problem solving process in healthcare

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9 Problem-Solving and Decision-Making Skills for Public Health Practice

• Published: October 2023
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This chapter provides an initial definition of problem-solving and the components of the problem-solving process. It identifies common mistakes early in the process and their implications. It explains that the first step toward successful problem-solving is thoroughly and accurately defining the problem and acknowledging that multiple solutions must be considered. It explores multiple approaches to problem-solving, such as rational problem-solving and organic problem-solving, as well as a type of organic problem-solving called appreciative inquiry. The chapter also explores seven decision-making styles and elaborates on common mistakes made during the process, as well as how to overcome them.

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Making Decisions and Solving Problems

CHAPTER 6 Making Decisions and Solving Problems Rose Aguilar Welch This chapter describes the key concepts related to problem solving and decision making. The primary steps of the problem-solving and decision-making processes, as well as analytical tools used for these processes, are explored. Moreover, strategies for individual or group problem solving and decision making are presented. Objectives •  Apply a decision-making format to list options to solve a problem, identify the pros and cons of each option, rank the options, and select the best option. •  Evaluate the effect of faulty information gathering on a decision-making experience. •  Analyze the decision-making style of a nurse leader/manager. •  Critique resources on the Internet that focus on critical thinking, problem solving, and decision making. Terms to Know autocratic creativity critical thinking decision making democratic optimizing decision participative problem solving satisficing decision The Challenge Vickie Lemmon RN, MSN Director of Clinical Strategies and Operations, WellPoint, Inc., Ventura, California Healthcare managers today are faced with numerous and complex issues that pertain to providing quality services for patients within a resource-scarce environment. Stress levels among staff can escalate when problems are not resolved, leading to a decrease in morale, productivity, and quality service. This was the situation I encountered in my previous job as administrator for California Children Services (CCS). When I began my tenure as the new CCS administrator, staff expressed frustration and dissatisfaction with staffing, workload, and team communications. This was evidenced by high staff turnover, lack of teamwork, customer complaints, unmet deadlines for referral and enrollment cycle times, and poor documentation. The team was in crisis, characterized by in-fighting, blaming, lack of respectful communication, and lack of commitment to program goals and objectives. I had not worked as a case manager in this program. It was hard for me to determine how to address the problems the staff presented to me. I wanted to be fair but thought that I did not have enough information to make immediate changes. My challenge was to lead this team to greater compliance with state-mandated performance measures. What do you think you would do if you were this nurse? Introduction Problem solving and decision making are essential skills for effective nursing practice. Carol Huston (2008) identified “expert decision-making skills” as one of the eight vital leadership competencies for 2020. These processes not only are involved in managing and delivering care but also are essential for engaging in planned change. Myriad technologic, social, political, and economic changes have dramatically affected health care and nursing. Increased patient acuity, shorter hospital stays, shortage of healthcare providers, increased technology, greater emphasis on quality and patient safety, and the continuing shift from inpatient to ambulatory and home health care are some of the changes that require nurses to make rational and valid decisions. Moreover, increased diversity in patient populations, employment settings, and types of healthcare providers demands efficient and effective decision making and problem solving. More emphasis is now placed on involving patients in decision making and problem solving and using multidisciplinary teams to achieve results. Nurses must possess the basic knowledge and skills required for effective problem solving and decision making. These competencies are especially important for nurses with leadership and management responsibilities. Definitions Problem solving and decision making are not synonymous terms. However, the processes for engaging in both processes are similar. Both skills require critical thinking, which is a high-level cognitive process, and both can be improved with practice. Decision making is a purposeful and goal-directed effort that uses a systematic process to choose among options. Not all decision making begins with a problem situation. Instead, the hallmark of decision making is the identification and selection of options or alternatives. Problem solving, which includes a decision-making step, is focused on trying to solve an immediate problem, which can be viewed as a gap between “what is” and “what should be.” Effective problem solving and decision making are predicated on an individual’s ability to think critically. Although critical thinking has been defined in numerous ways, Scriven and Paul (2007) refer to it as “ the intellectually disciplined process of actively and skillfully conceptualizing, applying, analyzing, synthesizing, and/or evaluating information gathered from, or generated by, observation, experience, reflection, reasoning, or communication, as a guide to belief and action.” Effective critical thinkers are self-aware individuals who strive to improve their reasoning abilities by asking “why,” “what,” or “how.” A nurse who questions why a patient is restless is thinking critically. Compare the analytical abilities of a nurse who assumes a patient is restless because of anxiety related to an upcoming procedure with those of a nurse who asks if there could be another explanation and proceeds to investigate possible causes. It is important for nurse leaders and managers to assess staff members’ ability to think critically and enhance their knowledge and skills through staff-development programs, coaching, and role modeling. Establishing a positive and motivating work environment can enhance attitudes and dispositions to think critically. Creativity is essential for the generation of options or solutions. Creative individuals can conceptualize new and innovative approaches to a problem or issue by being more flexible and independent in their thinking. It takes just one person to plant a seed for new ideas to generate . The model depicted in Figure 6-1 demonstrates the relationship among related concepts such as professional judgment, decision making, problem solving, creativity, and critical thinking. Sound clinical judgment requires critical or reflective thinking. Critical thinking is the concept that interweaves and links the others. An individual, through the application of critical-thinking skills, engages in problem solving and decision making in an environment that can promote or inhibit these skills. It is the nurse leader’s and manager’s task to model these skills and promote them in others. FiGURE 6-1 Problem-solving and decision-making model. Decision Making This section presents an overview of concepts related to decision models, decision-making styles, factors affecting decision making, group decision making (advantages and challenges), and strategies and tools. The phases of the decision-making process include defining objectives, generating options, identifying advantages and disadvantages of each option, ranking the options, selecting the option most likely to achieve the predefined objectives, implementing the option, and evaluating the result. Box 6-1 contains a form that can be used to complete these steps. BOX 6-1    Decision-Making Format Objective: _____________________________________ Options Advantages Disadvantages Ranking                                 Add more rows as necessary. Rank priority of options, with “1” being most preferred. Select the best option. Implementation plan: ______________________________________________________________________________ Evaluation plan: __________________________________________________________________________________ A poor-quality decision is likely if the objectives are not clearly identified or if they are inconsistent with the values of the individual or organization. Lewis Carroll illustrates the essential step of defining the goal, purpose, or objectives in the following excerpt from Alice’s Adventures in Wonderland: One day Alice came to a fork in the road and saw a Cheshire Cat in a tree. “Which road do I take?” she asked. His response was a question: “Where do you want to go?” “I don’t know,” Alice answered. “Then,” said the cat, “it doesn’t matter.” Decision Models The decision model that a nurse uses depends on the circumstances. Is the situation routine and predictable or complex and uncertain? Is the goal of the decision to make a decision conservatively that is just good enough or one that is optimal? If the situation is fairly routine, nurse leaders and managers can use a normative or prescriptive approach. Agency policy, standard procedures, and analytical tools can be applied to situations that are structured and in which options are known. If the situation is subjective, non-routine, and unstructured or if outcomes are unknown or unpredictable, the nurse leader and manager may need to take a different approach. In this case, a descriptive or behavioral approach is required. More information will need to be gathered to address the situation effectively. Creativity, experience, and group process are useful in dealing with the unknown. In the business world, Camillus described complex problems that are difficult to describe or resolve as “wicked” (as cited in Huston, 2008 ). This term is apt in describing the issues that nurse leaders face. In these situations, it is especially important for nurse leaders to seek expert opinion and involve key stakeholders. Another strategy is satisficing. In this approach, the decision maker selects the solution that minimally meets the objective or standard for a decision. It allows for quick decisions and may be the most appropriate when time is an issue. Optimizing is a decision style in which the decision maker selects the option that is best, based on an analysis of the pros and cons associated with each option. A better decision is more likely using this approach, although it does take longer to arrive at a decision. For example, a nursing student approaching graduation is contemplating seeking employment in one of three acute care hospitals located within a 40-mile radius of home. The choices are a medium-size, not-for-profit community hospital; a large, corporate-owned hospital; and a county facility. A satisficing decision might result if the student nurse picked the hospital that offered a decent salary and benefit packet or the one closest to home. However, an optimizing decision is more likely to occur if the student nurse lists the pros and cons of each acute care hospital being considered such as salary, benefits, opportunities for advancement, staff development, and mentorship programs. Decision-Making Styles The decision-making style of a nurse manager is similar to the leadership style that the manager is likely to use. A manager who leans toward an autocratic style may choose to make decisions independent of the input or participation of others. This has been referred to as the “decide and announce” approach, an authoritative style. On the other hand, a manager who uses a democratic or participative approach to management involves the appropriate personnel in the decision-making process. It is imperative for managers to involve nursing personnel in making decisions that affect patient care. One mechanism for doing so is by seeking nursing representation on various committees or task forces. Participative management has been shown to increase work performance and productivity, decrease employee turnover, and enhance employee satisfaction. Any decision style can be used appropriately or inappropriately. Like the tenets of situational leadership theory, the situation and circumstances should dictate which decision-making style is most appropriate. A Code Blue is not the time for managers to democratically solicit volunteers for chest compressions! The autocratic method results in more rapid decision making and is appropriate in crisis situations or when groups are likely to accept this type of decision style. However, followers are generally more supportive of consultative and group approaches. Although these approaches take more time, they are more appropriate when conflict is likely to occur, when the problem is unstructured, or when the manager does not have the knowledge or skills to solve the problem. Exercise 6-1 Interview colleagues about their most preferred decision-making model and style. What barriers or obstacles to effective decision making have your colleagues encountered? What strategies are used to increase the effectiveness of the decisions made? Based on your interview, is the style effective? Why or why not? Factors Affecting Decision Making Numerous factors affect individuals and groups in the decision-making process. Tanner (2006) conducted an extensive review of the literature to develop a Clinical Judgment Model. Out of the research, she concluded that five principle factors influence decision making. (See the Literature Perspective below.) Literature Perspective Resource: Tanner, C. A. (2006). Thinking like a nurse: A research-based model of clinical judgment in nursing. Journal of Nursing Education, 45 (6), 204-211. Tanner engaged in an extensive review of 200 studies focusing on clinical judgment and clinical decision making to derive a model of clinical judgment that can be used as a framework for instruction. The first review summarized 120 articles and was published in 1998. The 2006 article reviewed an additional 71 studies published since 1998. Based on an analysis of the entire set of articles, Tanner proposed five conclusions which are listed below. The reader is referred to the article for detailed explanation of each of the five conclusions. The author considers clinical judgment as a “problem-solving activity.” She notes that the terms “clinical judgment,” “problem solving,” “decision making,” and “critical thinking” are often used interchangeably. For the purpose of aiding in the development of the model, Tanner defined clinical judgment as actions taken based on the assessment of the patient’s needs. Clinical reasoning is the process by which nurses make their judgments (e.g., the decision-making process of selecting the most appropriate option) ( Tanner, 2006 , p. 204): 1.  Clinical judgments are more influenced by what nurses bring to the situation than the objective data about the situation at hand. 2.  Sound clinical judgment rests to some degree on knowing the patient and his or her typical pattern of responses, as well as an engagement with the patient and his or her concerns. 3.  Clinical judgments are influenced by the context in which the situation occurs and the culture of the nursing care unit. 4.  Nurses use a variety of reasoning patterns alone or in combination. 5.  Reflection on practice is often triggered by a breakdown in clinical judgment and is critical for the development of clinical knowledge and improvement in clinical reasoning. The Clinical Judgment Model developed through the review of the literature involves four steps that are similar to problem-solving and decision-making steps described in this chapter. The model starts with a phase called “Noticing.” In this phase, the nurse comes to expect certain responses resulting from knowledge gleaned from similar patient situations, experiences, and knowledge. External factors influence nurses in this phase such as the complexity of the environment and values and typical practices within the unit culture. The second phase of the model is “Interpreting,” during which the nurse understands the situation that requires a response. The nurse employs various reasoning patterns to make sense of the issue and to derive an appropriate action plan. The third phase is “Responding,” during which the nurse decides on the best option for handling the situation. This is followed by the fourth phase, “Reflecting,” during which the nurse assesses the patient’s responses to the actions taken. Tanner emphasized that “reflection-in-action” and “reflection-on-action” are major processes required in the model. Reflection-in-action is real-time reflection on the patient’s responses to nursing action with modifications to the plan based on the ongoing assessment. On the other hand, reflection-on-action is a review of the experience, which promotes learning for future similar experiences. Nurse educators and managers can employ this model with new and experienced nurses to aid in understanding thought processes involved in decision making. As Tanner (2006) so eloquently concludes, “If we, as nurse educators, help our students understand and develop as moral agents, advance their clinical knowledge through expert guidance and coaching, and become habitual in reflection-on-practice, they will have learned to think like a nurse” ( p. 210 ). Implications for Practice Nurse educators and managers can employ this model with new and experienced nurses to aid in understanding thought processes involved in decision making. For example, students and practicing nurses can be encouraged to maintain reflective journals to record observations and impressions from clinical experiences. In clinical post-conferences or staff development meetings, the nurse educator and manager can engage them in applying to their lived experiences the five conclusions Tanner proposed. The ultimate goal of analyzing their decisions and decision-making processes is to improve clinical judgment, problem-solving, decision-making, and critical-thinking skills. Internal and external factors can influence how the situation is perceived. Internal factors include variables such as the decision maker’s physical and emotional state, personal philosophy, biases, values, interests, experience, knowledge, attitudes, and risk-seeking or risk-avoiding behaviors. External factors include environmental conditions, time, and resources. Decision-making options are externally limited when time is short or when the environment is characterized by a “we’ve always done it this way” attitude. Values affect all aspects of decision making, from the statement of the problem/issue through the evaluation. Values, determined by one’s cultural, social, and philosophical background, provide the foundation for one’s ethical stance. The steps for engaging in ethical decision making are similar to the steps described earlier; however, alternatives or options identified in the decision-making process are evaluated with the use of ethical resources. Resources that can facilitate ethical decision making include institutional policy; principles such as autonomy, nonmaleficence, beneficence, veracity, paternalism, respect, justice, and fidelity; personal judgment; trusted co-workers; institutional ethics committees; and legal precedent. Certain personality factors, such as self-esteem and self-confidence, affect whether one is willing to take risks in solving problems or making decisions. Keynes (2008) asserts that individuals may be influenced based on social pressures. For example, are you inclined to make decisions to satisfy people to whom you are accountable or from whom you feel social pressure? Characteristics of an effective decision maker include courage, a willingness to take risks, self-awareness, energy, creativity, sensitivity, and flexibility. Ask yourself, “Do I prefer to let others make the decisions? Am I more comfortable in the role of ‘follower’ than leader? If so, why?” Exercise 6-2 Identify a current or past situation that involved resource allocation, end-of-life issues, conflict among healthcare providers or patient/family/significant others, or some other ethical dilemma. Describe how the internal and external factors previously described influenced the decision options, the option selected, and the outcome. Group Decision Making There are two primary criteria for effective decision making. First, the decision must be of a high quality; that is, it achieves the predefined goals, objectives, and outcomes. Second, those who are responsible for its implementation must accept the decision. Higher-quality decisions are more likely to result if groups are involved in the problem-solving and decision-making process. In reality, with the increased focus on quality and safety, decisions cannot be made alone. When individuals are allowed input into the process, they tend to function more productively and the quality of the decision is generally superior. Taking ownership of the process and outcome provides a smoother transition. Multidisciplinary teams should be used in the decision-making process, especially if the issue, options, or outcome involves other disciplines. Research findings suggest that groups are more likely to be effective if members are actively involved, the group is cohesive, communication is encouraged, and members demonstrate some understanding of the group process. In deciding to use the group process for decision making, it is important to consider group size and composition. If the group is too small, a limited number of options will be generated and fewer points of view expressed. Conversely, if the group is too large, it may lack structure, and consensus becomes more difficult. Homogeneous groups may be more compatible; however, heterogeneous groups may be more successful in problem solving. Research has demonstrated that the most productive groups are those that are moderately cohesive. In other words, divergent thinking is useful to create the best decision. For groups to be able to work effectively, the group facilitator or leader should carefully select members on the basis of their knowledge and skills in decision making and problem solving. Individuals who are aggressive, are authoritarian, or manifest self-oriented behaviors tend to decrease the effectiveness of groups. The nurse leader or manager should provide a nonthreatening and positive environment in which group members are encouraged to participate actively. Using tact and diplomacy, the facilitator can control aggressive individuals who tend to monopolize the discussion and can encourage more passive individuals to contribute by asking direct, open-ended questions. Providing positive feedback such as “You raised a good point,” protecting members and their suggestions from attack, and keeping the group focused on the task are strategies that create an environment conducive to problem solving. Advantages of Group Decision Making The advantages of group decision making are numerous. The adage “two heads are better than one” illustrates that when individuals with different knowledge, skills, and resources collaborate to solve a problem or make a decision, the likelihood of a quality outcome is increased. More ideas can be generated by groups than by individuals functioning alone. In addition, when followers are directly involved in this process, they are more apt to accept the decision, because they have an increased sense of ownership or commitment to the decision. Implementing solutions becomes easier when individuals have been actively involved in the decision-making process. Involvement can be enhanced by making information readily available to the appropriate personnel, requesting input, establishing committees and task forces with broad representation, and using group decision-making techniques. The group leader must establish with the participants what decision rule will be followed. Will the group strive to achieve consensus, or will the majority rule? In determining which decision rule to use, the group leader should consider the necessity for quality and acceptance of the decision. Achieving both a high-quality and an acceptable decision is possible, but it requires more involvement and approval from individuals affected by the decision. Groups will be more committed to an idea if it is derived by consensus rather than as an outcome of individual decision making or majority rule. Consensus requires that all participants agree to go along with the decision. Although achieving consensus requires considerable time, it results in both high-quality and high-acceptance decisions and reduces the risk of sabotage. Majority rule can be used to compromise when 100% agreement cannot be achieved. This method saves time, but the solution may only partially achieve the goals of quality and acceptance. In addition, majority rule carries certain risks. First, if the informal group leaders happen to fall in the minority opinion, they may not support the decision of the majority. Certain members may go so far as to build coalitions to gain support for their position and block the majority choice. After all, the majority may represent only 51% of the group. In addition, group members may support the position of the formal leader, although they do not agree with the decision, because they fear reprisal or they wish to obtain the leader’s approval. In general, as the importance of the decision increases, so does the percentage of group members required to approve it. To secure the support of the group, the leader should maintain open communication with those affected by the decision and be honest about the advantages and disadvantages of the decision. The leader should also demonstrate how the advantages outweigh the disadvantages, suggest ways the unwanted outcomes can be minimized, and be available to assist when necessary.

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Want to Solve Problems in Public Health? Here's How

I have many loves in family medicine. I love delivering a newborn directly into a mother's arms. I love excisional biopsies of funny looking moles. I love giving someone hope after a chronic disease diagnosis.  

What I love most, however, is community-based preventive medicine. As such, I wrangled a contract directly out of residency that is 20 percent population medicine. (Lesson No. 1: Ask for what you want. You might actually get it).

As part of this endeavor, I am pursuing a master's degree in public health. I hope to use this training to make connections in the world of public health policy; to learn how to create, implement, message and evaluate programming; and perhaps to eventually break into creation of, or participation in, policy. I will have some required coursework in, for example, biostatistics and epidemiology, but I will also have myriad electives on topics like environmental public health and behavioral economics.

Throughout the course of my program, I hope to distill the most useful-to-the-family-medicine-doc public health pearls from my classes and pass them along. This post is the first in this series. Thus far, I have taken courses on problem-solving in public health and intro to persuasive communication.

The course on problem-solving in public health taught me two things: a remarkably egalitarian way to run a meeting and a systematic approach to solving problems.

The Nominal Group Technique (NGT)

In this setup for running a meeting, start by imagining a group of eight people. During each session, one of them is the moderator, one a notetaker and one a timekeeper. The moderator's job is to decide how long each part of the session ought to take, and the timekeeper's job is to cut people off once time is reached. The notetaker … takes notes.

Each meeting uses the following series of steps, and as participants get used to the process, they get faster and more efficient.

• Clarify the purpose and goals. The moderator reminds everyone about the specific question or questions for the session, reviews time limits for each ensuing step and allows for adjustments on each of these points.
• Brainstorm solutions. Group members brainstorm answers to the session's central question, a step that can take place before the meeting.
• Share ideas in a round robin. Going around in a circle, each person briefly shares one idea, adding more brief ideas -- avoiding duplicates -- when the circle comes back around until time runs out or all ideas have been voiced. In this manner, no one dominates the discussion and everyone is heard.
• Discuss as a group. Here the group focuses on clarifying, not debating. The goal is to add salient details or reasons for a certain suggestion. This is a time to ask questions rather than make arguments. Some time can also be spent discussing criteria for voting in the next step.
• Rank the suggestions. Each group member ranks the options based on the set criteria -- perhaps voting for their three favorites, using two votes however they want, or casting one vote each -- and the group ends up with two or three leading suggestions.
• Wrap up with conclusions and assignments. Participants are assigned roles or tasks to complete before the next meeting, and a new moderator, timekeeper and notetaker are assigned.

The NGT is delightfully efficient and focused. Moreover, it imposes a thoughtful, respectful and inclusive methodology to traditionally explosive or at least controversial topics. By using this technique, you can assure all members of the group that each of their voices will be heard with equal weight, as will also be the case in the problem-solving process below.

The Problem-solving Process

Usually applied to public health problems, this series of steps offers a framework through which one can approach just about any problem that involves groups of people. Whether your problem is developing a group visit program or decreasing smoking in pregnant women, you can approach the problem with success in this way. Notably, this process works well in combination with the NGT.

• Define the problem. A good problem definition has a specific group, timeframe and outcome of interest. For example, the definition could be, "Childhood obesity rates in the United States among school-aged children have been rising since the 1970s."
• Identify indicators of the problem. If your problem is childhood obesity, your direct indicators would be things like body mass index, waist circumference or waist-to-hip ratio. Indirect indicators -- things that give you a clue your endpoint might be happening -- would be rates of childhood hypertension, diabetes or obesity-related sleep apnea. Using the NGT would lead your group to brainstorm as many direct and indirect indicators as possible, then you vote on which ones to track and change.
• Find data for the indicators. Without data, you will have a hard time convincing others to do what you want.
• Identify stakeholders. Find out who cares about the outcome. A meeting held in the NGT style would come out of brainstorming and round robin with a diverse, inclusive and thorough list of potential stakeholders. For childhood obesity, the stakeholders could be parents, students, educators, elected officials, etc. The ranking step would narrow the list to the stakeholders that your group wants to work with.
• Identify key determinants. These are the things that might make the outcome of interest more or less likely. For childhood obesity, these factors might be diet, exercise, dangerous neighborhoods that prevent exercise, food deserts, genes, obesity in parents, television watching, school lunches and poverty.
• Identify intervention strategies. Here is when you brainstorm actions to change the outcome. Some of the group's ideas might be school lunch programs, educational programs for parents, active recess or adjusting food aid programs. All ideas are welcome for discussion and ranking. At the end of the meeting, your group will have decided on an intervention strategy to pursue.
• Identify implementation strategies. It is all well and good to have an intervention, but the next step is to figure out how to get it off the ground. You need to use all the resources you have -- friends in high places, friends in low places, grants, national organizations, local fundraising, city council meetings and more.
• Evaluate. All good interventions need to be evaluated. Be sure to figure out how to do so. Is it working? Is it costing too much? Does it have any unintended benefits or consequences?

I already have used each of these techniques to great effect. By addressing problems in this step-by-step fashion, I find myself suddenly more organized, and you know what that means: more time for more projects!

Just kidding. I get to read books for fun these days and go on long runs. It is amazing.

Stewart Decker, M.D., is a family physician practicing in southern Oregon. He focuses on the intersection of public health and primary care. You can follow him on Twitter at @drstewartdecker.

The opinions and views expressed here are those of the authors and do not necessarily represent or reflect the opinions and views of the American Academy of Family Physicians. This blog is not intended to provide medical, financial, or legal advice. All comments are moderated and will be removed if they violate our  Terms of Use .

Copyright © 2024 American Academy of Family Physicians. All Rights Reserved.

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Overview of the Problem-Solving Mental Process

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Rachel Goldman, PhD FTOS, is a licensed psychologist, clinical assistant professor, speaker, wellness expert specializing in eating behaviors, stress management, and health behavior change.

• Identify the Problem
• Define the Problem
• Form a Strategy
• Organize Information
• Allocate Resources
• Monitor Progress
• Evaluate the Results

Frequently Asked Questions

Problem-solving is a mental process that involves discovering, analyzing, and solving problems. The ultimate goal of problem-solving is to overcome obstacles and find a solution that best resolves the issue.

The best strategy for solving a problem depends largely on the unique situation. In some cases, people are better off learning everything they can about the issue and then using factual knowledge to come up with a solution. In other instances, creativity and insight are the best options.

It is not necessary to follow problem-solving steps sequentially, It is common to skip steps or even go back through steps multiple times until the desired solution is reached.

In order to correctly solve a problem, it is often important to follow a series of steps. Researchers sometimes refer to this as the problem-solving cycle. While this cycle is portrayed sequentially, people rarely follow a rigid series of steps to find a solution.

The following steps include developing strategies and organizing knowledge.

1. Identifying the Problem

While it may seem like an obvious step, identifying the problem is not always as simple as it sounds. In some cases, people might mistakenly identify the wrong source of a problem, which will make attempts to solve it inefficient or even useless.

Some strategies that you might use to figure out the source of a problem include :

• Asking questions about the problem
• Breaking the problem down into smaller pieces
• Looking at the problem from different perspectives
• Conducting research to figure out what relationships exist between different variables

2. Defining the Problem

After the problem has been identified, it is important to fully define the problem so that it can be solved. You can define a problem by operationally defining each aspect of the problem and setting goals for what aspects of the problem you will address

At this point, you should focus on figuring out which aspects of the problems are facts and which are opinions. State the problem clearly and identify the scope of the solution.

3. Forming a Strategy

After the problem has been identified, it is time to start brainstorming potential solutions. This step usually involves generating as many ideas as possible without judging their quality. Once several possibilities have been generated, they can be evaluated and narrowed down.

The next step is to develop a strategy to solve the problem. The approach used will vary depending upon the situation and the individual's unique preferences. Common problem-solving strategies include heuristics and algorithms.

• Heuristics are mental shortcuts that are often based on solutions that have worked in the past. They can work well if the problem is similar to something you have encountered before and are often the best choice if you need a fast solution.
• Algorithms are step-by-step strategies that are guaranteed to produce a correct result. While this approach is great for accuracy, it can also consume time and resources.

Heuristics are often best used when time is of the essence, while algorithms are a better choice when a decision needs to be as accurate as possible.

4. Organizing Information

Before coming up with a solution, you need to first organize the available information. What do you know about the problem? What do you not know? The more information that is available the better prepared you will be to come up with an accurate solution.

When approaching a problem, it is important to make sure that you have all the data you need. Making a decision without adequate information can lead to biased or inaccurate results.

5. Allocating Resources

Of course, we don't always have unlimited money, time, and other resources to solve a problem. Before you begin to solve a problem, you need to determine how high priority it is.

If it is an important problem, it is probably worth allocating more resources to solving it. If, however, it is a fairly unimportant problem, then you do not want to spend too much of your available resources on coming up with a solution.

At this stage, it is important to consider all of the factors that might affect the problem at hand. This includes looking at the available resources, deadlines that need to be met, and any possible risks involved in each solution. After careful evaluation, a decision can be made about which solution to pursue.

6. Monitoring Progress

After selecting a problem-solving strategy, it is time to put the plan into action and see if it works. This step might involve trying out different solutions to see which one is the most effective.

It is also important to monitor the situation after implementing a solution to ensure that the problem has been solved and that no new problems have arisen as a result of the proposed solution.

Effective problem-solvers tend to monitor their progress as they work towards a solution. If they are not making good progress toward reaching their goal, they will reevaluate their approach or look for new strategies .

7. Evaluating the Results

After a solution has been reached, it is important to evaluate the results to determine if it is the best possible solution to the problem. This evaluation might be immediate, such as checking the results of a math problem to ensure the answer is correct, or it can be delayed, such as evaluating the success of a therapy program after several months of treatment.

Once a problem has been solved, it is important to take some time to reflect on the process that was used and evaluate the results. This will help you to improve your problem-solving skills and become more efficient at solving future problems.

A Word From Verywell​

It is important to remember that there are many different problem-solving processes with different steps, and this is just one example. Problem-solving in real-world situations requires a great deal of resourcefulness, flexibility, resilience, and continuous interaction with the environment.

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You can become a better problem solving by:

• Practicing brainstorming and coming up with multiple potential solutions to problems
• Being open-minded and considering all possible options before making a decision
• Breaking down problems into smaller, more manageable pieces
• Asking for help when needed
• Researching different problem-solving techniques and trying out new ones
• Learning from mistakes and using them as opportunities to grow

It's important to communicate openly and honestly with your partner about what's going on. Try to see things from their perspective as well as your own. Work together to find a resolution that works for both of you. Be willing to compromise and accept that there may not be a perfect solution.

Take breaks if things are getting too heated, and come back to the problem when you feel calm and collected. Don't try to fix every problem on your own—consider asking a therapist or counselor for help and insight.

If you've tried everything and there doesn't seem to be a way to fix the problem, you may have to learn to accept it. This can be difficult, but try to focus on the positive aspects of your life and remember that every situation is temporary. Don't dwell on what's going wrong—instead, think about what's going right. Find support by talking to friends or family. Seek professional help if you're having trouble coping.

Davidson JE, Sternberg RJ, editors.  The Psychology of Problem Solving .  Cambridge University Press; 2003. doi:10.1017/CBO9780511615771

Sarathy V. Real world problem-solving .  Front Hum Neurosci . 2018;12:261. Published 2018 Jun 26. doi:10.3389/fnhum.2018.00261

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

A3 Methodology

Purpose  A3 problem solving is part of the Lean management approach to quality improvement (QI). However, few tools are available to assess A3 problem-solving skills. The authors sought to develop an assessment tool for problem-solving A3s with an accompanying self-instruction package and to test agreement in assessments made by individuals who teach A3 problem solving.

Methods  After reviewing relevant literature, the authors developed an A3 assessment tool and self-instruction package over five improvement cycles. Lean experts and individuals from two institutions with QI proficiency and experience teaching QI provided iterative feedback on the materials. Tests of inter-rater agreement were conducted in cycles 3, 4 and 5. The final assessment tool was tested in a study involving 12 raters assessing 23 items on six A3s that were modified to enable testing a range of scores.

Results  The intraclass correlation coefficient (ICC) for overall assessment of an A3 (rater’s mean on 23 items per A3 compared across 12 raters and 6 A3s) was 0.89 (95% CI 0.75 to 0.98), indicating excellent reliability. For the 20 items with appreciable variation in scores across A3s, ICCs ranged from 0.41 to 0.97, indicating fair to excellent reliability. Raters from two institutions scored items similarly (mean ratings of 2.10 and 2.13, p=0.57). Physicians provided marginally higher ratings than QI professionals (mean ratings of 2.17 and 2.00, p=0.003). Raters averaged completing the self-instruction package in 1.5 hours, then rated six A3s in 2.0 hours.

Conclusion  This study provides evidence of the reliability of a tool to assess healthcare QI project proposals that use the A3 problem-solving approach. The tool also demonstrated evidence of measurement, content and construct validity. QI educators and practitioners can use the free online materials to assess learners’ A3s, provide formative and summative feedback on QI project proposals and enhance their teaching.

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Eight-Step Problem Solving Process for Medical Practices

Whether you are hoping to solve a problem at your practice or simply trying to improve a process, the easy-to-follow OODA Loop method can help.

Practice managers know that there are four key objectives at the core of process improvement:

• To remove waste and inefficiencies • To increase productivity and asset availability • To improve response time and agility • To sustain safe and reliable operations

The question is, how do we do all this? I would suggest a proven technique known as the OODA Loop.

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Problem solving

Sometimes, it is not enough to just cope with the problems – they need to be solved.

Most people engage in problem solving every day. It occurs automatically for many of the small decisions that need to be made on a daily basis.

For example, when making a decision about whether to get up now or sleep in for an extra 10 minutes, the possible choices and the relative risks and benefits of obeying the alarm clock or sleeping later come automatically to mind.

Larger problems are addressed in a similar way. For example: “I have tasks that need to be done by the end of the week. How am I going to get them all done on time?”

After considering the possible strategies, 1 is chosen and implemented. If it proves to be ineffective, a different strategy is tried.

People who can define problems, consider options, make choices, and implement a plan have all the basic skills required for effective problem solving.

Sometimes following a step-by-step procedure for defining problems, generating solutions, and implementing solutions can make the process of problem solving seem less overwhelming.

Six step guide to help you solve problems

Step 1: identify and define the problem.

• State the problem as clearly as possible. For example: “I don’t have enough money to pay the bills.”
• Be specific about the behaviour, situation, timing, and circumstances that make it a problem. For example: “I need to pay the phone and gas bills, and I don’t have enough money to cover both this month.”

Step 2: Generate possible solutions

• List all the possible solutions; don’t worry about the quality of the solutions at this stage.
• Try to list at least 15 solutions, be creative and forget about the quality of the solution.
• If you allow yourself to be creative you may come up with some solutions that you would not otherwise have thought about.

Step 3: Evaluate alternatives      

• The next step is to go through and eliminate less desirable or unreasonable solutions.
• Order the remaining solutions in order of preference.
• Evaluate the remaining solutions in terms of their advantages and disadvantages.

Step 4: Decide on a solution      

• Specify who will take action.
• Specify how the solution will be implemented.
• Specify when the solution will be implemented. For example: tomorrow morning, phone the gas company and negotiate to pay the gas bill next month.

Step 5: Implement the solution

• Implement the solution as planned.

Step 6: Evaluate the outcome

• Evaluate how effective the solution was.
• Decide whether the existing plan needs to be revised, or whether a new plan is needed to better address the problem.
• If you are not pleased with the outcome, return to step 2 to select a new solution or revise the existing solution, and repeat the remaining steps.

Problem solving is something we do every day.

Some problems are small or easily solved - others are more complicated and can seem overwhelming.

One way of tackling problems is to use a specific and systematic problem solving procedure. If you’ve tried to solve certain problems without much success, try these steps out and see if they help.

Learning to solve problems effectively will help you to minimise the level of stress in your life and improve your overall sense of well-being.

Try it out and see.

Where to get help

Centre for Clinical Interventions (CCI)

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See your doctor

Visit healthdirect (external site) or call 1800 022 222, mental health emergency response line (mherl).

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• Most people engage in problem solving daily.
• Sometimes following a step-by-step process to define problems, consider options and make choices can make problem solving less overwhelming.
• You can always talk to your doctor or mental health practitioner and ask for help.

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• Published: 16 May 2024

Promoting equality, diversity and inclusion in research and funding: reflections from a digital manufacturing research network

• Oliver J. Fisher 1 ,
• Debra Fearnshaw   ORCID: orcid.org/0000-0002-6498-9888 2 ,
• Nicholas J. Watson 3 ,
• Peter Green 4 ,
• Fiona Charnley 5 ,
• Duncan McFarlane 6 &
• Sarah Sharples 2  

Research Integrity and Peer Review volume  9 , Article number:  5 ( 2024 ) Cite this article

146 Accesses

Metrics details

Equal, diverse, and inclusive teams lead to higher productivity, creativity, and greater problem-solving ability resulting in more impactful research. However, there is a gap between equality, diversity, and inclusion (EDI) research and practices to create an inclusive research culture. Research networks are vital to the research ecosystem, creating valuable opportunities for researchers to develop their partnerships with both academics and industrialists, progress their careers, and enable new areas of scientific discovery. A feature of a network is the provision of funding to support feasibility studies – an opportunity to develop new concepts or ideas, as well as to ‘fail fast’ in a supportive environment. The work of networks can address inequalities through equitable allocation of funding and proactive consideration of inclusion in all of their activities.

This study proposes a strategy to embed EDI within research network activities and funding review processes. This paper evaluates 21 planned mitigations introduced to address known inequalities within research events and how funding is awarded. EDI data were collected from researchers engaging in a digital manufacturing network activities and funding calls to measure the impact of the proposed method.

Quantitative analysis indicates that the network’s approach was successful in creating a more ethnically diverse network, engaging with early career researchers, and supporting researchers with care responsibilities. However, more work is required to create a gender balance across the network activities and ensure the representation of academics who declare a disability. Preliminary findings suggest the network’s anonymous funding review process has helped address inequalities in funding award rates for women and those with care responsibilities, more data are required to validate these observations and understand the impact of different interventions individually and in combination.

Conclusions

In summary, this study offers compelling evidence regarding the efficacy of a research network's approach in advancing EDI within research and funding. The network hopes that these findings will inform broader efforts to promote EDI in research and funding and that researchers, funders, and other stakeholders will be encouraged to adopt evidence-based strategies for advancing this important goal.

Peer Review reports

Introduction

Achieving equality, diversity, and inclusion (EDI) is an underpinning contributor to human rights, civilisation and society-wide responsibility [ 1 ]. Furthermore, promoting and embedding EDI within research environments is essential to make the advancements required to meet today’s research challenges [ 2 ]. This is evidenced by equal, diverse and inclusive teams leading to higher productivity, creativity and greater problem-solving ability [ 3 ], which increases the scientific impact of research outputs and researchers [ 4 ]. However, there remains a gap between EDI research and the everyday implementation of inclusive practices to achieve change [ 5 ]. This paper presents and reflects on the EDI measures trialled by the UK Engineering and Physical Sciences Research Council (EPSRC) funded digital manufacturing research network, Connected Everything (grant number: EP/S036113/1) [ 6 ]. The EPSRC is a UK research council that funds engineering and physical sciences research. By sharing these reflections, this work aims to contribute to the wider effort of creating an inclusive research culture. The perceptions of equality, diversity, and inclusion may vary among individuals. For the scope of this study, the following definitions are adopted:

Equality: Equality is about ensuring that every individual has an equal opportunity to make the most of their lives and talents. No one should have poorer life chances because of the way they were born, where they come from, what they believe, or whether they have a disability.

Diversity: Diversity concerns understanding that each individual is unique, recognising our differences, and exploring these differences in a safe, positive, and nurturing way to value each other as individuals.

Inclusion: Inclusion is an effort and practice in which groups or individuals with different backgrounds are culturally and socially accepted, welcomed and treated equally. This concerns treating each person as an individual, making them feel valued, and supported and being respectful of who they are.

Research networks have varied goals, but a common purpose is to create new interdisciplinary research communities, by fostering interactions between researchers and appropriate scientific, technological and industrial groups. These networks aim to offer valuable career progression opportunities for researchers, through access to research funding, forming academic and industrial collaborations at network events, personal and professional development, and research dissemination. However, feedback from a 2021 survey of 19 UK research networks, suggests that these research networks are not always diverse, and whilst on the face of it they seem inclusive, they are perceived as less inclusive by minority groups (including non-males, those with disabilities, and ethnic minority respondents) [ 7 ]. The exclusivity of these networks further exacerbates the inequality within the academic community as it prevents certain groups from being able to engage with all aspects of network activities.

Research investigating the causes of inequality and exclusivity has identified several suggestions to make research culture more inclusive, including improving diverse representation within event programmes and panels [ 8 , 9 ]; ensuring events are accessible to all [ 10 ]; providing personalised resources and training to build capacity and increase engagement [ 11 ]; educating institutions and funders to understand and address the barriers to research [ 12 ]; and increasing diversity in peer review and funding panels [ 13 ]. Universities, research institutions and research funding bodies are increasingly taking responsibility to ensure the health of the research and innovation system and to foster inclusion. For example, the EPSRC has set out their own ‘Expectation for EDI’ to promote the formation of a diverse and inclusive research culture [ 14 ]. To drive change, there is an emphasis on the importance of measuring diversity and links to measured outcomes to benchmark future studies on how interventions affect diversity [ 5 ]. Further, collecting and sharing EDI data can also drive aspirations, provide a target for actions, and allow institutions to consider common issues. However, there is a lack of available data regarding the impact of EDI practices on diversity that presents an obstacle, impeding the realisation of these benefits and hampering progress in addressing common issues and fostering diversity and inclusion [ 5 ].

Funding acquisition is important to an academic’s career progression, yet funding may often be awarded in ways that feel unequal and/or non-transparent. The importance of funding in academic career progression means that, if credit for obtaining funding is not recognised appropriately, careers can be damaged, and, as a result of the lack of recognition for those who have been involved in successful research, funding bodies may not have a complete picture of the research community, and are unable to deliver the best value for money [ 15 ]. Awarding funding is often a key research network activity and an area where networks can have a positive impact on the wider research community. It is therefore important that practices are established to embed EDI consideration within the funding process and to ensure that network funding is awarded without bias. Recommendations from the literature to make the funding award process fairer include: ensuring a diverse funding panel; funders instituting reviewer anti-bias training; anonymous review; and/or automatic adjustments to correct for known biases [ 16 ]. In the UK, the government organisation UK Research and Innovation (UKRI), tasked with overseeing research and innovation funding, has pledged to publish data to enhance transparency. This initiative aims to furnish an evidence base for designing interventions and evaluating their efficacy. While the data show some positive signs (e.g., the award rates for male and female PI applicants were equal at 29% in 2020–21), Ottoline Leyser (UKRI Chief Executive) highlights the ‘persistent pernicious disparities for under-represented groups in applying for and winning research funding’ [ 17 ]. This suggests that a more radical approach to rethinking the traditional funding review process may be required.

This paper describes the approach taken by the ‘Connected Everything’ EPSRC-funded Network to embed EDI in all aspects of its research funding process, and evaluates the impact of this ambition, leading to recommendations for embedding EDI in research funding allocation.

Connected everything’s equality diversity and inclusion strategy

Connected Everything aims to create a multidisciplinary community of researchers and industrialists to address key challenges associated with the future of digital manufacturing. The network is managed by an investigator team who are responsible for the strategic planning and, working with the network manager, to oversee the delivery of key activities. The network was first funded between 2016–2019 (grant number: EP/P001246/1) and was awarded a second grant (grant number: EP/S036113/1). The network activities are based around three goals: building partnerships, developing leadership and accelerating impact.

The Connected Everything network represents a broad range of disciplines, including manufacturing, computer science, cybersecurity, engineering, human factors, business, sociology, innovation and design. Some of the subject areas, such as Computer Science and Engineering, tend to be male-dominated (e.g., in 2021/22, a total of 185,42 higher education student enrolments in engineering & technology subjects was broken down as 20.5% Female and 79.5% Male [ 18 ]). The networks also face challenges in terms of accessibility for people with care responsibilities and disabilities. In 2019, Connected Everything committed to embedding EDI in all its network activities and published a guiding principle and goals for improving EDI (see Additional file 1 ). When designing the processes to deliver the second iteration of Connected Everything, the team identified several sources of potential bias/exclusion which have the potential to impact engagement with the network. Based on these identified factors, a series of mitigation interventions were implemented and are outlined in Table  1 .

Connected everything anonymous review process

A key Connected Everything activity is the funding of feasibility studies to enable cross-disciplinary, foresight, speculative and risky early-stage research, with a focus on low technology-readiness levels. Awards are made via a short, written application followed by a pitch to a multidisciplinary diverse panel including representatives from industry. Six- to twelve-month-long projects are funded to a maximum value of £60,000.

The current peer-review process used by funders may reveal the applicants’ identities to the reviewer. This can introduce dilemmas to the reviewer regarding (a) deciding whether to rely exclusively on information present within the application or search for additional information about the applicants and (b) whether or not to account for institutional prestige [ 34 ]. Knowing an applicant’s identity can bias the assessment of the proposal, but by focusing the assessment on the science rather than the researcher, equality is more frequently achieved between award rates (i.e., the proportion of successful applications) [ 15 ]. To progress Connected Everything’s commitment to EDI, the project team created a 2-stage review process, where the applicants’ identity was kept anonymous during the peer review stage. This anonymous process, which is outlined in Fig.  1 , was created for the feasibility study funding calls in 2019 and used for subsequent funding calls.

Connected Everything’s anonymous review process [EDI: Equality, diversity, and inclusion]

To facilitate the anonymous review process, the proposal was submitted in two parts: part A the research idea and part B the capability-to-deliver statement. All proposals were first anonymously reviewed by a random selection of two members from the Connected Everything executive group, which is a diverse group of digital manufacturing experts and peers from academia, industry and research institutions that provide guidance and leadership on Connected Everything activities. The reviewers rated the proposals against the selection criteria (see Additional file 1 , Table 1) and provided overall comments alongside a recommendation on whether or not the applicant should be invited to the panel pitch. This information was summarised and shared with a moderation sift panel, made up of a minimum of two Connected Everything investigators and a minimum of one member of the executive group, that tensioned the reviewers’ comments (i.e. comments and evaluations provided by the peer reviewers are carefully considered and weighed against each other) and ultimately decided which proposals to invite to the panel. This tension process included using the identifying information to ensure the applicants did have the capability to deliver the project. If this remained unclear, the applicants were asked to confirm expertise in an area the moderation sift panel thought was key or asked to bring in additional expertise to the project team during the panel pitch.

During stage two the applicants were invited to pitch their research idea to a panel of experts who were selected to reflect the diversity of the community. The proposals, including applicants’ identities, were shared with the panel at least two weeks ahead of the panel. Individual panel members completed a summary sheet at the end of the pitch session to record how well the proposal met the selection criteria (see Additional file 1 , Table 1). Panel members did not discuss their funding decision until all the pitches had been completed. A panel chair oversaw the process but did not declare their opinion on a specific feasibility study unless the panel could not agree on an outcome. The panel and panel chair were reminded to consider ways to manage their unconscious bias during the selection process.

Due to the positive response received regarding the anonymous review process, Connected Everything extended its use when reviewing other funded activities. As these awards were for smaller grant values (~ £5,000), it was decided that no panel pitch was required, and the researcher’s identity was kept anonymous for the entire process.

Data collection and analysis methods

Data collection.

Equality, diversity and inclusion data were voluntarily collected from applicants for Connected Everything research funding and from participants who won scholarships to attend Connected Everything funded activities. Responses to the EDI data requests were collected from nine Connected Everything coordinated activities between 2019 and 2022. Data requests were sent after the applicant had applied for Connected Everything funding or had attended a Connected Everything funded activity. All data requests were completed voluntarily, with reassurance given that completion of the data requested in no way affected their application. In total 260 responses were received, of which the three feasibility study calls comprised 56.2% of the total responses received. Overall, there was a 73.8% response rate.

To understand the diversity of participants engaging with Connected Everything activities and funding, the data requests asked for details of specific diversity characteristics: gender, transgender, disability, ethnicity, age, and care responsibilities. Although sex and gender are terms that are often used interchangeably, they are two different concepts. To clarify, the definitions used by the UK government describe sex as a set of biological attributes that is generally limited to male or female, and typically attributed to individuals at birth. In contrast, gender identity is a social construction related to behaviours and attributes, and is self-determined based on a person’s internal perception, identification and experience. Transgender is a term used to describe people whose gender identity is not the same as the sex they were registered at birth. Respondents were first asked to identify their gender and then whether their gender was different from their birth sex.

For this study, respondents were asked to (voluntarily) self-declare whether they consider themselves to be disabled or not. Ethnicity within the data requests was based on the 2011 census classification system. When reporting ethnicity data, this study followed the AdvanceHE example to aggregate the census categories into six groups to enable benchmarking against the available academic ethnicity data. AdvanceHE is a UK charity that works to improve the higher education system for staff, students and society. However, it was acknowledged that there were limitations with this grouping, including the assumption that minority ethnic staff or students are a homogenous group [ 16 ]. Therefore, this study made sure to breakdown these groups during the discussion of the results. The six groups are:

Asian: Asian/Asian British: Indian, Pakistani, Bangladeshi, and any other Asian background;

Black: Black/African/Caribbean/Black British: African, Caribbean, and any other Black/African/Caribbean background;

Other ethnic backgrounds, including Arab.

White: all white ethnic groups.

Benchmarking data

Published data from the Higher Education Statistics Agency [ 26 ] (a UK organisation responsible for collecting, analysing, and disseminating data related to higher education institutions and students), UKRI funding data [ 19 , 35 ] and 2011 census data [ 36 ] were used to benchmark the EDI data collected within this study. The responses to the data collected were compared to the engineering and technology cluster of academic disciplines, as this is most represented by Connected Everything’s main funded EPSRC. The Higher Education Statistics Agency defines the engineering and technology cluster as including the following subject areas: general engineering; chemical engineering; mineral, metallurgy & materials engineering; civil engineering; electrical, electronic & computer engineering; mechanical, aero & production engineering and; IT, systems sciences & computer software engineering [ 37 ].

When assessing the equality in funding award rates, previous studies have focused on analysing the success rates of only the principal investigators [ 15 , 16 , 38 ]; however, Connected Everything recognised that writing research proposals is a collaborative task, so requested diversity data from the whole research team. The average of the last six years of published principal investigator and co-investigator diversity data for UKRI and EPSRC funding awards (2015–2021) was used to benchmark the Connected Everything funding data [ 35 ]. The UKRI and EPSRC funding review process includes a peer review stage followed by panel pitch and assessment stage; however, the applicant's track record is assessed during the peer review stage, unlike the Connected Everything review process.

The data collected have been used to evaluate the success of the planned migrations to address EDI factors affecting the higher education research ecosystem, as outlined in Table  1 (" Connected Everything’s Equality Diversity and Inclusion Strategy " Section).

Dominance of small number of research-intensive universities receiving funding from network

The dominance of a small number of research-intensive universities receiving funding from a network can have implications for the field of research, including: the unequal distribution of resources; a lack of diversity of research, limited collaboration opportunities; and impact on innovation and progress. Analysis of published EPSRC funding data between 2015 and 2021 [ 19 ], shows that the funding has been predominately (74.1%, 95% CI [71.%, 76.9%] out of £3.98 billion) awarded to Russell Group universities. The Russell Group is a self-selected association of 24 research-intensive universities (out of the 174 universities) in the UK, established in 1994. Evaluation of the universities that received Connected Everything feasibility study funding between 2016–2019, shows that Connected Everything awarded just over half (54.6%, 95% CI [25.1%, 84.0%] out of 11 awards) to Russell Group universities. Figure  2 shows that the Connected Everything funding awarded to Russell Group universities reduced to 44.4%, 95% CI [12.0%, 76.9%] of 9 awards between 2019–2022.

A comparison of funding awarded by EPSRC (total = £3.98 billion) across Russell Group universities and non-Russell Group universities, alongside the allocations for Connected Everything I (total = £660 k) and Connected Everything II (total = £540 k)

Dominance of successful applications from men

The percentage point difference between the award rates of researchers who identified as female, those who declare a disability, or identified as ethnic minority applicants and carers and their respective counterparts have been plotted in Fig.  3 . Bars to the right of the axis mean that the award rate of the female/declared-disability/ethnic-minority/carer applicants is greater than that of male/non- disability/white/not carer applicants.

Percentage point (PP) differences in award rate by funding provider for gender, disability status, ethnicity and care responsibilities (data not collected by UKRI and EPSRC [ 35 ]). The total number of applicants for each funder are as follows: Connected Everything = 146, EPSRC = 37,960, and UKRI = 140,135. *The numbers of applicants were too small (< 5) to enable a meaningful discussion

Figure  3 (A) shows that between 2015 and 2021 research team applicants who identified as male had a higher award rate than those who identified as female when applying for EPSRC and wider UKRI research council funding. Connected Everything funding applicants who identified as female achieved a higher award rate (19.4%, 95% CI [6.5%, 32.4%] out of 146) compared to male applicants (15.6%, 95% CI [8.8%, 22.4%] out of 146). These data suggest that biases have been reduced by the Connected Everything review process and other mitigation strategies (e.g., visible gender diversity in panel pitch members and publishing CE principal and goals to demonstrate commitment to equality and fairness). This finding aligns with an earlier study that found gender bias during the peer review process, resulting in female investigators receiving less favourable evaluations than their male counterparts [ 15 ].

Over-representation of people identifying as male in engineering and technology academic community

Figure  4 shows the response to the gender question, with 24.2%, 95% CI [19.0%, 29.4%] of 260 responses identifying as female. This aligns with the average for the engineering and technology cluster (21.4%, 95% CI [20.9%, 21.9%] female of 27,740 academic staff), which includes subject areas representative of our main funder, EPSRC [ 22 ]. We also sought to understand the representation of transgender researchers within the network. However, following the rounding policy outlined by UK Government statistics policies and procedures [ 39 ], the number of responses that identified as a different sex to birth was too low (< 5) to enable a meaningful discussion.

Gender question responses from a total of 260 respondents

Dominance of successful applications from white academics

Figure  3 (C) shows that researchers with a minority ethnicity consistently have a lower award rate than white researchers when applying for EPSRC and UKRI funding. Similarly, the results in Fig.  3 (C) indicate that white researchers are more successful (8.0% percentage point, 95% CI [-8.6%, 24.6%]) when applying for Connected Everything funding. These results indicate that more measures should be implemented to support the ethnic minority researchers applying for Connected Everything funding, as well as sense checking there is no unconscious bias in any of the Connected Everything funding processes. The breakdown of the ethnicity diversity of applicants at different stages of the Connected Everything review process (i.e. all applications, applicants invited to panel pitch and awarded feasibility studies) has been plotted in Fig.  5 to help identify where more support is needed. Figure  5 shows an increase in the proportion of white researchers from 54%, 95% CI [45.4%, 61.8%] of all 146 applicants to 66%, 95% CI [52.8%, 79.1%] of the 50 researchers invited to the panel pitch. This suggests that stage 1 of the Connected Everything review process (anonymous review of written applications) may favour white applicants and/or introduce unconscious bias into the process.

Ethnicity questions responses from different stages during the Connected Everything anonymous review process. The total number of applicants is 146, with 50 at the panel stage and 23 ultimately awarded

Under-representation of those from black or minority ethnic backgrounds

Connected Everything appears to have a wide range of ethnic diversity, as shown in Fig.  6 . The ethnicities Asian (18.3%, 95% CI [13.6%, 23.0%]), Black (5.1%, 95% CI [2.4%, 7.7%]), Chinese (12.5%, 95% CI [8.4%, 16.5%]), mixed (3.5%, 95% CI [1.3%, 5.7%]) and other (7.8%, 95% CI [4.5%, 11.1%]) have a higher representation among the 260 individuals engaging with network’s activities, in contrast to both the engineering and technology academic community and the wider UK population. When separating these groups into the original ethnic diversity answers, it becomes apparent that there is no engagement with ‘Black or Black British: Caribbean’, ‘Mixed: White and Black Caribbean’ or ‘Mixed: White and Asian’ researchers within Connected Everything activities. The lack of engagement with researchers from a Caribbean heritage is systemic of a lack of representation within the UK research landscape [ 25 ].

Ethnicity question responses from a total of 260 respondents compared to distribution of the 13,085 UK engineering and technology (E&T) academic staff [ 22 ] and 56 million people recorded in the UK 2011 census data [ 36 ]

Under-representation of disabilities, chronic conditions, invisible illnesses and neurodiversity in funded activities and events.

Figure  7 (A) shows that 5.7%, 95% CI [2.4%, 8.9%] of 194 responses declared a disability. This is higher than the average of engineering and technology academics that identify as disabled (3.4%, 95% CI [3.2%, 3.7%] of 27,730 academics). Between Jan-March 2022, 9.0 million people of working age (16–64) within the UK were identified as disabled by the Office for National Statistics [ 40 ], which is 21% of the working age population [ 27 ]. Considering these statistics, there is a stark under-representation of disabilities, chronic conditions, invisible illnesses and neurodiversity amongst engineering and technology academic staff and those engaging in Connected Everything activities.

Responses to A  Disability and B  Care responsibilities questions colected from a total of 194 respondents

Between 2015 and 2020 academics that declared a disability have been less successful than academics without a disability in attracting UKRI and EPSRC funding, as shown in Fig.  3 (B). While Fig.  3 (B) shows that those who declare a disability have a higher Connected Everything funding award rate, the number of applicants who declared a disability was too small (< 5) to enable a meaningful discussion regarding this result.

Under-representation of those with care responsibilities in funded activities and events

In response to the care responsibilities question, Fig.  7 (B) shows that 27.3%, 95% CI [21.1%, 33.6%] of 194 respondents identified as carers, which is higher than the 6% of adults estimated to be providing informal care across the UK in a UK Government survey of the 2020/2021 financial year [ 41 ]. However, the ‘informal care’ definition used by the 2021 survey includes unpaid care to a friend or family member needing support, perhaps due to illness, older age, disability, a mental health condition or addiction [ 41 ]. The Connected Everything survey included care responsibilities across the spectrum of care that includes partners, children, other relatives, pets, friends and kin. It is important to consider a wide spectrum of care responsibilities, as key academic events, such as conferences, have previously been demonstrably exclusionary sites for academics with care responsibilities [ 42 ]. Breakdown analysis of the responses to care responsibilities by gender in Fig.  8 reveals that 37.8%, 95% CI [25.3%, 50.3%] of 58 women respondents reported care responsibilities, compared to 22.6%, 95% CI [61.1%, 76.7%] of 136 men respondents. Our findings reinforce similar studies that conclude the burden of care falls disproportionately on female academics [ 43 ].

Responses to care responsibilities when grouped by A  136 males and B  58 females

Figure  3 (D) shows that researchers with careering responsibilities applying for Connected Everything funding have a higher award rate than those researchers applying without care responsibilities. These results suggest that the Connected Everything review process is supportive of researchers with care responsibilities, who have faced barriers in other areas of academia.

Reduced opportunities for ECRs

Early-career researchers (ECRs) represent the transition stage between starting a PhD and senior academic positions. EPSRC defines an ECR as someone who is either within eight years of their PhD award, or equivalent professional training or within six years of their first academic appointment [ 44 ]. These periods exclude any career break, for example, due to family care; health reasons; and reasons related to COVID-19 such as home schooling or increased teaching load. The median age for starting a PhD in the UK is 24 to 25, while PhDs usually last between three and four years [ 45 ]. Therefore, these data would imply that the EPSRC median age of ECRs is between 27 and 37 years. It should be noted, however, that this definition is not ideal and excludes ECRs who may have started their research career later in life.

Connected Everything aims to support ECRs via measures that include mentoring support, workshops, summer schools and podcasts. Figure  9 shows a greater representation of researchers engaging with Connected Everything activities that are aged between 30–44 (62.4%, 95% CI [55.6%, 69.2%] of 194 respondents) when compared to the wider engineering and technology academic community (43.7%, 95% CI [43.1%, 44.3%] of 27,780 academics) and UK population (26.9%, 95% CI [26.9%, 26.9%]).

Age question responses from a total of 194 respondents compared to distribution of the 27,780 UK engineering and technology (E&T) academic staff [ 22 ] and 56 million people recorded in the UK 2011 census data [ 36 ]

High competition for funding has a greater impact on ECRs

Figure  10 shows that the largest age bracket applying for and winning Connected Everything funding is 31–45, whereas 72%, CI 95% [70.1%, 74.5%] of 12,075 researchers awarded EPSRC grants between 2015 and 2021 were 40 years or older. These results suggest that measures introduced by Connected Everything has been successful at providing funding opportunities for researchers who are likely to be early-mid career stage.

Age of researchers at applicant and awarded funding stages for A  Connected Everything between 2019–2022 (total of 146 applicants and 23 awarded) and B  EPSRC funding between 2015–2021 [ 35 ] (total of 35,780 applicants and 12,075 awarded)

The results of this paper provide insights into the impact that Connected Everything’s planned mitigations have had on promoting equality, diversity, and inclusion (EDI) in research and funding. Collecting EDI data from individuals who engage with network activities and apply for research funding enabled an evaluation of whether these mitigations have been successful in achieving the intended outcomes outlined at the start of the study, as summarised in Table  2 .

The results in Table  2 indicate that Connected Everything’s approach to EDI has helped achieve the intended outcome to improve representation of women, ECRs, those with a declared disability and black/minority ethnic backgrounds engaging with network events when compared to the engineering and technology academic community. In addition, the network has helped raise awareness of the high presence of researchers with care responsibilities at network events, which can help to track progress towards making future events inclusive and accessible towards these carers. The data highlights two areas for improvement: (1) ensuring a gender balance; and (2) increasing representation of those with declared disabilities. Both these discrepancies are indicative of the wider imbalances and underrepresentation of these groups in the engineering and technology academic community [ 26 ], yet represent areas where networks can strive to make a difference. Possible strategies include: using targeted outreach; promoting greater representation of these groups in event speakers; and going further to create a welcoming and inclusive environment. One barrier that can disproportionately affect women researchers is the need to balance care responsibilities with attending network events [ 46 ]. This was reflected in the Connected Everything data that reported 37.8%, 95% CI [25.3%, 50.3%] of women engaging with network activities had care responsibilities, compared to 22.6%, 95% CI [61.1%, 76.7%] of men. Providing accommodations such as on-site childcare, flexible scheduling, or virtual attendance options can therefore help to promote inclusivity and allow more women researchers to attend.

Only 5.7%, 95% CI [2.4%, 8.9%] of responses engaging with Connected Everything declared a disability, which is higher than the engineering and technology academic community (3.4%, 95% CI [3.2%, 3.7%]) [ 26 ], but unrepresentative of the wider UK population. It has been suggested that academics can be uncomfortable when declaring disabilities because scholarly contributions and institutional citizenship are so prized that they feel they cannot be honest about their issues or health concerns and keep them secret [ 47 ]. In research networks, it is important to be mindful of this hidden group within higher education and ensure that measures are put in place to make the network’s activities inclusive to all. Future considerations for accommodations to improve research events inclusivity include: improving physical accessibility of events; providing assistive technology such as screen readers, audio descriptions, and captioning can help individuals with visual or hearing impairments to access and participate; providing sign language interpreters; offering flexible scheduling options; and the provision of quiet rooms, written materials in accessible formats, and support staff trained to work with individuals with cognitive disabilities.

Connected Everything introduced measures (e.g., anonymised reviewing process, Q&A sessions before funding calls, inclusive design of panel pitch) to help address inequalities in how funding is awarded. Table 2 shows success in reducing the dominance of researchers who identify as male and research-intensive universities in winning research funding and that researchers with care responsibilities were more successful at winning funding than those without care responsibilities. The data revealed that the proposed measures were unable to address the inequality in award rates between white and ethnic minority researchers, which is an area to look to improve. The inequality appears to occur during the anonymous review stage, with a greater proportion of white researchers being invited to panel. Recommendations to make the review process fairer include: ensuring greater diversity of reviewers; reviewer anti-bias training; and automatic adjustments to correct for known biases in writing style [ 16 , 32 ].

When reflecting on the development of a strategy to embed EDI throughout the network, Connected Everything has learned several key lessons that may benefit other networks undergoing a similar activity. These include:

EDI is never ‘done’: There is a constant need to review approaches to EDI to ensure they remain relevant to the network community. Connected Everything could review its principles to include the concept of justice in its approach to diversity and inclusion. The concept of justice concerning EDI refers to the removal of systematic barriers that stop fair and equitable distribution of resources and opportunities among all members of society, regardless of their individual characteristics or backgrounds. The principles and subsequent actions could be reviewed against the EDI expectations [ 14 ], paying particular attention to areas where barriers may still be present. For example, shifting from welcoming people into existing structures and culture to creating new structures and culture together, with specific emphasis on decision or advisory mechanisms within the network. This activity could lend itself to focusing more on tailored support to overcome barriers, thus achieving equity, if it is not within the control of the network to remove the barrier itself (justice).

Widen diversity categories: By collecting data on a broad range of characteristics, we can identify and address disparities and biases that might otherwise be overlooked. A weakness of this dataset is that ignores the experience of those with intersectional identities, across race, ethnicity, gender, class, disability and/ or LGBTQI. The Wellcome Trust noted how little was known about the socio-economic background of scientists and researchers [ 48 ].

Collect data on whole research teams: For the first two calls for feasibility study funding, Connected Everything only asked the Principal Investigator to voluntarily provide their data. We realised that this was a limited approach and, in the third call, asked for the data regarding the whole research team to be shared anonymously. Furthermore, we do not currently measure the diversity of our event speakers, panellists or reviewers. Collecting these data in the future will help to ensure the network is accountable and will ensure that all groups are represented during our activities and in the funding decision-making process.

High response rate: Previous surveys measuring network diversity (e.g., [ 7 ]) have struggled to get responses when surveying their memberships; whereas, this study achieved a response rate of 73.8%. We attribute this high response rate to sending EDI data requests on the point of contact with the network (e.g., on submitting funding proposals or after attending network events), rather than trying to survey the entire network membership at anyone point in time.

Improve administration: The administration associated with collecting EDI data requires a commitment to transparency, inclusivity, and continuous improvement. For example, during the first feasibility funding call, Connected Everything made it clear that the review process would be anonymous, but the application form was not in separate documents. This made anonymising the application forms extremely time-consuming. For the subsequent calls, separate documents were created – Part A for identifying information (Principal Investigator contact details, Project Team and Industry collaborators) and Part B for the research idea.

Accepting that this can be uncomfortable: Trying to improve EDI can be uncomfortable because it often requires challenging our assumptions, biases, and existing systems and structures. However, it is essential if we want to make real progress towards equity and inclusivity. Creating processes to support embedding EDI takes time and Connected Everything has found it is rare to get it right the first time. Connected Everything is sharing its learning as widely as possible both to support others in their approaches and continue our learning as we reflect on how to continually improve, even when it is challenging.

Enabling individual engagement with EDI: During this work, Connected Everything recognised that methods for engaging with such EDI issues in research design and delivery are lacking. Connected Everything, with support from the Future Food Beacon of Excellence at the University of Nottingham, set out to develop a card-based tool [ 49 ] to help researchers and stakeholders identify questions around how their work may promote equity and increase inclusion or have a negative impact towards one or more protected groups and how this can be overcome. The results of this have been shared at conference presentations [ 50 ] and will be published later.

While this study provides insights into how EDI can be improved in research network activities and funding processes, it is essential to acknowledge several limitations that may impact the interpretation of the findings.

Sample size and generalisability: A total of 260 responses were received, which may not be representative of our overall network of 500 + members. Nevertheless, this data provides a sense of the current diversity engaging in Connected Everything activities and funding opportunities, which we can compare with other available data to steer action to further diversify the network.

Handling of missing data: Out of the 260 responses, 66 data points were missing for questions regarding age, disability, and caring responsibilities. These questions were mistakenly omitted from a Connected Everything summer school survey, contributing to 62 missing data points. While we assumed the remainer of missing data to be at random during analysis, it's important to acknowledge it could be related to other factors, potentially introducing bias into our results.

Emphasis on quantitative data: The study relies on using quantitative data to evaluate the impact of the EDI measures introduced by Connected Everything. However, relying solely on quantitative metrics may overlook nuanced aspects of EDI that cannot be easily quantified. For example, EDI encompasses multifaceted issues influenced by historical, cultural, and contextual factors. These nuances may not be fully captured by numbers alone. In addition, some EDI efforts may not yield immediate measurable outcomes but still contribute to a more inclusive environment.

Diversity and inclusion are not synonymous: The study proposes 21 measures to contribute towards creating an equal, diverse and inclusive research culture and collects diversity data to measure the impact of these measures. However, while diversity is simpler to monitor, increasing diversity alone does not guarantee equality or inclusion. Even with diverse research groups, individuals from underrepresented groups may still face barriers, microaggressions, or exclusion.

Balancing anonymity and rigour in grant reviews:The proposed anonymous review process proposed by Connected Everything removes personal and organisational details from the research ideas under reviewer evaluation. However, there exists a possibility that a reviewer could discern the identity of the grant applicant based on the research idea. Reviewers are expected to be subject matter experts in the field relevant to the grant proposal they are evaluating. Given the specialised nature of scientific research, it is conceivable that a well-known applicant could be identified through the specifics of the work, the methodologies employed, and even the writing style.

Expanding gender identity options: A limitation of this study emerged from the restricted gender options (male, female, other, prefer not to say) provided to respondents when answering the gender identity question. This limitation reflects the context of data collection in 2018, a time when diversity monitoring guidance was still limited. As our understanding of gender identity evolves beyond binary definitions, future data collection efforts should embrace a more expansive and inclusive approach, recognising the diverse spectrum of gender identities.

In conclusion, this study provides evidence of the effectiveness of a research network's approach to promoting equality, diversity, and inclusion (EDI) in research and funding. By collecting EDI data from individuals who engage with network activities and apply for research funding, this study has shown that the network's initiatives have had a positive impact on representation and fairness in the funding process. Specifically, the analysis reveals that the network is successful at engaging with ECRs, and those with care responsibilities and has a diverse range of ethnicities represented at Connected Everything events. Additionally, the network activities have a more equal gender balance and greater representation of researchers with disabilities when compared to the engineering and technology academic community, though there is still an underrepresentation of these groups compared to the national population.

Connected Everything introduced measures to help address inequalities in how funding is awarded. The measures introduced helped reduce the dominance of researchers who identified as male and research-intensive universities in winning research funding. Additionally, researchers with care responsibilities were more successful at winning funding than those without care responsibilities. However, inequality persisted with white researchers achieving higher award rates than those from ethnic minority backgrounds. Recommendations to make the review process fairer include: ensuring greater diversity of reviewers; reviewer anti-bias training; and automatic adjustments to correct for known biases in writing style.

Connected Everything’s approach to embedding EDI in network activities has already been shared widely with other EPSRC-funded networks and Hubs (e.g. the UKRI Circular Economy Hub and the UK Acoustics Network Plus). The network hopes that these findings will inform broader efforts to promote EDI in research and funding and that researchers, funders, and other stakeholders will be encouraged to adopt evidence-based strategies for advancing this important goal.

Availability of data and materials

The data collected was anonymously, however, it may be possible to identify an individual by combining specific records of the data request form data. Therefore, the study data has been presented in aggregate form to protect the confidential of individuals and the data utilised in this study cannot be made openly accessible due to ethical obligations to protect the privacy and confidentiality of the data providers.

Abbreviations

Early career researcher

Equality, diversity and inclusion

Engineering physical sciences research council

UK research and innovation

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Acknowledgements

The authors would like to acknowledge the support Engineering and Physical Sciences Research Council (EPSRC) [grant number EP/S036113/1], Connected Everything II: Accelerating Digital Manufacturing Research Collaboration and Innovation. The authors would also like to gratefully acknowledge the Connected Everything Executive Group for their contribution towards developing Connected Everything’s equality, diversity and inclusion strategy.

This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) [grant number EP/S036113/1].

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OJF analysed and interpreted the data, and was the lead author in writing and revising the manuscript. DF led the data acquisition and supported the interpretation of the data. DF was also a major contributor to the design of the equality diversity and inclusion (EDI) strategy proposed in this work. NJW supported the design of the EDI strategy and was a major contributor in reviewing and revising the manuscript. PG supported the design of the EDI strategy, and was a major contributor in reviewing and revising the manuscript. FC supported the design of the EDI strategy and the interpretation of the data. DM supported the design of the EDI strategy. SS led the development EDI strategy proposed in this work, and was a major contributor in data interpretation and reviewing and revising the manuscript. All authors read and approved the final manuscript.

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Correspondence to Debra Fearnshaw .

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Fisher, O.J., Fearnshaw, D., Watson, N.J. et al. Promoting equality, diversity and inclusion in research and funding: reflections from a digital manufacturing research network. Res Integr Peer Rev 9 , 5 (2024). https://doi.org/10.1186/s41073-024-00144-w

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Daniel Nguyen: Faculty Lecturer and Occupational Therapist Working in Long-term, Geriatric Care, and Mental Health

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Daniel Nguyen, erg., completed his MSc (Applied) in Occupational Therapy in 2012. Since that time, he has worked in geriatric care in several long-term care settings within the CIUSSS Ouest-de-l’ile de Montreal, supervised 19 OT students, and is currently working in return-to-work mental health. He has been a faculty lecturer at the School of Physical and Occupational Therapy since 2018. As part of our Question-and-Answer series to showcase the McGill Occupational Therapy program and illustrate the diversity of the profession, Daniel recently answered these questions.

How did you decide on OT as a career choice?

When I applied out of CEGEP, I was looking for a career in health care which was practical and hands-on, and I didn’t know too much about the profession, it just seemed like an interesting option. It was during my clinical placements, especially my placement at Villa Medica Hospital where I realized the important, and positive, impact we have on a client’s recovery process and their personal life. I remember being surprised that I was able to apply much of the learning we had done in class right into the clinic! It made me reflect on the impact of our decisions in improving other peoples’ conditions and how important participation was in one’s quality of life. That professional objective, to maintain that for our clients, is so important but the broadness of it also makes it difficult to describe in words alone.

What are some of the skills you feel make a great OT?

Being an active listener with a genuine interest in other people is important. OT practice is very client centered, so you may encounter the same condition or problem and even the same environment but completely different issues just because it's a different client with different personal needs.

Another key characteristic is to be interested in problem solving. We learn anatomy, physiology, and pathology of different conditions but there is no guidebook to solving every problem and each issue is different from person to person. In my current work, I help healthcare workers who are going through mental health issues that have led them to take time off work. Each work environment is different, everyone has different life experiences and stressors affecting them so helping them build up their skills to get back to work, identifying strategies to cope with different stressors combined with the right intervention plans during their return-to-work timeline requires creative problem solving.

Just to give you a few examples, my interventions in my current practice have ranged from helping a client develop strategies to dial in their day to day schedule at home in preparation for their return to work, deal with an overloaded Outlook inbox to role-playing and strategies on how to navigate workplace discussion to avoid sensitive topics.

Finally, a holistic view is important. I’ve heard one of my colleagues say that the OT’s superpower is their activity analysis. OTs are skilled at instantly breaking down a specific activity in multiple components and seeing how all these components interact with each other. To understand how personal systems and environmental components affect the participation in an activity, that's a very specific OT skill.

How would you describe OT to someone? 

I usually explain that OTs answer three fundamental questions. When observing a client, we ask ourselves: What can’t they do, why can't they do it and what can we do about it? That's it.

If for example, you meet a client with carpal tunnel syndrome (CTS) who has difficulties at their office job. Your first part would be to identify which tasks are affected by the CTS. In this case, typing on the keyboard could be an answer to the first question of what they can’t do. Then we ask ourselves the reasons why the client can’t type. This will be identified through our different assessment tools. In this case, we could measure multiple factors such as range of motion, endurance, strength, fine motor dexterity, body positioning, office set-up, the type of keyboard, potential accommodations in the workspace, the relationship that the client has with their employer that could affect the implementation of said accommodations, etc. Finally, we ask ourselves what we, as OTs, do about it, which would be to develop a treatment plan to manage the symptoms, improve on the physical limitations that have been assessed and make recommendations on optimizing the office area so that in the end, the client performs their task in a more efficient and satisfactory manner.

Why did you choose McGill University?

I am born and raised in Montreal and had always attended school in French. Although I spoke both English and French, I wanted the opportunity to learn in English at the University Level.

Any advice for someone deciding on a career in OT?

I would advise them to get a good understanding of what OT is and the role of an OT from multiple health care professionals. OT is a very broad and holistic program and having a better understanding of it when going into the program will help you contextualise what you are learning and imagine what kind of practice you may see yourself doing later. You also have to enjoy anatomy, physiology and learning about the development through the lifespan.

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12 Major Challenges Facing the Healthcare Industry in 2024

Healthcare was a $4.3 trillion industry in 2021 for the United States alone, accounting for 18.3% of the country’s gross domestic product. From primary care to life-saving emergency treatment, healthcare provides essential services to people in need — and jobs that require a wide range of skills and expertise. As such a large and impactful industry, healthcare faces a host of challenges, from delivering high-quality and equitable care to managing and securing data and supporting its workforce. Beyond that are challenges stemming from government agencies seeking to increase their influence over how those trillions could be better spent and a growing number of disruptors from outside the industry that all want a piece of the pie.

What Are Healthcare’s Top Industry Challenges?

Hospitals and health systems are challenged by maintaining continuous operations, keeping patients safe and ensuring their data is secure. Those challenges come at a significant cost in terms of hiring the right personnel, deploying strong technology and making clinical and business processes efficient without compromising care quality. What’s more, healthcare institutions must do their work while responding to an ever-changing regulatory environment and facing new kinds of competitors that promise patients more convenience or less costly care. So healthcare providers must find a balance between provisioning care that meets the needs of patients and expectations of regulators and maintaining financial strength.

Key Takeaways

• Many healthcare industry challenges stem from a need to respond to external forces, be they from regulators, competitors or cybercriminals.
• Inefficient workflows for documenting patient appointments, submitting insurance claims and normalizing unstructured data add to organizations’ expenses.
• From telehealth to electronic health records, hospitals and health systems face looming questions about how to deploy technology to improve patient experience without further burdening clinical staff.
• Healthcare providers stand to benefit from adopting enterprise resource planning (ERP) systems that can offer systemwide insight into operational performance.

Healthcare Industry Challenges Explained

Healthcare comes with the highest stakes: people’s lives. The dozen healthcare industry challenges outlined below cover a wide range of areas. Some are the result of external forces, such as cybersecurity, competition and government regulation. Others stem from internal processes that need improvement, such as inefficient workflows for documenting clinical care or submitting claims. Others, still, may be influenced by internal and external forces, such as the adoption of telehealth or the movement to provide more equitable care. But woven through them all is the common thread of uncertainty: uncertainty about disruptive competitors, the next cyberattack, the future of telehealth services and a whole host of other technology innovations that hold great promise. There’s also uncertainty about how providers can move to value-based care without creating a back-breaking administrative burden — and that’s only one of healthcare’s many regulatory-induced challenges.

Each of these challenges is significant on its own but, together, they create a tough environment. Not meeting these challenges could harm patient care, financial stability and the reputation of a health or hospital system’s reputation. Successfully meeting the challenges requires a proactive approach. Providers must embrace technology that supports their ability to make decisions based on data. They need to foster a culture of continuous learning and improvement and prioritize patient-centered care. Collaboration — both within healthcare organizations and with external partners — is vital. As the landscape continues to change, flexibility and adaptability will be key for successful healthcare providers.

12 Healthcare Industry Challenges

A wide range of challenges impact healthcare organizations and will impact how they provide care, use technology and otherwise do business in 2024.

1. Cybersecurity: The healthcare industry is especially susceptible to cyberattacks due to the volume of personally identifiable information (PII) and protected health information (PHI) that hospitals and health systems store. Research has shown that 60% of healthcare organizations have been hit with ransomware attacks in the past 12 months, while the number of successful attacks targeting the healthcare industry has more than doubled in the same time frame.

It has become crucial for healthcare companies to address their cybersecurity issues because more than financial consequences are at stake: Patient outcomes are affected and, sometimes, it’s literally a matter of life and death. Roughly 80% of ransomware attacks that hit hospitals disrupt patient care, with disruptions typically lasting two weeks. These disruptions often force organizations to divert care to other facilities, which has been linked to increased complications with medical procedures and higher mortality rates.

Meanwhile, the cost of mitigating a data breach in the healthcare industry is more than $10 million, and the potential loss of annual operating income from a single ransomware attack is as much as 30%. With hospital and health system operating margins at less than 2% and still recovering from largely negative margins in 2022, that is a cost few organizations find themselves able to pay.

2. Telehealth: The healthcare industry was quick to embrace telehealth in the early days of COVID-19; in April 2020, its use was 78 times greater than it was two months prior. That said, the gradual return to in-person care coupled with the expiration of the public health emergency, which relaxed many restrictions on when and how telehealth could be used, has led to increased doubt about telehealth’s future.

Currently, telehealth use represents about 5% of all medical claims. However, nearly 70% of all telehealth claims represent mental or behavioral health appointments, which suggests that other medical specialties haven’t fully embraced telehealth use. It’s also possible that utilization will drop in 2024 and beyond given uncertainty about what services Medicare will cover, how much physicians will be paid for telehealth visits and whether providers will be able to prescribe controlled substances in telehealth visits.

Further complicating telehealth’s future matters is the potential for vendor churn. Many hospitals and health systems are nearing the end of the contracts they signed with telehealth vendors early in the pandemic. As these organizations reevaluate the technology they have and whether it meets their needs, the healthcare industry may need to prepare for significant disruption in telehealth.

3. Competition: Brick-and-mortar health systems increasingly face competition and disruption. Standalone urgent care clinics are growing at a 7% annual rate, and today 80% of the U.S. population lives within a 10-minute drive of an urgent care center. (Notably, that total excludes clinics inside retail stores, which number more than 2,500.) The popularity of these clinics comes from convenience, as they tend to be open longer than the typical doctor’s office.

In addition, retail companies that have not traditionally provided care delivery services are getting into the game. Amazon acquired primary care provider One Medical in 2022, both CVS Health and Walgreens have acquired primary care and home health companies, and Best Buy has focused on supporting in-home remote patient monitoring. To top it off, venture capital firm General Catalyst has recently hinted that it may purchase a hospital.

All these moves are poised to have a significant impact on the healthcare industry in 2024 and beyond. Hospitals and doctor’s offices with long wait times to schedule an appointment or to see a doctor may have trouble competing with clinics that have longer hours or are even willing to send a care provider into someone’s home.

4. Invoicing and payment processing: The healthcare industry is especially susceptible to revenue leakage , with as much as 15 cents on every dollar earned going uncollected. The primary challenge organizations face is an inefficient revenue cycle management (RCM) process. Manual workflows are common for tasks such as verifying a patient’s insurance information, obtaining prior authorization, checking the status of a claim and appealing denials. These manual processes are time-consuming and subject to human error, so they lead to added expenses — most notably, in the form of employee time. This contributes to payment delays, which means it takes healthcare organizations longer to get paid for the services they provide. Unfortunately, about 75% of providers use manual processes for collections and, as a result, about 70% need more than 30 days to collect payments from patients.

It is possible to streamline these processes using electronic invoicing and invoice processing , which automatically tracks invoices from the time they are received. This can reduce errors, shorten time to payment and improve a healthcare provider’s cash flow. It’s worth noting, however, that certain claims or prior authorization denials will still require manual intervention.

5. Price transparency: Two U.S. regulations aim to help patients understand the cost of healthcare services and avoid unanticipated bills. First, the Hospital Price Transparency rule requires hospitals to provide both machine-readable and consumer-friendly lists of prices for common services and procedures. Second, the No Surprises Act requires hospitals to provide good-faith estimates of what services will cost and bans out-of-network charges for services provided at an in-network facility.

While organizations such as the American Medical Association and the American Hospital Association support these regulations, they have also noted the strains they can put on healthcare providers. For example, it’s difficult to determine a single, fixed rate for a medical service, as organizations often negotiate different rates with different insurance companies. In addition, creating price transparency tools takes time and requires financial and staff resources that are in short supply in healthcare organizations. Finally, the arbitration process for insurers and providers to negotiate a “surprise bill” issued to a patient is likely to result in lower payments, which could adversely impact the hospitals and health systems that provide the services.

6. Big data: The average hospital system produces 137 terabytes of data every day. This data is valuable for many reasons: It documents the care that has been delivered, it offers insight into a patient’s overall health and wellness, and it provides an audit trail for compliance and legal purposes. The healthcare industry struggles to manage big data because as much as 80% of its data is unstructured, such as free-text physicians’ notes or medical images, and cannot easily be captured in the rows of a database. Normalizing unstructured data to look like structured data makes it more useful for clinical and business decision-making, but the normalization process is expensive and time-consuming when done manually.

Here, data crunching is necessary to convert raw data into a machine-readable format. Key to successful data crunching is knowing the use case, understanding the data sources and documenting the process to help make it more efficient in the future. Data crunching will be an important consideration for the healthcare industry in 2024 as organizations seek to study their data to better understand clinical and financial outcomes.

7. Health equity: The negative impact that economic and social marginalization have on health outcomes was well documented prior to the 2020 pandemic. However, higher rates of hospitalization and death from COVID-19 among non-white Americans — coupled with lower vaccination rates — further highlight the healthcare industry’s ongoing struggle to provide equitable care. Other examples include higher rates of death from cancer and higher maternal mortality rates.

In response, the Centers for Medicare & Medicaid Services (CMS) has set five priorities for improving health equity in the United States over the next decade. These priorities include collecting more accurate data about patients’ barriers to receiving care, building the capacity to address disparities in care and making healthcare services more accessible.

The Centers for Disease Control and Prevention (CDC) has indicated that addressing health equity “requires ongoing societal efforts” to remove barriers to care and address long-standing injustices that patients face based on their race, gender, sexual orientation, disability status or other factors. The healthcare industry has an important role to play, but institutions in education, government, public safety and the private sector also need to be involved.

8. Slow clinical workflows: Nearly 80% of office-based physicians and 96% of hospitals in the United States use electronic health record (EHR) systems. This is a significant increase from less than 20% of physicians in 2001, influenced largely by federal reimbursements allocated in the Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009. In terms of clinical workflow, EHR systems appear to come with both yin and yang. They have improved care delivery by streamlining note-taking, improving decision-making and providing reminders and alerts to clinical staff. However, multiple features of EHR systems have been linked to an adverse impact on clinical workflows, ranging from long load times to information overload. Clinical staff also spend more time looking at their computer screens and less time with patients, and more time completing their clinical notes or documenting visits to submit bills.

A range of technology innovations have the potential to improve the clinical workflow within EHR systems, including the use of generative AI to automate documentation and make it easier to search for information. The challenge for the healthcare industry in 2024 is to determine the appropriate use cases for such innovations — and ensure that using them does not further distract clinical staff during patient visits.

9. Provider shortages: The healthcare industry faces a significant shortage of qualified professionals to deliver care. The Association of American Medical Colleges has projected a total shortage of between 37,800 and 124,000 physicians by 2034. Meanwhile, a 2023 analysis of data from the 2022 National Nursing Workforce Survey showed that more than 610,000 experienced registered nurses — nearly one-third of nurses in the U.S. — are considering leaving their jobs in the next five years due to stress directly linked to the pandemic.

Several factors contribute to these expected shortages. Chief among them is burnout, fueled by a combination of increased workloads during the pandemic and the growing number of administrative tasks that clinical staff are forced to complete. Additionally, nearly 45% of physicians are over the age of 55; as these physicians reach retirement age, there are not enough medical school and residency program graduates to replace them.

The shortage of physicians and nurses is hitting rural areas of the United States especially hard. Nearly 200 rural hospitals have closed since 2005 and another 600 are at risk of closing, in part because they struggle to compete with the higher salaries and better working conditions of suburban and urban hospitals. This will have a significant impact on the healthcare industry in 2024 and beyond. Patients in rural areas will have to travel farther to get the care they need, which contributes to poorer health outcomes.

10. Patient experience: Slow clinical workflows and provider shortages have contributed to declining satisfaction with the patient experience. Also bringing down patient experience are long wait times — nearly a month (26 days), on average, between scheduling a new-patient appointment and the appointment date — and persistent manual processes for managing appointments, renewing medications and discussing test results. These increase the likelihood that a patient will switch doctors, which leads to lost revenue and hurts the reputation of a hospital or health system.

The healthcare industry will continue to respond to these challenges in 2024 through physical changes, such as state-of-the-art facilities, and a range of technology offerings. Self-scheduling capabilities, automated reminders, digital check-in and real-time payments are some examples of technologies that can improve the patient experience. Increasing the level of care provided in the home, whether through home visits or telehealth appointments, can also help.

For many organizations, these are large-scale changes. Leaders must ensure that new technologies or workflows to improve the patient experience do not adversely impact the experience of clinical staff, who are already feeling overwhelmed.

11. Move to value-based care: CMS has set ambitious goals for transitioning the healthcare industry to value-based care, which reimburses providers based on the clinical outcomes they achieve and not simply on the volume of services they perform. For example, the agency hopes all Medicare beneficiaries and most Medicaid beneficiaries will be enrolled in value-based programs by 2030.

Healthcare providers face two core challenges in achieving this goal. One is the volume of value-based care models that CMS has created. The accountable care organization (ACO) is the most notable, but there are also separate models for certain chronic conditions, prescription drugs and medical procedures, as well as models for specific types of Medicare and Medicaid health plans. A patient seen by a hospital or health system could conceivably be part of multiple value-based care models, which makes it difficult to document where, when and from whom they receive care.

The second challenge is the amount of documentation required to demonstrate value. There are several components of quality care, including safety, equity, timeliness and cost. Organizations must report their performance on these metrics, and many others, to show both CMS and commercial insurers that they are delivering high-value care. This is creating a significant administrative burden likely to leave hospitals and health systems with fewer resources to devote to patient care.

12. Regulatory changes: Healthcare, of course, is heavily regulated, as patients’ lives are at stake. The pace of regulatory change has picked up since the pandemic, partly to give healthcare organizations greater flexibility to provide necessary care and partly to give patients improved access to their own health records — while keeping that information secure. But regulations covering the use of telehealth, transparent pricing, health equity and value-based care continue to evolve. In addition, the healthcare industry must contend with recently passed regulations, such as the information blocking rule (which requires organizations to share patients’ records with them), and new requirements for documenting evaluation and management (E&M) care encounters more accurately.

The healthcare industry is struggling to keep up with this pace of change. Staff need to be educated about the new rules, particularly if their day-to-day roles are directly affected. Legal and compliance teams, meanwhile, need to review and update policies on everything from releasing information to documenting care — and often must sort through multiple regulations that apply to the same process.

How ERP Can Help Solve Healthcare Challenges

While healthcare’s challenges have disparate origins and require different mitigation strategies, a common thread is the pitfall of inefficiency. Manual workflows for submitting claims, departmental data silos, time-consuming processes for normalizing data and outdated practices for engaging with patients all result in healthcare providers operating less efficiently than they could. Furthermore, that inefficiency makes it difficult to provide high-quality care, meet compliance requirements and adapt to competitive pressures.

But unifying data and automating manual processes is where enterprise resource planning (ERP) systems shine. An ERP system can bring together previously siloed data from across the healthcare organization. Through this unified view of data, executives have greater oversight into administrative and clinical operations, while hospital leaders have greater access to the information they need to make informed decisions quickly. Oversight and informed decision-making improve efficiency, which enables high-quality care, reduces costs and sets the stage for growth.

ERP systems support growing healthcare organizations in at least three important ways. First is their easy scalability. Cloud-based ERP systems help organizations deploy a single system across multiple locations without the need for additional infrastructure or resources. Second is unification. With ERP, reporting, purchasing, accounting and many other departments are served from a single platform, removing the inefficiencies and redundancies of managing dozens of different applications. And third is integration with external systems. ERP systems are designed to facilitate that integration, so they can automatically extract data from business and clinical systems, eliminating the need to pull manual reports and getting data into decision-makers’ hands faster.

Adapt to the Ever-Changing Healthcare Landscape With NetSuite

Healthcare organizations must do business in a complex and dynamic environment where it’s imperative to respond to changing market conditions, industry regulations and competitive pressures, all while providing quality care to patients in need and supporting the staff that do this meaningful work. NetSuite cloud-based ERP software for healthcare and life sciences is well-suited to help providers adapt to change through workflow reconfiguration, process automation, real-time reporting and better visibility into data across the enterprise. Leveraging NetSuite Enterprise Resource Planning (ERP) empowers the data-driven decision-making that health and hospital systems need to maintain operations at a time of great uncertainty.

For example, using NetSuite, Ohio-based Crossroads Health , a provider of behavioral health and mental health services, was able to submit stronger applications for grant funding, refresh its annual budgeting cycle and prepare budget reports that are individually tailored to the interests of board members. This has supported Crossroads Health as it doubled in size, expanded into primary care and added pharmacy services even as it managed increases in wages and expenses that outpaced Medicare and Medicaid reimbursements.

The healthcare industry faces no shortage of challenges to how it documents, delivers and pays for patient care. Health organizations must prepare to expect the unexpected — whether in the form of new regulations, new competitors or new cyber threats — while maintaining the level of service their patients expect and improving access for those who have been underserved. While there is no single solution for addressing all these challenges, greater visibility into clinical, operational and performance data enables healthcare providers to take proactive steps to meet the heady challenges they face.

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Healthcare Industry Challenges FAQs

What is the biggest challenge in the healthcare industry?

Healthcare organizations’ greatest challenge is adapting to pressure from a wide range of external stakeholders that they cannot control, from government regulators, to competitors in adjacent vertical markets, to cyberattackers seeking valuable financial and personal information. This is a difficult and expensive challenge for organizations that must maintain 24/7 operations while providing high-quality patient care.

What are the biggest issues in healthcare 2024?

The healthcare industry maintains many inefficient workflows. Some, such as processes for submitting claims to insurance companies or sending bills to patients, are largely manual. Others have been automated but remain challenging due to requirements for clinical and administrative documentation. In some cases, such as leveraging big data, the inefficiency is a by-product of the nature of healthcare. Because the bulk of the industry’s data is in unstructured formats, it can be difficult and time-consuming to normalize it for interpretation and analysis.

What are three common barriers to growth in the healthcare industry?

Healthcare organizations struggle to grow due to inefficient workflows that make it difficult to see more patients or pay bills on time. In addition, new documentation required as part of the transition to value-based care and other regulatory requirements has pushed the healthcare industry to add administrative personnel, which means fewer resources are available to hire physicians and nurses. Finally, the healthcare industry must continue to address inequity in access to care, which contributes to poorer outcomes in marginalized and underrepresented communities.

What is a major disruptor facing healthcare currently?

Traditional providers of healthcare services, such as hospitals, health systems and physicians’ offices, face increased competition from retailers, urgent-care providers and direct-to-consumer telehealth providers. These competitors aim to provide more convenient care at a lower cost than a trip to the doctor’s office or emergency room. At the same time, these entities often lack the brand awareness and market presence of health systems that have been part of their communities for decades, if not centuries.

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A Problem Solving Process for Health Care

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