A Straightforward Recipe for Changing Culture

Source: COS website

We recently came across a clear, easily communicated road map for implementing cultural change.*  We’ll provide some background information on the author’s motivation for developing the road map, a summary of it, and our perspective on it.

The author, Brian Nosek, is executive director of the Center for Open Science (COS).  The mission of COS is to increase the openness, integrity, and reproducibility of scientific research.  Specifically, they propose that researchers publish the initial description of their studies so that original plans can be compared with actual results.  In addition, researchers should “share the materials, protocols, and data that they produced in the research so that others could confirm, challenge, extend, or reuse the work.”  Overall, the COS proposes a major change from how much research is presently conducted.

Currently, a lot of research is done in private, i.e., more or less in secret, usually with the objective of getting results published, preferably in a prestigious journal.  Frequent publishing is fundamental to getting and keeping a job, being promoted, and obtaining future funding for more research, in other words, having a successful career.  Researchers know that publishers generally prefer findings that are novel, positive (e.g., a treatment is effective), and tidy (the evidence fits together).

Getting from the present to the future requires a significant change in the culture of scientific research.  Nosek describes the steps to implement such change using a pyramid, shown below, as his visual model.  Similar to Abraham Maslow’s Hierarchy of Needs, a higher level of the pyramid can only be achieved if the lower levels are adequately satisfied.

Source: "Strategy for Culture Change"

Each level represents a different step for changing a culture:

•    Infrastructure refers to an open source database where researchers can register their projects, share their data, and show their work.
•    The User Interface of the infrastructure must be easy to use and compatible with researchers' existing workflows.
•    New research Communities will be built around new norms (e.g., openness and sharing) and behavior, supported and publicized by the infrastructure.
•    Incentives refer to redesigned reward and recognition systems (e.g., research funding and prizes, and institutional hiring and promotion schemes) that motivate desired behaviors.
•    Public and private Policy changes codify and normalize the new system, i.e., specify the new requirements for conducting research.
     
Our Perspective

As long-time consultants to senior managers, we applaud Nosek’s change model.  It is straightforward and adequately complete, and can be easily visualized.  We used to spend a lot of time distilling complicated situations into simple graphics that communicated strategically important points.

We also totally support his call to change the reward system to motivate the new, desirable behaviors.  We have been promoting this viewpoint for years with respect to safety culture: If an organization or other entity values safety and wants safe activities and outcomes, then they should compensate the senior leadership accordingly, i.e., pay for safety performance, and stop promoting the nonsense that safety is intrinsic to the entity’s functioning and leaders should provide it basically for free.

All that said, implementing major cultural change is not as simple as Nosek makes it sound.

First off, the status quo can have enormous sticking power.  Nosek acknowledges it is defined by strong norms, incentives, and policies.  Participants know the rules and how the system works, in particular they know what they must do to obtain the rewards and recognition.  Open research is an anathema to many researchers and their sponsors; this is especially true when a project is aimed at creating some kind of competitive advantage for the researcher or the institution.  Secrecy is also valued when researchers may (or do) come up with the “wrong answer” – findings that show a product is not effective or has dangerous side effects, or an entire industry’s functioning is hazardous for society.

Second, the research industry exists in a larger environment of social, political and legal factors.  Many elected officials, corporate and non-profit bosses, and other thought leaders may say they want and value a world of open research but in private, and in their actions, believe they are better served (and supported) by the existing regime.  The legal system in particular is set up to reinforce the current way of doing business, e.g., through patents.

Finally, systemic change means fiddling with the system dynamics, the physical and information flows, inter-component interfaces, and feedback loops that create system outcomes.  To the extent such outcomes are emergent properties, they are created by the functioning of the system itself and cannot be predicted by examining or adjusting separate system components.  Large-scale system change can be a minefield of unexpected or unintended consequences.

Bottom line: A clear model for change is essential but system redesigners need to tread carefully.  


*  B. Nosek, “Strategy for Culture Change,” blog post (June 11th, 2019).

Real Systems Pursue Goals

System Model Control Panel

On March 10, 2023 we posted about a medical journal editorial that advocated for incorporating more systems thinking in hospital emergency rooms’ (ERs) diagnostic processes.  Consistent with Safetymatters’ core beliefs, we approved of using systems thinking in complicated decision situations such as those arising in the ER. 

The article prompted a letter to the editor in which the author said the approach described in the original editorial wasn’t a true systems approach because it wasn’t specifically goal-oriented.  We agree with that author’s viewpoint.  We often argue for more systems thinking and describe mental models of systems with components, dynamic relationships among the components, feedback loops, control functions such as rules and culture, and decision maker inputs.  What we haven’t emphasized as much, probably because we tend to take it for granted, is that a bona fide system is teleological, i.e., designed to achieve a goal. 

It’s important to understand what a system’s goal is.  This may be challenging because the system’s goal may contain multiple sub-goals.  For example, a medical clinician may order a certain test.  The lab has a goal: to produce accurate, timely, and reliable results for tests that have been ordered.  But the clinician’s goal is different: to develop a correct diagnosis of a patient’s condition.  The goal of the hospital of which the clinician and lab are components may be something else: to produce generally acceptable patient outcomes, at reasonable cost, without incurring undue legal problems or regulatory oversight.  System components (the clinician and the lab) may have goals which are hopefully supportive of, or at least consistent with, overall system goals.

The top-level system, e.g., a healthcare provider, may not have a single goal, it may have multiple, independent goals that can conflict with one another.  Achieving the best quality may conflict with keeping costs within budgets.  Achieving perfect safety may conflict with the need to make operational decisions under time pressure and with imperfect or incomplete information.  One of the most important responsibilities of top management is defining how the system recognizes and deals with goal conflict.

In addition to goals, we need to discuss two other characteristics of full-fledged systems: a measure of performance and a defined client.* 

The measure of performance shows the system designers, users, managers, and overseers how well the system’s goal(s) are being achieved through the functioning of system components as affected by the system’s decision makers.  Like goals, the measure of performance may have multiple dimensions or sub-measures.  In a well-designed system, the summation of the set of sub-measures should be sufficient to describe overall system performance.  

The client is the entity whose interests are served by the system.  Identifying the client can be tricky.  Consider a city’s system for serving its unhoused population.  The basic system consists of a public agency to oversee the services, entities (often nongovernmental organizations, or NGOs) that provide the services, suppliers (e.g., landlords who offer buildings for use as housing), and the unhoused population.  Who is the client of this system, i.e., who benefits from its functioning?  The politicians, running for re-election, who authorize and sustain the public agency?  The public agency bureaucrats angling for bigger budgets and more staff?  The NGOs who are looking for increased funding?  Landlords who want rent increases?  Or the unhoused who may be looking for a private room with a lockable door, or may be resistant to accepting any services because of their mental, behavioral, or social problems?  It’s easy to see that many system participants do better, i.e., get more pie, if the “homeless problem” is never fully resolved.

For another example, look at the average public school district in the U.S.  At first blush, the students are the client.  But what about the elected state commissioner of education and the associated bureaucracy that establish standards and curricula for the districts?  And the elected district directors and district bureaucracy?  And the parents’ rights organizations?  And the teachers’ unions?  All of them claim to be working to further the students’ interests but what do they really care about?  How about political or organizational power, job security, and money?  The students could be more of a secondary consideration.

We could go on.  The point is we are surrounded by many social-legal-political-technical systems and who and what they are actually serving may not be those they purport to serve.

  

*  These system characteristics are taken from the work of a systems pioneer, Prof. C. West Churchman of UC Berkeley.  For more information, see his The Design of Inquiring Systems (New York: Basic Books) 1971.

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).

A Lesson from the Accounting Profession: Don’t Cheat on the Ethics Test

SEC Order

Accounting, like many professions, requires practitioners to regularly demonstrate competence and familiarity with relevant knowledge and practices.  One requirement for Certified Public Accountants (CPAs) is to take an on-line, multiple-choice test covering professional ethics.  Sounds easy but the passing grade is relatively high so it’s not a slam dunk.  Some Ernest & Young (EY) audit accountants found it was easier to pass if they cheated by using answer keys and sharing the keys with their colleagues.  They were eventually caught and got into big trouble with the U.S. Securities and Exchange Commission (SEC).  Following is a summary of the scandal as it evolved over time per the SEC order* and our view on what the incident says about EY’s culture.

During 2012-15, some EY employees were exploiting weaknesses in the company’s test software to pass tests despite not having a sufficient number of correct answers.  EY learned about this problem in 2014.  In 2016, EY learned that professionals in one office improperly shared answer keys.  EY repeatedly warned personnel that cheating on tests was a violation of the firm’s code of ethics but did not implement any additional controls to detect this misconduct.  The cheating continued into 2021.

In 2019 the SEC discovered cheating at another accounting firm and fined them $50 million.  As part of the SEC’s 2019 investigation, the agency asked EY if they had any problems with cheating.  In their response, EY said they had uncovered instances in the past but implied they had no current problems.  In fact, EY management had recently received a tip about cheating and initiated what turned out to be an extensive investigation that by late 2019 “confirmed that audit professionals in multiple offices cheated on CPA ethics exams.” (p. 6)  However, EY never updated their response to the SEC.  Eventually EY told the Public Company Accounting Oversight Board (PCAOB)** about the problems, and the PCAOB informed the SEC – 9 months after the SEC’s original request for information from EY.

In the U.S., the relationship between government regulators and regulated entities is based on the expectation that communications from the regulated entities will be complete, truthful, and updated on a timely basis if new information is discovered or developed.  Lying to or misleading the government, either through commission or omission, is a serious matter.

Because of EY’s violation of a PCAOB rule and EY’s misleading behavior with the SEC, the company was censured, fined $100 million, and required to implement a host of corrective actions, summarized below.

Review of Policies and Procedures

“EY shall evaluate . . . the sufficiency and adequacy of its quality controls, policies, and procedures relevant to ethics and integrity and to responding to Information Requests” (p. 9)  In particular, EY will evaluate “whether EY’s culture [emphasis added] is supportive of ethical and compliant conduct and maintaining integrity, including strong, explicit, and visible support and commitment by the firm’s management” (p. 10)

Independent Review of EY’s Policies and Procedures

“EY shall require that the Policies and Procedures IC [Independent Consultant] conduct a review of EY’s Policies and Procedures to determine whether they are designed and being implemented in a manner that provides reasonable assurance of compliance with all professional standards . . . . EY shall adopt, as soon as practicable, all recommendations of the Policies and Procedures IC in its report. . . . EY’s Principal Executive Officer must certify to the Commission staff in writing that (i) EY has adopted and has implemented or will implement all recommendations of the Policies and Procedures IC in its report . . .” (pp. 10-12)

Independent Review of EY’s Disclosure Failures

“EY’s Special Review Committee shall require that the Remedial IC conduct a review . . . of EY’s conduct relating to the Commission staff’s June 2019 Information Request, including whether any member of EY’s executive team, General Counsel’s Office, compliance staff, or other EY employees contributed to the firm’s failure to correct its misleading submission.” (p. 12)  Like the Policies and Procedures review, EY must adopt the recommendations in the Remedial IC Report and EY’s Principal Executive Officer must certify their adoption to the SEC.

Notice to Audit Clients, Training, and Certifications

“Within 10 business days after entry of this Order, EY shall provide all of its issuer audit clients and SEC-registered broker-dealer audit clients a copy of this Order. . . . all audit professionals and all EY partners and employees who, at any time prior to March 3, 2020, were aware (i) of the Division of Enforcement’s June 19, 2019 request, (ii) of EY’s June 20, 2019 response, and (iii) that an employee had made a tip on June 19, 2019 concerning cheating shall complete a minimum of 6 hours every 6 months of ethics and integrity training by an independent training provider . . . . EY’s Principal Executive Officer shall also certify that the training requirements . . . have been completed.” (pp. 14-15)

Our Perspective

A company’s culture includes the values and assumptions that underlie daily work life and influence decision making.  What can we infer about EY’s culture from the behavior described above?

First, what managers did after they discovered the cheating – issuing memos and waving their arms – did not work.  Even if EY terminated some employees, perhaps the worst offenders or maybe the least productive ones, EY did not make their testing process more robust or secure.

Second, senior leadership has not suffered from this scandal.  There is no indication any senior managers have been disciplined or terminated because of the misconduct.  The head of EY’s U.S. operations left at the end of her 4-year term, but her departure was apparently due to a disagreement with her boss, EY’s global chief executive. 

Third, there has been no apparent change in the employees’ task environments, e.g., their workload expectations and compensation program.

Conclusion: EY management tolerated the cheating because their more important priorities were elsewhere.  It’s safe to assume that EY, like other professional service firms, primarily values and rewards technical competence and maximizing billable hours.

We see two drivers for possible changes: the $100 million fine and the mandated review by “Independent Consultants.”  (EY’s self-review will likely be no more useful than their previous memos and posturing.)

What needs to be done? 

To begin, senior leadership has to say fixing the cheating problem is vitally important, and walk the talk by adjusting company practices to reinforce the task’s importance.  Leadership has to commit to a company corrective action program that recognizes, analyzes, and permanently fixes all significant company problems as they arise – not after their noses are rubbed into action by the regulator.  

In addition, there have to be visible changes in the audit professionals’ task environment.  The employees need to get work time, in the form of unbilled overhead hours, to prepare for tests.  The compensation scheme needs to add a component to recognize and reward ethical behavior – with clients and internally.  The administration of ethics tests needs to be made more secure, on a par with the accounting exams the employees take.

*  Securities and Exchange Commission, Other Release No.: 34-95167 Re: Ernst & Young LLP (June 28, 2022).  All quotes in our post are from the SEC order.  There is also an associated SEC press release.

**  The Public Company Accounting Oversight Board establishes auditing and professional practice standards for registered public accounting firms, such as EY, to follow in the preparation of audit reports for public companies.  PCAOB members are appointed by the SEC.

Guiding People to Better Decisions: Lessons from Nudge by Richard Thaler and Cass Sunstein

Safetymatters reports on organizational culture, the values and beliefs that underlie an organization’s essential activities.  One such activity is decision-making (DM) and we’ve said an organization’s DM processes should be robust and replicable.  DM must incorporate the organization’s priorities, allocate its resources, and handle the inevitable goal conflicts which arise.

In a related area, we’ve written about the biases that humans exhibit in their personal DM processes, described most notably in the work by Daniel Kahneman.*  These biases affect decisions people make, or contribute to, on behalf of their organizations, and personal decisions that only impact the decision maker himself.

Thaler and Sunstein also recognize that humans are not perfectly rational decision makers (citing Kahneman’s work, among others) and seek to help people make better decisions based on insights from behavioral science and applied economics.  Nudge** focuses on the presentation of decision situations and alternatives to decision makers on public and private sector websites.  It describes the nitty-gritty of identifying, analyzing, and manipulating decision factors, i.e., the architecture of choice. 

The authors examine the choice architecture for a specific class of decisions: where groups of people make individual choices from a set of alternatives.  Choice architecture consists of curation and navigation tools.  Curation refers to the set of alternatives presented to the decision maker.  Navigation tools sound neutral but small details can have a significant effect on a decider’s behavior. 

The authors discuss many examples including choosing a healthcare or retirement plan, deciding whether or not to become an organ donor, addressing climate change, and selecting a home mortgage.  In each case, they describe different ways of presenting the decision choices, and their suggestions for an optimal approach.  Their recommendations are guided by their philosophy of “libertarian paternalism” which means decision makers should be free to choose, but should be guided to an alternative that would maximize the decider’s utility, as defined by the decision maker herself.

Nudge concentrates on which alternatives are presented to a decider and how they are presented.  Is the decision maker asked to opt-in or opt-out with respect to major decisions?  Are many alternatives presented or a subset of possibilities?  A major problem in the real world is that people can have difficulty in seeing how choices will end up affecting their lives.  What is the default if the decision maker doesn’t make a selection?  This is important: default options are powerful nudges; they can be welfare enhancing for the decider or self-serving for the organization.  Ideally, default choices should be “consistent with choices people would make if they all the relevant information, were not subject to behavioral biases, and had the time to make a thoughtful choice.” (p. 261)

Another real world problem is that much choice architecture is bogged down with sludge - the inefficiency in the choice system – including barriers, red tape, delays, opaque costs, and hidden or difficult to use off-ramps (e.g., finding the path to unsubscribe from a publication).

The authors show how private entities like social media companies and employers, and public ones like the DMV, present decision situations to users.  Some entities have the decider’s welfare and benefit in mind, others are more concerned with their own power and profits.  It’s no secret that markets give companies an incentive to exploit our DM frailties to increase profits.  The authors explicitly do not support the policy of “presumed consent” embedded in many choice situations where the designer has assumed a desirable answer and is trying to get more deciders to end up there. 

The authors’ view is their work has led to many governments around the world establishing “nudge” departments to identify better routes for implementing social policies.

Our Perspective

First, the authors have a construct that is totally consistent with our notion of a system.  A true teleological system includes a designer (the authors), a client (the individual deciders), and a measure of performance (utility as experienced by the decider).  Because we all agree, we’ll give them an A+ for conceptual clarity and completeness.

Second, they pull back the curtain to reveal the deliberate (or haphazard) architecture that underlies many of our on-line experiences where we are asked or required to interact with the source entities.  The authors make clear how often we are being prodded and nudged.  Even the most ostensibly benign sites can suggest what we should be doing through their selection of default choices.  (In fairness, some site operators, like one’s employer, are themselves under the gun to provide complete data to government agencies or insurance companies.  They simply can’t wait indefinitely for employees to make up their minds.)  We need to be alert to defaults that we accept without thinking and choices we make when we know what others have chosen; in both cases, we may end up with a sub-optimal choice for our particular circumstances. 

Thaler and Sunstein are respectable academics so they include lots of endnotes with references to books, journals, mainstream media, government publications, and other sources.  Sunstein was Kahneman’s co-author for Noise, which we reviewed on July 1, 2021.

Bottom line: Nudge is an easy read about how choice architects shape our everyday experiences in the on-line world where user choices exist. 

 

*  Click on the Kahneman label for all our posts related to his work.

**  R.H. Thaler and C.R. Sunstein, Nudge, final ed. (New Haven: Yale University Press) 2021.