Faculty Bibliography

PROBLEM/OBJECTIVE:Internal medicine training programs operate under the assumption that the three-year residency training period is sufficient for trainees to achieve the depth and breadth of clinical experience necessary for independent practice; however, the medical conditions to which residents are exposed in clinical practice are not easily measured. As a result, residents' clinical educational experiences are poorly understood. APPROACH/METHODS:A crosswalk tool (a repository of international classification of diseases [ICD]-10 codes linked to medical content areas) was developed to query routinely collected inpatient principal diagnosis codes and translate them into an educationally meaningful taxonomy. This tool provides a robust characterization of residents' inpatient clinical experiences. OUTCOMES/RESULTS:This pilot study has provided proof of principle that the crosswalk tool can effectively map one year of resident-attributed diagnosis codes to both the broad content category level (for example "Cardiovascular Disease") and to the more specific condition category level (for example "Myocardial Disease"). The authors uncovered content areas in their training program that are overrepresented and some that are underrepresented relative to material on the American Board of Internal Medicine (ABIM) Certification Exam. NEXT STEPS/UNASSIGNED:The crosswalk tool introduced here translated residents' patient care activities into discrete, measurable educational content and enabled one internal medicine residency training program to characterize residents' inpatient educational experience with a high degree of resolution. Leaders of other programs seeking to profile the clinical exposure of their trainees may adopt this strategy. Such clinical content mapping drives innovation in the experiential curriculum, enables comparison across practice sites, and lays the groundwork to test associations between individual clinical exposure and competency-based outcomes, which, in turn, will allow medical educators to draw conclusions regarding how clinical experience reflects clinical competency.

BACKGROUND:It is not yet known whether socioeconomic factors (ie, social determinants of health) are associated with readmission following hospitalization for coronavirus disease 2019 (COVID-19). METHODS:We conducted a retrospective cohort study of 6191 adult patients hospitalized with COVID-19 in a large New York City safety-net hospital system between March 1 and June 1, 2020. Associations between 30-day readmission and selected demographic characteristics, socioeconomic factors, prior health care utilization, and relevant features of the index hospitalization were analyzed using a multivariable generalized estimating equation model. RESULTS:The readmission rate was 7.3%, with a median of 7 days between discharge and readmission. The following were risk factors for readmission: age 65 and older [adjusted odds ratio (aOR): 1.32; 95% confidence interval (CI): 1.13-1.55], history of homelessness, (aOR: 2.03 95% CI: 1.49-2.77), baseline coronary artery disease (aOR: 1.68; 95% CI: 1.34-2.10), congestive heart failure (aOR: 1.34; 95% CI: 1.20-1.49), cancer (aOR: 1.68; 95% CI: 1.26-2.24), chronic kidney disease (aOR: 1.74; 95% CI: 1.46-2.07). Patients' sex, race/ethnicity, insurance, and presence of obesity were not associated with increased odds of readmission. A longer length of stay (aOR: 0.98; 95% CI: 0.97-1.00) and use of noninvasive supplemental oxygen (aOR: 0.68; 95% CI: 0.56-0.83) was associated with lower odds of readmission. Upon readmission, 18.4% of patients required intensive care, and 13.7% expired. CONCLUSION:We have found some factors associated with increased odds of readmission among patients hospitalized with COVID-19. Awareness of these risk factors, including patients' social determinants of health, may ultimately help to reduce readmission rates.

Importance/UNASSIGNED:There is clinical equipoise for COVID-19 convalescent plasma (CCP) use in patients hospitalized with COVID-19. Objective/UNASSIGNED:To determine the safety and efficacy of CCP compared with placebo in hospitalized patients with COVID-19 receiving noninvasive supplemental oxygen. Design, Setting, and Participants/UNASSIGNED:CONTAIN COVID-19, a randomized, double-blind, placebo-controlled trial of CCP in hospitalized adults with COVID-19, was conducted at 21 US hospitals from April 17, 2020, to March 15, 2021. The trial enrolled 941 participants who were hospitalized for 3 or less days or presented 7 or less days after symptom onset and required noninvasive oxygen supplementation. Interventions/UNASSIGNED:A unit of approximately 250 mL of CCP or equivalent volume of placebo (normal saline). Main Outcomes and Measures/UNASSIGNED:The primary outcome was participant scores on the 11-point World Health Organization (WHO) Ordinal Scale for Clinical Improvement on day 14 after randomization; the secondary outcome was WHO scores determined on day 28. Subgroups were analyzed with respect to age, baseline WHO score, concomitant medications, symptom duration, CCP SARS-CoV-2 titer, baseline SARS-CoV-2 serostatus, and enrollment quarter. Outcomes were analyzed using a bayesian proportional cumulative odds model. Efficacy of CCP was defined as a cumulative adjusted odds ratio (cOR) less than 1 and a clinically meaningful effect as cOR less than 0.8. Results/UNASSIGNED:Of 941 participants randomized (473 to placebo and 468 to CCP), 556 were men (59.1%); median age was 63 years (IQR, 52-73); 373 (39.6%) were Hispanic and 132 (14.0%) were non-Hispanic Black. The cOR for the primary outcome adjusted for site, baseline risk, WHO score, age, sex, and symptom duration was 0.94 (95% credible interval [CrI], 0.75-1.18) with posterior probability (P[cOR<1] = 72%); the cOR for the secondary adjusted outcome was 0.92 (95% CrI, 0.74-1.16; P[cOR<1] = 76%). Exploratory subgroup analyses suggested heterogeneity of treatment effect: at day 28, cORs were 0.72 (95% CrI, 0.46-1.13; P[cOR<1] = 93%) for participants enrolled in April-June 2020 and 0.65 (95% CrI, 0.41 to 1.02; P[cOR<1] = 97%) for those not receiving remdesivir and not receiving corticosteroids at randomization. Median CCP SARS-CoV-2 neutralizing titer used in April to June 2020 was 1:175 (IQR, 76-379). Any adverse events (excluding transfusion reactions) were reported for 39 (8.2%) placebo recipients and 44 (9.4%) CCP recipients (P = .57). Transfusion reactions occurred in 2 (0.4) placebo recipients and 8 (1.7) CCP recipients (P = .06). Conclusions and Relevance/UNASSIGNED:In this trial, CCP did not meet the prespecified primary and secondary outcomes for CCP efficacy. However, high-titer CCP may have benefited participants early in the pandemic when remdesivir and corticosteroids were not in use. Trial Registration/UNASSIGNED:ClinicalTrials.gov Identifier: NCT04364737.

UNLABELLED:To describe relationships between compromised integrity (CI), burnout, and intent-to-leave (ITL) practice in critical care (CC) and noncritical care (non-CC) nurses and physicians. DESIGN/METHODS:CC nurses (RNs) and physicians (MDs) from the American Medical Association Coping with COVID survey were matched by gender, race, years in practice, and role with non-CC clinicians to determine likelihood of ITL in relation to burnout and CI. SETTING/METHODS:U.S. Healthcare organizations; July-December 2020. SUBJECTS/METHODS:= 313) matched with 165 non-CC RNs and 148 non-CC MDs from 83 healthcare organizations. MEASUREMENTS AND MAIN RESULTS/RESULTS:< 0.001). In multivariate regressions, CC clinicians experiencing burnout had 50% greater odds of ITL than non-CC clinicians experiencing burnout; odds of ITL were substantially higher (odds ratio, 2.8-3.2) in those with CI regardless of location or burnout. In the ICU, those feeling valued by their organization had one-third the odds of ITL. CONCLUSIONS:Burnout (EE) is high (>50%) among CC RNs and MDs, which may result in losses of CC clinicians while demand rises. Preventing CI independent of burnout may reduce turnover in all settings and especially in ICUs. Feeling valued may promote staff retention.

Rationale: Allergic and non-allergic adverse reactions (ARs) to Covid-19 vaccine (Cov19V) have been reported. Understanding the characteristics of Cov19V ARs, particularly those that are allergic in nature, may help us to better counsel patients who are at risk of developing a vaccine AR.
Method(s): We performed a retrospective chart review of ARs voluntarily reported to our Occupational Health Services following Cov19V at a multi-site academic medical center between December 2020-June 2021.
Result(s): 464 Cov19V ARs among 71,281 vaccine doses given (0.65%) were reported. 57 ARs (12.3%) were determined to be allergic (10 after the second dose), 356 were nonallergic, and 51 (11.0%) were undetermined. Of the 47 first-dose allergic ARs, 30 (63.8%) received a second dose, 16 did not complete the vaccine series, and 1 had no data. 3 employees received an alternative Cov19V. Of the 356 nonallergic ARs, 110 were following second dose, 2 were following Janssen, and 4 had no data. 228 of first dose reactions (95.0%, 228/240) completed the vaccine series. 22/57 (38.6%) allergic ARs versus 38/356 (10.7%) nonallergic ARs required ER transfer. More allergic ARs were categorized as moderate/severe (80.7%, 46/57) than nonallergic ARs (66.3%, 236/356).
Conclusion(s): Cov19V ARs are extremely uncommon with nonallergic AR more common than allergic. A vast majority of ARs, allergic or nonallergic, are able to receive subsequent Cov19V. Employees with allergic ARs were less likely to receive a second Cov19V and more frequently required emergent medical evaluation compared to those with nonallergic ARs.
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BACKGROUND:Residents and fellows receive little feedback on their clinical reasoning documentation. Barriers include lack of a shared mental model and variability in the reliability and validity of existing assessment tools. Of the existing tools, the IDEA assessment tool includes a robust assessment of clinical reasoning documentation focusing on four elements (interpretive summary, differential diagnosis, explanation of reasoning for lead and alternative diagnoses) but lacks descriptive anchors threatening its reliability. OBJECTIVE:Our goal was to develop a valid and reliable assessment tool for clinical reasoning documentation building off the IDEA assessment tool. DESIGN, PARTICIPANTS, AND MAIN MEASURES/UNASSIGNED:The Revised-IDEA assessment tool was developed by four clinician educators through iterative review of admission notes written by medicine residents and fellows and subsequently piloted with additional faculty to ensure response process validity. A random sample of 252 notes from July 2014 to June 2017 written by 30 trainees across several chief complaints was rated. Three raters rated 20% of the notes to demonstrate internal structure validity. A quality cut-off score was determined using Hofstee standard setting. KEY RESULTS/RESULTS:The Revised-IDEA assessment tool includes the same four domains as the IDEA assessment tool with more detailed descriptive prompts, new Likert scale anchors, and a score range of 0-10. Intraclass correlation was high for the notes rated by three raters, 0.84 (95% CI 0.74-0.90). Scores ≥6 were determined to demonstrate high-quality clinical reasoning documentation. Only 53% of notes (134/252) were high-quality. CONCLUSIONS:The Revised-IDEA assessment tool is reliable and easy to use for feedback on clinical reasoning documentation in resident and fellow admission notes with descriptive anchors that facilitate a shared mental model for feedback.

PURPOSE/OBJECTIVE:To determine the effectiveness of a shared decision-making (SDM) tool versus guideline-informed usual care in translating evidence into primary care, and to explore how use of the tool changed patient perspectives about diabetes medication decision making. METHODS:In this mixed methods multicenter cluster randomized trial, we included patients with type 2 diabetes mellitus and their primary care clinicians. We compared usual care with or without a within-encounter SDM conversation aid. We assessed participant-reported decisions made and quality of SDM (knowledge, satisfaction, and decisional conflict), clinical outcomes, adherence, and observer-based patient involvement in decision-making (OPTION12-scale). We used semi-structured interviews with patients to understand their perspectives. RESULTS:We enrolled 350 patients and 99 clinicians from 20 practices and interviewed 26 patients. Use of the conversation aid increased post-encounter patient knowledge (correct answers, 52% vs. 45%, p = 0.02) and clinician involvement of patients (Mean between-arm difference in OPTION12, 7.3 (95% CI 3, 12); p = 0.003). There were no between-arm differences in treatment choice, patient or clinician satisfaction, encounter length, medication adherence, or glycemic control. Qualitative analyses highlighted differences in how clinicians involved patients in decision making, with intervention patients noting how clinicians guided them through conversations using factors important to them. CONCLUSIONS:Using an SDM conversation aid improved patient knowledge and involvement in SDM without impacting treatment choice, encounter length, medication adherence or improved diabetes control in patients with type 2 diabetes. Future interventions may need to focus specifically on patients with signs of poor treatment fit. CLINICAL TRIAL REGISTRATION/BACKGROUND:ClinicalTrial.gov: NCT01502891.

PURPOSE/UNASSIGNED:Earlier literature has reported on the utility of diagnostic codes and demographic information for identifying transgender patients. We aim to assess which method identifies the most transgender patients utilizing readily available tools from within the electronic health record (EHR). METHODS/UNASSIGNED:(ICD-10) diagnostic codes and demographic data specific to transgender patients from January 2011 to April 2019. RESULTS/UNASSIGNED:Demographic data and ICD-10 codes yielded 1494 individual EHRs with transgender-specific data domains. ICD-10 diagnostic codes alone identified 942 (63.05%) unique EHRs. Demographics alone identified 218 (14.59%) unique EHRs. A total of 334 (22.36%) unique EHRs had both ICD-10 and demographic identifiers. Of those identified by transgender-specific demographic data (552), 294 (53.26%) were trans masculine, 215 (38.95%) were trans feminine, and 43 (7.79%) were nonbinary. Of the 552 demographic-identified transgender patients, 141 (25.86%) were identified by a two-part gender identity demographic question. CONCLUSIONS/UNASSIGNED:ICD-10 diagnostic codes, not demographic data, identified the most transgender patient records, but neither diagnostic codes alone nor demographic data captured the full population. Only 26.36% of the charts identified as transgender patients had both ICD-10 codes and demographic data. We recommend that when identifying transgender populations through EHR domains, a combination of diagnostic codes and demographic data be used. Furthermore, research is needed to optimize disclosure and collection of demographic information for gender minority populations.