A Strategist's Guide to Clinician Well-being in a Time of Crisis

As health care professionals across the globe face the continued onslaught of the coronavirus disease 2019 (COVID-19) pandemic, health care organizations must brace for the anticipated near and long term consequences for clinician well-being. Preventing a parallel crisis of clinician burnout will take careful planning and thoughtful allocation of resources.¹ ² However, strategic planning during a crisis is incredibly challenging for any organization — never mind one at the front lines of a pandemic. In this report, we outline principles of decision making and strategic execution that health care organizations must consider in addressing the near- and long-term mental health needs of their clinical work force.

I. Forecasting Outcomes

Although the COVID-19 pandemic has been described as a central health crisis of our generation,³ prior disasters scenarios offer grim insights into the pandemic’s impact on clinician well-being. Indeed, following the 2003 severe acute respiratory syndrome (SARS) outbreak and the 2015 Middle East Respiratory Syndrome (MERS) outbreak, responding clinicians displayed higher rates of psychological distress, including fear, sleep problems, “numbness,” and survival guilt.⁴ And while most individuals are resilient enough to avoid clinical trauma responses, they can still experience subclinical symptoms that greatly impair their quality of life and result in professional burnout.⁴ These symptoms pose a danger not just to clinicians — whose rates of suicide exceed those of other professions⁵ ⁶ –but also to their patients.⁷ ⁸ ⁹ ¹⁰ ¹¹ ¹² ¹³

In a June, 2020 viewpoint, Shanafelt and colleagues published early insights into the anxieties facing health care professionals as a consequence of the COVID-19 pandemic. Among other issues, the authors cited anxieties related to finding necessary child care services during school closures and increased work hours.³ While this viewpoint was published over 6 months ago, it is unlikely these anxieties have eased in the current climate given the recurrent viral surges and lack of return to “normal” life.

II. Aiming to Fail Fast

Health care organizations have had to contend with significant operational and financial uncertainty as a result of the pandemic.¹⁴ ¹⁵ The challenges of modern-day decision making only amplify this uncertainty. As Day and Schoemaker have argued, modern organizations face pervasive information overload, which can compromise detection of critical signals and lead to confusion and diversion in decision making.¹⁶ To mitigate these risks, organizations must be able to “fail fast” in strategic planning. This requires them to rapidly develop, scale and evaluate programs in order to understand where limited resources can be allocated most effectively.¹⁴

However, “failing fast” with programs aimed at clinician well-being is incredibly challenging. The drivers of clinician well-being are multi-factorial¹⁷ and often require high effort, systems-level changes, such as reducing clerical burden for physicians.¹⁸ Changes of this magnitude take time and resources — both of which are limited in a crisis. Organizations must therefore rely on data to inform a selection of effective programs.

Unfortunately, there is a lack of robust data currently available to guide organizational responses to clinician well-being. A recent Cochrane review highlighted this point: in a systematic meta-analysis, the authors evaluated interventions for frontline health care professionals during or after a disease outbreak, epidemic or pandemic. They identified a lack of quantitative and qualitative evidence to support interventions that are beneficial to the resilience and mental health of these frontline workers.¹⁹

Without data to inform strategic planning, health care organizations must instead rely on research and improvement science to facilitate the rapid identification and scaling of effective interventions. In practice, however, this process is hindered by data collection. This is because organizations have traditionally assessed clinician well-being using survey and qualitative methods⁹ ²⁰ ²¹ — which rely on adequate participation from busy clinicians. Paradoxically, then, organizations are forced to make strategic decisions based on engagement from the very clinicians at highest risk of burnout.

This feedback mechanism is problematic for several reasons. Beyond the fact that professional burnout can be accompanied by exhaustion and disengagement — which can lead to selection and other sources of bias in data collection — these methods also functionally limit the timeliness of data, as leaders try to avoid “piling on” to clinicians’ schedules with additional tasks. In practice, this leads to stochastic assessments of clinician well-being that may be of questionable validity.

III. Dynamic Scanning

With insights into clinician well-being provided at a quarterly or annually cadence, how can a health care organization plan to effectively mitigate burnout and other symptoms of distress? The simple answer is it can’t — at least relying on traditional feedback mechanisms alone. Organizations must be willing to innovate beyond these methods to perform meaningful surveillance of clinician well-being at a systems level. And while data collected from clinicians through surveys, interviews, and focus groups are incredibly valuable, they are not sufficient in providing continuous signal detection and timely attention from leaders — critical tasks in any organization.²²

Increasingly, machine learning has emerged as a potential approach to this type of dynamic scanning. We recently explored this approach in our health care strategy consulting business through partnership with Receptiviti — a social psychology and data science technology platform that uses natural language processing, social psychology and artificial intelligence to drive business insights across a variety of industries. In collaboration with our firm, Adaptive Strategic Partners, Receptiviti recently developed a Healthcare Workers Wellness Index to test the viability of their software in trending clinician well-being.²³ The Healthcare Workers Wellness Index uses machine learning analysis of publicly-available data on Reddit, where self-identified doctors and nurses post daily submissions and comments in medical subreddits. Over the last several months, the platform’s language psychology algorithms assessed daily submissions and comments from thousands of self-identified doctors and nurses to understand their emotions and psychological wellbeing longitudinally across COVID-19. The platform also conducted comparative analyses with data from 2019, to study the impact of the pandemic.

The results from this proof-of-concept stage are staggering: in analyzing thousands of submissions from doctors and nurses, the Receptiviti platform identified a clear and progressive increase in signs of psychological distress over the course of the COVID-19 pandemic in relation to 2019. A particularly interesting trend was a steady decline in signs of analytical thinking, such as complex problem solving and higher order executive functioning.²³ These findings are congruent with anticipated trends in burnout among clinicians during the pandemic. However, unlike other data collection methods, machine learning can empirically quantifytrends in real-time — offering crucial insights into the magnitude of these outcomes.

IV. Systematically Scanning for Early Indicators

Imagine a scenario in which clinical staff in a busy health care organization are routinely monitored for signs of burnout or psychological distress. If a disturbing signal is noticed within an individual clinician or team, organizational leaders can systematically deploy targeted resources to offer clinicians dynamic levels of support, and to reduce stressors. With this technology, programmatic interventions for well-being can be rapidly tested and scaled. Well-being can be trended longitudinally against other performance indicators to identify common predictors of burnout across patient care settings. Once identified, these predictors can be buttressed by targeted interventions to mitigate the risk of burnout.

This scenario may sound inconceivable in the current health care climate, but advances in machine learning and other technologies have made this a realistic scenario in the near term. Health care organizations must recognize this potential and plan to incorporate clinician well-being as a target for automated, systems-based improvement. This will require health care organizations to identify a systematic process for scanning for early indicators of clinician burnout — much like they have invested in trigger tool technologies to detect adverse patient safety events.²⁴ Afterall, clinician well-being is a systems issue that impacts all levels of a health care organization.⁷ It should thus be assessed and addressed with the same rigor and technology investments as other patient safety and quality indicators. It’s a strategy that is certain to succeed.

References

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