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Johnson WR, Durning SJ, Artino AR. The dynamics of self-monitoring in medicine: Safety, efficiency and clinical implications. MEDICAL EDUCATION 2024; 58:488-490. [PMID: 38251418 DOI: 10.1111/medu.15312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Revised: 12/29/2023] [Accepted: 01/03/2024] [Indexed: 01/23/2024]
Abstract
.@MedEdDoc et al. delve into how the concepts of 'safety' and 'efficiency' in self‐monitoring can be used to influence clinical practice and #MedEd.
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Affiliation(s)
- W Rainey Johnson
- Uniformed Services University of Health Sciences, Bethesda, Maryland, USA
| | - Steven J Durning
- Uniformed Services University of Health Sciences, Bethesda, Maryland, USA
| | - Anthony R Artino
- George Washington University School of Medicine and Health Sciences, Washington, DC, USA
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Patel B, Gheihman G, Katz JT, Begin AS, Solomon SR. Navigating Uncertainty in Clinical Practice: A Structured Approach. J Gen Intern Med 2024; 39:829-836. [PMID: 38286969 PMCID: PMC11043270 DOI: 10.1007/s11606-023-08596-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 12/28/2023] [Indexed: 01/31/2024]
Abstract
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.
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Affiliation(s)
- Badar Patel
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Galina Gheihman
- Harvard Medical School, Boston, MA, USA
- Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Joel T Katz
- Harvard Medical School, Boston, MA, USA
- Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, MA, USA
| | - Arabella Simpkin Begin
- Harvard Medical School, Boston, MA, USA
- Lincoln College, University of Oxford, Oxford, UK
| | - Sonja R Solomon
- Harvard Medical School, Boston, MA, USA.
- Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, MA, USA.
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Smulowitz PB, Burke RC, Ostrovsky D, Novack V, Isbell L, Kan V, Landon BE. Clinician Risk Tolerance and Rates of Admission From the Emergency Department. JAMA Netw Open 2024; 7:e2356189. [PMID: 38363570 PMCID: PMC10873771 DOI: 10.1001/jamanetworkopen.2023.56189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 12/21/2023] [Indexed: 02/17/2024] Open
Abstract
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.
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Affiliation(s)
- Peter B. Smulowitz
- Department of Emergency Medicine, University of Massachusetts Medical School, Worcester
- Milford Regional Medical Center, Milford, Massachusetts
| | - Ryan C. Burke
- Department of Emergency Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts
| | - Daniel Ostrovsky
- Soroka University Medical Center, Ben-Gurion University of the Negev, Be’er-Sheva, Israel
| | - Victor Novack
- Soroka University Medical Center, Ben-Gurion University of the Negev, Be’er-Sheva, Israel
| | - Linda Isbell
- Department of Psychological and Brain Sciences, University of Massachusetts, Amherst
| | - Vincent Kan
- Department of Emergency Medicine, University of Massachusetts Medical School, Worcester
| | - Bruce E. Landon
- Department of Health Care Policy, Harvard Medical School and Division of General Internal Medicine, Beth Israel Deaconess Medical Center, Boston
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Marshall TL, Nickels LC, Brady PW, Edgerton EJ, Lee JJ, Hagedorn PA. Developing a machine learning model to detect diagnostic uncertainty in clinical documentation. J Hosp Med 2023; 18:405-412. [PMID: 36919861 DOI: 10.1002/jhm.13080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 02/11/2023] [Accepted: 02/25/2023] [Indexed: 03/16/2023]
Abstract
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.
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Affiliation(s)
- Trisha L Marshall
- Division of Hospital Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
- Department of Pediatrics, College of Medicine, University of Cincinnati, Cincinnati, Ohio, USA
| | - Lindsay C Nickels
- Digital Scholarship Center, University of Cincinnati Libraries and College of Arts and Sciences, Cincinnati, Ohio, USA
- AI for All Lab, Digital Futures Program, University of Cincinnati, Cincinnati, Ohio, USA
| | - Patrick W Brady
- Division of Hospital Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
- Department of Pediatrics, College of Medicine, University of Cincinnati, Cincinnati, Ohio, USA
- James M. Anderson Center for Health Systems Excellence, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
| | - Ezra J Edgerton
- Digital Scholarship Center, University of Cincinnati Libraries and College of Arts and Sciences, Cincinnati, Ohio, USA
- AI for All Lab, Digital Futures Program, University of Cincinnati, Cincinnati, Ohio, USA
| | - James J Lee
- Digital Scholarship Center, University of Cincinnati Libraries and College of Arts and Sciences, Cincinnati, Ohio, USA
- AI for All Lab, Digital Futures Program, University of Cincinnati, Cincinnati, Ohio, USA
| | - Philip A Hagedorn
- Division of Hospital Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
- Department of Pediatrics, College of Medicine, University of Cincinnati, Cincinnati, Ohio, USA
- Department of Information Services, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA
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Khazen M, Mirica M, Carlile N, Groisser A, Schiff GD. Developing a Framework and Electronic Tool for Communicating Diagnostic Uncertainty in Primary Care: A Qualitative Study. JAMA Netw Open 2023; 6:e232218. [PMID: 36892841 PMCID: PMC9999246 DOI: 10.1001/jamanetworkopen.2023.2218] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/10/2023] Open
Abstract
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.
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Affiliation(s)
- Maram Khazen
- Department of Health Systems Management, Harvard Medical School and Brigham and Women’s Hospital, Boston, Massachusetts
- Now with Max Stern Yezreel Valley College, Yezreel Valle, Israel
| | - Maria Mirica
- Department of Medicine, Division of General Medicine Center for Patient Research and Practice, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Narath Carlile
- Department of Medicine, Division of General Medicine Center for Patient Research and Practice, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Alissa Groisser
- Department of Pediatrics, Children’s National Hospital, Washington, DC
| | - Gordon D. Schiff
- Center for Patient Safety Research and Practice, Division of General Internal Medicine, Brigham and Women’s Hospital, Boston, Massachusetts
- Harvard Medical School Center for Primary Care, Boston, Massachusetts
- Center for Primary Care, Harvard Medical School, Boston, Massachusetts
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