Resident Burnout, Wellness, Professional Development, and Engagement Before and After New Training Schedule Implementation.

Importance: Burnout is a work-related syndrome of depersonalization (DP), emotional exhaustion (EE), and low personal achievement (PA) that is prevalent among internal medicine resident trainees. Prior interventions have had modest effects on resident burnout. The association of a new 4 + 4 block schedule (4 inpatient weeks plus 4 outpatient weeks) with resident burnout has not previously been evaluated. Objective: To evaluate the association of a 4 + 4 block schedule, compared with a 4 + 1 schedule, with burnout, wellness, and self-reported professional engagement and clinical preparedness among resident physicians. Design, Setting, and Participants: This nonrandomized preintervention and postintervention survey study was conducted in a single academic-based internal medicine residency program from June 2019 to June 2021. The study included residents in the categorical, hospitalist, and primary care tracks in postgraduate years 1 and 2 (PGY1 and PGY2). Data analysis was conducted from October to December 2022. Intervention: In the 4 + 4 structure, resident schedules alternated between 4-week inpatient call-based rotations and 4-week ambulatory non-call-based rotations. Main Outcomes and Measures: The primary outcome was burnout, assessed using the Maslach Burnout Inventory subcategories of EE (range, 0-54), DP (range, 0-30), and PA (range, 0-48), adjusted for sex and PGY. Secondary outcomes included In-Training Examination (ITE) scores and a questionnaire on professional, educational, and health outcomes. Multivariable logistic regression was used to assess the primary outcome, 1-way analysis of variance was used to compare ITE percentiles, and a Bonferroni-adjusted Kruskal Wallis test was used for the remaining secondary outcomes. The findings were reexamined with several sensitivity analyses, and Cohen's D was used to estimate standardized mean differences (SMDs). Results: Of the 313 eligible residents, 216 completed the surveys. A total of 107 respondents (49.5%) were women and 109 (50.5%) were men; 119 (55.1%) were PGY1 residents. The survey response rates were 78.0% (85 of 109) in the preintervention cohort and 60.6% (63 of 104) and 68.0% (68 of 100) in the 2 postintervention cohorts. The PGY1 residents had higher response rates than the PGY2 residents (119 of 152 [78.2%] vs 97 of 161 [60.2%]; P < .001). Adjusted EE scores (mean difference [MD], -6.78 [95% CI, -9.24 to -4.32]) and adjusted DP scores (MD, -3.81 [95% CI, -5.29 to -2.34]) were lower in the combined postintervention cohort. The change in PA scores was not statistically significant (MD, 1.4 [95% CI, -0.49 to 3.29]). Of the 15 items exploring professional, educational, and health outcomes, a large positive association was observed for 11 items (SMDs >1.0). No statistically significant change in ITE percentile ranks was noted. Conclusions and Relevance: In this survey study of internal medicine resident physicians, a positive association was observed between a 4 + 4 block training schedule and internal medicine resident burnout scores and improved self-reported professional, educational, and health outcomes. These results suggest that specific 4 + 4 block combinations may better improve resident burnout than a 4 + 1 combination used previously.

Patient-provider communications about pharmacogenomic results increase patient recall of medication changes.

Effective doctor-patient communication is critical for disease management, especially when considering genetic information. We studied patient-provider communications after implementing a point-of-care pharmacogenomic results delivery system to understand whether pharmacogenomic results are discussed and whether medication recall is impacted. Outpatients undergoing preemptive pharmacogenomic testing (cases), non-genotyped controls, and study providers were surveyed from October 2012-May 2017. Patient responses were compared between visits where pharmacogenomic results guided prescribing versus visits where pharmacogenomics did not guide prescribing. Provider knowledge of pharmacogenomics, before and during study participation, was also analyzed. Both providers and case patients frequently reported discussions of genetic results after visits where pharmacogenomic information guided prescribing. Importantly, medication changes from visits where pharmacogenomics influenced prescribing were more often recalled than non-pharmacogenomic guided medication changes (OR = 3.3 [1.6-6.7], p = 0.001). Case patients who had separate visits where pharmacogenomics did and did not, respectively, influence prescribing more often remembered medication changes from visits where genomic-based guidance was used (OR = 3.4 [1.2-9.3], p = 0.02). Providers also displayed dramatic increases in personal genomic understanding through program participation (94% felt at least somewhat informed about pharmacogenomics post-participation, compared to 61% at baseline, p = 0.04). Using genomic information during prescribing increases patient-provider communications, patient medication recall, and provider understanding of genomics, important ancillary benefits to clinical use of pharmacogenomics.

Adoption of a clinical pharmacogenomics implementation program during outpatient care--initial results of the University of Chicago "1,200 Patients Project".

Pharmacogenomic testing is viewed as an integral part of precision medicine. To achieve this, we originated The 1,200 Patients Project which offers broad, preemptive pharmacogenomic testing to patients at our institution. We analyzed enrollment, genotype, and encounter-level data from the first year of implementation to assess utility of providing pharmacogenomic results. Results were delivered via a genomic prescribing system (GPS) in the form of traffic lights: green (favorable), yellow (caution), and red (high risk). Additional supporting information was provided as a virtual pharmacogenomic consult, including citation to relevant publications. Currently, 812 patients have participated, representing 90% of those approached; 608 have been successfully genotyped across a custom array. A total of 268 clinic encounters have occurred at which results were accessible via the GPS. At 86% of visits, physicians accessed the GPS, receiving 367 result signals for medications patients were taking: 57% green lights, 41% yellow lights, and 1.4% red lights. Physician click frequencies to obtain clinical details about alerts varied according to color severity (100% of red were clicked, 72% yellow, 20% green). For 85% of visits, clinical pharmacogenomic information was available for at least one drug the patient was taking, suggesting relevance of the delivered information. We successfully implemented an individualized health care model of preemptive pharmacogenomic testing, delivering results along with pharmacogenomic decision support. Patient interest was robust, physician adoption of information was high, and results were routinely utilized. Ongoing examination of a larger number of clinic encounters and inclusion of more physicians and patients is warranted.