Quality improvement project to reduce medicare 1-day write-offs due to inappropriate admission orders.

BACKGROUND: We identified that Stanford Health Care had a significant number of patients who after discharge are found by the utilization review committee not to meet Center for Mediare and Medicaid Services (CMS) 2-midnight benchmark for inpatient status. Some of the charges incurred during the care of these patients are written-off and known as Medicare 1-day write-offs. This study which aims to evaluate the use of a Best Practice Alert (BPA) feature on the electronic medical record, EPIC, to ensure appropriate designation of a patient's hospitalization status as either inpatient or outpatient in accordance with Center for Medicare and Medicaid services (CMS) 2 midnight length of stay benchmark thereby reducing the number of associated write-offs. METHOD: We incorporated a best practice alert (BPA) into the Epic Electronic Medical Record (EMR) that would prompt the discharging provider and the case manager to review the patients' inpatient designation prior to discharge and change the patient's designation to observation when deemed appropriate. Patients who met the inclusion criteria (Patients must have Medicare fee-for-service insurance, inpatient length of stay (LOS) less than 2 midnights, inpatient designation as hospitalization status at time of discharge, was hospitalized to an acute level of care and belonged to one of 37 listed hospital services at the time of signing of the discharge order) were randomized to have the BPA either silent or active over a three-month period from July 18, 2019, to October 18, 2019. RESULT: A total of 88 patients were included in this study: 40 in the control arm and 48 in the intervention arm. In the intervention arm, 8 (8/48, 16.7%) had an inpatient status designation despite potentially meeting Medicare guidelines for an observation stay, comparing to 23 patients (23/40, 57.5%) patients in the control group (p = 0.001). The estimated number of write-offs in the control arm was 17 (73.9%, out of 23 inpatient patients) while in the intervention arm was 1 (12.5%, out of 8 inpatient patient) after accounting for patients who may have met inpatient criteria for other reasons based on case manager note review. CONCLUSION: This is the first time to our knowledge that a BPA has been used in this manner to reduce the number of Medicare 1-day write-offs.

Interpreter and limited-English proficiency patient training helps develop medical and physician assistant students' cross-cultural communication skills.

BACKGROUND: The increasing linguistic and cultural diversity in the United States underscores the necessity of enhancing healthcare professionals' cross-cultural communication skills. This study focuses on incorporating interpreter and limited-English proficiency (LEP) patient training into the medical and physician assistant student curriculum. This aims to improve equitable care provision, addressing the vulnerability of LEP patients to healthcare disparities, including errors and reduced access. Though training is recognized as crucial, opportunities in medical curricula remain limited. METHODS: To bridge this gap, a novel initiative was introduced in a medical school, involving second-year students in clinical sessions with actual LEP patients and interpreters. These sessions featured interpreter input, patient interactions, and feedback from interpreters and clinical preceptors. A survey assessed the perspectives of students, preceptors, and interpreters. RESULTS: Outcomes revealed positive reception of interpreter and LEP patient integration. Students gained confidence in working with interpreters and valued interpreter feedback. Preceptors recognized the sessions' value in preparing students for future clinical interactions. CONCLUSIONS: This study underscores the importance of involving experienced interpreters in training students for real-world interactions with LEP patients. Early interpreter training enhances students' communication skills and ability to serve linguistically diverse populations. Further exploration could expand languages and interpretation modes and assess long-term effects on students' clinical performance. By effectively training future healthcare professionals to navigate language barriers and cultural diversity, this research contributes to equitable patient care in diverse communities.

A randomized study of a best practice alert for platelet transfusions.

BACKGROUND AND OBJECTIVES: Inappropriate platelet transfusions represent an opportunity for improvements in patient care. Use of a best practice alert (BPA) as clinical decision support (CDS) for red cell transfusions has successfully reduced unnecessary red blood cell (RBC) transfusions in prior studies. We studied the impact of a platelet transfusion BPA with visibility randomized by patient chart. MATERIALS AND METHODS: A BPA was built to introduce CDS at the time of platelet ordering in the electronic health record. Alert visibility was randomized at the patient encounter level. BPA eligible platelet transfusions for patients with both visible and non-visible alerts were recorded along with reasons given for override of the BPA. Focused interviews were performed with providers who interacted with the BPA to assess its impact on their decision making. RESULTS: Over a 9-month study period, 446 patient charts were randomized. The visible alert group used 25.3% fewer BPA eligible platelets. Mean monthly usage of platelets eligible for BPA display was 65.7 for the control group and 49.1 for the visible alert group (p = 0.07). BPA-eligible platelets used per inpatient day at risk per month were not significantly different between groups (2.4 vs. 2.1, p = 0.53). CONCLUSION: It is feasible to study CDS via chart-based randomization. A platelet BPA reduced total platelets used over the study period and may have resulted in $151,069 in yearly savings, although there were no differences when adjusted for inpatient days at risk. During interviews, providers offered additional workflow insights allowing further improvement of CDS for platelet transfusions.

Clinical sites of the Undiagnosed Diseases Network: unique contributions to genomic medicine and science.

PURPOSE: The NIH Undiagnosed Diseases Network (UDN) evaluates participants with disorders that have defied diagnosis, applying personalized clinical and genomic evaluations and innovative research. The clinical sites of the UDN are essential to advancing the UDN mission; this study assesses their contributions relative to standard clinical practices. METHODS: We analyzed retrospective data from four UDN clinical sites, from July 2015 to September 2019, for diagnoses, new disease gene discoveries and the underlying investigative methods. RESULTS: Of 791 evaluated individuals, 231 received 240 diagnoses and 17 new disease-gene associations were recognized. Straightforward diagnoses on UDN exome and genome sequencing occurred in 35% (84/240). We considered these tractable in standard clinical practice, although genome sequencing is not yet widely available clinically. The majority (156/240, 65%) required additional UDN-driven investigations, including 90 diagnoses that occurred after prior nondiagnostic exome sequencing and 45 diagnoses (19%) that were nongenetic. The UDN-driven investigations included complementary/supplementary phenotyping, innovative analyses of genomic variants, and collaborative science for functional assays and animal modeling. CONCLUSION: Investigations driven by the clinical sites identified diagnostic and research paradigms that surpass standard diagnostic processes. The new diagnoses, disease gene discoveries, and delineation of novel disorders represent a model for genomic medicine and science.

A minimalist electronic health record-based intervention to reduce standing lab utilisation.

BACKGROUND: Repetitive laboratory testing in stable patients is low-value care. Electronic health record (EHR)-based interventions are easy to disseminate but can be restrictive. OBJECTIVE: To evaluate the effect of a minimally restrictive EHR-based intervention on utilisation. SETTING: One year before and after intervention at a 600-bed tertiary care hospital. 18 000 patients admitted to General Medicine, General Surgery and the Intensive Care Unit (ICU). INTERVENTION: Providers were required to specify the number of times each test should occur instead of being able to order them indefinitely. MEASUREMENTS: For eight tests, utilisation (number of labs performed per patient day) and number of associated orders were measured. RESULTS: Utilisation decreased for some tests on all services. Notably, complete blood count with differential decreased 9% (p<0.001) on General Medicine and 21% (p<0.001) in the ICU. CONCLUSIONS: Requiring providers to specify the number of occurrences of labs changes significantly reduces utilisation in some cases.

A Patient with Sjogren's Syndrome and Subsequent Diagnosis of Inclusion Body Myositis and Light-Chain Amyloidosis.

We discuss a challenging case of a 58-year-old Vietnamese-American woman who presented to her new primary care provider with an 8-year history of slowly progressive dysphagia, hoarseness, muscle weakness with associated frequent falls, and weight loss. She eventually reported dry eyes and dry mouth, and she was diagnosed with Sjogren's syndrome. Subsequently, she was additionally diagnosed with inclusion body myositis and gastric light-chain (AL) amyloidosis. Although inclusion body myositis has been previously associated with Sjogren's syndrome, inclusion body myositis is rare in non-Caucasians, and the trio of Sjogren's syndrome, inclusion body myositis, and AL amyloidosis has not been previously reported. Sjogren's syndrome is a systemic autoimmune condition characterized by ocular and oral dryness. It is one of the most common rheumatologic disorders in the USA and worldwide. Early diagnosis of Sjogren's is particularly important given the frequency and variety of associated autoimmune diseases and extraglandular manifestations. Furthermore, although inclusion body myositis has a low prevalence, it is the most common inflammatory myopathy in older adults and is unfortunately associated with long delays in diagnosis, so knowledge of this disorder is also crucial for practicing internists.

Carotid Artery Imaging Is More Strongly Associated With the 10-Year Atherosclerotic Cardiovascular Disease Score Than Coronary Artery Imaging.

PURPOSE: The aim of this study was to compare coronary and carotid artery imaging and determine which one shows the strongest association with atherosclerotic cardiovascular disease (ASCVD) score. PATIENTS AND METHODS: Two separate series patients who underwent either coronary computed tomography angiography (CTA) or carotid CTA were included. We recorded the ASCVD scores and assessed the CTA imaging. Two thirds were used to build predictive models, and the remaining one third generated predicted ASCVD scores. The Bland-Altman analysis analyzed the concordance. RESULTS: A total of 110 patients were included in each group. There was no significant difference between clinical characteristics. Three imaging variables were included in the carotid model. Two coronary models (presence of calcium or Agatston score) were created. The bias between true and predicted ASCVD scores was 0.37 ± 5.72% on the carotid model, and 2.07 ± 7.18% and 2.47 ± 7.82% on coronary artery models, respectively. CONCLUSIONS: Both carotid and coronary artery imaging features can predict ASCVD score. The carotid artery was more associated to the ASCVD score than the coronary artery.

Semiautomated Characterization of Carotid Artery Plaque Features From Computed Tomography Angiography to Predict Atherosclerotic Cardiovascular Disease Risk Score.

PURPOSE: To investigate whether selected carotid computed tomography angiography (CTA) quantitative features can predict 10-year atherosclerotic cardiovascular disease (ASCVD) risk scores. METHODS: One hundred seventeen patients with calculated ASCVD risk scores were considered. A semiautomated imaging analysis software was used to segment and quantify plaque features. Eighty patients were randomly selected to build models using 14 imaging variables and the calculated ASCVD risk score as the end point (continuous and binarized). The remaining 37 patients were used as the test set to generate predicted ASCVD scores. The predicted and observed ASCVD risk scores were compared to assess properties of the predictive model. RESULTS: Nine of 14 CTA imaging variables were included in a model that considered the plaque features in a continuous fashion (model 1) and 6 in a model that considered the plaque features dichotomized (model 2). The predicted ASCVD risk scores were 18.87% ± 13.26% and 18.39% ± 11.6%, respectively. There were strong correlations between the observed ASCVD and the predicted ASCVDs, with r = 0.736 for model 1 and r = 0.657 for model 2. The mean biases between observed ASCVD and predicted ASCVDs were -1.954% ± 10.88% and -1.466% ± 12.04%, respectively. CONCLUSIONS: Selected quantitative imaging carotid features extracted from the semiautomated carotid artery analysis can predict the ASCVD risk scores.

Genomics in medicine: a novel elective rotation for internal medicine residents.

It is well recognised that medical training globally and at all levels lacks sufficient incorporation of genetics and genomics education to keep up with the rapid advances and growing application of genomics to clinical care. However, the best strategy to implement these desired changes into postgraduate medical training and engage learners is still unclear. We developed a novel elective rotation in 'Genomic Medicine and Undiagnosed Diseases' for categorical Internal Medicine Residents to address this educational gap and serve as an adaptable model for training that can be applied broadly across different specialties and at other institutions. Key curriculum goals achieved include increased understanding about genetic testing modalities and tools available for diagnosis and risk analysis, the role of genetics-trained allied health professionals, and indications and limitations of genetic and genomic testing in both rare and common conditions.

Thrombophilia testing in the inpatient setting: impact of an educational intervention.

BACKGROUND: Thrombophilia testing is frequently ordered in the inpatient setting despite its limited impact on clinical decision-making and unreliable results in the setting of acute thrombosis or ongoing anticoagulation. We sought to determine the effect of an educational intervention in reducing inappropriate thrombophilia testing for hospitalized patients. METHODS: During the 2014 academic year, we implemented an educational intervention with a phase implementation design for Internal Medicine interns at Stanford University Hospital. The educational session covering epidemiology, appropriate thrombophilia evaluation and clinical rationale behind these recommendations. Their ordering behavior was compared with a contemporaneous control (non-medicine and private services) and a historical control (interns from prior academic year). From the analyzed data, we determined the proportion of inappropriate thrombophilia testing of each group. Logistic generalized estimating equations were used to estimate odds ratios for inappropriate thrombophilia testing associated with the intervention. RESULTS: Of 2151 orders included, 934 were deemed inappropriate (43.4%). The two intervention groups placed 147 orders. A pooled analysis of ordering practices by intervention groups revealed a trend toward reduction of inappropriate ordering (p = 0.053). By the end of the study, the intervention groups had significantly lower rates of inappropriate testing compared to historical or contemporaneous controls. CONCLUSION: A brief educational intervention was associated with a trend toward reduction in inappropriate thrombophilia testing. These findings suggest that focused education on thrombophilia testing can positively impact inpatient ordering practices.

An evaluation of clinical order patterns machine-learned from clinician cohorts stratified by patient mortality outcomes.

OBJECTIVE: Evaluate the quality of clinical order practice patterns machine-learned from clinician cohorts stratified by patient mortality outcomes. MATERIALS AND METHODS: Inpatient electronic health records from 2010 to 2013 were extracted from a tertiary academic hospital. Clinicians (n = 1822) were stratified into low-mortality (21.8%, n = 397) and high-mortality (6.0%, n = 110) extremes using a two-sided P-value score quantifying deviation of observed vs. expected 30-day patient mortality rates. Three patient cohorts were assembled: patients seen by low-mortality clinicians, high-mortality clinicians, and an unfiltered crowd of all clinicians (n = 1046, 1046, and 5230 post-propensity score matching, respectively). Predicted order lists were automatically generated from recommender system algorithms trained on each patient cohort and evaluated against (i) real-world practice patterns reflected in patient cases with better-than-expected mortality outcomes and (ii) reference standards derived from clinical practice guidelines. RESULTS: Across six common admission diagnoses, order lists learned from the crowd demonstrated the greatest alignment with guideline references (AUROC range = 0.86-0.91), performing on par or better than those learned from low-mortality clinicians (0.79-0.84, P < 10-5) or manually-authored hospital order sets (0.65-0.77, P < 10-3). The same trend was observed in evaluating model predictions against better-than-expected patient cases, with the crowd model (AUROC mean = 0.91) outperforming the low-mortality model (0.87, P < 10-16) and order set benchmarks (0.78, P < 10-35). DISCUSSION: Whether machine-learning models are trained on all clinicians or a subset of experts illustrates a bias-variance tradeoff in data usage. Defining robust metrics to assess quality based on internal (e.g. practice patterns from better-than-expected patient cases) or external reference standards (e.g. clinical practice guidelines) is critical to assess decision support content. CONCLUSION: Learning relevant decision support content from all clinicians is as, if not more, robust than learning from a select subgroup of clinicians favored by patient outcomes.

Assessing the Relationship Between American Heart Association Atherosclerotic Cardiovascular Disease Risk Score and Coronary Artery Imaging Findings.

OBJECTIVE: The aim of this study was to characterize the relationship between computed tomography angiography imaging characteristics of coronary artery and atherosclerotic cardiovascular disease (ASCVD) score. METHODS: We retrospectively identified all patients who underwent a coronary computed tomography angiography at our institution from December 2013 to July 2016, then we calculated the 10-year ASCVD score. We characterized the relationship between coronary artery imaging findings and ASCVD risk score. RESULTS: One hundred fifty-one patients met our inclusion criteria. Patients with a 10-year ASCVD score of 7.5% or greater had significantly more arterial segments showing stenosis (46.4%, P = 0.008) and significantly higher maximal plaque thickness (1.25 vs 0.53, P = 0.001). However, among 56 patients with a 10-year ASCVD score of 7.5% or greater, 30 (53.6%) had no arterial stenosis. Furthermore, among the patients with a 10-year ASCVD score of less than 7.5%, 24 (25.3%) had some arterial stenosis. CONCLUSIONS: There is some concordance but not a perfect overlap between 10-year ASCVD risk scores and coronary artery imaging findings.

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

PURPOSE: Most residency programmes do not have a formal high value care curriculum. Our goal was to design and implement a multidisciplinary high value care curriculum specifically targeted at interns. DESIGN: Our curriculum was designed with multidisciplinary input from attendings, fellows and residents at Stanford. Curricular topics were inspired by the American Board of Internal Medicine's Choosing Wisely campaign, Alliance for Academic Internal Medicine, American College of Physicians and Society of Hospital Medicine. Our topics were as follows: introduction to value-based care; telemetry utilisation; lab ordering; optimal approach to thrombophilia work-ups and fresh frozen plasma use; optimal approach to palliative care referrals; antibiotic stewardship; and optimal approach to imaging for low back pain. Our curriculum was implemented at the Stanford Internal Medicine residency programme over the course of two academic years (2014 and 2015), during which 100 interns participated in our high value care curriculum. After each high value care session, interns were offered the opportunity to complete surveys regarding feedback on the curriculum, self-reported improvements in knowledge, skills and attitudinal module objectives, and quiz-based knowledge assessments. RESULTS: The overall survey response rate was 67.1%. Overall, the material was rated as highly useful on a 5-point Likert scale (mean 4.4, SD 0.6). On average, interns reported a significant improvement in their self-rated knowledge, skills and attitudes after the six seminars (mean improvement 1.6 points, SD 0.4 (95% CI 1.5 to 1.7), p<0.001). CONCLUSIONS: We successfully implemented a novel high value care curriculum that specifically targets intern physicians.

Fulfilling outpatient medicine responsibilities during internal medicine residency: a quantitative study of housestaff participation with between visit tasks.

BACKGROUND: Internal Medicine residents experience conflict between inpatient and outpatient medicine responsibilities. Outpatient "between visit" responsibilities such as reviewing lab and imaging data, responding to medication refill requests and replying to patient inquiries compete for time and attention with inpatient duties. By examining Electronic Health Record (EHR) audits, our study quantitatively describes this balance between competing responsibilities, focusing on housestaff participation with "between visit" outpatient responsibilities. METHODS: We examined EHR log-in data from 2012-2013 for 41 residents (R1 to R3) assigned to a large academic center's continuity clinic. From the EHR log-in data, we examined housestaff compliance with "between visit" tasks, based on official clinic standards. We used generalized estimating equations to evaluate housestaff compliance with between visit tasks and amount of time spent on tasks. We examined the relationship between compliance with between visit tasks and resident year of training, rotation type (elective or required) and interest in primary care. RESULTS: Housestaff compliance with logging in to complete "between visit" tasks varied significantly depending on rotation, with overall compliance of 45% during core inpatient rotations compared to 68% during electives (p = 0.01). Compliance did not significantly vary by interest in primary care or training level. Once logged in, housestaff spent a mean 53 min per week logged in while on electives, compared to 55 min on required rotations (p = 0.90). CONCLUSIONS: Our study quantitatively highlights the difficulty of attending to outpatient responsibilities during busy core inpatient rotations, which comprise the bulk of residency at our institution and at others. Our results reinforce the need to continue development and study of innovative systems for coverage of "between visit" responsibilities, including shared coverage models among multiple residents and shared coverage models between residents and clinic attendings, both of which require a balance between clinic efficiency and resident ownership, autonomy and learning.

Influence of a Large Language Model on Diagnostic Reasoning: A Randomized Clinical Vignette Study.

Importance: Diagnostic errors are common and cause significant morbidity. Large language models (LLMs) have shown promise in their performance on both multiple-choice and open-ended medical reasoning examinations, but it remains unknown whether the use of such tools improves diagnostic reasoning. Objective: To assess the impact of the GPT-4 LLM on physicians' diagnostic reasoning compared to conventional resources. Design: Multi-center, randomized clinical vignette study. Setting: The study was conducted using remote video conferencing with physicians across the country and in-person participation across multiple academic medical institutions. Participants: Resident and attending physicians with training in family medicine, internal medicine, or emergency medicine. Interventions: Participants were randomized to access GPT-4 in addition to conventional diagnostic resources or to just conventional resources. They were allocated 60 minutes to review up to six clinical vignettes adapted from established diagnostic reasoning exams. Main Outcomes and Measures: The primary outcome was diagnostic performance based on differential diagnosis accuracy, appropriateness of supporting and opposing factors, and next diagnostic evaluation steps. Secondary outcomes included time spent per case and final diagnosis. Results: 50 physicians (26 attendings, 24 residents) participated, with an average of 5.2 cases completed per participant. The median diagnostic reasoning score per case was 76.3 percent (IQR 65.8 to 86.8) for the GPT-4 group and 73.7 percent (IQR 63.2 to 84.2) for the conventional resources group, with an adjusted difference of 1.6 percentage points (95% CI -4.4 to 7.6; p=0.60). The median time spent on cases for the GPT-4 group was 519 seconds (IQR 371 to 668 seconds), compared to 565 seconds (IQR 456 to 788 seconds) for the conventional resources group, with a time difference of -82 seconds (95% CI -195 to 31; p=0.20). GPT-4 alone scored 15.5 percentage points (95% CI 1.5 to 29, p=0.03) higher than the conventional resources group. Conclusions and Relevance: In a clinical vignette-based study, the availability of GPT-4 to physicians as a diagnostic aid did not significantly improve clinical reasoning compared to conventional resources, although it may improve components of clinical reasoning such as efficiency. GPT-4 alone demonstrated higher performance than both physician groups, suggesting opportunities for further improvement in physician-AI collaboration in clinical practice.

Adapted large language models can outperform medical experts in clinical text summarization.

Analyzing vast textual data and summarizing key information from electronic health records imposes a substantial burden on how clinicians allocate their time. Although large language models (LLMs) have shown promise in natural language processing (NLP) tasks, their effectiveness on a diverse range of clinical summarization tasks remains unproven. Here we applied adaptation methods to eight LLMs, spanning four distinct clinical summarization tasks: radiology reports, patient questions, progress notes and doctor-patient dialogue. Quantitative assessments with syntactic, semantic and conceptual NLP metrics reveal trade-offs between models and adaptation methods. A clinical reader study with 10 physicians evaluated summary completeness, correctness and conciseness; in most cases, summaries from our best-adapted LLMs were deemed either equivalent (45%) or superior (36%) compared with summaries from medical experts. The ensuing safety analysis highlights challenges faced by both LLMs and medical experts, as we connect errors to potential medical harm and categorize types of fabricated information. Our research provides evidence of LLMs outperforming medical experts in clinical text summarization across multiple tasks. This suggests that integrating LLMs into clinical workflows could alleviate documentation burden, allowing clinicians to focus more on patient care.