case study approach in swedish

Bridging the Research to Practice Gap: A Case Study Approach to Understanding EIBI Supports and Barriers in Swedish Preschools

• December 2016
• lnternational Electronic Journal of Elementary Education 9((2)):317- 336
• 9((2)):317- 336

• Stockholm University

• Uppsala University
• This person is not on ResearchGate, or hasn't claimed this research yet.

Discover the world's research

• 25+ million members
• 160+ million publication pages
• 2.3+ billion citations

No full-text available

To read the full-text of this research, you can request a copy directly from the authors.

• J AUTISM DEV DISORD
• Nina Linder

• Jessica E. Van Stratton

• Amber Paulk

• Shahla Ala’i-Rosales

• EVAL PROGRAM PLANN

• Louis Jr Baillargeon

• Susanne Garvis
• Gunilla Westman Andersson

• Kristina Andersson

• B Munoz Chereau

• Zuilma Gabriela Sigurðardóttir

• Laura J. Hall

• Hui-Ting Wang
• O. Ivar Lovaas
• Dean L. Fixsen

• Frances Wallace
• Justin B. Leaf

• Ronald Leaf

• INFANT YOUNG CHILD

• Samuel L. Odom

• Katie Wolfe

• RES DEV DISABIL

• Jane S. Howard

• Howard G Cohen

• RES AUTISM SPECT DIS

• Marilyn Goodwin

• Nicole M. Zeug
• Jessa R. Love

• Season M Almason

• Zachary Warren

• BEHAV MODIF
• Gerald L. Shook

• D L Sackett
• William Rosenberg

• W. Scott Richardson
• EARLY INTERV PSYCHIA

• Martin Hammar
• AM J MENT RETARD

• Peter Bibby

• PSYCHOL SCHOOLS

• Johnny L. Matson
• J Res Spec Educ Needs
• R. A. Pring

• Paige E. Cervantes
• Mitchell Taubman
• Kathleen Tsuji

• Robin Taylor
• Lauren Brookman-Frazee

• Elisabeth Fernell

• Joakim Westerlund
• Christopher Gillberg
• ADM POLICY MENT HLTH

• S Ala'i-Rosales
• L Roll-Petterson
• S Pinkelman
• K Dillenburger
• H.-R Röttger
• C Nikopolous
• S Kucharczyk
• B Smith Myles
• L Tuchman-Ginsberg
• L Roll-Pettersson
• S Ala I'-Rosales
• B Scheuermann
• Health Care
• Stockholm County
• Recruit researchers
• Join for free
• Login Email Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google Welcome back! Please log in. Email · Hint Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google No account? Sign up

Vol. 9 No. 2 (2016) IEJEE Special Issue Autism Spectrum Disorders (ASD): Approaches to Training, Teaching, and Treatment

Bridging the research to practice gap: A case study approach to understanding eıbı supports and barriers in Swedish preschools

• Lise Roll-Petterson
• Ingrid Olsson
• Shahla Ala'i Rosales

The present study examined proximal and distal barriers and supports within the Swedish service system that may affect implementation of early and intensive behavioral intervention (EIBI) for children with autism. A case study approach with roots in ethnography was chosen to explore this issue. Two preschools exemplifying ‘high quality practice’ were studied and information was collected through multiple sources during a 12 month period, this included participant observations, direct observations, semi-structured interviews with key informants; paraprofessionals, parents, special educators, habilitation specialists and a focus group interview. Interview transcripts and field notes were combined and analyzed using an abductive grounded theory approach. Findings highlight the relevance of researchers understanding and taking into consideration the effect that distal variables have on implementation within proximal settings. A theoretical model of factors affecting implementation was conceptualised to include: staff entry knowledge and competence, development through supervision, the role of the preschool administrator, as well as distal influences and inter-organizational tensions, values, and bridges. Findings are discussed within the context of implementation science. Implications for future research are discussed as well as areas in need of further development to bridge the gap between research and practice.

Ala'i-Rosales. S., Roll-Petterson, L., Pinkelman, S., & Cihon, T. (2010) Higher Education Programs in Applied Behavior Analysis and Autism: Emerging Opportunities. European Journal of Behavior Analysis, 11, 207-216. Ala'i-Rosales. S., & Zueg, N. (2010). Important things to consider when starting intervention for a child newly diagnosed with autism. Behavior Analysis in Practice, 3, 54-55. American Psychological Association (2013). Diagnostic and Statistical Manual of Mental Disorders. (5th ed.). Washington, DC. Behavior Analyst Certification Board (2016, May 22). BACB Creditentials. Retrieved from www.BACB.com Bibby, P., Eikeseth, S., Martin, N. T., Mudford, O. C., & Reeves, D. (2001). Progress and outcomes for children with autism receiving parent-managed intensive interventions. Research in Developmental Disabilities, 22, 425-447. Bromark, G., & Granat, T. (2012). Rekommendationer. Reviderad och uppdaterad ur rapporten Mångsidiga intensiva insatser för barn med autism i förskoleålder. (Rev. ed.). Lund: Sveriges Habiliteringschefer. Brookman-Frazee, L., Baker-Ericzén, M., Stadnick, N., & Taylor, R. (2012). Parent perspectives on community mental health services for children with autism spectrum disorders. Journal of Child and Family Studies, 21, 533-544. Brookman-Frazee, L., Drahota, A., Stadnick, N., & Palinkas, L. A. (2012). Therapist perspectives on community mental health services for children with autism spectrum disorders. Administration and Policy in Mental Health and Mental Health Services Research, 39, 365-373. Bölte, S. (2014). Is autism curable? Developmental Medicine and Child Neurology, 56, 927-931. Callahan, K., Hughes, H., Mehta, S., Toussaint, K.A., Nichols, S, M., Ma, P. S., ... Wang, H.-T. (2016). Social validity of evidence-based practices and emerging interventions in autism. Focus in Autism and Other Developmental Disabilities, 1-10. http://dx.doi:10.1177/1088357616632446. Centers of Disease Control (2014, January 21). Data and Statistics. Retrieved from http://www.cdc.gov/ncbddd/autism/data.html. Chasson, G. S., Harris, G. E., & Neely, W. J. (2007). Cost comparison of early intensive behavioral intervention and special education for children with autism. Journal of Child and Family Studies, 16, 401–413. Cook, B. G., & Odom, S. L. (2013). Evidence-based practice and implementation science in special education. Exceptional Children, 79(2), 135-144. Daniels, A. M., & Mandell, D. S. (2014). Explaining differences in age at autism spectrum disorder diagnosis: a critical review. Autism, 18(5), 583-97. Denne, L. D., Hastings, R. P., Hughes, C. J., Bovellc, V., & Redford, L. (2011) Developing a competence framework for ABA and autism: What can we learn from others? European Journal of Behavior Analysis, 12, 217-230. Dey, I. (2012). Grounded Theory. In C. Seale, G. Gobo, J. Gubrium, & D. Silverman (Eds.), Qualitative Research Practice. Thousand Oakes, California: Sage Publications. Dillenburger, K., Röttger, H.-R., Dounavi, K., Sparkman, C., Keenan, M., Thyer, B., & Nikopolous, C. (2014). Multidisciplinary teamwork in autism: Can one size fit all? The Australian Educational and Developmental Psychologist, 31, 97-112. Dunst, C. J. (2015). Improving the design and implementation of in-service professional development in early childhood intervention. Infants and Young Children, 28(3), 210-219. Eikeseth, S., Klintwall, L., Jahr, E., & Karlsson, P. (2012). Outcome for children with autism receiving early and intensive behavioral intervention in mainstream preschool and kindergarten settings. Research in Autism Spectrum Disorders, 6, 829-835. Fawcett, S. B. (1991). Some values guiding community research and action. Journal of Applied Behavior Analysis, 24(4), 621-636. Fernell, E., Hedvall, Å., Westerlund, J., Höglund Carlsson, L., Eriksson, M., Barnevik Olsson, … Gillberg, C. (2011). Early intervention in 208 Swedish preschoolers with autism spectrum disorder. A prospective naturalistic study. Research in Developmental Disabilities, 32, 2092–2101. Fixsen, D. L., Naoom, S.F., Blase, K. A., Friedman, R. M., & Wallace, F. (2005). Implementation Reseach: A synthesis of the literature. Tampa, FL: University of South Florida. Howard, J. S., Sparkman, C. R., Cohen, H. G., Green, G., & Stanislaw, H. (2005). A comparison of intensive behavior analytic and eclectic treatments for young children with autism. Research in Developmental Disabilities, 26, 359–383. Howard, J. S., Stanislaw, H., Green, G., Sparkman, C. R., & Cohen, H. G. (2014). Comparison of behavior analytic and eclectic early interventions for young children with autism after three years. Research in Developmental Disabilities, 35(12), 3326–3344. Jang, J., Matson, J. L., Williams, L. W., Tureck, K., Goldin, R. L., & Cervantes, P. E. (2013). Rates of comorbid symptoms of children with ASD, ADHD and comorbid ASD and ADHD. Research in Developmental Disabilities, 34(8), 2369-2378. Keenan, M., Dillenburger, K., Röttgers, H.-R., Dounavi, K., Jónsdóttir, S. L., Moderato, P., … Martin, N. (2014). Autism and ABA: The gulf between North America and Europe. Review Journal of Autism and Developmental Disorders, 2(2), 167-183. Kelly, A., & Tincani, M. (2013). Collaborative training and practice among applied behavior analysts who support individuals with autism spectrum disorder. Education and Training in Autism and Developmental Disabilities, 48, 120-131. Kucharczyk, S., Shaw. E., Smith Myles, B., Sullivan, L., Szidon, K., & Tuchman-Ginsberg, L. (2012). Guidance and Coaching on Evidence-Based Practices for Learners with Autism Spectrum Disorders. Chapel Hill: The University of North Carolina, Frank Porter Graham Child Development Institute, National Professional Development Center on Autism Spectrum Disorders. Leaf, J. B., McEachin, J., Taubman, M., Ala’i-Rosales, S., Ross, R. K., Smith, T., & Weiss, M. J. (2016). Applied Behavior Analysis is a science and therefore progressive. Journal of Autism and Developmental Disorders, 42, 720-731. Leaf, R. B, Taubman, M. T., McEachin, J. J., Leaf, J. B., & Tsuji, K. H. (2011). A program description of a community-based intensive behavioral intervention program for individuals with autism spectrum disorders. Education and Treatment of Children, 34, 259-285. Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic Inquiry. Beverly Hills: Sage. Lovaas, I. O. (1987). Behavioral treatment and normal educational and intellectual functioning in young autistic children. Journal of Consulting and Clinical Psychology, 55, 3-9. Love, J. R., Carr, J. E., Almason, S. M., & Petursdottir, A. I. (2009). Early and intensive behavioral intervention for autism: A survey of clinical practices. Research in Autism Spectrum Disorders, 3, 421–428. Långh, U., Hammar, M., Klintwall, L., & Bölte, S. (2016). Allegiance and knowledge levels of professionals working with early intensive behavioural intervention in autism. Early Intervention in Psychiatry. http://dx.doi:10/1111/eip.12335. Matson, J. L., & Konst M. J. (2014). Early intervention for autism: Who provides treatment and in what settings? Research in Autism Spectrum Disorders, 8(11), 1585-1590. Metz, A. (2016). Practice profiles: A process for capturing evidence and operationalizing innovations. National Implementation Research Network White Paper. Chapel Hill: The University of North Carolina, Frank Porter Graham Child Development Institute, National Implementation Research Network. McEachin, J., Smith, T., & Lovaas, I. O. (1993). Long-term outcome for children who have received early intensive behavioral treatment. American Journal on Mental Retardation, 97, 359-372. National Autism Center at May Institute (2014, December 11) National Standards Project Report 2009; 2014. Retrieved from: http://www.nationalautismcenter.org/national-standards-project/history/. National Implementation Research Network (2016, August 16). Retrieved from: http://nirn.fpg.unc.edu/. Odom, S. L., Cox, A., & Brock, M. E. (2013). Implementation science, professional development, and autism spectrum disorders. Exceptional Children, 79(2), 233-251. Pinkelman, S. E., McIntosh, K., Rasplica, C. K., Berg, T., & Strickland-Cohen, M. K. (2015). Perceived enablers and barriers related to sustainability of school-wide positive behavioral interventions and supports. Behavioral Disorders, 40(3), 171–183. Pring, R. (2006). Philosophy of Education. London: Continuum Books. Roll-Pettersson, L., & Ala’i-Rosales, S. (2009). Using blended and guided technologies in a university course for scientist-practitioners: Teaching Applied Behavior Analysis to autism professionals. Journal of Intellectual Disabilities, 13, 113-142. Roll-Pettersson, L., Ala i’-Rosales, S., Kennan, M., & Dillenburger, K. (2010). Teaching and learning technologies in higher education: Applied behavior analysis and Autism “Necessity is the mother of invention”. European Journal of Behavior Analysis, 11, 247-260. Roll-Pettersson, L., Ek, U., & Ramnerö, J. (2010). Benefits of BACB Certification for Universities in Europe: A Case Study from Sweden. Association of Professional Behavior Analysts, issue 17. Sackett, D.L., Rosenberg, W. M., Gray, J. A. M., Haynes, R. B., & Richardson, W. S. (1996). Evidence-Based medicine, what it is and what it isn’t. British Medical Association, 312, 71-71. Scheuermann, B., Webber, J., Boutot, E., & Goodwin, M. (2003). Problems with personnel preparation in Autism Spectrum Disorders. Focus on Autism and other Developmental Disabilities, 18, 197-06. Shook, J., Ala’i-Rosales, S. & Glenn, S. (2002). Training and certifying behavior analysts to work with children with autism. Behavior Modification, 2, 253-258. Slocum, T, Detrich, R., Wilcynski, S., Spencer, T. D., Lewis, T., & Wolf, K. (2014). The evidence based practice of applied behavior analysis. The Behavior Analyst, 37, 41-56. Smith, T., & Iadarola, S. (2015). Evidence base update for Autism Spectrum Disorder. Journal of Clinical Child and Adolescent Psychology, 44, 897-922. Stahmer, A. C., Rieth, S., Lee, E., Reisinger, E. M., Mandell, D. S., & Connell, J. E. (2015). Training teachers to use evidence-based practices for autism: Examining procedural implementation fidelity. Psychology in the Schools, 52(2), 181-195. Stockholm County Council. (2014, November 12). Health Care. Retrieved from: http://www.sll.se. The Swedish National Agency for Education (2010a). The Swedish National Curriculum for the Preschool Lpfö 98 Revised 2010. Stockholm: Fritzes. The Swedish National Agency for Education (2010b). The Swedish Education Act. Stockholm: Nordsteds Juridik. Volkmar, F. R. (2014). Editorial: The importance of early intervention. Journal of Autism and Developmental Disorders, 44, 2979-2980. Warren, Z., McPheeters, M. L., Sathe, N., Foss-Feig, J. H., Glasser, A., & Veenstra-Vanderweele, J. (2012). A systematic review of early intensive intervention for autism spectrum disorders. Pediatrics, 127, 1303-1312. Yin, R. K. (2009). Case Study Research; Designs and Methods (4th ed.). Los Angles: Sage Publications. Zakirova Engstrand, R. & Roll-Pettersson, L. (2012). Inclusion of preschool children with autism in Sweden: attitudes and perceived efficacy of preschool teachers. Journal of Research in Special Educational Needs, 14(3), 170-179.

Autism , Case-study , Distal , EIBI , Implementation science

Affiliations

How to cite.

• [ Chicago ]
• [ Harvard ]
• [ Turabian ]
• [ Vancouver ]
• [ Endnote/Zotero/Mendeley (RIS) ]

SUBSCRIBE AND RECEIVE LATEST ISSUES

Subscribe to IEJEE to get information on the latest issue. None of your information will be shared with third parties.

• Infrastructure-as-a-Service (IaaS)

exclusive-design - stock.adobe.c

Case study: Kingfisher Group takes DIY approach to AI roll-out across e-commerce sites

International home improvement retailer kingfisher group opens up about the evolution of its ai strategy, and the rewards it is reaping.

• Caroline Donnelly, Senior Editor, UK

Several months into the start of the global Covid-19 coronavirus pandemic, international home improvement retail group Kingfisher debuted a revamped company strategy focused on repositioning the organisation as a digital and service-oriented entity.

Kingfisher, which owns the B&Q, Screwfix and DIY.com brands in the UK, had seen several of its brands suffer sales declines as a result of what it termed in its 2020 financial results  as “the company’s operating model becoming overly complex”.

“While some of our banners [brands] have delivered growth over the past four years … our performance has been disappointing. Group sales and retail profit need to improve,” its financial report, published in June 2020, stated.

In the wake of this realisation, the Powered by Kingfisher strategy was created, with an emphasis on ensuring each of the company’s brands was meeting the diverse and distinct needs of their respective customer bases, while also drawing on the businesses “core strengths and commercial assets”.    

“To serve customers effectively today, we also need to be digital and service-orientated, while leveraging our strong store assets,” the report added.

A month after going public with its plans for a strategic shift in how the company operates, Kingfisher announced the creation of a new role within its customer team with the appointment of Tom Betts as group data director.

Fast forward several years, and these two events have led to Kingfisher having its own in-house data and artificial intelligence (AI) team whose efforts have seen it centrally develop and roll out various digital tools that have boosted sales across its brands.

On this point, the company’s 2024 financial report stated: “Our [brands] are leveraging data and artificial intelligence to build customer-centric tools and solutions, support better commercial decision-making and higher productivity, thereby unlocking significant new sources of revenue, profit and cash.”

Speaking to Computer Weekly, Mohsen Ghasempour, group AI director at Kingfisher, said the appointment of Betts led to the creation of a team that has steadily grown in size and whose work has led to a notable uptick in sale across the group.

“We started with almost zero people on AI, and today we have around 28 – a mixture of machine learning engineers, data scientists, and engineers – so we [have the internal capabilities] to develop our own AI solutions,” he said,

“If you look at our portfolio of AI offerings today, we have 30-plus different initiatives on the go … and it might surprise people to know how much AI technology is impacting the way the DIY industry is operating.”

The company is using AI in its supply chain management and logistics function to deliver a demand forecasting model that can predict how demand for certain products will change over a 12-month period, as well as to pick up on patterns within the reviews customers leave about its products.

“We have services that sit on top of our customer reviews to extract actionable insights. Our AI algorithm can detect that 200 reviews are about product quality, and what specifically they are complaining about,” said Ghasempour.  

The company is also working on some “very cool technology” that will help the group’s in-store customers find the products they are looking for more efficiently, he added. “There is a lot happening with AI here at the moment.”

AI at the beginning

However, when Ghasempour first joined the company three years ago, Kingfisher knew it wanted to use AI to help achieve its strategic goals, but was still figuring out what role the technology would play in its business.

“When we started, there was no plan in terms of ‘This is how we’re going to use AI’,” he said. “So, the question became ‘How are we going to use it?’”

The answer to that came through trying to address what Ghasempour describes as one of the businesses’ biggest problems: a customer wanting to buy a product online that is no longer in stock.

“It wasn’t an AI problem, it was a product availability issue [that needed solving] that was affecting customer experience,” he said. “At that time, the challenge was ‘How are we going to solve it?’, but we did not necessarily think the answer was in using AI.”

While addressing this challenge, the idea of creating an “alternative product” recommendation algorithm emerged, which Ghasempour said gave way to an exploration of what role AI could play in the process.

“We started investigating how we can use AI when customers are at the point of buying a product that is not available, and how you can recommend a product which is very similar to the product that they’re looking for as an alternative,” he said. “That was the first recommendation service we developed, it went live in early 2023 on [B&Q’s online site] diy.com.”

This service has now been rolled out, in one form or another, across all of Kingfisher’s brands, and since B&Q became the early adopter of the technology, the brand has seen more than 10% of its e-commerce sales originate from product recommendations, according to the company’s own stats.

“From the basic algorithm to solve one problem, today we have 10 different recommendation algorithms that try to help the customer journey in different ways by offering [serving customers information about] frequently bought together products and personalised recommendations,” said Ghasempour.

And the early success achieved from its first forays into building AI-powered recommendation engines allowed the company to take the concept of Powered by Kingfisher even further by providing it with the proof points needed to ditch some of its legacy tech providers, he added.

“We had some legacy recommendation providers on [our]  e-commerce platform, and we started running tests A-B tests against those providers to demonstrate that we can achieve better performance, which justified building [out] this in-house [data and AI] capability even more,” he said.

“We completely replaced all the third-party providers we used for recommendation engines, so all of that, across all of our e-commerce platforms, is now powered by internal capabilities.”

These capabilities have also been created using Google Cloud’s portfolio of AI tools , with Ghasempour revealing that Kingfisher has partnerships in place with Microsoft and Amazon Web Services (AWS) too.

“Anybody wanting to build any kind of AI capability needs some infrastructure and at Kingfisher we have a partnership with all three cloud providers, but when it comes to AI and data science capability, Google has a bit more of a mature platform, from our point of view,” he said. “It was more intuitive and easier to use, so we started building that capability in Google’s infrastructure.”

Attuned to AI with Athena

Google Cloud’s fully managed development platform, Vertex AI, is playing a foundational role in the delivery of Kingfisher’s AI and data strategy, as it forms the basis of the company’s AI orchestration framework Athena.

Before the introduction of Athena, Kingfisher was effectively setting about addressing individual customer pain points, such as lack of product availability, by creating the AI microservices needed to address these problems from scratch each time.

In Kingfisher’s own words , this way of working resulted in lengthy development times for each microservice, which in turn slowed down the release time for them and caused scalability issues.

What Athena does is allow the Kingfisher team to automatically select the correct, ready-made Microsoft needed to answer a specific user issue or query, which it claims has cut the development time for new AI services from months to weeks.

“This is a fairly new technology for us, and is probably about a year old,” said Ghasempour. “And the idea behind Athena was, ‘How can we actually build a framework that means we can start to utilise the services in a in a safe and secure way, but also move fast because whoever is using this technology fastest is going to get the competitive advantage?’”

Athena acts as a “wrapper” around existing large language models, such as Google Gemini and Chat GPT, that allows Kingfisher to tap into the respective capabilities of these competing tools at once.   

“Athena can wrap around all of those large language models, and provide a stronger and more powerful service because it can utilise all of those language models at the same time, plus build the security model around them. So, we can we can track all the conversation and we can make sure there is nothing inappropriate happening,” said Ghasempour.

This means Kingfisher can essentially take a “build once, apply everywhere” approach to rolling out AI services across its retail brands.

“You can just do the development once but you can scale it up to more banners [brands] while you’re still secure in the safe environment,” said Ghasempour.

Presently, Kingfisher is using Athena to create services that will make it even easier for the company’s customers to find products using AI-based conversational, image and text searches.

For instance, if a customer does not know the name of the piece of equipment they need to replace on a household item or what the name of a certain tool is, Athena makes it possible for the customer to search the product catalogue for what they need using an image and get a result in seconds.

“All they have to do is upload a photo of the part and we’ll show them exactly what they need,” said Ghasempour.

It is also experimenting with using Athena to moderate the content of the listings published on the marketplace section of diy.com, which allows third-party sellers to sell their home improvement wares online through its website.

“Athena assesses the description of the product to check for any racism or sexism, for example, and offers visual moderation of all the product images,” said Ghasempour.  

Furthermore, the technology is being put to use internally at Kingfisher, to assist its 82,000-strong workforce with finding information about the group’s employment policies and guidelines that are contained within hundreds of internal staff documents.

“In any organisation you have a lot of documentation, from the legal team or HR, that tell staff what the rules of working there are, but people don’t go read the documents. So, at the moment, we’re putting [Athena] on top of those documents, so staff can ask an [internal chatbot] about the maternity leave policy, for example, and get the information they need,” said Ghasempour.

“Over the next couple of months, we’ve got a few more services going live internally to empower our colleagues using this technology to do their day-to-day jobs more efficiently.”

Read more about cloud AI use in retail

• Retail-related studies into AI’s influence suggest its capabilities are welcome by the sector and shoppers alike – but tech leaders advise treading cautiously.
• The French retailer has some catching up to do on its data strategy and digital transformation – and its new data chief has an ambitious roadmap to deliver on data science, business intelligence and artificial intelligence.

Read more on Infrastructure-as-a-Service (IaaS)

Optimize Amazon Athena performance with these 5 tuning tips

Kingfisher develops AI-agnostic platform to power DIY assistant

New Microsoft AI chip pressures Nvidia and hardware market

Google Cloud lands five-year deal with DIY specialist retailer Kingfisher

The next U.S. president will set the tone on tech issues such as AI regulation, data privacy and climate tech. This guide breaks ...

Financial services firm JPMorgan Chase seeks to balance technology modernization and cost optimization as it pursues initiatives...

Businesses nationwide will be able to forego the U.S. Federal Trade Commission's Sept. 4 deadline for compliance with the ...

The Tor browser has sparked discussion and dissension since its debut. Does the software, which promises anonymous and secure web...

As cyberattacks continue to rise, infosec professionals address the need to increase private and public sector partnerships to ...

A possible cyberattack against Washington's Port of Seattle has caused significant service disruptions to airline travel at the ...

Cloud and on-premises subnets use IP ranges, subnet masks or prefixes, and security policies. But cloud subnets are simpler to ...

Satellite connectivity lets Broadcom offer the VeloCloud SD-WAN as an option for linking IoT devices to the global network from ...

Enterprises have been slow to adopt Wi-Fi 7, as they find previous generations suit their needs. However, experts predict ...

New capabilities for VMware VCF can import and manage existing VMware services through a single console interface for a private ...

Due to rapid AI hardware advancement, companies are releasing advanced products yearly to keep up with the competition. The new ...

AMD plans to acquire AI systems designer and manufacturer ZT Systems for $5 billion. AMD CEO Lisa Su said hyperscalers want more ...

Serverless, first launched on AWS, is now available on all three major public clouds in a move aimed at enabling customers to ...

Implementing a data governance strategy requires a roadmap to keep everyone on track and overcome challenges. Follow eight key ...

Enable Microsoft Copilot in Power BI to automate key features using generative AI capabilities that improve insights and ...

• Cambridge Dictionary +Plus

Translation of case study – English–Swedish dictionary

(Translation of case study from the PASSWORD English–Swedish Dictionary © 2023 K Dictionaries Ltd)

Examples of case study

Translations of case study.

Get a quick, free translation!

Word of the Day

get away from it all

to go somewhere, usually on holiday, where you can completely relax and forget your responsibilities or problems

It’s not really my thing (How to say you don’t like something)

Learn more with +Plus

• Recent and Recommended {{#preferredDictionaries}} {{name}} {{/preferredDictionaries}}
• Definitions Clear explanations of natural written and spoken English English Learner’s Dictionary Essential British English Essential American English
• Grammar and thesaurus Usage explanations of natural written and spoken English Grammar Thesaurus
• Pronunciation British and American pronunciations with audio English Pronunciation
• English–Chinese (Simplified) Chinese (Simplified)–English
• English–Chinese (Traditional) Chinese (Traditional)–English
• English–Dutch Dutch–English
• English–French French–English
• English–German German–English
• English–Indonesian Indonesian–English
• English–Italian Italian–English
• English–Japanese Japanese–English
• English–Norwegian Norwegian–English
• English–Polish Polish–English
• English–Portuguese Portuguese–English
• English–Spanish Spanish–English
• English–Swedish Swedish–English
• Dictionary +Plus Word Lists
• PASSWORD English–Swedish    Noun
• Translations
• All translations

To add case study to a word list please sign up or log in.

Add case study to one of your lists below, or create a new one.

{{message}}

Something went wrong.

There was a problem sending your report.

Working with a Democratic Curriculum: The Swedish Case Study

• First Online: 10 October 2011

Cite this chapter

• Marja Kuisma 4 &
• Anette Sandberg 5  

Part of the book series: International perspectives on early childhood education and development ((CHILD,volume 6))

2178 Accesses

1 Citations

In this chapter we focus on what it means to act professionally in a particular early childhood education context in Sweden. We highlight the macro-level structure of early childhood policy and administration in Swedish society and explore the micro-level reality of one pre-school teacher working with 3- to 5-year-olds in a pre-school on the outskirts of a town in central Sweden. Using data from video observations of the teacher’s day and a follow-up interview, we offer insights into how the teacher thinks and acts in professional ways. The study shows that for this Swedish teacher, to act professionally in pre-school meant to interact and actively communicate with children, staff members and parents. These interactions made visible the ethical approach of the teacher, as well as a focus on the curriculum content areas of reading and mathematical skills. The study also throws light on some discrepancies between the thoughts and the actions of the pre-school teacher.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or eBook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime
• Available as PDF
• Read on any device
• Instant download
• Own it forever
• Available as EPUB and PDF
• Compact, lightweight edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info
• Durable hardcover edition

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Changing Mode of Regulation: Role Models in Moral Education Textbooks for Primary Schools in China, 1999–2005

Professional Learning to Support the Development of Inclusive Curricula in Scotland

‘Little School, Big Heart’: Embracing a New Partnership for Learning Generous and Ethical Judgements

Asplund-Carlsson, M., Pramling-Samuelsson, I., & Kärrby, G. (2001 ). Strukturella faktorer och pedagogisk kvalitet i barnomsorgen och skola; en kunskapsöversikt. [Structural factors and pedagogical quality in pre-school education and school] . Stockholm: Skolverket.

Google Scholar  

Dalli, C. (2008). The new teacher in New Zealand. In L. K. Miller & C. Cable (Eds.), Professionalism in the early years (pp. 142–153). Abingdon, Oxon: Hodder Education.

Dewey, J. (2004). Individ, skola och samhälle; utbildningsfilosofiska texter . Stockholm: Natur och Kultur.

Gustafsson, J.-E., & Myrberg, E. (2002). Ekonomiska resursers betydelse för pedagogiska resultat; en kunskapsöversikt. [Economical resources and their importance in the educational result] . Stockholm: Skolverket. Liber Distribution.

Sandberg, A., & Vuorinen, T. (2008). Preschool-home cooperation in change. International Journal of Early Years Education, 16 (2), 151–161.

Article   Google Scholar  

Swedish Ministry of Education. (2006). Curriculum for the pre-school, Lpfö 98 . Stockholm, Sweden: Fritzes.

Swedish National Agency for Education. (2004/2005). Förskolan i brytningstid. Nationell utvärdering av förskolan. [Pre-school in the changing period. A National assessment of the pre-school] . Stockholm: Skolverket, Nr 239.

Swedish National Agency for Education. (2005). Kvalitet i förskolan. [Quality in pre-school] . Stockholm: Skolverket: Fritzes förlag.

Swedish National Agency for Education. (2008). Descriptive data on pre-school activities, school-age childcare, schools and adult education in Sweden . Report no. 320.

Swedish National Agency for Education. (2009). Statistics, PM . 2009-04-02.

Vygotsky, L. (1986). Thought and language . Cambridge, MA: MIT Press.

Download references

Author information

Authors and affiliations.

Department of Didactics, Uppsala University, 75002, Uppsala, Sweden

Marja Kuisma

School of Education, Culture and Communication, Mälardalen University, 721 23, Västerås, Sweden

Anette Sandberg

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Anette Sandberg .

Editor information

Editors and affiliations.

North End 120, Royston, Herts, SG8 5NZ, United Kingdom

Linda Miller

School of Education Policy &, Implementation, Victoria University of Wellington, Wellington, New Zealand

Carmen Dalli

Cass School of Education, University of East London, Water Lane, London, E15 4LZ, United Kingdom

Mathias Urban

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer Science+Business Media B.V.

About this chapter

Kuisma, M., Sandberg, A. (2012). Working with a Democratic Curriculum: The Swedish Case Study. In: Miller, L., Dalli, C., Urban, M. (eds) Early Childhood Grows Up. International perspectives on early childhood education and development, vol 6. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-2718-2_7

Download citation

DOI : https://doi.org/10.1007/978-94-007-2718-2_7

Published : 10 October 2011

Publisher Name : Springer, Dordrecht

Print ISBN : 978-94-007-2717-5

Online ISBN : 978-94-007-2718-2

eBook Packages : Humanities, Social Sciences and Law Education (R0)

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

• Free Samples
• Best DSOs to Work For
• Infection Control
• Patient Care
• Career & Profession
• Solutions Lab

Case study: Pyogenic granuloma localized on lingual palatal mucosa of nos. 14-15 in a 72-year-old male

This case report describes an unusual presentation of a pyogenic granuloma in a 72-year-old male. Pyogenic granulomas, while common benign vascular lesions, are typically found in younger individuals and pregnant women. Their occurrence in older males underscores the importance of considering a broad differential diagnosis when encountering oral lesions.

Pyogenic granuloma: An overview

Pyogenic granuloma, though the name suggests an infectious process, is a benign vascular tumor characterized by excessive growth of capillaries and fibroblasts. While its exact etiology remains elusive, the pathogenesis is often linked to a combination of factors, including local irritation, trauma, hormonal fluctuations, and certain medications. The interaction of these factors triggers a cascade of events that leads to the characteristic clinical presentation of this lesion. 1

Local irritation or trauma, such as chronic inflammation from poor oral hygiene or accidental injury, can initiate an inflammatory response that stimulates the release of growth factors and cytokines, promoting angiogenesis (new blood vessel formation) and fibroblast proliferation. 2

Hormonal changes, particularly during pregnancy or puberty, can also influence the development of pyogenic granulomas due to their effects on vascular reactivity and tissue growth. Certain medications, such as oral contraceptives and retinoids, have been implicated in some cases, though the exact mechanism is unclear.

The rapid proliferation of capillaries and fibroblasts results in a highly vascularized, friable lesion that bleeds easily. This explains its clinical presentation of a solitary, red-to-purple nodule, often pedunculated or sessile, and prone to bleeding upon even slight manipulation.

These lesions can vary significantly in size, ranging from a few millimeters to several centimeters, and they typically occur on the gingiva, lips, tongue, and buccal mucosa. 3 However, they can also manifest in extraoral locations, such as the skin and nasal septum.

Understanding the pathogenesis of pyogenic granuloma is crucial for accurate diagnosis and appropriate management. 4 Recognizing the potential triggers and the resulting cellular changes allows clinicians to differentiate this lesion from other conditions with similar clinical presentations, such as peripheral giant cell granuloma, peripheral ossifying fibroma, hemangioma, and oral squamous cell carcinoma. 4  

The medically challenging patient needs compassion and education: A case study

Intuitive patient care: Humans helping humans

Case presentation

During a routine hygiene appointment, a 72-year-old man reported a "growth" on the roof of his mouth behind his upper left molar. He noted its presence for about two weeks with occasional bleeding. An oral examination revealed a solitary, reddish nodule measuring 7 mm in diameter in the gingival mucosa between teeth nos. 14 and 15.

The lesion was soft, tender to the touch, asymptomatic, and bled slightly when examined. Additionally, a defective crown on tooth no. 14 was observed. The patient indicated that the only recent change in his oral hygiene routine was using a different size of interdental brush in that area.

Diagnosis and management

An excisional biopsy was performed, and a histopathological examination confirmed the diagnosis of pyogenic granuloma. The patient was given oral hygiene instructions that focused on gentle brushing and flossing, and advised to use a smaller interdental brush in the affected area.

Discussion and conclusion

This case underscores a crucial point in clinical practice: even seemingly straightforward oral lesions can present in atypical ways. Although uncommon, the occurrence of pyogenic granuloma in an older male patient highlights the necessity of maintaining a broad differential diagnosis. While the clinical presentation was suggestive, the patient's age and the lesion's location prompted a biopsy to exclude other possibilities, such as oral squamous cell carcinoma.

This case highlights how important it is for dental professionals to take a comprehensive approach when evaluating oral lesions. Always remember to gather a detailed patient history and conduct a thorough clinical examination, even when a lesion seems familiar and inoffensive. This will ensure we can accurately identify any potential issues and intervene promptly. Early detection and proper management can truly make a difference in preventing complications and improving our patient’s overall health. Dental hygienists play a vital role in this process!

By carefully observing and raising concerns about any unusual oral findings, we actively contribute to early diagnosis and improved patient care. We have the power to make a real impact!

1.Lomeli Martinez SM, Carrillo Contreras NG, Gómez Sandoval JR, et al.  Oral pyogenic granuloma: a narrative review. Int J Molecular Sci. 2023;4 (23):16885. doi:10.3390/ijms242316885

2. Ribeiro JL, Moraes RM, Carvalho BFC, Nascimento AO, Milhan NVM, Anbinder AL. Oral pyogenic granuloma: an 18-year retrospective clinicopathological and immunohistochemical study. J Cutaneous Path .  2021; 48 (7):863-869. doi:10.1111/cup.13970

3. Sonar PR, Panchbhai AS. Pyogenic granuloma in the mandibular anterior gingiva: a case study. Cureus.  2024; 16 (1):e52273. doi:10.7759/cureus.52273

4. Ramón Ramirez J, Seoane J, Montero J, Esparza Gómez GC, Cerero R. Isolated gingival metastasis from hepatocellular carcinoma mimicking a pyogenic granuloma.  J Clin Perio. 2003; 30 (10):926-929. doi:10.1034/j.1600-051x.2003.00391.x

Andreina Sucre, MSc, RDH

Andreina Sucre, MSc, RDH, is an international dentist, oral pathology, and oral surgery specialist practicing dental hygiene in Miami, Florida. A passionate advocate for early pathological diagnosis, she empowers colleagues through lectures focused on oral pathologies. Andreina spoke on this topic at the 2024 ADHA Annual Conference, 2023 RDH Under One Roof, and she writes about oral pathology for  RDH magazine. Committed to community outreach, she educates non-native English-speaking children on oral health and actively volunteers in dental initiatives.

.ebm-content-item .title-wrapper .title-text-wrapper .title-text.items-with-images { font-size: 16px; @container (width > calc(400px + 145px)) { font-size: 22px; } @container (width > calc(600px + 145px)) { font-size: 24px; } @container (width > calc(750px + 145px)) { font-size: 26px; } } Whitening products: What's right for your patients?

The power of alignment: Yoga therapy for dental hygienists

Are natural disinfectants a good choice for your operatory?

From gums to gut: Harnessing the power of oral probiotics

Depression and oral health: A comprehensive review

Finding balance and defining your worth

To read this content please select one of the options below:

Please note you do not have access to teaching notes, crisis management at the government offices: a swedish case study.

Disaster Prevention and Management

ISSN : 0965-3562

Article publication date: 2 November 2015

The purpose of this paper is to gain a deeper understanding of crisis management at the Swedish Government office level in an international crisis by using a multiperspective approach, and paying particular attention to factors contributing favorably to the management process.

Design/methodology/approach

The Eyjafjallajökull volcano eruption on Iceland in 2010 was accompanied by an ash cloud that caused serious air traffic problems in large parts of Europe. Interviews were conducted with seven high-level informants at the Swedish Government offices and two informants at the Swedish Aviation Authority. An interview guide inspired by governance, command and control, and leadership perspectives was used.

A Crisis Coordination Secretariat, organizationally placed directly under the prime minister, coordinated the operation. A combination of mandate (hard power) and social smoothness (soft power) on part of the Crisis Coordination Secretariat contributed to confidence building and a collaboration norm between the ministries, and between the ministries and their underlying agencies. Preparatory training, exercises and a high level of system knowledge on part of the Crisis Coordination Secretariat – contextual intelligence – also contributed to a favorable crisis management.

Research limitations/implications

The study relies on retrospective self-report data only from a limited group of informants making generalizations difficult.

Practical implications

The organizational positioning of the Crisis Coordination Secretariat directly under the prime minister gave its members formal authority. These members in turn skillfully used social flexibility to build confidence and a will to collaborate. This combination of hard and soft power is recommended.

Originality/value

The multiperspective approach used when designing the interview guide and when interpreting the responses was new as well as the focus on factors contributing to crisis management success.

• Emergency response
• Crisis management
• Natural hazard

Acknowledgements

This research was supported by the Swedish Defence University.

Larsson, G. , Bynander, F. , Ohlsson, A. , Schyberg, E. and Holmberg, M. (2015), "Crisis management at the government offices: a Swedish case study", Disaster Prevention and Management , Vol. 24 No. 5, pp. 542-552. https://doi.org/10.1108/DPM-11-2014-0232

Emerald Group Publishing Limited

Copyright © 2015, Emerald Group Publishing Limited

Related articles

All feedback is valuable.

Please share your general feedback

Report an issue or find answers to frequently asked questions

Contact Customer Support

arXiv's Accessibility Forum starts next month!

Help | Advanced Search

Computer Science > Machine Learning

Title: data-centric approach to constrained machine learning: a case study on conway's game of life.

Abstract: This paper focuses on a data-centric approach to machine learning applications in the context of Conway's Game of Life. Specifically, we consider the task of training a minimal architecture network to learn the transition rules of Game of Life for a given number of steps ahead, which is known to be challenging due to restrictions on the allowed number of trainable parameters. An extensive quantitative analysis showcases the benefits of utilizing a strategically designed training dataset, with its advantages persisting regardless of other parameters of the learning configuration, such as network initialization weights or optimization algorithm. Importantly, our findings highlight the integral role of domain expert insights in creating effective machine learning applications for constrained real-world scenarios.

Submission history

Access paper:.

• HTML (experimental)
• Other Formats

References & Citations

• Google Scholar
• Semantic Scholar

BibTeX formatted citation

Bibliographic and Citation Tools

Code, data and media associated with this article, recommenders and search tools.

• Institution

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .

Reimagining insurance with a comprehensive approach to gen AI

Despite forging ahead with generative AI (gen AI) use cases and capabilities, many insurance companies are finding themselves stuck in the pilot phase, unable to scale or extract value. Jörg Mußhoff  sat down with Cameron Talischi and Khaled Rifai to discuss how organizations can escape “pilot purgatory” by leveraging traditional AI and robotic process automation in addition to gen AI; the importance of reimagining domains such as claims, underwriting, and distribution; and how to address data privacy and security concerns regarding intellectual property (IP) and other issues early on. This transcript has been edited for clarity.

Jörg Mußhoff: To us, gen AI is not just hype. McKinsey has estimated that the total gen AI potential for the global economy is $4.4 trillion. 1 The economic potential of generative AI: The next productivity frontier , McKinsey, June 14, 2024. Many insurance leaders are asking, “How do we get the benefits from first use cases, and how do we scale and make it real across geographies and business models?” Cam, could you start us off by telling us what you see in the overarching trends in gen AI and what applications and domains have the greatest potential impact for clients?

Cameron Talischi: We’ve seen a lot of interest and activity in the insurance sector on this topic, which is not surprising given that the insurance industry is knowledge-based and involves processing unstructured types of data. That is precisely what gen AI models are very good for.

In terms of promising applications and domains, three categories of use cases are gaining traction. First, and most common, is that carriers are exploring the use of gen AI models to extract insights and information from unstructured sources. In the context of claims, for example, this could be synthesizing medical records or pulling information from demand packages. In the context of underwriting for a commercial P&C [property and casualty insurance carrier], this could look like pulling information from submissions that come from brokers or allowing underwriters to more seamlessly search and query risk appetite and underwriting guidelines.

The second category is the generation of content—namely, creative content. Think about it in the context of marketing or personalization. Again, in the context of claims, it’s communicating the status of a claim to a claimant by capturing some of the details and nuances specific to that claim or for supporting underwriters, and it’s communicating or negotiating with brokers. Use cases for coding and software development make up the last category. These are notable given the imperative for tech modernization and digitalization and that many insurance companies are still dealing with legacy systems.

Khaled Rifai: I would add one more in the context of client engagement and self-service. Think about the insured wanting to know whether they’re covered, what the statuses of their claims are, or whether they need to update their addresses or names. Many insurers are still employing people to handle these requests. With the help of gen AI, those tasks can be automated or designed for self-service. I think the long-term effects of gen AI are underrated, and the short-term effects are overrated. And that’s the dilemma many insurance companies and other corporations find themselves in. They want fast results from the benefits of gen AI applications but hesitate to invest in data management, technology modernization, organizational change, and budgetary allocations.

While we believe in the potential of gen AI, it will take a lot of engagement, investment, and commitment from top management teams and organizations to make it real. To make gen AI truly successful, you must combine gen AI with more-traditional AI and traditional robotic process automation. These technologies combined make the secret sauce that helps you rethink your customer journeys and processes with the right ROI.

Want to subscribe to the McKinsey on Insurance podcast?

Jörg Mußhoff: That’s exactly what we’re seeing many players do. But we are still in that pilot phase. Why do organizations get stuck in this phase, and how can they successfully scale up from there?

Cameron Talischi: We are seeing a lot of organizations getting stuck in what we call “pilot purgatory” for several reasons. One is misplaced focus on technology versus what matters from a business perspective. Many organizations have identified several use cases and have development teams building these assets. But a lot of time is being spent on testing, analyzing, and benchmarking different tools such as LLMs [language learning models] even though the choice of the language model may be dictated by other factors and, ultimately, has a marginal impact on performance.

While there’s value in learning and experimenting with use cases, these need to be properly planned so they don’t become a distraction. Conversely, leading organizations that are thinking about scaling are shifting their focus to identifying the common code components behind applications. Earlier, we talked about extracting information from unstructured sources. Typically, these applications have similar architecture operating in the background. So, it’s possible to create reusable modules that can accelerate building similar use cases while also making it easier to manage them on the back end.

Another area where organizations get stuck is how they think about impact. We’ve seen many organizations source ideas from various parts of the business and prioritize them. But many of the use cases are very isolated and don’t generate much value, so the organization prolongs the pilot. If you’re not seeing value from a use case, even in isolation, you may want to move on. The better approach to driving business value is to reimagine domains and explore all the potential actions within each domain that can collectively drive meaningful change in the way work is accomplished. So that includes looking at all the levers at your disposal, not just gen AI. That approach better lends itself to scaling versus piloting an isolated use case.

Khaled Rifai: I fully agree. Reimaging domains is key because you can very quickly get to the restrictions connected to isolated use cases because of the dependencies with other systems and processes. We are at a point in time with gen AI where we should take a step back and really reimagine claims, underwriting, and distribution. By combining these technologies and thinking about how to design processes that capture the right data at the right point, we can drive meaningful change. This approach requires investments in more than just tech; it also takes quite some commitment, quite some investment, and quite some change to do so.

About QuantumBlack, AI by McKinsey

QuantumBlack, McKinsey’s AI arm, helps companies transform using the power of technology, technical expertise, and industry experts. With thousands of practitioners at QuantumBlack (data engineers, data scientists, product managers, designers, and software engineers) and McKinsey (industry and domain experts), we are working to solve the world’s most important AI challenges. QuantumBlack Labs is our center of technology development and client innovation, which has been driving cutting-edge advancements and developments in AI through locations across the globe.

Jörg Mußhoff: Do you have any pragmatic advice for our clients about what they should do to set this up and develop these capabilities over time?

Cameron Talischi: Everything must be anchored in a strategic vision and a road map, but in terms of capabilities, the data setup is critically important, especially as you think about gaining scale. You need to make sure that the data underpinning the possible use cases are in usable condition. We talked about the technology stack and this notion of creating infrastructure that can build and deliver use cases at an accelerated pace. You are touching on talent and operating models, which are equally important. One of the failures of some operating models is when the effort is solely tech-led versus business-led with the technology function as an enabler. It’s important to assess how much of the development is done centrally versus within the business.

You shouldn’t wait it out, because you need to build that muscle to understand what solutions you should buy. Khaled Rifai

On the talent side, organizations will most likely pursue a combination of building and buying: purchasing some of the capabilities and use cases from external vendors and building some internally, such as use cases that tie to your IP and ways of working. To build internally, you’ll need the requisite talent to create those capabilities. For example, new roles such as prompt engineers address how we interact with models and get the right behavior out of them. You need to build that muscle and some of those capabilities through a combination of tech and business to deploy them as part of the right operating model.

Khaled Rifai: Some companies wonder what to do about data management now that gen AI is being implemented at large vendors. Should they just wait it out? Our answer is no—you shouldn’t wait it out, because, as Cam said, you need to build that muscle to understand not only how to keep your organization safe but also what solutions you should buy that will fit your needs.

Jörg Mußhoff: Besides data privacy and security, there’s also a big regulatory question. Gen AI can be biased, which raises ethical questions. In the mid- to long-term use of these technologies, what should insurance carriers focus on to avoid risk?

Cameron Talischi: First and foremost, it’s important for insurance carriers to have a comprehensive framework in place that covers major AI-related risks such as data privacy issues or issues and concerns about accuracy and hallucinations. Incidentally, insurance carriers need to account for risks that they’re exposed to via the use of gen AI by customers or other parties they interact with. The use of image generation is a good example of this because it could lead to fraudulent claims.

Regarding data privacy, it is possible to have automated routines to identify PII [personal identifiable information] and strip that data—if it’s not needed—to ensure that it doesn’t leave a secure environment. With accuracy, it’s important to, in tandem with the business, have objective measures and targets for performance. Test these in advance of the application or use case going into production, but also implement routine audits postproduction to make sure that the performance reached expected levels.

Khaled Rifai: In terms of regulation in Europe, the EU Artificial Intelligence Act has recently been passed. With room for national regulations, national regulators of the insurance industry will look at certain cases to determine standards. In my experience, the regulations are good enough for clients to work with. I wouldn’t start with high-risk cases concerning decisions that impact the life and health of the insured, but instead begin with other use cases that we’re certain we can implement in a secure, customer-friendly way. The thing to remember is that nothing is static, and the ongoing process of shaping regulations means taking things one step at a time.

Cameron Talischi is a partner in McKinsey’s Chicago office, and Jörg Mußhoff is a senior partner in the Berlin office, where Khaled Rifai is a partner.

Explore a career with us

Related articles.

McKinsey insurance leadership on 2024 trends and innovations

A generative AI reset: Rewiring to turn potential into value in 2024

Navigating shifting risks in the insurance industry

Information

• Author Services

Initiatives

You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.

All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .

Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.

Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.

Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.

Original Submission Date Received: .

• Active Journals
• Find a Journal
• Proceedings Series
• For Authors
• For Reviewers
• For Editors
• For Librarians
• For Publishers
• For Societies
• For Conference Organizers
• Open Access Policy
• Institutional Open Access Program
• Special Issues Guidelines
• Editorial Process
• Research and Publication Ethics
• Article Processing Charges
• Testimonials
• Preprints.org
• SciProfiles
• Encyclopedia

Article Menu

• Subscribe SciFeed
• Recommended Articles
• Google Scholar
• on Google Scholar
• Table of Contents

Find support for a specific problem in the support section of our website.

Please let us know what you think of our products and services.

Visit our dedicated information section to learn more about MDPI.

JSmol Viewer

Ecological and health risk assessment of heavy metals in groundwater within an agricultural ecosystem using gis and multivariate statistical analysis (msa): a case study of the mnasra region, gharb plain, morocco.

1. Introduction

2. materials and methods, 2.1. description of the study area, 2.2. sampling collection and analysis, 2.3. multivariate statistical analysis, 2.4. pollution evaluation indices, 2.4.1. heavy metal pollution index (hpi), 2.4.2. metal index (mi), 2.4.3. degree of contamination (c d ), 2.4.4. ecological risk index (eri), 2.4.5. pollution index (pi), 2.5. human health risk assessment (hhra), 3. results and discussion, 3.1. hms analysis and spatial variation, 3.2. multivariate statistical analysis, 3.2.1. correlation matrix analysis, 3.2.2. principal component analysis, 3.2.3. hierarchical cluster analysis, 3.3. pollution assessment using hms pollution indices, 3.3.1. heavy metal pollution index (hpi), 3.3.2. metal index (mi), 3.3.3. degree of contamination (c d ), 3.3.4. ecological risk index (eri), 3.3.5. pollution index (pi), 3.4. human health risk assessment (hhra), 4. conclusions, author contributions, data availability statement, acknowledgments, conflicts of interest.

• Wu, F.; Yang, X.; Cui, Z.; Ren, L.; Jiang, S.; Liu, Y.; Yuan, S. The Impact of Human Activities on Blue-Green Water Resources and Quantification of Water Resource Scarcity in the Yangtze River Basin. Sci. Total Environ. 2024 , 909 , 168550. [ Google Scholar ] [ CrossRef ]
• Andrabi, S.; Bakhtiyar, Y.; Yousuf, T.; Akhtar, M.; Nissar, S. Water Quality Assessment in Relation to Fish Assemblage Using Multivariate Analysis in Manasbal Lake, Kashmir. Water Sci. 2024 , 38 , 92–108. [ Google Scholar ] [ CrossRef ]
• Scanlon, B.R.; Fakhreddine, S.; Rateb, A.; de Graaf, I.; Famiglietti, J.; Gleeson, T.; Grafton, R.Q.; Jobbagy, E.; Kebede, S.; Kolusu, S.R.; et al. Global Water Resources and the Role of Groundwater in a Resilient Water Future. Nat. Rev. Earth Environ. 2023 , 4 , 87–101. [ Google Scholar ] [ CrossRef ]
• Saleh, H.N.; Panahande, M.; Yousefi, M.; Asghari, F.B.; Oliveri Conti, G.; Talaee, E.; Mohammadi, A.A. Carcinogenic and Non-Carcinogenic Risk Assessment of Heavy Metals in Groundwater Wells in Neyshabur Plain, Iran. Biol. Trace Elem. Res. 2019 , 190 , 251–261. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Abanyie, S.K.; Apea, O.B.; Abagale, S.A.; Amuah, E.E.Y.; Sunkari, E.D. Sources and Factors Influencing Groundwater Quality and Associated Health Implications: A Review. Emerg. Contam. 2023 , 9 , 100207. [ Google Scholar ] [ CrossRef ]
• Wang, Y.; Yuan, X.; Zhang, Y.; Zhang, X.; Xiao, Y.; Duo, J.; Huang, X.; Sun, M.; Lv, G. Hydrochemical, D–O–Sr Isotopic and Electromagnetic Characteristics of Geothermal Waters from the Erdaoqiao Area, SW China: Insights into Genetic Mechanism and Scaling Potential. Ore Geol. Rev. 2023 , 158 , 105486. [ Google Scholar ] [ CrossRef ]
• Gao, Y.; Qian, H.; Zhou, Y.; Chen, J.; Wang, H.; Ren, W.; Qu, W. Cumulative Health Risk Assessment of Multiple Chemicals in Groundwater Based on Deterministic and Monte Carlo Models in a Large Semiarid Basin. J. Clean. Prod. 2022 , 352 , 131567. [ Google Scholar ] [ CrossRef ]
• Islam, M.S. Groundwater: Sources, Functions, and Quality. In Hydrogeochemical Evaluation and Groundwater Quality ; Springer Nature: Cham, Switzerland, 2023; pp. 17–36. ISBN 978-3-031-44304-6. [ Google Scholar ]
• Sharma, K.; Rajan, S.; Nayak, S.K. Chapter 1—Water Pollution: Primary Sources and Associated Human Health Hazards with Special Emphasis on Rural Areas. Water Resour. Manag. Rural. Dev. 2024 , 35 , 3–14. [ Google Scholar ]
• Maharjan, A.K.; Kamei, T.; Amatya, I.M.; Mori, K.; Kazama, F.; Toyama, T. Ammonium-Nitrogen (NH 4 + -N) Removal from Groundwater by a Dropping Nitrification Reactor: Characterization of NH 4 + -N Transformation and Bacterial Community in the Reactor. Water 2020 , 12 , 599. [ Google Scholar ] [ CrossRef ]
• Nurtazin, S.; Pueppke, S.; Ospan, T.; Mukhitdinov, A.; Elebessov, T. Quality of Drinking Water in the Balkhash District of Kazakhstan’s Almaty Region. Water 2020 , 12 , 392. [ Google Scholar ] [ CrossRef ]
• Rao, K.N.; Latha, P.S. Groundwater Quality Assessment Using Water Quality Index with a Special Focus on Vulnerable Tribal Region of Eastern Ghats Hard Rock Terrain, Southern India. Arab. J. Geosci. 2019 , 12 , 267. [ Google Scholar ] [ CrossRef ]
• Kana, A.A. Heavy Metal Assessment of Groundwater Quality in Part of Karu, Central Nigeria. Water Pract. Technol. 2022 , 17 , 1802–1817. [ Google Scholar ] [ CrossRef ]
• Feng, B.; Ma, Y.; Qi, Y.; Zhong, Y.; Sha, X. Health Risk Assessment of Groundwater Nitrogen Pollution in Yinchuan Plain. J. Contam. Hydrol. 2022 , 249 , 104031. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Marghade, D. Detailed Geochemical Assessment & Indexing of Shallow Groundwater Resources in Metropolitan City of Nagpur (Western Maharashtra, India) with Potential Health Risk Assessment of Nitrate Enriched Groundwater for Sustainable Development. Geochemistry 2020 , 80 , 125627. [ Google Scholar ] [ CrossRef ]
• Toi Bissang, B.; Aragón-Barroso, A.J.; Baba, G.; González-López, J.; Osorio, F. Integrated Assessment of Heavy Metal Pollution and Human Health Risks in Waters from a Former Iron Mining Site: A Case Study of the Canton of Bangeli, Togo. Water 2024 , 16 , 471. [ Google Scholar ] [ CrossRef ]
• Saddik, M.; Fadili, A.; Makan, A. Assessment of Heavy Metal Contamination in Surface Sediments along the Mediterranean Coast of Morocco. Environ. Monit. Assess. 2019 , 191 , 197. [ Google Scholar ] [ CrossRef ]
• Belkhiri, L.; Mouni, L.; Narany, T.S.; Tiri, A. Evaluation of Potential Health Risk of Heavy Metals in Groundwater Using the Integration of Indicator Kriging and Multivariate Statistical Methods. Groundw. Sustain. Dev. 2017 , 4 , 12–22. [ Google Scholar ] [ CrossRef ]
• Khan, M.; Ellahi, A.; Niaz, R.; Ghoneim, M.E.; Tag-eldin, E.; Rashid, A. Water Quality Assessment of Alpine Glacial Blue Water Lakes and Glacial-Fed Rivers. Geomat. Nat. Hazards Risk 2022 , 13 , 2597–2617. [ Google Scholar ] [ CrossRef ]
• Yin, X.; Shao, P.; Ding, L.; Xi, Y.; Zhang, K.; Yang, L.; Shi, H.; Luo, X. Protonation of Rhodanine Polymers for Enhancing the Capture and Recovery of Ag+ from Highly Acidic Wastewater. Environ. Sci. Nano 2019 , 6 , 3307–3315. [ Google Scholar ] [ CrossRef ]
• Ge, X.; Ma, Y.; Song, X.; Wang, G.; Zhang, H.; Zhang, Y.; Zhao, H. β-FeOOH Nanorods/Carbon Foam-Based Hierarchically Porous Monolith for Highly Effective Arsenic Removal. ACS Appl. Mater. Interfaces 2017 , 9 , 13480–13490. [ Google Scholar ] [ CrossRef ]
• Aithani, D.; Kushawaha, J. Heavy Metals Contamination in Environment. In Remediation of Heavy Metals ; John Wiley & Sons, Ltd.: Hoboken, NJ, USA, 2024; pp. 15–30. ISBN 978-1-119-85358-9. [ Google Scholar ]
• Rashid, A.; Schutte, B.J.; Ulery, A.; Deyholos, M.K.; Sanogo, S.; Lehnhoff, E.A.; Beck, L. Heavy Metal Contamination in Agricultural Soil: Environmental Pollutants Affecting Crop Health. Agronomy 2023 , 13 , 1521. [ Google Scholar ] [ CrossRef ]
• Lace, A.; Cleary, J. A Review of Microfluidic Detection Strategies for Heavy Metals in Water. Chemosensors 2021 , 9 , 60. [ Google Scholar ] [ CrossRef ]
• Kou, B.; Yuan, Y.; Zhu, X.; Ke, Y.; Wang, H.; Yu, T.; Tan, W. Effect of Soil Organic Matter-Mediated Electron Transfer on Heavy Metal Remediation: Current Status and Perspectives. Sci. Total Environ. 2024 , 917 , 170451. [ Google Scholar ] [ CrossRef ]
• Gao, J.; Han, H.; Gao, C.; Wang, Y.; Dong, B.; Xu, Z. Organic Amendments for in Situ Immobilization of Heavy Metals in Soil: A Review. Chemosphere 2023 , 335 , 139088. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Yasin, M.U.; Haider, Z.; Munir, R.; Zulfiqar, U.; Rehman, M.; Javaid, M.H.; Ahmad, I.; Nana, C.; Saeed, M.S.; Ali, B.; et al. The Synergistic Potential of Biochar and Nanoparticles in Phytoremediation and Enhancing Cadmium Tolerance in Plants. Chemosphere 2024 , 354 , 141672. [ Google Scholar ] [ CrossRef ]
• Edo, G.I.; Samuel, P.O.; Oloni, G.O.; Ezekiel, G.O.; Ikpekoro, V.O.; Obasohan, P.; Ongulu, J.; Otunuya, C.F.; Opiti, A.R.; Ajakaye, R.S.; et al. Environmental Persistence, Bioaccumulation, and Ecotoxicology of Heavy Metals. Chem. Ecol. 2024 , 40 , 322–349. [ Google Scholar ] [ CrossRef ]
• Bețianu, C.; Cozma, P.; Gavrilescu, M. Human Health Hazards and Risks Generated by the Bioaccumulation of Lead from the Environment in the Food Chain. In Lead Toxicity Mitigation: Sustainable Nexus Approaches ; Kumar, N., Jha, A.K., Eds.; Springer Nature: Cham, Switzerland, 2024; pp. 73–123. ISBN 978-3-031-46146-0. [ Google Scholar ]
• Sable, H.; Singh, V.; Kumar, V.; Roy, A.; Pandit, S.; Kaur, K.; Rustagi, S.; Malik, S. Toxicological and Bioremediation Profiling of Nonessential Heavy Metals (Mercury, Chromium, Cadmium, Aluminium) and Their Impact on Human Health: A Review. Toxicol. Anal. Clin. 2024 , 36 , 205–234. [ Google Scholar ] [ CrossRef ]
• Tyczyńska, M.; Gędek, M.; Brachet, A.; Stręk, W.; Flieger, J.; Teresiński, G.; Baj, J. Trace Elements in Alzheimer’s Disease and Dementia: The Current State of Knowledge. J. Clin. Med. 2024 , 13 , 2381. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Ceramella, J.; De Maio, A.C.; Basile, G.; Facente, A.; Scali, E.; Andreu, I.; Sinicropi, M.S.; Iacopetta, D.; Catalano, A. Phytochemicals Involved in Mitigating Silent Toxicity Induced by Heavy Metals. Foods 2024 , 13 , 978. [ Google Scholar ] [ CrossRef ]
• Anchidin-Norocel, L.; Gutt, G.; Tătăranu, E.; Amariei, S. Electrochemical Sensors and Biosensors: Effective Tools for Detecting Heavy Metals in Water and Food with Possible Implications for Children’s Health. Int. J. Electrochem. Sci. 2024 , 19 , 100643. [ Google Scholar ] [ CrossRef ]
• Zhu, Y.; Yang, Q.; Wang, H.; Yang, J.; Zhang, X.; Li, Z.; Martín, J.D. A Hydrochemical and Isotopic Approach for Source Identification and Health Risk Assessment of Groundwater Arsenic Pollution in the Central Yinchuan Basin. Environ. Res. 2023 , 231 , 116153. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Budi, H.S.; Opulencia, M.J.C.; Afra, A.; Abdelbasset, W.K.; Abdullaev, D.; Majdi, A.; Taherian, M.; Ekrami, H.A.; Mohammadi, M.J. Source, Toxicity and Carcinogenic Health Risk Assessment of Heavy Metals. Rev. Environ. Health 2022 , 39 , 77–90. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Ray, S.; Vashishth, R. From Water to Plate: Reviewing the Bioaccumulation of Heavy Metals in Fish and Unraveling Human Health Risks in the Food Chain. Emerg. Contam. 2024 , 10 , 100358. [ Google Scholar ] [ CrossRef ]
• Althomali, R.H.; Abbood, M.A.; Saleh, E.A.M.; Djuraeva, L.; Abdullaeva, B.S.; Habash, R.T.; Alhassan, M.S.; Alawady, A.H.R.; Alsaalamy, A.H.; Najafi, M.L. Exposure to Heavy Metals and Neurocognitive Function in Adults: A Systematic Review. Environ. Sci. Eur. 2024 , 36 , 18. [ Google Scholar ] [ CrossRef ]
• Wu, Y.-S.; Osman, A.I.; Hosny, M.; Elgarahy, A.M.; Eltaweil, A.S.; Rooney, D.W.; Chen, Z.; Rahim, N.S.; Sekar, M.; Gopinath, S.C.B.; et al. The Toxicity of Mercury and Its Chemical Compounds: Molecular Mechanisms and Environmental and Human Health Implications: A Comprehensive Review. ACS Omega 2024 , 9 , 5100–5126. [ Google Scholar ] [ CrossRef ]
• Howard, R.; Al-Mayhani, T.; Carr, A.; Leff, A.; Morrow, J.; Rossor, A. Toxic, Metabolic and Physical Insults to the Nervous System. In Neurology ; John Wiley & Sons, Ltd.: Hoboken, NJ, USA, 2024; pp. 903–943. ISBN 978-1-119-71567-2. [ Google Scholar ]
• Shukla, S.; Mbingwa, G.; Khanna, S.; Dalal, J.; Sankhyan, D.; Malik, A.; Badhwar, N. Environment and Health Hazards Due to Military Metal Pollution: A Review. Environ. Nanotechnol. Monit. Manag. 2023 , 20 , 100857. [ Google Scholar ] [ CrossRef ]
• Witkowski, A.J.; Dąbrowska, D.; Wróbel, J. Groundwater Quality Assessment in the Area of the Zinc Smelter in Miasteczko Śląskie (Poland) Using Selected Metal Indices. Water 2024 , 16 , 279. [ Google Scholar ] [ CrossRef ]
• Zainudin, A.M.; Zulkefli, S.N.; Looi, L.J.; Aris, A.Z.; Sefie, A.; Isa, N.M. Spatial and Temporal Evaluation of Groundwater Hydrochemistry in an Active Phreatic Zone of Developed Basin in Selangor, Malaysia. Appl. Geochem. 2023 , 154 , 105656. [ Google Scholar ] [ CrossRef ]
• Singh, K.K.; Tewari, G.; Kumar, S.; Busa, R.; Chaturvedi, A.; Rathore, S.S.; Singh, R.K.; Gangwar, A. Understanding Urban Groundwater Pollution in the Upper Gangetic Alluvial Plains of Northern India with Multiple Industries and Their Impact on Drinking Water Quality and Associated Health Risks. Groundw. Sustain. Dev. 2023 , 21 , 100902. [ Google Scholar ] [ CrossRef ]
• Sarhat, A.R.; Al-Obaidi, B.S. Contamination by Heavy Metals in the Sediments of Sirwan/Diyala River, Garmian Region. IOP Conf. Ser. Earth Environ. Sci. 2023 , 1158 , 022010. [ Google Scholar ] [ CrossRef ]
• Gad, M.; Abou El-Safa, M.M.; Farouk, M.; Hussein, H.; Alnemari, A.M.; Elsayed, S.; Khalifa, M.M.; Moghanm, F.S.; Eid, E.M.; Saleh, A.H. Integration of Water Quality Indices and Multivariate Modeling for Assessing Surface Water Quality in Qaroun Lake, Egypt. Water 2021 , 13 , 2258. [ Google Scholar ] [ CrossRef ]
• Mukherjee, I.; Singh, U.K.; Singh, R.P.; Anshumali; Kumari, D.; Jha, P.K.; Mehta, P. Characterization of Heavy Metal Pollution in an Anthropogenically and Geologically Influenced Semi-Arid Region of East India and Assessment of Ecological and Human Health Risks. Sci. Total Environ. 2020 , 705 , 135801. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Chaturvedi, A.; Bhattacharjee, S.; Mondal, G.C.; Kumar, V.; Singh, P.K.; Singh, A.K. Exploring New Correlation between Hazard Index and Heavy Metal Pollution Index in Groundwater. Ecol. Indic. 2019 , 97 , 239–246. [ Google Scholar ] [ CrossRef ]
• Sawut, R.; Kasim, N.; Maihemuti, B.; Hu, L.; Abliz, A.; Abdujappar, A.; Kurban, M. Pollution Characteristics and Health Risk Assessment of Heavy Metals in the Vegetable Bases of Northwest China. Sci. Total Environ. 2018 , 642 , 864–878. [ Google Scholar ] [ CrossRef ]
• Patel, N.; Bhatt, D. Insights of Ground Water Quality Assessment Methods—A Review. Mater. Today Proc. 2024 . [ Google Scholar ] [ CrossRef ]
• Kandel, K.; Sharma, C.M.; Rawat, B.; Paudyal, R.; Li, M.; Pandey, A.; Zhang, Q. Synthesis Analysis of Hydrogeochemistry of Nepal Himalayan Rivers: Perspective from Major Ions and Trace Elements. Ecol. Indic. 2024 , 163 , 112080. [ Google Scholar ] [ CrossRef ]
• Ribeiro, P.G.; de Oliveira, C.; Guerra, M.B.B.; de Carvalho, T.S.; Martins, G.C.; Pereira, W.V.d.S.; Ramos, S.J.; Guilherme, L.R.G. Rare Earths as Emerging Trace Element Contaminants in the Soil. Curr. Pollut. Rep. 2024 , 10 , 443–458. [ Google Scholar ] [ CrossRef ]
• Onyena, A.P.; Folorunso, O.M.; Nwanganga, N.; Udom, G.J.; Ekhator, O.C.; Frazzoli, C.; Ruggieri, F.; Bocca, B.; Orisakwe, O.E. Engaging One Health in Heavy Metal Pollution in Some Selected Nigerian Niger Delta Cities. A Systematic Review of Pervasiveness, Bioaccumulation and Subduing Environmental Health Challenges. Biol. Trace Elem. Res. 2024 , 202 , 1356–1389. [ Google Scholar ] [ CrossRef ]
• Zhang, S.; Han, Y.; Peng, J.; Chen, Y.; Zhan, L.; Li, J. Human Health Risk Assessment for Contaminated Sites: A Retrospective Review. Environ. Int. 2023 , 171 , 107700. [ Google Scholar ] [ CrossRef ]
• Li, P.; Karunanidhi, D.; Subramani, T.; Srinivasamoorthy, K. Sources and Consequences of Groundwater Contamination. Arch. Environ. Contam. Toxicol. 2021 , 80 , 1–10. [ Google Scholar ] [ CrossRef ]
• Wang, X.; Liu, B.; He, S.; Yuan, H.; Ji, D.; Li, R.; Song, Y.; Xu, W.; Liu, B.; Xu, Y. Groundwater Environment and Health Risk Assessment in an In Situ Oil Shale Mining Area. Water 2024 , 16 , 185. [ Google Scholar ] [ CrossRef ]
• Yu, Z.; Yao, R.; Huang, X.; Yan, Y. Health Risk Appraisal of Trace Elements in Groundwater in an Urban Area: A Case Study of Sichuan Basin, Southwest China. Water 2023 , 15 , 4286. [ Google Scholar ] [ CrossRef ]
• Badeenezhad, A.; Soleimani, H.; Shahsavani, S.; Parseh, I.; Mohammadpour, A.; Azadbakht, O.; Javanmardi, P.; Faraji, H.; Babakrpur Nalosi, K. Comprehensive Health Risk Analysis of Heavy Metal Pollution Using Water Quality Indices and Monte Carlo Simulation in R Software. Sci. Rep. 2023 , 13 , 15817. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Munene, E.N.; Hashim, N.O.; Ambusso, W.N. Human Health Risk Assessment of Heavy Metal Concentration in Surface Water of Sosian River, Eldoret Town, Uasin-Gishu County Kenya. MethodsX 2023 , 11 , 102298. [ Google Scholar ] [ CrossRef ]
• Selvam, S.; Manimaran, G.; Sivasubramanian, P.; Balasubramanian, N.; Seshunarayana, T. GIS-Based Evaluation of Water Quality Index of Groundwater Resources around Tuticorin Coastal City, South India. Environ. Earth Sci. 2014 , 71 , 2847–2867. [ Google Scholar ] [ CrossRef ]
• Nduka, J.K.; Kelle, H.I.; Umeh, T.C.; Okafor, P.C.; Iloka, G.C.; Okoyomon, E. Ecological and Health Risk Assessment of Radionuclides and Heavy Metals of Surface and Ground Water of Ishiagu–Ezillo Quarry Sites of Ebonyi, Southeast Nigeria. J. Hazard. Mater. Adv. 2023 , 10 , 100307. [ Google Scholar ] [ CrossRef ]
• Dippong, T.; Mihali, C.; Avram, A. Evaluating Groundwater Metal and Arsenic Content in Piatra, North-West of Romania. Water 2024 , 16 , 539. [ Google Scholar ] [ CrossRef ]
• Rashid, A.; Ayub, M.; Ullah, Z.; Ali, A.; Sardar, T.; Iqbal, J.; Gao, X.; Bundschuh, J.; Li, C.; Khattak, S.A.; et al. Groundwater Quality, Health Risk Assessment, and Source Distribution of Heavy Metals Contamination around Chromite Mines: Application of GIS, Sustainable Groundwater Management, Geostatistics, PCAMLR, and PMF Receptor Model. Int. J. Environ. Res. Public Health 2023 , 20 , 2113. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Liu, Z.; Tao, S.; Sun, Z.; Chen, Y.; Xu, J. Determination of Heavy Metals and Health Risk Assessment in Tap Water from Wuhan, China, a City with Multiple Drinking Water Sources. Water 2023 , 15 , 3709. [ Google Scholar ] [ CrossRef ]
• Egbueri, J.C. Groundwater Quality Assessment Using Pollution Index of Groundwater (PIG), Ecological Risk Index (ERI) and Hierarchical Cluster Analysis (HCA): A Case Study. Groundw. Sustain. Dev. 2020 , 10 , 100292. [ Google Scholar ] [ CrossRef ]
• Sanad, H.; Moussadek, R.; Mouhir, L.; Oueld Lhaj, M.; Dakak, H.; El Azhari, H.; Yachou, H.; Ghanimi, A.; Zouahri, A. Assessment of Soil Spatial Variability in Agricultural Ecosystems Using Multivariate Analysis, Soil Quality Index (SQI), and Geostatistical Approach: A Case Study of the Mnasra Region, Gharb Plain, Morocco. Agronomy 2024 , 14 , 1112. [ Google Scholar ] [ CrossRef ]
• El Azhari, H.; Cherif, E.K.; El Halimi, R.; Azzirgue, E.M.; Ou Larbi, Y.; Coren, F.; Salmoun, F. Predicting the Production and Depletion of Rare Earth Elements and Their Influence on Energy Sector Sustainability through the Utilization of Multilevel Linear Prediction Mixed-Effects Models with R Software. Sustainability 2024 , 16 , 1951. [ Google Scholar ] [ CrossRef ]
• Azhari, H.E.; Cherif, E.K.; Sarti, O.; Azzirgue, E.M.; Dakak, H.; Yachou, H.; Esteves Da Silva, J.C.G.; Salmoun, F. Assessment of Surface Water Quality Using the Water Quality Index (IWQ), Multivariate Statistical Analysis (MSA) and Geographic Information System (GIS) in Oued Laou Mediterranean Watershed, Morocco. Water 2022 , 15 , 130. [ Google Scholar ] [ CrossRef ]
• Sanad, H.; Mouhir, L.; Zouahri, A.; Moussadek, R.; El Azhari, H.; Yachou, H.; Ghanimi, A.; Oueld Lhaj, M.; Dakak, H. Assessment of Groundwater Quality Using the Pollution Index of Groundwater (PIG), Nitrate Pollution Index (NPI), Water Quality Index (WQI), Multivariate Statistical Analysis (MSA), and GIS Approaches: A Case Study of the Mnasra Region, Gharb Plain, Morocco. Water 2024 , 16 , 1263. [ Google Scholar ] [ CrossRef ]
• Aabad, M.; Bouaziz, A.; Falisse, A.; Martin, J.F. Improving Yield and Water Use Efficiency of Sugarcane under Drip. Rev. Marocaine Des Sci. Agron. Vétérinaires 2016 , 5 , 32–40. [ Google Scholar ]
• Mrabet, L.; Loukili, A.; Belghyti, D.; Attarassi, B. Impact of Leachates from the Landfill in the Kenitra City (Morocco) on the Water Resources. Int. J. Appl. Environ. Sci. 2019 , 14 , 541–553. [ Google Scholar ]
• Chow, A.T.-S.; Ulus, Y.; Huang, G.; Kline, M.A.; Cheah, W.-Y. Challenges in Quantifying and Characterizing Dissolved Organic Carbon: Sampling, Isolation, Storage, and Analysis. J. Environ. Qual. 2022 , 51 , 837–871. [ Google Scholar ] [ CrossRef ]
• Wang, H.; Wu, Z.; Chen, B.; He, M.; Hu, B. Chip-Based Array Magnetic Solid Phase Microextraction on-Line Coupled with Inductively Coupled Plasma Mass Spectrometry for the Determination of Trace Heavy Metals in Cells. Analyst 2015 , 140 , 5619–5626. [ Google Scholar ] [ CrossRef ]
• Tao, S.; Zhang, X.; Xu, J.; Pan, G.; Gu, F. Anthropogenic Impacts on Isotopic and Geochemical Characteristics of Urban Streams: A Case Study in Wuhan, China. Environ. Sci. Pollut. Res. 2021 , 28 , 39186–39198. [ Google Scholar ] [ CrossRef ]
• El-Zeiny, A.M.; Kafrawy, S.B.E.; Ahmed, M.H. Geomatics Based Approach for Assessing Qaroun Lake Pollution. Egypt. J. Remote Sens. Space Sci. 2019 , 22 , 279–296. [ Google Scholar ] [ CrossRef ]
• Pan, Y.; She, D.; Ding, J.; Abulaiti, A.; Zhao, J.; Wang, Y.; Liu, R.; Wang, F.; Shan, J.; Xia, Y. Coping with Groundwater Pollution in High-Nitrate Leaching Areas: The Efficacy of Denitrification. Environ. Res. 2024 , 250 , 118484. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Prasad, M.; Sunitha, V.; Reddy, Y.S.; Suvarna, B.; Reddy, B.M.; Reddy, M.R. Data on Water Quality Index Development for Groundwater Quality Assessment from Obulavaripalli Mandal, YSR District, A.P India. Data Brief 2019 , 24 , 103846. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Sheng, D.; Meng, X.; Wen, X.; Wu, J.; Yu, H.; Wu, M. Contamination Characteristics, Source Identification, and Source-Specific Health Risks of Heavy Metal(Loid)s in Groundwater of an Arid Oasis Region in Northwest China. Sci. Total Environ. 2022 , 841 , 156733. [ Google Scholar ] [ CrossRef ]
• WHO World Health Organization. Guidelines for Drinking-Water Quality: Fourth Edition Incorporating First Addendum ; World Health Organization: Geneva, Switzerland, 2017; ISBN 978-92-4-154995-0.
• Wagh, V.M.; Panaskar, D.B.; Mukate, S.V.; Gaikwad, S.K.; Muley, A.A.; Varade, A.M. Health Risk Assessment of Heavy Metal Contamination in Groundwater of Kadava River Basin, Nashik, India. Model. Earth Syst. Environ. 2018 , 4 , 969–980. [ Google Scholar ] [ CrossRef ]
• Tamasi, G.; Cini, R. Heavy Metals in Drinking Waters from Mount Amiata (Tuscany, Italy). Possible Risks from Arsenic for Public Health in the Province of Siena. Sci. Total Environ. 2004 , 327 , 41–51. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Alam, R.; Ahmed, Z.; Howladar, M.F. Evaluation of Heavy Metal Contamination in Water, Soil and Plant around the Open Landfill Site Mogla Bazar in Sylhet, Bangladesh. Groundw. Sustain. Dev. 2020 , 10 , 100311. [ Google Scholar ] [ CrossRef ]
• Caeiro, S.; Costa, M.H.; Ramos, T.B.; Fernandes, F.; Silveira, N.; Coimbra, A.; Medeiros, G.; Painho, M. Assessing Heavy Metal Contamination in Sado Estuary Sediment: An Index Analysis Approach. Ecol. Indic. 2005 , 5 , 151–169. [ Google Scholar ] [ CrossRef ]
• Edet, A.E.; Offiong, O.E. Evaluation of Water Quality Pollution Indices for Heavy Metal Contamination Monitoring. A Study Case from Akpabuyo-Odukpani Area, Lower Cross River Basin (Southeastern Nigeria). GeoJournal 2002 , 57 , 295–304. [ Google Scholar ] [ CrossRef ]
• Economou-Eliopoulos, M.; Megremi, I. Contamination of the Soil–Groundwater–Crop System: Environmental Risk and Opportunities. Minerals 2021 , 11 , 775. [ Google Scholar ] [ CrossRef ]
• Botle, A.; Salgaonkar, S.; Tiwari, R.; Ambadekar, S.; Barabde, G.R. Brief Status of Contamination in Surface Water of Rivers of India by Heavy Metals: A Review with Pollution Indices and Health Risk Assessment. Environ. Geochem. Health 2023 , 45 , 2779–2801. [ Google Scholar ] [ CrossRef ]
• Sudharshan Reddy, Y.; Sunitha, V. Assessment of Heavy Metal Pollution and Its Health Implications in Groundwater for Drinking Purpose around Inactive Mines, SW Region of Cuddapah Basin, South India. Total Environ. Res. Themes 2023 , 8 , 100069. [ Google Scholar ] [ CrossRef ]
• Goher, M.E.; Farhat, H.I.; Abdo, M.H.; Salem, S.G. Metal Pollution Assessment in the Surface Sediment of Lake Nasser, Egypt. Egypt. J. Aquat. Res. 2014 , 40 , 213–224. [ Google Scholar ] [ CrossRef ]
• Bineshpour, M.; Payandeh, K.; Nazarpour, A.; Sabzalipour, S. Status, Source, Human Health Risk Assessment of Potential Toxic Elements (PTEs), and Pb Isotope Characteristics in Urban Surface Soil, Case Study: Arak City, Iran. Environ. Geochem. Health 2021 , 43 , 4939–4958. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Selvam, S.; Jesuraja, K.; Roy, P.D.; Venkatramanan, S.; Khan, R.; Shukla, S.; Manimaran, D.; Muthukumar, P. Human Health Risk Assessment of Heavy Metal and Pathogenic Contamination in Surface Water of the Punnakayal Estuary, South India. Chemosphere 2022 , 298 , 134027. [ Google Scholar ] [ CrossRef ]
• United States Environmental Protection Agency; Office of Emergency; Remedial Response. Risk Assessment Guidance for Superfund: Pt. A. Human Health Evaluation Manual ; Office of Emergency and Remedial Response, US Environmental Protection Agency: Washington, DC, USA, 1989; Volume 1.
• Tokatli, C.; Ustaoğlu, F. Health Risk Assessment of Toxicants in Meriç River Delta Wetland, Thrace Region, Turkey. Environ. Earth Sci. 2020 , 79 , 426. [ Google Scholar ] [ CrossRef ]
• Mahato, M.K.; Singh, P.K.; Tiwari, A.K.; Singh, A.K. Risk Assessment Due to Intake of Metals in Groundwater of East Bokaro Coalfield, Jharkhand, India. Expo. Health 2016 , 8 , 265–275. [ Google Scholar ] [ CrossRef ]
• Kumar, V.; Parihar, R.D.; Sharma, A.; Bakshi, P.; Singh Sidhu, G.P.; Bali, A.S.; Karaouzas, I.; Bhardwaj, R.; Thukral, A.K.; Gyasi-Agyei, Y.; et al. Global Evaluation of Heavy Metal Content in Surface Water Bodies: A Meta-Analysis Using Heavy Metal Pollution Indices and Multivariate Statistical Analyses. Chemosphere 2019 , 236 , 124364. [ Google Scholar ] [ CrossRef ]
• United States Environmental Protection Agency. Regional Screening Level (RSL) Summary Table ; United States Environmental Protection Agency: Washington, DC, USA, 2013.
• Das Sharma, S. Risk Assessment via Oral and Dermal Pathways from Heavy Metal Polluted Water of Kolleru Lake—A Ramsar Wetland in Andhra Pradesh, India. Environ. Anal. Health Toxicol. 2020 , 35 , e2020019. [ Google Scholar ] [ CrossRef ]
• Kumar, R.N.; Solanki, R.; Kumar, J.I.N. Seasonal Variation in Heavy Metal Contamination in Water and Sediments of River Sabarmati and Kharicut Canal at Ahmedabad, Gujarat. Environ. Monit. Assess. 2013 , 185 , 359–368. [ Google Scholar ] [ CrossRef ]
• Giri, S.; Singh, A.K. Spatial Distribution of Metal(Loid)s in Groundwater of a Mining Dominated Area: Recognising Metal(Loid) Sources and Assessing Carcinogenic and Non-Carcinogenic Human Health Risk. Int. J. Environ. Anal. Chem. 2016 , 96 , 1313–1330. [ Google Scholar ] [ CrossRef ]
• Gao, B.; Gao, L.; Gao, J.; Xu, D.; Wang, Q.; Sun, K. Simultaneous Evaluations of Occurrence and Probabilistic Human Health Risk Associated with Trace Elements in Typical Drinking Water Sources from Major River Basins in China. Sci. Total Environ. 2019 , 666 , 139–146. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Qu, L.; Huang, H.; Xia, F.; Liu, Y.; Dahlgren, R.A.; Zhang, M.; Mei, K. Risk Analysis of Heavy Metal Concentration in Surface Waters across the Rural-Urban Interface of the Wen-Rui Tang River, China. Environ. Pollut. 2018 , 237 , 639–649. [ Google Scholar ] [ CrossRef ]
• Sinha, D.; Datta, S.; Mishra, R.; Agarwal, P.; Kumari, T.; Adeyemi, S.B.; Kumar Maurya, A.; Ganguly, S.; Atique, U.; Seal, S.; et al. Negative Impacts of Arsenic on Plants and Mitigation Strategies. Plants 2023 , 12 , 1815. [ Google Scholar ] [ CrossRef ]
• Singh, A.; Kostova, I. Health Effects of Heavy Metal Contaminants Vis-à-Vis Microbial Response in Their Bioremediation. Inorganica Chim. Acta 2024 , 568 , 122068. [ Google Scholar ] [ CrossRef ]
• Obasi, P.N.; Akudinobi, B.B. Potential Health Risk and Levels of Heavy Metals in Water Resources of Lead–Zinc Mining Communities of Abakaliki, Southeast Nigeria. Appl. Water Sci. 2020 , 10 , 184. [ Google Scholar ] [ CrossRef ]
• Mubeen, S.; Ni, W.; He, C.; Yang, Z. Agricultural Strategies to Reduce Cadmium Accumulation in Crops for Food Safety. Agriculture 2023 , 13 , 471. [ Google Scholar ] [ CrossRef ]
• Zulfiqar, U.; Haider, F.U.; Ahmad, M.; Hussain, S.; Maqsood, M.F.; Ishfaq, M.; Shahzad, B.; Waqas, M.M.; Ali, B.; Tayyab, M.N.; et al. Chromium Toxicity, Speciation, and Remediation Strategies in Soil-Plant Interface: A Critical Review. Front. Plant Sci. 2023 , 13 , 1081624. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Fahim, R.; Cheng, L.; Mishra, S. Structural and Functional Perspectives of Carbon Filter Media in Constructed Wetlands for Pollutants Abatement from Wastewater. Chemosphere 2023 , 345 , 140514. [ Google Scholar ] [ CrossRef ]
• Hassan, M.U.; Lihong, W.; Nawaz, M.; Ali, B.; Tang, H.; Rasheed, A.; Zain, M.; Alqahtani, F.M.; Hashem, M.; Qari, S.H.; et al. Silicon a Key Player to Mitigate Chromium Toxicity in Plants: Mechanisms and Future Prospective. Plant Physiol. Biochem. 2024 , 208 , 108529. [ Google Scholar ] [ CrossRef ]
• Samantara, M.K.; Padhi, R.K.; Sowmya, M.; Kumaran, P.; Satpathy, K.K. Heavy Metal Contamination, Major Ion Chemistry and Appraisal of the Groundwater Status in Coastal Aquifer, Kalpakkam, Tamil Nadu, India. Groundw. Sustain. Dev. 2017 , 5 , 49–58. [ Google Scholar ] [ CrossRef ]
• Mesquita, A.F.; Gonçalves, F.J.; Gonçalves, A.M. The Lethal and Sub-Lethal Effects of Fluorinated and Copper-Based Pesticides—A Review. Int. J. Environ. Res. Public Health 2023 , 20 , 3706. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Pant, R.; Mathpal, N.; Chauhan, R.; Singh, A.; Gupta, A. A Review of Mercury Contamination in Water and Its Impact on Public Health. In Mercury Toxicity Mitigation: Sustainable Nexus Approach ; Springer Nature: Cham, Switzerland, 2024; pp. 93–115. [ Google Scholar ] [ CrossRef ]
• Vácha, R. Heavy Metal Pollution and Its Effects on Agriculture. Agronomy 2021 , 11 , 1719. [ Google Scholar ] [ CrossRef ]
• Zheng, X.; Fang, Y.; Lin, J.; Luo, J.; Li, S.; Aschner, M.; Jiang, Y. Signal Transduction Associated with Mn-Induced Neurological Dysfunction. Biol. Trace Elem. Res. 2023 , 202 , 1–12. [ Google Scholar ] [ CrossRef ]
• Mustafa, A.; Zulfiqar, U.; Mumtaz, M.Z.; Radziemska, M.; Haider, F.U.; Holatko, J.; Hammershmiedt, T.; Naveed, M.; Ali, H.; Kintl, A.; et al. Nickel (Ni) Phytotoxicity and Detoxification Mechanisms: A Review. Chemosphere 2023 , 328 , 138574. [ Google Scholar ] [ CrossRef ]
• Kazberuk, W.; Szulc, W.; Rutkowska, B. Use Bottom Sediment to Agriculture—Effect on Plant and Heavy Metal Content in Soil. Agronomy 2021 , 11 , 1077. [ Google Scholar ] [ CrossRef ]
• Menšík, L.; Hlisnikovský, L.; Nerušil, P.; Kunzová, E. Comparison of the Concentration of Risk Elements in Alluvial Soils Determined by pXRF In Situ, in the Laboratory, and by ICP-OES. Agronomy 2021 , 11 , 938. [ Google Scholar ] [ CrossRef ]
• Piwowarska, D.; Kiedrzyńska, E.; Jaszczyszyn, K. A Global Perspective on the Nature and Fate of Heavy Metals Polluting Water Ecosystems, and Their Impact and Remediation. Crit. Rev. Environ. Sci. Technol. 2024 , 54 , 1436–1458. [ Google Scholar ] [ CrossRef ]
• Duan, M.; Li, T.; Liu, B.; Yin, S.; Zang, J.; Lv, C.; Zhao, G.; Zhang, T. Zinc Nutrition and Dietary Zinc Supplements. Crit. Rev. Food Sci. Nutr. 2023 , 63 , 1277–1292. [ Google Scholar ] [ CrossRef ]
• Oueld Lhaj, M.; Moussadek, R.; Mouhir, L.; Mdarhri Alaoui, M.; Sanad, H.; Iben Halima, O.; Zouahri, A. Assessing the Evolution of Stability and Maturity in Co-Composting Sheep Manure with Green Waste Using Physico-Chemical and Biological Properties and Statistical Analyses: A Case Study of Botanique Garden in Rabat, Morocco. Agronomy 2024 , 14 , 1573. [ Google Scholar ] [ CrossRef ]
• Das, S.; Nag, S.K. Application of Multivariate Statistical Analysis Concepts for Assessment of Hydrogeochemistry of Groundwater—A Study in Suri I and II Blocks of Birbhum District, West Bengal, India. Appl. Water Sci. 2017 , 7 , 873–888. [ Google Scholar ] [ CrossRef ]
• Kumar, M.; Das, N.; Tripathi, S.; Verma, A.; Jha, P.K.; Bhattacharya, P.; Mahlknecht, J. Global Co-Occurrences of Multi-(Emerging)-Contaminants in the Hotspots of Arsenic Polluted Groundwater: A Pattern of Menace. Curr. Opin. Environ. Sci. Health 2023 , 34 , 100483. [ Google Scholar ] [ CrossRef ]
• Shi, J.; Zhao, D.; Ren, F.; Huang, L. Spatiotemporal Variation of Soil Heavy Metals in China: The Pollution Status and Risk Assessment. Sci. Total Environ. 2023 , 871 , 161768. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Wang, C.-C.; Zhang, Q.-C.; Yan, C.-A.; Tang, G.-Y.; Zhang, M.-Y.; Ma, L.Q.; Gu, R.-H.; Xiang, P. Heavy Metal(loid)s in Agriculture Soils, Rice, and Wheat across China: Status Assessment and Spatiotemporal Analysis. Sci. Total Environ. 2023 , 882 , 163361. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• George, S.E.; Wan, Y. Microbial Functionalities and Immobilization of Environmental Lead: Biogeochemical and Molecular Mechanisms and Implications for Bioremediation. J. Hazard. Mater. 2023 , 457 , 131738. [ Google Scholar ] [ CrossRef ] [ PubMed ]
• Pradhan, B.; Chand, S.; Chand, S.; Rout, P.R.; Naik, S.K. Emerging Groundwater Contaminants: A Comprehensive Review on Their Health Hazards and Remediation Technologies. Groundw. Sustain. Dev. 2023 , 20 , 100868. [ Google Scholar ] [ CrossRef ]
• Kükrer, S.; Mutlu, E. Assessment of Surface Water Quality Using Water Quality Index and Multivariate Statistical Analyses in Saraydüzü Dam Lake, Turkey. Environ. Monit. Assess. 2019 , 191 , 71. [ Google Scholar ] [ CrossRef ]
• Ward, J.H., Jr. Hierarchical Grouping to Optimize an Objective Function. J. Am. Stat. Assoc. 1963 , 58 , 236–244. [ Google Scholar ] [ CrossRef ]
• Stefan, D.S.; Bosomoiu, M.; Teodorescu, G. The Behavior of Polymeric Pipes in Drinking Water Distribution System—Comparison with Other Pipe Materials. Polymers 2023 , 15 , 3872. [ Google Scholar ] [ CrossRef ]

Click here to enlarge figure

Share and Cite

Sanad, H.; Moussadek, R.; Dakak, H.; Zouahri, A.; Oueld Lhaj, M.; Mouhir, L. Ecological and Health Risk Assessment of Heavy Metals in Groundwater within an Agricultural Ecosystem Using GIS and Multivariate Statistical Analysis (MSA): A Case Study of the Mnasra Region, Gharb Plain, Morocco. Water 2024 , 16 , 2417. https://doi.org/10.3390/w16172417

Sanad H, Moussadek R, Dakak H, Zouahri A, Oueld Lhaj M, Mouhir L. Ecological and Health Risk Assessment of Heavy Metals in Groundwater within an Agricultural Ecosystem Using GIS and Multivariate Statistical Analysis (MSA): A Case Study of the Mnasra Region, Gharb Plain, Morocco. Water . 2024; 16(17):2417. https://doi.org/10.3390/w16172417

Sanad, Hatim, Rachid Moussadek, Houria Dakak, Abdelmjid Zouahri, Majda Oueld Lhaj, and Latifa Mouhir. 2024. "Ecological and Health Risk Assessment of Heavy Metals in Groundwater within an Agricultural Ecosystem Using GIS and Multivariate Statistical Analysis (MSA): A Case Study of the Mnasra Region, Gharb Plain, Morocco" Water 16, no. 17: 2417. https://doi.org/10.3390/w16172417

Article Metrics

Article access statistics, further information, mdpi initiatives, follow mdpi.

Subscribe to receive issue release notifications and newsletters from MDPI journals

This paper is in the following e-collection/theme issue:

Published on 26.8.2024 in Vol 26 (2024)

Evaluation of a Natural Language Processing Approach to Identify Diagnostic Errors and Analysis of Safety Learning System Case Review Data: Retrospective Cohort Study

Authors of this article:

Original Paper

• Azade Tabaie 1, 2 , PhD   ; 
• Alberta Tran 3 , RN, CCRN, PhD   ; 
• Tony Calabria 3 , MA, CPHQ, CSSBB   ; 
• Sonita S Bennett 1 , MSc   ; 
• Arianna Milicia 4 , BSc   ; 
• William Weintraub 5, 6 , MACC, MD   ; 
• William James Gallagher 6, 7 , MD   ; 
• John Yosaitis 6, 8 , MD   ; 
• Laura C Schubel 4 , MPH   ; 
• Mary A Hill 9, 10 , MS   ; 
• Kelly Michelle Smith 9, 10 , PhD   ; 
• Kristen Miller 4, 6 , MSPH, MSL, CPPS, DrPH  

1 Center for Biostatistics, Informatics, and Data Science, MedStar Health Research Institute, Washington, DC, United States

2 Department of Emergency Medicine, Georgetown University School of Medicine, Washington, DC, United States

3 Department of Quality and Safety, MedStar Health Research Institute, Washington, DC, United States

4 National Center for Human Factors in Healthcare, MedStar Health Research Institute, Washington, DC, United States

5 Population Health, MedStar Health Research Institute, Washington, DC, United States

6 Georgetown University School of Medicine, Washington, DC, United States

7 Family Medicine Residency Program, MedStar Health Georgetown-Washington Hospital Center, Washington, DC, United States

8 MedStar Simulation Training & Education Lab (SiTEL), MedStar Institute for Innovation, Washington, DC, United States

9 Institute of Health Policy, Management & Evaluation, University of Toronto, Toronto, ON, Canada

10 Michael Garron Hospital, Toronto, ON, Canada

Corresponding Author:

Azade Tabaie, PhD

Center for Biostatistics, Informatics, and Data Science

MedStar Health Research Institute

3007 Tilden Street NW

Washington, DC, 20008

United States

Phone: 1 202 244 9810

Email: [email protected]

Background: Diagnostic errors are an underappreciated cause of preventable mortality in hospitals and pose a risk for severe patient harm and increase hospital length of stay.

Objective: This study aims to explore the potential of machine learning and natural language processing techniques in improving diagnostic safety surveillance. We conducted a rigorous evaluation of the feasibility and potential to use electronic health records clinical notes and existing case review data.

Methods: Safety Learning System case review data from 1 large health system composed of 10 hospitals in the mid-Atlantic region of the United States from February 2016 to September 2021 were analyzed. The case review outcome included opportunities for improvement including diagnostic opportunities for improvement. To supplement case review data, electronic health record clinical notes were extracted and analyzed. A simple logistic regression model along with 3 forms of logistic regression models (ie, Least Absolute Shrinkage and Selection Operator, Ridge, and Elastic Net) with regularization functions was trained on this data to compare classification performances in classifying patients who experienced diagnostic errors during hospitalization. Further, statistical tests were conducted to find significant differences between female and male patients who experienced diagnostic errors.

Results: In total, 126 (7.4%) patients (of 1704) had been identified by case reviewers as having experienced at least 1 diagnostic error. Patients who had experienced diagnostic error were grouped by sex: 59 (7.1%) of the 830 women and 67 (7.7%) of the 874 men. Among the patients who experienced a diagnostic error, female patients were older (median 72, IQR 66-80 vs median 67, IQR 57-76; P =.02), had higher rates of being admitted through general or internal medicine (69.5% vs 47.8%; P =.01), lower rates of cardiovascular-related admitted diagnosis (11.9% vs 28.4%; P =.02), and lower rates of being admitted through neurology department (2.3% vs 13.4%; P =.04). The Ridge model achieved the highest area under the receiver operating characteristic curve (0.885), specificity (0.797), positive predictive value (PPV; 0.24), and F 1 -score (0.369) in classifying patients who were at higher risk of diagnostic errors among hospitalized patients.

Conclusions: Our findings demonstrate that natural language processing can be a potential solution to more effectively identifying and selecting potential diagnostic error cases for review and therefore reducing the case review burden.

Introduction

Diagnostic errors are an underappreciated cause of preventable mortality in hospitals, estimated to affect a quarter million hospital inpatients, and account for an estimated 40,000-80,000 deaths annually in the United States [ 1 ]. These errors pose a risk for severe patient harm [ 2 , 3 ], increase hospital length of stay [ 4 ], and made up 22% and accounted for US $5.7 billion of paid malpractice claims in hospitalized patients throughout a nearly 13-year period [ 5 ]. In their analysis of malpractice claims occurring in the US National Practitioner Database from 1999 to 2011, Gupta et al [ 5 ] found that diagnosis-related paid claims were most likely to be associated with death and cost (following surgery); among diagnosis-related paid claims, failure to diagnose was the most common subtype and was more likely than other types to be associated with mortality. Several factors have been proposed as contributors to inpatient diagnostic errors including time constraints related to the concurrent care of multiple patients, unpredictable workflows, distractions, and competing priorities for trainees. From their systematic review and meta-analysis, Gunderson et al [ 2 ] estimate that 250,000 diagnostic adverse events occur annually among hospitalized patients in the United States, and this is likely an underestimation of the problem due to several challenges in diagnostic error measurement [ 6 ].

Challenges in identifying and measuring diagnostic errors occur due to the evolving and iterative nature of the diagnostic process, making it difficult to determine when, if at all, a correct or more specific diagnosis could have been established by clinicians to start the appropriate treatment [ 6 ]. Since its landmark report, Improving Diagnosis in Health Care , the National Academies of Science, Engineering, and Medicine (NASEM) has produced a common understanding of diagnostic error that includes accuracy, timeliness, and communication of the explanation to the patient or patient’s family member [ 3 ]. Diagnostic errors often involve missed opportunities related to various aspects of the diagnostic process [ 7 - 9 ] and diagnostic adverse events resulting in harm [ 10 ]. However, many hospitals currently do not capture or include surveillance for diagnostic errors, despite having robust systems in place to report and analyze patient safety issues [ 6 , 11 , 12 ].

A crucial first step to improving diagnosis in hospitals is the creation of programs to identify, analyze, and learn from diagnostic errors. Ongoing efforts by the Agency for Health Care Research and Quality have supported pragmatic measurement approaches for health organizations to build a diagnostic safety program and identify and learn from diagnostic errors such as those described in the Measure Dx resource [ 9 ]. One proposed and promising solution for hospitals to improve diagnostic surveillance is to build on existing efforts to collect patient safety data, root cause analyses, or other forms of case reviews for quality improvement purposes. Cases that have already been reviewed or investigated in the organization for general patient safety and quality purposes may be able to inform or be rereviewed for information and learning opportunities specific to diagnostic safety. Widely used case-based learning methodologies in particular, such as the “Learning From Every Death” initiative developed at Mayo Clinic [ 13 ] used both nationally and worldwide, offer an excellent opportunity for hospitals to augment their existing quality and safety efforts and support diagnostic safety.

Clinical notes in electronic health records (EHRs) written by health providers in free-text format are rich sources of a patient’s diagnoses and care trajectory through hospitalization time. Approaches to processing free text, such as through natural language processing (NLP) and machine learning (ML), have demonstrated significant opportunities to improve quality and safety within health care organizations in diverse applications [ 14 - 16 ] such as cancer research [ 17 , 18 ] and infection prediction [ 19 ] to sleep issues [ 20 ] and neurological outcome prediction [ 21 ]. Besides its use in the diagnostic process, ML models proved to have added benefits when used in diagnostic error identification [ 22 , 23 ]. However, despite significant progress and evidence about the use of these ML and NLP approaches to improve patient safety, the use of ML and NLP approaches to diagnostic safety and surveillance has largely remained untapped. A 2022 study demonstrates how an academic medical center’s implementation of an NLP-based algorithm to flag safety event reports for manual review enabled early detection of emerging diagnostic risks from large volumes of safety reports, and was among the first to apply an NLP approach to safety event reports to facilitate identification of COVID-19 related diagnostic errors [ 24 ]. Meanwhile, progress in the use of data mining approaches to develop electronic trigger tools offers promising methods to detect potential diagnostic events, promote organizational learning, and support the monitoring of data prospectively to identify patients at high risk for future adverse events [ 25 ]. To our knowledge, however, NLP has not yet been applied to case review data to facilitate the identification of diagnostic errors and understand its features and sources.

While free-text formatted clinical notes provide unique opportunities to incorporate ML models, the lack of reliable labels to represent diagnostic errors often limits the use of clinical notes for diagnostic safety surveillance efforts. The opportunity to train ML and NLP algorithms to identify diagnostic errors and opportunities depends on the collation of EHR data with existing efforts to identify diagnostic errors such as through case review findings from the Safety Learning System (SLS). To further explore the potential for this approach to be used to improve diagnostic safety surveillance, a rigorous evaluation of the feasibility and potential of using EHR and existing case review data is needed.

We hypothesized that ML and NLP methods can be applied to train models based on available case review data to examine content potentially related to diagnostic errors within EHR clinical notes. These approaches automatically identify features or information from free text using controlled vocabularies, rule sets, reference dictionaries, or lexicons.

Data Sets and Case Review Approach

We analyzed SLS data from 1 large health system comprised of 10 hospitals in the mid-Atlantic region of the United States. The SLS is one example of a holistic case review methodology delivered by health care organizations in the United States and globally. Established in 2015, the SLS builds upon the Mayo Clinic Mortality Review System of Huddleston et al [ 13 ] to review and analyze EHR data from patient mortality cases to find safety issues that could be found and mitigated. This approach was designed to enhance current quality improvement projects done within health organizations, providing a perspective and strategy based on the Safety II lens and rooted in the belief that every death provides an opportunity to improve care. With a Safety II lens, participating organizations use a holistic case review methodology designed to identify vulnerabilities in systems and processes of care delivery. Reviewers identify and translate these into different categories and labels to (1) define and quantify types of process of care and system failures contributing to adverse outcomes (errors) and (2) identify the components of the process of care and system failures that when fixed will improve performance (opportunities for improvement [OFIs]).

To ensure a sufficient cross-sampling of patients across different specialties and areas, patients are selected for case reviews at this health system based on their primary provider service line category (eg, medicine, surgery, etc) and hospital length of stay; patients in primary and ambulatory care settings are not included for case review selection. The case review process occurs according to the standardized SLS methodology and recommendations [ 13 , 26 ], and between at least 1 physician and 1 nurse within the health system who have both received training in the SLS approach. The case review outcome and identification of OFIs, including diagnostic OFIs, relies on the reviewer’s consensus of any findings and through multiple multidisciplinary and multispecialty meetings that may involve a committee Chair member, clinical department leader, or escalation to other leadership.

We obtained SLS data from February 2016 to September 2021; data in later years were available but not included because of key changes to the case selection process made during and in response to the COVID-19 pandemic. All hospitalized adult patients older than 18 years were included for analysis, regardless of their hospitalization outcome (eg, mortality or discharge location). Pediatric and neonatal patients were excluded.

Ethical Considerations

The original data collection and study protocol was approved by the institutional review board (00001245) at MedStar Health Research Institute on August 26, 2019.

Data Extraction

Medical record number, encounter number, length of stay, age, date of birth, sex, diagnosis at the time of admission (ie, ICD-10 [ International Statistical Classification of Diseases, Tenth Revision ] diagnosis codes), mortality, OFI categories (eg, delayed or missed diagnosis and diagnostic opportunities), number of identified OFIs and diagnosis issues (eg, the accuracy of diagnosis and confirmation or fixation bias) were the features and patient identifiers which were extracted from SLS data [ 13 , 26 ].

Because chart reviews generally occur at a single point in time within the patient’s care trajectory, they often do not contain information or details of the patient’s full hospital course. However, clinical notes written by health care providers are rich sources of patient’s health status throughout their hospitalization period [ 27 - 29 ]. Therefore, to supplement these chart review data, we additionally extracted and included all clinical notes from the EHR for patients who could be matched by patient identifiers (eg, encounter number and date of birth).

Coding Diagnostic Errors

Case reviewers can select any number of labels to describe a diagnosis issue or an OFI identified and agreed upon by consensus. For this study, diagnostic errors were defined by the available features from chart review pertaining to diagnosis and impacting the timeliness, accuracy, or communication of a diagnosis. Our definition of diagnostic errors was limited to the categories identified during chart reviews and recorded within the SLS data set; therefore, our diagnostic error definition does not include all aspects of the definition developed by the NASEM report [ 3 ]. Table 1 describes the SLS categories and values that were labeled as diagnostic errors and used to train our classification models. Patients were coded as having experienced a diagnostic error if one or more of the conditions listed in Table 1 were identified in their SLS case review.

a OFI: opportunity for improvement.

NLP Approach

We used an NLP approach on critical incident reporting system data to explore the features and risk of diagnostic error among hospitalized patients.

Features From Free-Text Data

Descriptive statistical analyses were performed to identify any differences among age, length of stay, and mortality between the female and male patients who had experienced diagnostic errors.

All EHR clinical notes were transformed to lowercase. Extra white spaces, numbers, punctuations, and stop words were removed and words were stemmed. The term frequency-inverse document frequency (TF-IDF) matrix was calculated for each clinical note using the bag-of-words from the preprocessed EHR clinical notes [ 30 ]. TF-IDF is a statistical measure that evaluates how relevant a word is to a document in a collection of documents and is a popular method to translate free text to numerical features in training ML models. The TF-IDF of a word in a document is calculated by multiplying 2 metrics: the number of times a word appeared in a document and the inverse document frequency of the word across a set of documents. TF-IDF is computationally efficient and easy to interpret. We excluded the most frequent words that had appeared in more than 95% of the EHR clinical notes, as these frequent words do not provide information to help with the classification. Moreover, we excluded the rare words that appeared in less than 5% of the EHR clinical notes [ 31 ].

In a TF-IDF matrix, the number of rows corresponds to the unique patients, and the number of columns represents the unique words found in EHR clinical notes. There are numerous unique words used in EHR clinical notes; therefore, the TF-IDF approach provides a high-dimensional input matrix for the classification task. The high-dimensional input matrix can lead to training inaccurate classifiers. To overcome that issue, we used the chi-square statistical test to select the most relevant words to identify diagnostic errors; therefore, if P values associated with a word (also called a feature) are less than .05, that word is selected and included in the feature matrix to train ML classification models.

Classification Models

In lieu of an existing model with the same objective in the literature, a simple logistic regression model was trained as the baseline classifier to identify patients within SLS data who were at higher risk of diagnostic error. Moreover, 3 forms of logistic regression models with regularization functions were trained on this data to compare classification performances and identify the best-performing model [ 32 ]: Least Absolute Shrinkage and Selection Operator (LASSO), Ridge, and Elastic Net.

• LASSO: for a more accurate prediction, LASSO regularization is used with a logistic regression model. The LASSO procedure encourages simple, sparse models which has fewer parameters in a way that the estimated coefficient of features with less effect will be set to zero. This characteristic makes LASSO well-suited for models showing high levels of multicollinearity or variable selection and parameter elimination is needed. LASSO is also called L1 regularization.
• Ridge: also called L2 regularization, Ridge is a regularization method used for models suffering from multicollinearity or high-dimensional feature space. Ridge regularization keeps all the features regardless of their effect on the model. However, it pushes the estimated coefficient of features with less effect toward zero to minimize their effect on the classification outcome. This characteristic of Ridge makes it well-suited when most features impact the outcome variable.
• Elastic Net: a logistic regression model with Elastic Net regularization is a weighted combination of LASSO (L1) and Ridge (L2) regularizations [ 33 ]. Elastic Net can remove the effect of the insignificant features by setting their estimated coefficient to zero and lower the effect of the less significant features by pushing their estimated coefficient toward zero while adding more weights to the more important features. From implementation and interpretation aspects, the Elastic Net model is simple to use. Such characteristics make this model an accepted baseline in ML-based studies [ 34 ].

The hyperparameters of the 3 classification models were optimized through cross-validation. All the analyses were conducted using Python 3 (Python Software Foundation).

Classification Performance Metrics

We calculated 7 common performance metrics reported for binary classifiers to compare the performance of the 4 classification models: area under receiver operating characteristics curve (AUROC), sensitivity or recall or true positive rate, specificity or true negative rate, positive predictive value (PPV) or precision, negative predictive value (NPV), F 1 -score, and area under precision-recall curve (AUPRC). The 7 metrics take values between 0 and 1. Values closer to 1 indicate a well-performing classifier. Multimedia Appendix 1 presents the definition of the performance metrics used in this study. Figure 1 presents the summary of the methods used in this analysis.

Descriptive Summary

In total, there were 2184 unique patient records within SLS data from February 2016 to September 2021. EHR clinical notes were cross-matched, extracted, and included in analyses for 1704 (78%) of these SLS patient records. Of those patients with cross-matched EHR data, 126 (7.4%) patients had been identified by case reviewers as having experienced at least 1 diagnostic error. A total number of 20,848 EHR clinical notes associated with the 1704 unique patients were used in this study.

Patients who had experienced diagnostic errors were grouped by sex: 59 (7.1%) of the 830 women and 67 (7.7%) of the 874 men in the larger cross-matched sample had been found to have a diagnostic error. Table 2 presents the descriptive statistics between female and male patient groups. We applied the Wilcoxon rank sum test for numerical features (ie, age and length of stay), and the chi-square test for mortality rate, admission diagnosis, and admission department or specialty. Patients in the female group were older than the male group by a median of 72 (IQR 66-80) versus a median of 67 (IQR 57-76; P =.02). Compared to the male group, female patients who experienced diagnostic error had higher rates of being admitted through general or internal medicine (69.5% vs 47.8%; P =.01), lower rates of cardiovascular-related admitted diagnosis (11.9% vs 28.4%; P =.02), and lower rates of being admitted through neurology department (2.3% vs 13.4%; P =.04). We observed no differences between groups in mortality rates and length of stay.

Classification Models’ Performance

Clinical notes were preprocessed for TF-IDF feature calculation. The bag-of-words included 2227 words, and each word was considered a feature (see Table S1 in Multimedia Appendix 2 for the top 100 words). We found that abscess, ascend, abnormality, scant, pair, and prefer were the top 5 features with the highest positive estimated coefficient (0.42 to 0.28); post, select, gave, muscl, hours, and unrespons were the top 5 features with the highest negative coefficients (–0.35 to –0.26). After applying the chi-square test, 250 features with a P value less than .05 were selected for the modeling process. All 4 ML classifiers were trained using the 250 selected features.

Table 3 presents the performances of the simple logistic regression and 3 regularized logistic regression models (LASSO, Ridge, and Elastic Net). The Ridge model achieved the highest AUROC (0.885), specificity (0.797), PPV (0.24), NPV (0.981), and F 1 -score (0.369) in classifying patients who were at higher risk of diagnostic errors among hospitalized patients in SLS system. The simple logistic regression model obtained the highest AUPRC (0.537). The simple logistic regression model classified all patients as the ones with diagnostic errors; therefore, it achieved a sensitivity of 1, and specificity and NPV of 0.

Figures 2 and 3 present the receiver operating characteristics curves and precision-recall curves for the 4 classifiers in this study. Models that give ROC curves closer to the top-left corner indicate a better performance. The AUROC values represent the probability that a patient who experienced a diagnostic error, chosen at random, is ranked higher by the Ridge model than a randomly chosen patient who did not experience a diagnostic error. The higher value of AUPRC indicates that the Ridge model can identify patients who experienced diagnostic errors more precisely with fewer false positives compared to LASSO and Elastic Net models.

a LASSO: Least Absolute Shrinkage and Selection Operator.

b AUROC: area under receiver operating characteristics curve.

c AUPRC: area under precision-recall curve.

Principal Findings

Our contribution is 2-fold; first, we integrated 2 data sources that are currently used by and available to many organizations across the United States, SLS and EHR data, to explore the use of ML and NLP algorithms to help identify diagnostic errors among hospitalized patients. Although case review methodologies offer rich insights into systems errors and OFIs, the predefined pull-down menus and structured data labels typically do not capture all the necessary clinical and contextual details that are considered by reviewers. Therefore, a large portion of these case review data are stored in free-text narratives that typically record key information and judgments decided upon by the multidisciplinary reviewers. However, given persistent issues of staff shortage and lack of time in health care settings, it is becoming increasingly important to lower the burden of systematic EHR data reviews for health care providers while maintaining the review systems in place. Second, any developed ML and NLP approaches can potentially be incorporated to generate a diagnostic error risk score for each patient. The predicted risk score can be used in identifying and prioritizing patients for focused chart reviews, thus lowering the burden of systematic EHR data reviews for health care providers while maintaining the review systems in place.

To our knowledge, this study is the first attempt to apply and test several different ML classification models to identify diagnostic errors within routinely collected organizational case review data. Despite a substantial body of literature about the prevalence of diagnostic errors in hospital settings, current efforts to identify diagnostic errors generally rely on reviews of patient cases and data by clinical or quality teams that often are resource-intensive. ML classification models and NLP techniques offer an opportunity to generate diagnostic error risk scores to sort through large data sets and identify signals of potential diagnostic errors that can be flagged for further review. However, these classification models require a high number of observations (and identified diagnostic errors) to perform well, which might not be feasible for health organizations that are just beginning to identify diagnostic errors or may have limited personnel and efforts to perform high numbers of case reviews. In this study, we accessed nearly 2000 patient records (and of those, only 126 cases of diagnostic errors), which is considered to be a limited data sample size in the field of ML. However, techniques, such as feature selection and n-fold cross-validations, can potentially be approaches to address small sample size challenges [ 35 ].

Using the results of the simple logistic regression model as the baseline performance, we found that 3 regularization functions, namely LASSO, Ridge, and Elastic Net, boosted the performance of the baseline model. The Ridge model outperformed the rest of the models in terms of multiple performance metrics: AUROC of 0.885, specificity of 0.797, PPV of 0.24, NPV of 0.981, and F 1 -score of 0.369. The Ridge algorithm tries to keep all features in the model even the features with a slight effect on the classification outcome. Since the patterns pointing at a diagnostic error were subtle in the clinical notes, even a small effect of a feature on the model’s classification outcome could be important for the classification model to learn. On the other hand, the LASSO algorithm rigorously removes features that have a small effect on the classification outcome. The Elastic Net model is a weighted combination of LASSO and Ridge. The performance results presented in Table 3 show that the values achieved by the Elastic Net model lie between those of the LASSO and Ridge models.

Insights From Diagnostic Errors Within Free-Text Clinical Notes

We did not find the free text formatted clinical notes in the EHR to reflect any sort of direct language around diagnostic errors. Our analysis identified no use of the terms misdiagnosis, missed diagnosis, or diagnostic error within clinical notes, finding instead more subtle signals pointing at diagnostic errors such as “there may be a chance of misreading the test,” or “insufficient data to make a diagnosis.” Our findings demonstrate that NLP algorithms can be used to identify such patterns and find the associations between diagnostic errors and the subtle signals in the clinical notes. A natural extension of this work can focus on using other feature extraction methods, such as Bidirectional Encoder Representations from Transformers contextualized word embeddings, and explore the use of the pretrained language models for this objective.

We found that the presence of terms, such as abscess, abnormality, “cp” (chest pain) , and dialysis in a patient’s EHR clinical note were associated with reviewer-identified diagnostic errors ( Multimedia Appendix 2 ). Misinterpretation of chest pain, specifically among female patients, has the potential to cause a cardiovascular-related diagnosis error [ 36 ]. Patients with chronic kidney disease are at higher risk of cardiovascular complications [ 37 ]. Missing such risk for a patient who is on dialysis, adds to the risk of diagnostic error.

Clinical and System Implications Around Diagnostic Inequity

Diagnostic inequity is defined as “the presence of preventable unwarranted variations in diagnostic process among population groups that are socially, economically, demographically, or geographically disadvantaged” [ 38 ]. Despite persistent and well-documented disparities in health care access and outcomes across different population groups, few studies have examined the association between diagnostic errors and health care disparities [ 39 ]. Recent evidence supports the notion that variation in diagnostic error rates across demographic groups may exist, particularly across sex. A systematic review of diagnostic errors in the emergency department, for example, found that female sex and non-White race were often associated with increased risk for diagnostic errors across several clinical conditions in emergency settings [ 40 ]. In cardiovascular medicine, a national cohort study of acute myocardial infarctions found that women were nearly twice as likely as men to receive the wrong initial diagnosis following signs of a heart attack [ 41 ]. Despite efforts to understand and reduce disparities in diagnosis and treatment, women not only continue to be understudied, underdiagnosed, and undertreated in cardiovascular medicine [ 42 ] but also may experience longer lengths of time to diagnosis than men in most patterns of disease diagnosis [ 43 ].

The analysis of case review data and other system-based data (eg, patient safety events or incident reporting) by subsets offer an opportunity to identify events in vulnerable patient populations and help sensitize clinicians to potential biases within the diagnostic process. To explore sex differences in diagnostic errors within our case review data, we statistically compared demographic and clinical differences between female and male patients who had been identified in case reviews as having experienced diagnostic error or errors. We found that of those patients who had experienced diagnostic error or errors, the female group of patients were older, had higher rates of being admitted through general or internal medicine or hospitalist (vs specialty) departments, and had lower rates of having a cardiovascular diagnosis on admission. These preliminary results of this study revealed unexpected differences between male and female diagnostic error groups, offering novel insights that warrant further investigation to fully understand the mechanisms underlying these relationships and their implications for clinical decision-making and practice. Future uses of NLP can potentially support clinical and system-based approaches to capture and increase the evidence around structural biases or disparities in diagnoses. Individual cases from these types of data sources could be used as example narratives to engage clinicians and improve clinician learning, contributing to the development of tailored clinician and systemic interventions that can improve quality and equity throughout the diagnostic process.

Limitations

This study has several limitations. Our definition of diagnostic errors was limited to the categories and labels used within the SLS data set, reviewer interpretations of cases (subject to reviewer bias), and does not include all aspects of the definition developed by the NASEM report [ 3 ]. Despite several continued differences in definitions of diagnostic error in the peer-reviewed literature [ 8 ], we recommend that quality and safety teams within health systems use the NASEM definition for diagnostic error—including errors in communicating the diagnosis to the patient—to develop any definitions, categories, or labels used in their case review and surveillance initiatives. Although a time-consuming task, future studies could consider EHR data chart reviews to have the ground truth for the diagnostic error cases and add to the accuracy of the data set used for training the ML classifiers. Additionally, due to staffing challenges and shifting organizational priorities, case review selection varies by hospital and has changed over time, resulting in a relatively small sample size and also introducing the potential for bias. Our data came from a single health system and may reflect the specific language, culture, and practices occurring within the system and therefore may not be similar to that of other health systems. To enhance the external validity and generalizability of results, future efforts and research studies should consider the random selection of cases to evaluate both diagnostic and general quality issues within the organization; studies with larger sample sizes can build on our preliminary findings and test differences between clinical subgroups. Finally, our classification models were developed and evaluated based on a retrospective cohort from EHR; therefore, the performance may deteriorate when the method is applied to real-time data. Further work or future studies should be conducted to prospectively validate the models.

Conclusions

We performed an NLP approach and compared 4 techniques to classify patients who were at a higher risk of experiencing diagnostic error during hospitalization. Our findings demonstrate that NLP can be a potential solution to more effectively identifying and selecting potential diagnostic error cases for review, and therefore, reducing the case review burden.

Acknowledgments

This work was supported by the Agency for Health Care Research and Quality (grant 5R18HS027280-02).

Conflicts of Interest

None declared.

Binary classification performance metrics.

The Estimated Coefficient from the Ridge Model.

• Newman-Toker DE, Wang Z, Zhu Y, Nassery N, Saber Tehrani AS, Schaffer AC, et al. Rate of diagnostic errors and serious misdiagnosis-related harms for major vascular events, infections, and cancers: toward a national incidence estimate using the "Big Three". Diagnosis (Berl). Feb 23, 2021;8(1):67-84. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Gunderson CG, Bilan VP, Holleck JL, Nickerson P, Cherry BM, Chui P, et al. Prevalence of harmful diagnostic errors in hospitalised adults: a systematic review and meta-analysis. BMJ Qual Saf. Dec 2020;29(12):1008-1018. [ CrossRef ] [ Medline ]
• Ball JR, Miller BT, Balogh EP. Improving Diagnosis in Health Care. Washington, DC. National Academies Press; 2015:472.
• Hautz WE, Kämmer JE, Hautz SC, Sauter TC, Zwaan L, Exadaktylos AK, et al. Diagnostic error increases mortality and length of hospital stay in patients presenting through the emergency room. Scand J Trauma Resusc Emerg Med. May 08, 2019;27(1):54. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Gupta A, Snyder A, Kachalia A, Flanders S, Saint S, Chopra V. Malpractice claims related to diagnostic errors in the hospital. BMJ Qual Saf. Aug 09, 2017;27(1):53-60. [ CrossRef ] [ Medline ]
• Zwaan L, Singh H. Diagnostic error in hospitals: finding forests not just the big trees. BMJ Qual Saf. 2020;29(12):961-964. [ CrossRef ] [ Medline ]
• Singh H. Editorial: helping health care organizations to define diagnostic errors as missed opportunities in diagnosis. Jt Comm J Qual Patient Saf. 2014;40(3):99-101. [ CrossRef ] [ Medline ]
• Giardina TD, Hunte H, Hill MA, Heimlich SL, Singh H, Smith KM. Defining diagnostic error: a scoping review to assess the impact of the national academies' report improving diagnosis in health care. J Patient Saf. 2022;18(8):770-778. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Measure Dx: a resource to identify, analyze, and learn from diagnostic safety events. AHRQ: Agency for Healthcare Research and Quality. URL: https://www.ahrq.gov/patient-safety/settings/multiple/measure-dx.html [accessed 2023-06-22]
• Zwaan L, de Bruijne M, Wagner C, Thijs A, Smits M, van der Wal G, et al. Patient record review of the incidence, consequences, and causes of diagnostic adverse events. Arch Intern Med. 2010;170(12):1015-1021. [ CrossRef ] [ Medline ]
• Singh H. Diagnostic errors: moving beyond 'no respect' and getting ready for prime time. BMJ Qual Saf. 2013;22(10):789-792. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Zwaan L, El-Kareh R, Meyer AND, Hooftman J, Singh H. Advancing diagnostic safety research: results of a systematic research priority setting exercise. J Gen Intern Med. 2021;36(10):2943-2951. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Huddleston JM, Diedrich DA, Kinsey GC, Enzler MJ, Manning DM. Learning from every death. J Patient Saf. 2014;10(1):6-12. [ CrossRef ] [ Medline ]
• Arnaud E, Elbattah M, Gignon M, Dequen G. NLP-based prediction of medical specialties at hospital admission using triage notes. 2021. Presented at: IEEE 9th International Conference on Healthcare Informatics (ICHI); August 09-12, 2021; Victoria, British Columbia, Canada. [ CrossRef ]
• Bansal A, Saraswat B, Sharma B, Nayan S, Kathuria K. Data extraction and integration from unstructured electronic health records. 2023. Presented at: 2023 3rd International Conference on Technological Advancements in Computational Sciences (ICTACS); November 01-03, 2023; Tashkent, Uzbekistan. [ CrossRef ]
• Sezgin E, Hussain S, Rust S, Huang Y. Extracting medical information from free-text and unstructured patient-generated health data using natural language processing methods: feasibility study with real-world data. JMIR Form Res. 2023;7:e43014. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Asri H, Mousannif H, Moatassime HA, Noel T. Using machine learning algorithms for breast cancer risk prediction and diagnosis. Procedia Comput Sci. 2016;83:1064-1069. [ CrossRef ]
• Goldenberg SL, Nir G, Salcudean SE. A new era: artificial intelligence and machine learning in prostate cancer. Nat Rev Urol. 2019;16(7):391-403. [ CrossRef ] [ Medline ]
• Henry KE, Adams R, Parent C, Soleimani H, Sridharan A, Johnson L, et al. Factors driving provider adoption of the TREWS machine learning-based early warning system and its effects on sepsis treatment timing. Nat Med. 2022;28(7):1447-1454. [ CrossRef ] [ Medline ]
• Nasiri S, Gari D. Attentive adversarial network for large-scale sleep staging. 2020. Presented at: Proceedings of the 5th Machine Learning for Healthcare Conference, PMLR; September, 2020:457-478; Atlanta, GA, USA. URL: https://proceedings.mlr.press/v126/nasiri20a.html
• Tabaie A, Nemati S, Allen JW, Chung C, Queiroga F, Kuk W, et al. Assessing contribution of higher order clinical risk factors to prediction of outcome in aneurysmal subarachnoid hemorrhage patients. AMIA Annu Symp Proc. 2019;2019:848-856. [ FREE Full text ] [ Medline ]
• Cheheltani R, King N, Lee S, North B, Kovarik D, Evans-Molina C, et al. Predicting misdiagnosed adult-onset type 1 diabetes using machine learning. Diabetes Res Clin Pract. 2022;191:110029. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Hercus C, Hudaib A. Delirium misdiagnosis risk in psychiatry: a machine learning-logistic regression predictive algorithm. BMC Health Serv Res. 2020;20(1):151. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Shen L, Levie A, Singh H, Murray K, Desai S. Harnessing event report data to identify diagnostic error during the COVID-19 pandemic. Jt Comm J Qual Patient Saf. 2022;48(2):71-80. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Murphy DR, Meyer AN, Sittig DF, Meeks DW, Thomas EJ, Singh H. Application of electronic trigger tools to identify targets for improving diagnostic safety. BMJ Qual Saf. 2019;28(2):151-159. [ FREE Full text ] [ CrossRef ] [ Medline ]
• The safety learning system®. Discover the Hidden Causes of Patient Harm. URL: http://hbhealthcaresafety.org/safety-learning-system/ [accessed 2023-06-22]
• Alsentzer E, Murphy J, Boag W, Weng W, Jindi D, Naumann T, et al. Publicly available clinical BERT embeddings. 2019. Presented at: Proceedings of the 2nd Clinical Natural Language Processing Workshop; June, 2019; Minneapolis, Minnesota, USA. [ CrossRef ]
• Huang K, Altosaar A, Ranganath R. ClinicalBERT: modeling clinical notes and predicting hospital readmission. ArXiv. Preprint posted online on April 10, 2019. 2019:190405342. [ CrossRef ]
• Zhang D, Thadajarassiri J, Sen C, Rundensteiner E. Time-aware transformer-based network for clinical notes series prediction. 2020. Presented at: Proceedings of the 5th Machine Learning for Healthcare Conference; September, 2020; Worcester, MA, USA. URL: https://proceedings.mlr.press/v126/zhang20c/zhang20c.pdf
• Jones KS. A statistical interpretation of term specificity and its application in retrieval. J Doc. 1972;28(1):11-21. [ CrossRef ]
• Tabaie A, Sengupta S, Pruitt ZM, Fong A. A natural language processing approach to categorise contributing factors from patient safety event reports. BMJ Health Care Inform. 2023;30(1):e100731. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Aheto JMK, Duah HO, Agbadi P, Nakua EK. A predictive model, and predictors of under-five child malaria prevalence in Ghana: how do LASSO, Ridge and Elastic Net regression approaches compare? Prev Med Rep. 2021;23:101475. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Zou H, Trevor H. Regularization and variable selection via the Elastic Net. J R Stat Soc Series B Stat Methodol. 2005;67(2):301-320. [ CrossRef ]
• Lee SJ, Weinberg BD, Gore A, Banerjee I. A scalable natural language processing for inferring BT-RADS categorization from unstructured brain magnetic resonance reports. J Digit Imaging. 2020;33(6):1393-1400. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Masino AJ, Harris MC, Forsyth D, Ostapenko S, Srinivasan L, Bonafide CP, et al. Machine learning models for early sepsis recognition in the neonatal intensive care unit using readily available electronic health record data. PLoS One. 2019;14(2):e0212665. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Mehta PK, Bess C, Elias-Smale S, Vaccarino V, Quyyumi A, Pepine CJ, et al. Gender in cardiovascular medicine: chest pain and coronary artery disease. Eur Heart J. 2019;40(47):3819-3826. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Jankowski J, Floege J, Fliser D, Böhm M, Marx N. Cardiovascular disease in chronic kidney disease: pathophysiological insights and therapeutic options. Circulation. 2021;143(11):1157-1172. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Giardina TD, Woodard LD, Singh H. Advancing diagnostic equity through clinician engagement, community partnerships, and connected care. J Gen Intern Med. 2023;38(5):1293-1295. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Ibrahim SA, Pronovost PJ. Diagnostic errors, health disparities, and artificial intelligence: a combination for health or harm? JAMA Health Forum. 2021;2(9):e212430. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Newman-Toker DE, Peterson SM, Badihian S, Hassoon A, Nassery N, Parizadeh D, et al. Diagnostic Errors in the Emergency Department: A Systematic Review [internet]. Rockville (MD). Agency for Healthcare Research and Quality; 2022.
• Wu J, Gale CP, Hall M, Dondo TB, Metcalfe E, Oliver G, et al. Editor's choice—impact of initial hospital diagnosis on mortality for acute myocardial infarction: a national cohort study. Eur Heart J Acute Cardiovasc Care. 2018;7(2):139-148. [ FREE Full text ] [ CrossRef ] [ Medline ]
• Burgess S. Understudied, under-recognized, underdiagnosed, and undertreated: sex-based disparities in cardiovascular medicine. Circ Cardiovasc Interv. 2022;15(2):e011714. [ CrossRef ] [ Medline ]
• Sun TY, Hardin J, Nieva HR, Natarajan K, Cheng R, Ryan P, et al. Large-scale characterization of gender differences in diagnosis prevalence and time to diagnosis. MedRxiv. Preprint posted online on October 16, 2023. 2023. [ FREE Full text ] [ CrossRef ] [ Medline ]

Abbreviations

Edited by S Ma, T Leung; submitted 17.07.23; peer-reviewed by D Chrimes, M Elbattah; comments to author 18.01.24; revised version received 21.03.24; accepted 20.06.24; published 26.08.24.

©Azade Tabaie, Alberta Tran, Tony Calabria, Sonita S Bennett, Arianna Milicia, William Weintraub, William James Gallagher, John Yosaitis, Laura C Schubel, Mary A Hill, Kelly Michelle Smith, Kristen Miller. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 26.08.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.