Association of Self-reported Primary Care Physician Tolerance for Uncertainty With Variations in Resource Use and Patient Experience

The dynamics of self-monitoring in medicine: Safety, efficiency and clinical implications

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Navigating Uncertainty in Clinical Practice: A Structured Approach

The practice of clinical medicine is imbued with uncertainty. The ways in which clinicians and patients think about, communicate about, and act within situations of heightened uncertainty can have significant implications for the therapeutic alliance and for the trajectory and outcomes of clinical care. Despite this, there is limited guidance about the best methods for physicians to recognize, acknowledge, communicate about, and manage uncertainty in clinical settings. In this paper, we propose a structured approach for discussing and managing uncertainty within the context of a clinician-patient relationship. The approach involves four steps: Recognize, Acknowledge, Partner, and Seek Support (i.e., the RAPS framework). The approach is guided by existing literature on uncertainty as well as our own experience as clinicians working at different stages of career. We define each component of the approach and present sample language and actions for how to implement it in practice. Our aim is to empower clinicians to regard situations of high uncertainty as an opportunity to deepen the therapeutic alliance with the patient, and simultaneously to grow and learn as practitioners.

Clinician Risk Tolerance and Rates of Admission From the Emergency Department

Importance

Much remains unknown about the extent of and factors that influence clinician-level variation in rates of admission from the emergency department (ED). In particular, emergency clinician risk tolerance is a potentially important attribute, but it is not well defined in terms of its association with the decision to admit.

Objective

To further characterize this variation in rates of admission from the ED and to determine whether clinician risk attitudes are associated with the propensity to admit.

Design, Setting, and Participants

In this observational cohort study, data were analyzed from the Massachusetts All Payer Claims Database to identify all ED visits from October 2015 through December 2017 with any form of commercial insurance or Medicaid. ED visits were then linked to treating clinicians and their risk tolerance scores obtained in a separate statewide survey to examine the association between risk tolerance and the decision to admit. Statistical analysis was performed from 2022 to 2023.

Main Outcomes and Measures

The ratio between observed and projected admission rates was computed, controlling for hospital, and then plotted against the projected admission rates to find the extent of variation. Pearson correlation coefficients were then used to examine the association between the mean projected rate of admission and the difference between actual and projected rates of admission. The consistency of clinician admission practices across a range of the most common conditions resulting in admission were then assessed to understand whether admission decisions were consistent across different conditions. Finally, an assessment was made as to whether the extent of deviation from the expected admission rates at an individual level was associated with clinician risk tolerance.

Results

The study sample included 392 676 ED visits seen by 691 emergency clinicians. Among patients seen for ED visits, 221 077 (56.3%) were female, and 236 783 (60.3%) were 45 years of age or older; 178 890 visits (46.5%) were for patients insured by Medicaid, 96 947 (25.2%) were for those with commercial insurance, 71 171 (18.5%) were Medicare Part B or Medicare Advantage, and the remaining 37 702 (9.8%) were other insurance category. Of the 691 clinicians, 429 (62.6%) were male; mean (SD) age was 46.5 (9.8) years; and 72 (10.4%) were Asian, 13 (1.9%) were Black, 577 (83.5%) were White, and 29 (4.2%) were other race. Admission rates across the clinicians included ranged from 36.3% at the 25th percentile to 48.0% at the 75th percentile (median, 42.1%). Overall, there was substantial variation in admission rates across clinicians; physicians were just as likely to overadmit or underadmit across the range of projected rates of admission (Pearson correlation coefficient, 0.046 [P = .23]). There also was weak consistency in admission rates across the most common clinical conditions, with intraclass correlations ranging from 0.09 (95% CI, 0.02-0.17) for genitourinary/syncope to 0.48 (95% CI, 0.42-0.53) for cardiac/syncope. Greater clinician risk tolerance (as measured by the Risk Tolerance Scale) was associated with a statistically significant tendency to admit less than the projected admission rate (coefficient, -0.09 [P = .04]). The other scales studied revealed no significant associations.

Conclusions and Relevance

In this cohort study of ED visits from Massachusetts, there was statistically significant variation between ED clinicians in admission rates and little consistency in admission tendencies across different conditions. Admission tendencies were minimally associated with clinician innate risk tolerance as assessed by this study's measures; further research relying on a broad range of measures of risk tolerance is needed to better understand the role of clinician attitudes toward risk in explaining practice patterns and to identify additional factors that may be associated with variation at the clinician level.

Developing a machine learning model to detect diagnostic uncertainty in clinical documentation

BACKGROUND AND OBJECTIVE

Diagnostic uncertainty, when unrecognized or poorly communicated, can result in diagnostic error. However, diagnostic uncertainty is challenging to study due to a lack of validated identification methods. This study aims to identify distinct linguistic patterns associated with diagnostic uncertainty in clinical documentation.

DESIGN, SETTING AND PARTICIPANTS

This case-control study compares the clinical documentation of hospitalized children who received a novel uncertain diagnosis (UD) diagnosis label during their admission to a set of matched controls. Linguistic analyses identified potential linguistic indicators (i.e., words or phrases) of diagnostic uncertainty that were then manually reviewed by a linguist and clinical experts to identify those most relevant to diagnostic uncertainty. A natural language processing program categorized medical terminology into semantic types (i.e., sign or symptom), from which we identified a subset of these semantic types that both categorized reliably and were relevant to diagnostic uncertainty. Finally, a competitive machine learning modeling strategy utilizing the linguistic indicators and semantic types compared different predictive models for identifying diagnostic uncertainty.

RESULTS

Our cohort included 242 UD-labeled patients and 932 matched controls with a combination of 3070 clinical notes. The best-performing model was a random forest, utilizing a combination of linguistic indicators and semantic types, yielding a sensitivity of 89.4% and a positive predictive value of 96.7%.

CONCLUSION

Expert labeling, natural language processing, and machine learning methods combined with human validation resulted in highly predictive models to detect diagnostic uncertainty in clinical documentation and represent a promising approach to detecting, studying, and ultimately mitigating diagnostic uncertainty in clinical practice.

Developing a Framework and Electronic Tool for Communicating Diagnostic Uncertainty in Primary Care: A Qualitative Study

IMPORTANCE

Communication of information has emerged as a critical component of diagnostic quality. Communication of diagnostic uncertainty represents a key but inadequately examined element of diagnosis.

OBJECTIVE

To identify key elements facilitating understanding and managing diagnostic uncertainty, examine optimal ways to convey uncertainty to patients, and develop and test a novel tool to communicate diagnostic uncertainty in actual clinical encounters.

DESIGN, SETTING, AND PARTICIPANTS

A 5-stage qualitative study was performed between July 2018 and April 2020, at an academic primary care clinic in Boston, Massachusetts, with a convenience sample of 24 primary care physicians (PCPs), 40 patients, and 5 informatics and quality/safety experts. First, a literature review and panel discussion with PCPs were conducted and 4 clinical vignettes of typical diagnostic uncertainty scenarios were developed. Second, these scenarios were tested during think-aloud simulated encounters with expert PCPs to iteratively draft a patient leaflet and a clinician guide. Third, the leaflet content was evaluated with 3 patient focus groups. Fourth, additional feedback was obtained from PCPs and informatics experts to iteratively redesign the leaflet content and workflow. Fifth, the refined leaflet was integrated into an electronic health record voice-enabled dictation template that was tested by 2 PCPs during 15 patient encounters for new diagnostic problems. Data were thematically analyzed using qualitative analysis software.

MAIN OUTCOMES AND MEASURES

Perceptions and testing of content, feasibility, usability, and satisfaction with a prototype tool for communicating diagnostic uncertainty to patients.

RESULTS

Overall, 69 participants were interviewed. A clinician guide and a diagnostic uncertainty communication tool were developed based on the PCP interviews and patient feedback. The optimal tool requirements included 6 key domains: most likely diagnosis, follow-up plan, test limitations, expected improvement, contact information, and space for patient input. Patient feedback on the leaflet was iteratively incorporated into 4 successive versions, culminating in a successfully piloted prototype tool as an end-of-visit voice recognition dictation template with high levels of patient satisfaction for 15 patients with whom the tool was tested.

CONCLUSIONS AND RELEVANCE

In this qualitative study, a diagnostic uncertainty communication tool was successfully designed and implemented during clinical encounters. The tool demonstrated good workflow integration and patient satisfaction.