A high value care curriculum for interns: a description of curricular design, implementation and housestaff feedback

Hom, Jason; Kumar, Andre; Evans, Kambria H.; Svec, David; Richman, Ilana B.; Fang, Daniel; Smeraglio, Andrea; Holubar, Marisa; Johnson, Tyler; Shah, Neil; Renault, Cybèle A.; Ahuja, Neera; Witteles, Ronald; Harman, Stephanie; Shieh, Lisa

doi:10.1136/postgradmedj-2016-134617

Cited by 8 publications

(11 citation statements)

References 12 publications

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“…[8][9][10][11] Numerous interventions have been studied to reduce inappropriate laboratory testing, including clinical education, audit feedback, financial incentives, and electronic medical record (EMR)-based ordering restrictions. [12][13][14][15] Interventions based on EMRs offer pertinent information for clinical decision-making, such as cost, turnaround time, prior stable results, and guideline-based best practice alerts. [16][17][18][19][20] Despite these efforts, unnecessary tests remain prolific when practitioners are influenced by fear of missing problems, medicolegal concerns, patient preferences, and the overall difficulty of systematically identifying low-value testing at the point of care, prompting behavior to check just in case.…”

Section: Introductionmentioning

confidence: 99%

Prevalence and Predictability of Low-Yield Inpatient Laboratory Diagnostic Tests

Hom

Balasubramanian

et al. 2019

JAMA Netw Open

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IMPORTANCE Laboratory testing is an important target for high-value care initiatives, constituting the highest volume of medical procedures. Prior studies have found that up to half of all inpatient laboratory tests may be medically unnecessary, but a systematic method to identify these unnecessary tests in individual cases is lacking.OBJECTIVE To systematically identify low-yield inpatient laboratory testing through personalized predictions. DESIGN, SETTING, AND PARTICIPANTSIn this retrospective diagnostic study with multivariable prediction models, 116 637 inpatients treated at Stanford MAIN OUTCOMES AND MEASURES Diagnostic accuracy measures, including sensitivity, specificity, negative predictive values (NPVs), positive predictive values (PPVs), and area under the receiver operating characteristic curve (AUROC), of machine learning models when predicting whether inpatient laboratory tests yield a normal result as defined by local laboratory reference ranges. RESULTS In the recent data sets (July 1, 2014, to June 30, 2017) from Stanford University Hospital (including 22 664 female inpatients with a mean [SD] age of 58.8 [19.0] years and 22 016 male inpatients with a mean [SD] age of 59.0 [18.1] years), among the top 20 highest-volume tests, 792 397 were repeats of orders within 24 hours, including tests that are physiologically unlikely to yield new information that quickly (eg, white blood cell differential, glycated hemoglobin, and serum albumin level). The best-performing machine learning models predicted normal results with an AUROC of 0.90 or greater for 12 stand-alone laboratory tests (eg, sodium AUROC, 0.92 [95% CI, 0.91-0.93]; sensitivity, 98%; specificity, 35%; PPV, 66%; NPV, 93%; lactate dehydrogenase AUROC, 0.93 [95% CI, 0.93-0.94]; sensitivity, 96%; specificity, 65%; PPV, 71%; NPV, 95%; and troponin I AUROC, 0.92 [95% CI, 0.91-0.93]; sensitivity, 88%; specificity, 79%; PPV, 67%; NPV, 93%) and 10 common laboratory test components (eg, hemoglobin AUROC, 0.94 [95% CI, 0.92-0.95]; sensitivity, 99%; specificity, 17%; PPV, 90%; NPV, 81%; creatinine AUROC, 0.96 [95% CI, 0.96-0.97]; sensitivity, 93%; specificity, 83%; PPV, 79%; NPV, 94%; and urea nitrogen AUROC, 0.95 [95% CI, 0.94, 0.96]; sensitivity, 87%; specificity, 89%; PPV, 77%; NPV 94%). CONCLUSIONS AND RELEVANCEThe findings suggest that low-yield diagnostic testing is common and can be systematically identified through data-driven methods and patient context-aware predictions. Implementing machine learning models appear to be able to quantify the level of uncertainty and expected information gained from diagnostic tests explicitly, with the potential to (continued) Key Points Question How prevalent are low-yield inpatient diagnostic laboratory tests for which results are predictable with machine learning models? Findings In this diagnostic study of 191 506 inpatients from 3 tertiary academic medical centers, common low-yield inpatient diagnostic laboratory test results were systematically identified through data-driven methods and personalized predic...

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Section: Introductionmentioning

confidence: 99%

Prevalence and Predictability of Low-Yield Inpatient Laboratory Diagnostic Tests

Hom

Balasubramanian

et al. 2019

JAMA Netw Open

Self Cite

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“…Other approaches to educate medical residents on proper telemetry use have embedded this skill in a broad curriculum containing multiple other educational objectives. 6 Our approach differs in that it is solely focused on improving knowledge and skills around telemetry and utilizes deliberate practice methodology to achieve this objective.…”

Section: Discussionmentioning

confidence: 99%

Design and Implementation of an Interactive Curriculum on Telemetry Use and Interpretation for Medicine Interns: a Survey Study

et al. 2020

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“…Curricula in high-value care and decision support have been well-received by medical trainees, and housestaff-led initiatives have led to significant reductions in unnecessary spending. [ 17 – 19 ] Crowdsourcing was used to solicit ideas from both leaders in medical education as well as trainees themselves as part of the “Choosing Wisely Challenge.”[ 20 ] Additional initiatives could be adapted to develop curricula and promote educational rotations in subspecialty areas. The ultimate goal is to create training programs that teach and role-model high-value, cost-conscious care in both general and subspecialty areas so that trainees graduates develop “good habits” early on.…”

Section: Discussionmentioning

confidence: 99%

Internal medicine trainees' knowledge and confidence in using the American Society of Hematology Choosing Wisely guidelines in hemostasis, thrombosis, and non-malignant hematology

et al. 2018

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BackgroundSeveral specialty societies participate in the Choosing Wisely (CW) campaign in an attempt to reduce waste in health care spending. We surveyed internal medicine (IM) residents with an objective of classifying knowledge of and confidence in using the American Society of Hematology (ASH) CW principles in hemostasis, thrombosis, and non-malignant hematology.MethodsMulti-institutional study of IM residents at 5 academic training programs in the United States. A 10-question, case-based multiple choice test, with each question accompanied by a 5-point Likert-scale confidence assessment, was distributed electronically. Responses were summarized with frequencies and percentages or medians and ranges, as appropriate. Two sample t-tests or Wilcoxon rank-sum tests were used to compare confidence and knowledge scores.ResultsOf 892 IM residents, 174 (19.5%) responded to all questions. Overall, residents answered a median of 7 of 10 questions correctly (range 2–10) and median resident confidence in their responses was 3.1 (on a 5-point scale). Correct responses were significantly associated with higher confidence for all but one question. Having a hematology rotation experience was significantly associated with more correct responses and with higher confidence (p = 0.001 and p<0.001, respectively).ConclusionsIM residents at several academic hospitals have variable knowledge of ASH-CW guidelines in thrombosis and hemostasis/non-malignant hematology. Residents who have done hematology rotations, particularly a hematology consult rotation, were more likely to answer questions correctly and to be more confident that their answers were correct. Adequate clinical exposure and training in cost-effective care is essential to train clinicians who are cost-conscious in any specialty.

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A high value care curriculum for interns: a description of curricular design, implementation and housestaff feedback

Abstract: We successfully implemented a novel high value care curriculum that specifically targets intern physicians.

Cited by 8 publications

References 12 publications

Prevalence and Predictability of Low-Yield Inpatient Laboratory Diagnostic Tests

Prevalence and Predictability of Low-Yield Inpatient Laboratory Diagnostic Tests

Design and Implementation of an Interactive Curriculum on Telemetry Use and Interpretation for Medicine Interns: a Survey Study

Internal medicine trainees' knowledge and confidence in using the American Society of Hematology Choosing Wisely guidelines in hemostasis, thrombosis, and non-malignant hematology

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