Dataset Information

Automated Reporting of Trainee Metrics Using Electronic Clinical Systems.

ABSTRACT:

Background

The Accreditation Council for Graduate Medical Education has called for increased emphasis on reporting objective performance measures to trainees and programs. However, reporting of objective measures, including clinical volume, is largely omitted from training programs.

Objective

To use automated electronic medical systems at a tertiary pediatric care hospital to create a dashboard that reports objective trainee and program metrics, including clinical volume and diagnoses in a pediatrics residency.

Methods

We queried an enterprise data warehouse that aggregates data daily from multiple hospital systems to identify patient encounters during which senior pediatrics residents at Boston Children's Hospital had entered documentation over a 9-month period. From this query, we created a filterable dashboard to display clinical volume and diagnosis data by individual resident and in aggregate.

Results

A total of 44 of 45 senior residents (98%) in the program were included in analysis. We identified 12?198 patient encounters during which a senior pediatrics resident had entered documentation; these included a median of 332 inpatient encounters per resident, 122 emergency department encounters, and 84 outpatient encounters. The most common diagnoses stratified by clinical site were: inpatient - dehydration (median = 61); emergency department - long-term/current drug therapy (median = 16); and outpatient - encounter for immunization (median = 48).

Conclusions

We used electronic health record systems to generate performance dashboards for trainees in a pediatrics residency across different sites of care with reported volume by diagnosis. Our dashboards provide feedback to program leadership regarding individual and aggregate trainee experience and allow individual trainees to compare their clinical exposure to peers.

SUBMITTER: Levin JC

PROVIDER: S-EPMC5476389 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Automated Reporting of Trainee Metrics Using Electronic Clinical Systems.

Levin Jonathan C JC Hron Jonathan J

Journal of graduate medical education 20170601 3

<h4>Background</h4>The Accreditation Council for Graduate Medical Education has called for increased emphasis on reporting objective performance measures to trainees and programs. However, reporting of objective measures, including clinical volume, is largely omitted from training programs.<h4>Objective</h4>To use automated electronic medical systems at a tertiary pediatric care hospital to create a dashboard that reports objective trainee and program metrics, including clinical volume and diagn ...[more]

PMID: 28638518

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Project description:BackgroundElectronic clinical quality measures (eCQMs) from electronic health records (EHRs) are a key component of quality improvement (QI) initiatives in small-to-medium size primary care practices, but using eCQMs for QI can be challenging. Organizational strategies are needed to effectively operationalize eCQMs for QI in these practice settings.ObjectiveThis study aimed to characterize strategies that seven regional cooperatives participating in the EvidenceNOW initiative developed to generate and report EHR-based eCQMs for QI in small-to-medium size practices.MethodsA qualitative study comprised of 17 interviews with representatives from all seven EvidenceNOW cooperatives was conducted. Interviewees included administrators were with both strategic and cooperative-level operational responsibilities and external practice facilitators were with hands-on experience helping practices use EHRs and eCQMs. A subteam conducted 1-hour semistructured telephone interviews with administrators and practice facilitators, then analyzed interview transcripts using immersion crystallization. The analysis and a conceptual model were vetted and approved by the larger group of coauthors.ResultsCooperative strategies consisted of efforts in four key domains. First, cooperative adaptation shaped overall strategies for calculating eCQMs whether using EHRs, a centralized source, or a "hybrid strategy" of the two. Second, the eCQM generation described how EHR data were extracted, validated, and reported for calculating eCQMs. Third, practice facilitation characterized how facilitators with backgrounds in health information technology (IT) delivered services and solutions for data capture and quality and practice support. Fourth, performance reporting strategies and tools informed QI efforts and how cooperatives could alter their approaches to eCQMs.ConclusionCooperatives ultimately generated and reported eCQMs using hybrid strategies because they determined neither EHRs alone nor centralized sources alone could operationalize eCQMs for QI. This required cooperatives to devise solutions and utilize resources that often are unavailable to typical small-to-medium-sized practices. The experiences from EvidenceNOW cooperatives provide insights into how organizations can plan for challenges and operationalize EHR-based eCQMs.

Project description:ImportanceWomen are underrepresented at higher ranks in academic medicine. However, the factors contributing to this disparity have not been fully elucidated. Implicit bias and unconscious mental attitudes toward a person or group may be factors. Although academic medical centers use physician trainee evaluations of faculty to inform promotion decisions, little is known about gender bias in these evaluations. To date, no studies have examined narrative evaluations of medical faculty by physician trainees for differences based on gender.ObjectiveTo characterize gender-associated linguistic differences in narrative evaluations of medical faculty written by physician trainees.Design, setting, and participantsThis retrospective cohort study included all faculty teaching evaluations completed for the department of medicine faculty by medical students, residents, and fellows at a large academic center in Pennsylvania from July 1, 2015, through June 30, 2016. Data analysis was performed from June 1, 2018, through July 31, 2018.Main outcomes and measuresWord use in faculty evaluations was quantified using automated text mining by converting free-text comments into unique 1- and 2-word phrases. Mixed-effects logistic regression analysis was performed to assess associations of faculty gender with frequencies of specific words and phrases present in a physician trainee evaluation.ResultsA total of 7326 unique evaluations were collected for 521 faculty (325 men [62.4%] and 196 women [37.6%]). The individual words art (odds ratio [OR], 7.78; 95% CI, 1.01-59.89), trials (OR, 4.43; 95% CI, 1.34-14.69), master (OR, 4.24; 95% CI, 1.69-10.63), and humor (OR, 2.32; 95% CI, 1.44-3.73) were significantly associated with evaluations of male faculty, whereas the words empathetic (OR, 4.34; 95% CI, 1.56-12.07), delight (OR, 4.26; 95% CI, 1.35-13.40), and warm (OR, 3.45; 95% CI, 1.83-6.49) were significantly associated with evaluations of female faculty. Two-word phrases associated with male faculty evaluations included run rounds (OR, 7.78; 95% CI, 1.01-59.84), big picture (OR, 7.15; 95% CI, 1.68-30.42), and master clinician (OR, 4.02; 95% CI, 1.21-13.36), whereas evaluations of female faculty were more likely to be associated with model physician (OR, 7.75; 95% CI, 1.70-35.39), just right (OR, 6.97; 95% CI, 1.51-32.30), and attention (to) detail (OR, 4.26; 95% CI, 1.36-13.40).Conclusions and relevanceThe data showed quantifiable linguistic differences between free-text comments about male and female faculty in physician trainee evaluations. Further evaluation of these differences, particularly in association with ongoing gender disparities in faculty promotion and retention, may be warranted.

Project description:PurposeIn this study, we investigate the ability of automated performance metrics (APMs) and task-evoked pupillary response (TEPR), as objective measures of surgeon performance, to distinguish varying levels of surgeon expertise during generic robotic surgical tasks. Additionally, we evaluate the association between APMs and TEPR.MethodsParticipants completed ten tasks on a da Vinci Xi Surgical System (Intuitive Surgical, Inc.), each representing a surgical skill type: EndoWrist® manipulation, needle targeting, suturing/knot tying, and excision/dissection. Automated performance metrics (instrument motion tracking, EndoWrist® articulation, and system events data) and TEPR were recorded by a systems data recorder (Intuitive Surgical, Inc.) and Tobii Pro Glasses 2 (Tobii Technologies, Inc.), respectively. The Kruskal-Wallis test determined significant differences between groups of varying expertise. Spearman's rank correlation coefficient measured associations between APMs and TEPR.ResultsTwenty-six participants were stratified by robotic surgical experience: novice (no prior experience; n = 9), intermediate (< 100 cases; n = 9), and experts (≥ 100 cases; n = 8). Several APMs differentiated surgeon experience including task duration (p < 0.01), time active of instruments (p < 0.03), linear velocity of instruments (p < 0.04), and angular velocity of dominant instrument (p < 0.04). Task-evoked pupillary response distinguished surgeon expertise for three out of four task types (p < 0.04). Correlation trends between APMs and TEPR revealed that expert surgeons move more slowly with high cognitive workload (ρ < - 0.60, p < 0.05), while novices move faster under the same cognitive experiences (ρ > 0.66, p < 0.05).ConclusionsAutomated performance metrics and TEPR can distinguish surgeon expertise levels during robotic surgical tasks. Furthermore, under high cognitive workload, there can be a divergence in robotic movement profiles between expertise levels.

Project description:BackgroundElectronic symptom self-reporting systems (e-SRS) have been shown to improve symptoms and survival in patients with cancer. However, patient engagement in using e-SRS for voluntary symptom self-reporting is less optimal. Multiple factors can potentially affect patients' acceptance and engagement in using home-based e-SRS. However, such factors have not been fully explored in cancer populations.ObjectiveThe aim of this study is to understand the acceptance and use of home-based e-SRS by patients with cancer and identify associated facilitators and barriers.MethodsPubMed, CINAHL, Scopus, and PsycINFO (January 2010 to March 2020) were searched using a combination of Medical Subject Headings (MeSH) terms and keywords such as symptom self-reporting, electronic/technology, cancer, and their synonyms. Included studies focused on the use of home-based e-SRS by patients with cancer and their families. Studies on patients' use of e-SRS in clinical settings only were excluded. Of the 3740 papers retrieved, 33 were included in the final review. Factors associated with patient acceptance and use of e-SRS were extracted and synthesized.ResultsMost e-SRS were web based (22/33, 66%) or mobile app based (9/33, 27%). The e-SRS initial acceptance, represented by patient enrollment rates, ranged from 40% (22/55) to 100% (100/100). High e-SRS acceptance was rated by 69% (59/85) to 77.6% (337/434) of the patients after they used the system. The e-SRS use, measured by patients' response rates to questionnaires (ranging from 1596/3521, 45.33% to 92%) or system log-on rates (ranging from 4/12, 33% to 99/100, 99%), declined over time in general patterns. Few studies (n=7) reported e-SRS use beyond 6 months, with the response rates ranging from 62% (40/64) to 85.1% (541/636) and the log-on rates ranging from 63.6% (103/162) to 77% (49/64). The availability of compatible devices and technical support, interactive system features, information accessibility, privacy, questionnaire quality, patient physical/psychosocial status, and age were associated with patient acceptance and use of home-based e-SRS.ConclusionsAcceptance and use of home-based e-SRS by patients with cancer varied significantly across studies, as assessed by a variety of approaches. The lack of access to technology has remained a barrier to e-SRS adoption. Interactive system features and personalized questionnaires may increase patient engagement. More studies are needed to further understand patients' long-term use of home-based e-SRS behavior patterns to develop personalized interventions to support symptom self-management and self-reporting of patients with cancer for optimal health outcomes.

Dataset Information

Automated Reporting of Trainee Metrics Using Electronic Clinical Systems.

Background

Objective

Methods

Results

Conclusions

Publications

Automated Reporting of Trainee Metrics Using Electronic Clinical Systems.

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