The National Academies on Behavioral Economics

Report cover

A National Academies of Sciences, Engineering, and Medicine (NASEM) committee recently published a report* on the contributions of behavioral economics (BE) to public policy.  BE is “an approach to understanding human behavior and decision making that integrates knowledge from psychology and other behavioral fields with economic analysis.” (p. Summ-1)

The report’s first section summarizes the history and development of the field of behavioral economics.  Classical economics envisions the individual person as a decision maker who has all relevant information available, and makes rational decisions that maximize his overall, i.e. short- and long-term, self-interest.  In contrast, BE recognizes that actual people making real decisions have many built-in biases, limitations, and constraints.  The following five principles apply to the decision making processes behavioral economists study:

Limited Attention and Cognition - The extent to which people pay limited attention to relevant aspects of their environment and often make cognitive errors.

Inaccurate Beliefs - Individuals can have incorrect perceptions or information about situations, relevant incentives, their own abilities, and the beliefs of others.

Present Bias - People tend to disproportionately focus on issues that are in front of them in the present moment.

Reference Dependence and Framing - Individuals tend to consider how their decision options relate to a particular reference point, e.g., the status quo, rather than considering all available possibilities. People are also sensitive to the way decision problems are framed, i.e., how options are presented, and this affects what comes to their attention and can lead to different perceptions, reactions, and choices.

Social Preferences and Social Norms - Decision makers often consider how their decisions affect others, how they compare with others, and how their decisions imply values and conformance with social norms.

The task of policy makers is to acknowledge these limitations and present decision situations to people in ways that people can comprehend and help them make decisions that will serve their own and society’s interests.  In practice this means decision situations “can be designed to modify the habitual and unconscious ways that people act and make decisions.” (p. Summ-3)

Decision situation designers use various interventions to inform and guide individuals’ decision making.  The NASEM committee mapped 23 possible interventions against the 5 principles.  It’s impractical to list all the interventions here but the more graspable ones include:

Defaults – The starting decision option is the designer’s preferred choice; the decision maker must actively choose a different option.

De-biasing – Attempt to correct inaccurate beliefs by presenting salient information related to past performance of the individual decision maker or a relevant reference group.

Mental Models – Update or change the decision maker’s mental representation of how the world works.

Reminders – Use reminders to cut through inattention, highlight desired behavior, and focus the decision maker on a future goal or desired state.

Framing – Focus the decision maker on a specific reference point, e.g., a default option or the negative consequences of inaction (not choosing any option).

Social Comparison and Feedback - Explicitly compare an individual’s performance with a relevant comparison or reference group, e.g., the individual’s professional peers.

Interventions can range from “nudges” that alter people’s behavior without forbidding any options to designs that are much stronger than nudges and are, in effect, efforts to enforce conformity.

The bulk of the report describes the theory, research, and application of BE in six public policy domains: health, retirement benefits, social safety net benefits, climate change, education, and criminal justice.  The NASEM committee reviewed current research and interventions in each domain and recommended areas for future research activity.  There is too much material to summarize so we’ll provide a single illustrative sample.

Because we have written about culture and safety practices in the healthcare industry, we will recap the report’s discussion of efforts to modify or support medical clinicians’ behavior.  Clinicians often work in busy, sometimes chaotic, settings that place multiple demands on their attention and must make frequent, critical decisions under time pressure.  On occasion, they provide more (or less) health care than a patient’s clinical condition warrants; they also make errors.  Research and interventions to date address present bias and limited attention by changing defaults, and invoke social norms by providing information on an individual’s performance relative to others.  An example of a default intervention is to change mandated checklists from opt-in (the response for each item must be specified) to opt-out (the most likely answer for each item is pre-loaded; the clinician can choose to change it).  An example of using social norms is to provide information on the behavior and performance of peers, e.g., in the quantity and type of prescriptions written.

Overall recommendations

The report’s recommendations are typical for this type of overview: improve the education of future policy makers, apply the key principles in public policy formulation, and fund and emphasize future research.  Such research should include better linkage of behavioral principles and insights to specific intervention and policy goals, and realize the potential for artificial intelligence and machine learning approaches to improve tailoring and targeting of interventions.

Our Perspective

We have written about decision making for years, mostly about how organizational culture (values and norms) affect decision making.  We’ve also reviewed the insights and principles highlighted in the subject report.  For example, our December 18, 2013 post on Daniel Kahneman’s work described people’s built-in decision making biases.  Our June 6, 2022 post on Thaler and Sunstein’s book Nudge discussed the application of behavioral economic principles in the design of ideal (and ethical) decision making processes.  These authors’ works are recognized as seminal in the subject report.

On the subject of ethics, the NASEM committee’s original mission included considering ethical issues related to the use of behavioral economics but ethics’ mention is the report is not much more than a few cautionary notes.  This is thin gruel for a field that includes many public and private actors deciding what people should do instead of letting them decide for themselves.

As evidenced by the report, the application of behavioral economics is widespread and growing.  It’s easy to see its use being supercharged by artificial intelligence and machine learning.  “Behavioral economics” sounds academic and benign.  Maybe we should start calling it behavioral engineering.

Bottom line: Read this report.  You need to know about this stuff.


*  National Academies of Sciences, Engineering, and Medicine, “Behavioral Economics: Policy Impact and Future Directions,” (Washington, DC: The National Academies Press, 2023).

A Systems Approach to Diagnosis in Healthcare Emergency Departments

JAMA logo

A recent op-ed* in JAMA advocated greater use of systems thinking to reduce diagnostic errors in emergency departments (EDs).  The authors describe the current situation – diagnostic errors occur at an estimated 5.7% rate – and offer 3 insights why systems thinking may contribute to interventions that reduce this error rate.  We will summarize their observations and then provide our perspective.

First, they point out that diagnostic errors are not limited to the ED, in fact, such errors occur in all specialties and areas of health care.  Diagnosis is often complicated and practitioners are under time pressure to come up with an answer.  The focus of interventions should be on reducing incorrect diagnoses that result in harm to patients.  Fortunately, studies have shown that “just 15 clinical conditions accounted for 68% of diagnostic errors associated with high-severity harms,” which should help narrow the focus for possible interventions.  However, simply doing more of the current approaches, e.g., more “testing,” is not going to be effective.  (We’ll explain why later.)

Second, diagnostic errors are often invisible; if they were visible, they would be recognized and corrected in the moment.  The system needs “practical value-added ways to define and measure diagnostic errors in real time, . . .”

Third, “Because of the perception of personal culpability associated with diagnostic errors, . . . health care professionals have relied on the heroism of individual clinicians . . . to prevent diagnostic errors.”  Because humans are not error-free, the system as it currently exists will inevitably produce some errors.  Possible interventions include checklists, cognitive aids, machine learning, and training modules aimed at the Top 15 problematic clinical conditions. “The paradigm of how we interpret diagnostic errors must shift from trying to “fix” individual clinicians to creating systems-level solutions to reverse system errors.”

Our Perspective

It will come as no surprise that we endorse the authors’ point of view: healthcare needs to utilize more systems thinking to increase the safety and effectiveness of its myriad diagnostic and treatment processes.  Stakeholders must acknowledge that the current system for delivering healthcare services has error rates consistent with its sub-optimal design.  Because of that, tinkering with incremental changes, e.g., the well-publicized effort to reduce infections from catheters, will yield only incremental improvements in safety.  At best, they will only expose the next stratum of issues that are limiting system performance.

Incremental improvements are based on fragmented mental models of the healthcare system.  Proper systems thinking starts with a complete mental model of a healthcare system and how it operates.  We have described a more complete mental model in other posts so we will only summarize it here.  A model has components, e.g., doctors, nurses, support staff, and facilities.  And the model is dynamic, which means components are not fixed entities but ones whose quality and quantity varies over time.  In addition, the inter-relationships between and among the components can also vary over time.  Component behavior is directed by both relatively visible factors – policies, procedures, and practices – and softer control functions such as the level of trust between individuals, different groups, and hierarchical levels, i.e., bosses and workers.  Importantly, component behavior is also influenced by feedback from other components.  These feedback loops can be positive or negative, i.e., they can reinforce certain behaviors or seek to reduce or eliminate them.  For more on mental models, see our May 21, 2021, Nov. 6, 2019, and Oct. 9, 2019 posts.

One key control factor is organizational culture, i.e., the values and assumptions about reality shared by members.  In the healthcare environment, the most important subset of culture is safety culture (SC).  Safety should be a primary consideration in all activities in a healthcare organization.  For example, in a strong SC, the reporting of an adverse event such as an error should be regarded as a routine and ordinary task.  The reluctance of doctors to report errors because of their feelings of personal and professional shame, or fear of malpractice allegations or discipline, must be overcome.  For more on SC, see our May 21, 2021 and July 31, 2020 posts.

Organizational structure is another control factor, one that basically defines the upper limit of organizational performance.  Does the existing structure facilitate communication, learning, and performance improvement or do silos create barriers?  Do professional organizations and unions create focal points the system designer can leverage to improve performance or are they separate power structures whose interests and goals may conflict with those of the larger system?  What is the quality of management’s behavior, especially their decision making processes, and how is management influenced by their goals, policy constraints, environmental pressures (e.g., to advance equity and diversity) and compensation scheme?

As noted earlier, the authors observe that EDs depend on individual doctors to arrive at correct diagnoses in spite of inadequate information or time pressure and doctors who can do this well are regarded as heroes.  We note that doctors who are less effective may be shuffled off to the side or in egregious cases, labeled “bad apples” and tossed out of the organization.  This is an incorrect viewpoint.  Competent, dedicated individuals are necessary, of course, but the system designer should focus on making the system more error tolerant (so any errors cause no or minimal harm) and resilient (so errors are recognized and corrective actions implemented.)          

Bottom line: more systems thinking is needed in healthcare and articles like this help move the needle in the correct direction.


*  J.A. Edlow and P.J. Pronovost, “Misdiagnosis in the Emergency Department: Time for a System Solution,” JAMA (Journal of the American Medical Association), Vol. 329, No. 8 (Feb. 28, 2023), pp. 631-632.

A Road Map for Reducing Diagnostic Errors in Healthcare

A recent article* about how to reduce diagnostic errors in healthcare caught our attention, for a couple of reasons.  First, it describes a fairly comprehensive checklist of specific practices to address diagnostic errors, and second, the practices include organizational culture and reflect systems thinking, both subjects dear to us.  The checklist’s purpose is to help an organization rate its current performance and identify areas for improvement.

The authors used a reasonable method to develop the checklist: they convened an anonymous Delphi group, identified and ranked initial lists of practices, shared the information among the group, then collected and organized the updated rankings.  The authors then sent the draft checklist to several hospital managers, i.e., the kind of people who would have to implement the approach, for their input on feasibility and clarity.  The final checklist was then published.

The checklist focuses on diagnostic errors, i.e., missed, delayed, or wrong diagnoses.  It does not address other major types of healthcare errors, e.g., botched procedures, drug mix-ups, or provider hygiene practices.

The authors propose 10 practices, summarized below, to assess current performance and direct interventions with respect to diagnostic errors:

1.    Senior leadership builds a “board-to-bedside” accountability framework to measure and improve diagnostic safety.

2.    Promote a just culture and create a psychologically safe environment that encourages clinicians and staff to share opportunities to improve diagnostic safety without fear of retribution.

3.    Create feedback loops to increase information flow about patients’ diagnostic and treatment-related outcomes after handoffs from one provider/department to another.

4.    Develop multidisciplinary perspectives to understand and address contributory factors in the analysis of diagnostic safety events.

5.    Seek patient and family feedback to identify and understand diagnostic safety concerns.

6.    Encourage patients to review their health records and ask questions about their diagnoses and related treatments.

7.    Prioritize equity in diagnostic safety efforts.

8-10.    Establish standardized systems and processes to (1) encourage direct, collaborative interactions between treating clinical teams and diagnostic specialties; (2) ensure reliable communication of diagnostic information between care providers and with patients and families; and (3) close the loop on communication and follow up on abnormal test results and referrals.

Our Perspective

We support the authors recognition that diagnostic errors are difficult to analyze; they can involve clinical uncertainty, the natural evolution of diagnosis as more information becomes available, and cognitive errors, all exacerbated by system vulnerabilities.  Addressing such errors requires a systems approach.  

The emphasis on a just culture and establishing feedback loops is good.  We would add the importance of management commitment to fixing and learning from identified problems, and a management compensation plan that includes monetary incentives for doing this.

However, we believe the probability of a healthcare organization establishing dedicated infrastructure to address diagnostic errors is very low.  First, the authors recognize there is no existing business case to address such errors.  In addition, we suspect there is some uncertainty around how often such errors occur.  The authors say these errors affect at least 5% of US adult outpatients annually but that number is based on a single mini-meta study.**

As a consequence, senior management is not currently motivated by either fear (e.g., higher costs, excessive losses to lawsuits, regulatory sanctions or fines, or reputational loss) or greed (e.g., professional recognition or monetary incentives) to take action.  So our recommended first step should be to determine which types of medical errors present the greatest threats to an institution, how many occur, and then determine what can be done to prevent them or minimize their consequences.  (See our July 31, 2020 post on Dr. Danielle Ofri’s book When We Do Harm for more on medical errors.)

Second, the organization has other competing goals demanding attention and resources so management’s inclination will be to minimize costs by simply extending any existing error identification and resolution program to include diagnostic errors.

Third, diagnosis is not a cut-and-dried process, like inserting a catheter, double-checking patients’ names, or hand washing.  The diagnostic process is essentially probabilistic, with different diagnoses possible from the same data, and to some degree, subjective.  Management probably does not want a stand-alone system that second guesses and retrospectively judges doctors’ decisions and opinions.  Such an approach could be perceived as intruding on doctors’ freedom to exercise professional judgment and is bad for morale.

Bottom line: The checklist is well-intentioned but a bit naïve.  It is a good guide for identifying weak spots and hazards in a healthcare organization, and the overall approach is not necessarily limited to diagnostic errors.   


*  Singh, H., Mushtaq, U., Marinez, A., Shahid, U., Huebner, J., McGaffigan, P., and Upadhyay, D.K., “Developing the Safer Dx Checklist of Ten Safety Recommendations for Health Care Organizations to Address Diagnostic Errors,” The Joint Commission Journal on Quality and Patient Safety, No. 48, Aug. 10, 2022, pp. 581–590.  The Joint Commission is an entity that inspects and accredits healthcare providers, mainly hospitals.

**  Singh, H., Meyer, A.N.D., and Thomas, E.J., “The frequency of diagnostic errors in outpatient care: estimations from three large observational studies involving US adult populations,” BMJ Quality and Safety, Vol. 23, No. 9, April 2014, pp. 727–731.

Culture in the Healthcare Industry

A couple of articles recognizing the importance of cultural factors in the healthcare space recently caught our attention.  The authors break no new ground but we’re reporting these articles because they appeared in a couple of the U.S.’s most prestigious medical journals.

We begin with an opinion piece in The Journal of the American Medical Association (JAMA).*  The authors’ focus is on clinician burnout (which we discussed on Nov. 6, 2019) but they cite earlier work on the importance of quality and culture in the healthcare workplace, including “the culture changes needed for effective teamwork and optimizing the authentic voice of every team member. . . . [and examining] the consequences of medical hierarchy and inequity.”  

One of the references in the JAMA piece is an earlier article by two of the authors in The New England Journal of Medicine.**  This article discusses how the National Academy of Medicine (NAM) and its predecessor entities have influenced the trajectory of the discussion of healthcare effectiveness, starting by documenting the wide scope of inappropriate care prescribed to patients, i.e., the overuse of ineffective medical practices.  Their seminal 1999 report, “To Err Is Human,” estimated that 44,000 to 98,000 Americans die in hospitals each year because of medical errors.  

Their 2001 report, “Crossing the Quality Chasm,” defined a framework for healthcare quality with six dimensions: safety, effectiveness, patient-centeredness, timeliness, efficiency, and equity.  In practice, the quality of healthcare services has improved since then in several specific areas, e.g., reduced rates of acquired infections, but “wholesale, systemic improvement in quality of care has proven difficult to bring to scale.”  We wrote about the lack of progress on Nov. 9, 1920.  One significant ongoing problem is that efforts to increase provider accountability, e.g., ascertaining if providers are delivering appropriate care, has resulted in a negative impact on clinicians’ morale.  “The United States has yet to find for health care the wisest balance between accountability, which is critical, and supports for a trusting culture of growth and learning, which, as the NAM asserts, is the essential foundation for continual improvement.”

Our Perspective

None of this information is new.  What is worth noting is how cultural aspects have become important topics for discussion at the highest levels of healthcare policy. 

If you have been following our healthcare posts on Safetymatters, you know we have discussed the challenges and the progress, or lack thereof, in reducing errors and increasing effectiveness.  We have emphasized the role of a strong safety culture in the delivery of high quality services.  Click on the healthcare label to see all of our related posts.

Healthcare has a long way to go to catch up with other industries that have integrated high levels of safety and quality into their daily operations.  To illustrate healthcare’s current position, we will repurpose a recent McKinsey article on corporate ESG (Environment, Social, Governance) attributes.***  McKinsey uses a 3-category framework (Minimum, Common, and Next level practices) to describe a business’s ESG character.  For our purposes, we will replace ESG with safety and quality (S&C), and excerpt and adapt specific attributes that could and should exist in a healthcare organization.

Minimum practices – focus on risk mitigation and do no harm measures

•    React to external social-legal-political trends
•    Address obvious vulnerabilities
•    Meet baseline standards
•    Pledge to minimal commitment levels

Common practices – substantive efforts, more proactive than reactive

•    Track major trends and develop strategies to address them
•    Identify strengths and use them to move toward S&C goals
•    Comply with voluntary standards and perform above average
•    Engage with stakeholder groups to understand what matters to them

Next level practices – full integration of S&C into strategy and operations

•    View S&C as essential components of overall strategy
•    Link clearly articulated leadership areas with S&C goals
•    Embed S&C in capital and resource allocation
•    Tie S&C to employee incentives and evaluations
•    Ensure that S&C reports cover the entity’s full set of operations

Our judgment is that most healthcare entities, especially hospitals, demonstrate minimum practices and are trying to get ahead of the curve by implementing some common practices.  Some entities may claim to be using next level practices, but these are generally narrow or limited efforts.  The industry’s biggest challenge is getting the entrenched guilds of doctors and nurses, accustomed to working in protective silos, to fully embrace increased accountability.  At the same time, senior management must create, maintain, and manage a non-punitive work environment and a just culture.


*  Rotenstein, L.S., Berwick, D.M., and Cassel, C.K., “Addressing Well-being Throughout the Health Care Workforce: The Next Imperative,” JAMA, Vol. 328, No. 6 (Aug. 9, 2022), pp. 521-22.  Published online July 18, 2022.  JAMA is a peer-reviewed journal published by the American Medical Association.

**  Berwick, D.M., and Cassel, C.K., “The NAM and the Quality of Health Care — Inflecting a Field,” The New England Journal of Medicine, Vol. 383, No. 6 (Aug. 6, 2020), pp. 505-08.

***  Pérez, L., Hunt, V., Samandari, H, Nuttall, R., and Bellone, D., “How to make ESG real,” McKinsey Quarterly (Aug. 2022).