Disease Heritability Inferred from Familial Relationships Reported in Medical Records.

Heritability is essential for understanding the biological causes of disease but requires laborious patient recruitment and phenotype ascertainment. Electronic health records (EHRs) passively capture a wide range of clinically relevant data and provide a resource for studying the heritability of traits that are not typically accessible. EHRs contain next-of-kin information collected via patient emergency contact forms, but until now, these data have gone unused in research. We mined emergency contact data at three academic medical centers and identified 7.4 million familial relationships while maintaining patient privacy. Identified relationships were consistent with genetically derived relatedness. We used EHR data to compute heritability estimates for 500 disease phenotypes. Overall, estimates were consistent with the literature and between sites. Inconsistencies were indicative of limitations and opportunities unique to EHR research. These analyses provide a validation of the use of EHRs for genetics and disease research.

Decoding the Genomics of Abdominal Aortic Aneurysm.

A key aspect of genomic medicine is to make individualized clinical decisions from personal genomes. We developed a machine-learning framework to integrate personal genomes and electronic health record (EHR) data and used this framework to study abdominal aortic aneurysm (AAA), a prevalent irreversible cardiovascular disease with unclear etiology. Performing whole-genome sequencing on AAA patients and controls, we demonstrated its predictive precision solely from personal genomes. By modeling personal genomes with EHRs, this framework quantitatively assessed the effectiveness of adjusting personal lifestyles given personal genome baselines, demonstrating its utility as a personal health management tool. We showed that this new framework agnostically identified genetic components involved in AAA, which were subsequently validated in human aortic tissues and in murine models. Our study presents a new framework for disease genome analysis, which can be used for both health management and understanding the biological architecture of complex diseases. VIDEO ABSTRACT.

DrugDoctor: enhancing drug recommendation in cold-start scenario via visit-level representation learning and training.

Medication recommendation is a crucial application of artificial intelligence in healthcare. Current methodologies mostly depend on patient-level longitudinal representation, which utilizes the entirety of historical electronic health records for making predictions. However, they tend to overlook a few key elements: (1) The need to analyze the impact of past medications on previous conditions. (2) Similarity in patient visits is more common than similarity in the complete medical histories of patients. (3) It is difficult to accurately represent patient-level longitudinal data due to the varying numbers of visits. To our knowledge, current models face difficulties in dealing with initial patient visits (i.e. in cold-start scenarios) which are common in clinical practice. This paper introduces DrugDoctor, an innovative drug recommendation model crafted to emulate the decision-making mechanics of human doctors. Unlike previous methods, DrugDoctor explores the visit-level relationship between prescriptions and diseases while considering the impact of past prescriptions on the patient's condition to provide more accurate recommendations. We design a plug-and-play block to effectively capture drug substructure-aware disease information and effectiveness-aware medication information, employing cross-attention and multi-head self-attention mechanisms. Furthermore, DrugDoctor adopts a fundamentally new visit-level training strategy, aligning more closely with the practices of doctors. Extensive experiments conducted on the MIMIC-III and MIMIC-IV datasets demonstrate that DrugDoctor outperforms 10 other state-of-the-art methods in terms of Jaccard, F1-score, and PRAUC. Moreover, DrugDoctor exhibits strong robustness in handling patients with varying numbers of visits and effectively tackles "cold-start" issues in medication combination recommendations.

Validation of an Electronic Health Record-Based Machine Learning Model Compared With Clinical Risk Scores for Gastrointestinal Bleeding.

BACKGROUND & AIMS: Guidelines recommend use of risk stratification scores for patients presenting with gastrointestinal bleeding (GIB) to identify very-low-risk patients eligible for discharge from emergency departments. Machine learning models may outperform existing scores and can be integrated within the electronic health record (EHR) to provide real-time risk assessment without manual data entry. We present the first EHR-based machine learning model for GIB. METHODS: The training cohort comprised 2546 patients and internal validation of 850 patients presenting with overt GIB (ie, hematemesis, melena, and hematochezia) to emergency departments of 2 hospitals from 2014 to 2019. External validation was performed on 926 patients presenting to a different hospital with the same EHR from 2014 to 2019. The primary outcome was a composite of red blood cell transfusion, hemostatic intervention (ie, endoscopic, interventional radiologic, or surgical), and 30-day all-cause mortality. We used structured data fields in the EHR, available within 4 hours of presentation, and compared the performance of machine learning models with current guideline-recommended risk scores, Glasgow-Blatchford Score, and Oakland Score. Primary analysis was area under the receiver operating characteristic curve. Secondary analysis was specificity at 99% sensitivity to assess the proportion of patients correctly identified as very low risk. RESULTS: The machine learning model outperformed the Glasgow-Blatchford Score (area under the receiver operating characteristic curve, 0.92 vs 0.89; P < .001) and Oakland Score (area under the receiver operating characteristic curve, 0.92 vs 0.89; P < .001). At the very-low-risk threshold of 99% sensitivity, the machine learning model identified more very-low-risk patients: 37.9% vs 18.5% for Glasgow-Blatchford Score and 11.7% for Oakland Score (P < .001 for both comparisons). CONCLUSIONS: An EHR-based machine learning model performs better than currently recommended clinical risk scores and identifies more very-low-risk patients eligible for discharge from the emergency department.

Tree-based subgroup discovery using electronic health record data: heterogeneity of treatment effects for DTG-containing therapies.

The rich longitudinal individual level data available from electronic health records (EHRs) can be used to examine treatment effect heterogeneity. However, estimating treatment effects using EHR data poses several challenges, including time-varying confounding, repeated and temporally non-aligned measurements of covariates, treatment assignments and outcomes, and loss-to-follow-up due to dropout. Here, we develop the subgroup discovery for longitudinal data algorithm, a tree-based algorithm for discovering subgroups with heterogeneous treatment effects using longitudinal data by combining the generalized interaction tree algorithm, a general data-driven method for subgroup discovery, with longitudinal targeted maximum likelihood estimation. We apply the algorithm to EHR data to discover subgroups of people living with human immunodeficiency virus who are at higher risk of weight gain when receiving dolutegravir (DTG)-containing antiretroviral therapies (ARTs) versus when receiving non-DTG-containing ARTs.

Multi-Omics Profiling for Health.

The world has witnessed a steady rise in both non-infectious and infectious chronic diseases, prompting a cross-disciplinary approach to understand and treating disease. Current medical care focuses on treating people after they become patients rather than preventing illness, leading to high costs in treating chronic and late-stage diseases. Additionally, a "one-size-fits all" approach to health care does not take into account individual differences in genetics, environment, or lifestyle factors, decreasing the number of people benefiting from interventions. Rapid advances in omics technologies and progress in computational capabilities have led to the development of multi-omics deep phenotyping, which profiles the interaction of multiple levels of biology over time and empowers precision health approaches. This review highlights current and emerging multi-omics modalities for precision health and discusses applications in the following areas: genetic variation, cardio-metabolic diseases, cancer, infectious diseases, organ transplantation, pregnancy, and longevity/aging. We will briefly discuss the potential of multi-omics approaches in disentangling host-microbe and host-environmental interactions. We will touch on emerging areas of electronic health record and clinical imaging integration with muti-omics for precision health. Finally, we will briefly discuss the challenges in the clinical implementation of multi-omics and its future prospects.

Nonsteroidal anti-inflammatory drug "allergy" labeling is associated with increased postpartum opioid utilization.

BACKGROUND: Current guidelines recommend a stepwise approach to postpartum pain management, beginning with acetaminophen and nonsteroidal anti-inflammatory drugs (NSAIDs), with opioids added only if needed. Report of a prior NSAID-induced adverse drug reaction (ADR) may preclude use of first-line analgesics, despite evidence that many patients with this allergy label may safely tolerate NSAIDs. OBJECTIVE: We assessed the association between reported NSAID ADRs and postpartum opioid utilization. METHODS: We performed a retrospective cohort study of birthing people who delivered within an integrated health system (January 1, 2017, to December 31, 2020). Study outcomes were postpartum inpatient opioid administrations and opioid prescriptions at discharge. Statistical analysis was performed on a propensity score-matched sample, which was generated with the goal of matching to the covariate distributions from individuals with NSAID ADRs. RESULTS: Of 38,927 eligible participants, there were 883 (2.3%) with an NSAID ADR. Among individuals with reported NSAID ADRs, 49.5% received inpatient opioids in the postpartum period, compared to 34.5% of those with no NSAID ADRs (difference = 15.0%, 95% confidence interval 11.4-18.6%). For patients who received postpartum inpatient opioids, those with NSAID ADRs received a higher total cumulative dose between delivery and hospital discharge (median 30.0 vs 22.5 morphine milligram equivalents [MME] for vaginal deliveries; median 104.4 vs 75.0 MME for cesarean deliveries). The overall proportion of patients receiving an opioid prescription at the time of hospital discharge was higher for patients with NSAID ADRs compared to patients with no NSAID ADRs (39.3% vs 27.2%; difference = 12.1%, 95% confidence interval 8.6-15.6%). CONCLUSION: Patients with reported NSAID ADRs had higher postpartum inpatient opioid utilization and more frequently received opioid prescriptions at hospital discharge compared to those without NSAID ADRs, regardless of mode of delivery.

Applications of machine learning on electronic health record data to combat antibiotic resistance.

There is growing excitement about the clinical use of artificial intelligence and machine learning technologies. Advancements in computing and the accessibility of machine learning frameworks enable researchers to easily train predictive models using electronic health record data. However, there are several practical factors that must be considered when employing machine learning on electronic health record data. We provide a primer on machine learning and approaches commonly taken to address these challenges. To illustrate how these approaches have been applied to address antimicrobial resistance, we review the use of electronic health record data to construct machine learning models for predicting pathogen carriage or infection, optimizing empiric therapy, and aiding antimicrobial stewardship tasks. Machine learning shows promise in promoting the appropriate use of antimicrobials, although clinical deployment is limited. We conclude by describing potential dangers of, and barriers to, implementation of machine learning models in the clinic.

Incidental Coronary Artery Calcium: Opportunistic Screening of Previous Nongated Chest Computed Tomography Scans to Improve Statin Rates (NOTIFY-1 Project).

BACKGROUND: Coronary artery calcium (CAC) can be identified on nongated chest computed tomography (CT) scans, but this finding is not consistently incorporated into care. A deep learning algorithm enables opportunistic CAC screening of nongated chest CT scans. Our objective was to evaluate the effect of notifying clinicians and patients of incidental CAC on statin initiation. METHODS: NOTIFY-1 (Incidental Coronary Calcification Quality Improvement Project) was a randomized quality improvement project in the Stanford Health Care System. Patients without known atherosclerotic cardiovascular disease or a previous statin prescription were screened for CAC on a previous nongated chest CT scan from 2014 to 2019 using a validated deep learning algorithm with radiologist confirmation. Patients with incidental CAC were randomly assigned to notification of the primary care clinician and patient versus usual care. Notification included a patient-specific image of CAC and guideline recommendations regarding statin use. The primary outcome was statin prescription within 6 months. RESULTS: Among 2113 patients who met initial clinical inclusion criteria, CAC was identified by the algorithm in 424 patients. After chart review and additional exclusions were made, a radiologist confirmed CAC among 173 of 194 patients (89.2%) who were randomly assigned to notification or usual care. At 6 months, the statin prescription rate was 51.2% (44/86) in the notification arm versus 6.9% (6/87) with usual care (P<0.001). There was also more coronary artery disease testing in the notification arm (15.1% [13/86] versus 2.3% [2/87]; P=0.008). CONCLUSIONS: Opportunistic CAC screening of previous nongated chest CT scans followed by clinician and patient notification led to a significant increase in statin prescriptions. Further research is needed to determine whether this approach can reduce atherosclerotic cardiovascular disease events. REGISTRATION: URL: https://www. CLINICALTRIALS: gov; Unique identifier: NCT04789278.

The Study of the Epidemiology of Pediatric Hypertension Registry (SUPERHERO): Rationale and Methods.

Despite increasing prevalence of hypertension in youth and high adult cardiovascular mortality rates, the long-term consequences of youth-onset hypertension remain unknown. This is due to limitations of prior research such as small sample sizes, reliance on manual record review, and limited analytic methods that did not address major biases. The Study of the Epidemiology of Pediatric Hypertension (SUPERHERO) is a multisite retrospective Registry of youth evaluated by subspecialists for hypertension disorders. Sites obtain harmonized electronic health record data using standardized biomedical informatics scripts validated with randomized manual record review. Inclusion criteria are index visit for International Classification of Diseases Diagnostic Codes, 10th Revision (ICD-10 code)-defined hypertension disorder ≥January 1, 2015 and age <19 years. We exclude patients with ICD-10 code-defined pregnancy, kidney failure on dialysis, or kidney transplantation. Data include demographics, anthropomorphics, U.S. Census Bureau tract, histories, blood pressure, ICD-10 codes, medications, laboratory and imaging results, and ambulatory blood pressure. SUPERHERO leverages expertise in epidemiology, statistics, clinical care, and biomedical informatics to create the largest and most diverse registry of youth with newly diagnosed hypertension disorders. SUPERHERO's goals are to (i) reduce CVD burden across the life course and (ii) establish gold-standard biomedical informatics methods for youth with hypertension disorders.

Feasibility of structuring electronic health record data to facilitate real-world data research: ICAREdata methods applied to multicenter cancer clinical trials.

BACKGROUND: The use of electronic health record (EHR) data for research is limited by a lack of structure and a standard data model. The objective of the ICAREdata (Integrating Clinical Trials and Real-World Endpoints Data) project was to structure key research data elements in EHRs using a minimal Common Oncology Data Elements (mCODE) data model to extract and transmit data. METHODS: The ICAREdata project captured two EHR data elements essential to clinical trials: cancer disease status and treatment plan change. The project was implemented in clinical sites participating in Alliance for Clinical Trials in Oncology trials. Data were extracted from EHRs and sent by secure Fast Healthcare Interoperability Resource messaging (a standard for exchanging EHRs) to a database. Selected elements were compared with corresponding data from the trial's electronic data capture (EDC) system, Medidata Rave. RESULTS: By December 2023, data were extracted and transmitted from 10 sites for 35 patients, involving 367 clinical encounters across 15 clinical trials. Data through March 2023 demonstrated that concordance for the elements treatment plan change and cancer disease status was 79% and 34%, respectively. When disease evaluation was reported by both EHR and EDC (n = 15), there was 87% agreement on cancer disease status. CONCLUSIONS: Documentation, extraction, and aggregation of structured data elements in EHRs using mCODE and ICAREdata methods is feasible in multi-institutional cancer clinical trials. EDC as a reference data set allowed assessment of the completeness of EHR data capture. Future initiatives will focus on elements with shared definitions in clinical and research environments and efficient workflows. PLAIN LANGUAGE SUMMARY: Clinical trials use electronic case report forms to report data, and data must be manually entered on these forms, which is costly and time consuming. ICAREdata methods use structured, organized data from clinical trials that can be more easily shared instead having to enter free text into electronic health records.

The American Cancer Society National Lung Cancer Roundtable strategic plan: Optimizing strategies for lung nodule evaluation and management.

Lung nodules are frequently detected on low-dose computed tomography scans performed for lung cancer screening and incidentally detected on imaging performed for other reasons. There is wide variability in how lung nodules are managed by general practitioners and subspecialists, with high rates of guideline-discordant care. This may be due in part to the level of evidence underlying current practice guideline recommendations (primarily based on findings from uncontrolled studies of diagnostic accuracy). The primary aims of lung nodule management are to minimize harms of diagnostic evaluations while expediting the evaluation, diagnosis, and treatment of lung cancer. Potentially useful tools such as lung cancer probability calculators, automated methods to identify patients with nodules in the electronic health record, and multidisciplinary team evaluation are often underused due to limited availability, accessibility, and/or provider knowledge. Finally, relatively little attention has been paid to identifying and reducing disparities among individuals with screening-detected or incidentally detected lung nodules. This contribution to the American Cancer Society National Lung Cancer Roundtable Strategic Plan aims to identify and describe these knowledge gaps in lung nodule management and propose recommendations to advance clinical practice and research. Major themes that are addressed include improving the quality of evidence supporting lung nodule evaluation guidelines, strategically leveraging information technology, and placing emphasis on equitable approaches to nodule management. The recommendations outlined in this strategic plan, when carried out through interdisciplinary efforts with a focus on health equity, ultimately aim to improve early detection and reduce the morbidity and mortality of lung cancer. PLAIN LANGUAGE SUMMARY: Lung nodules may be identified on chest scans of individuals who undergo lung cancer screening (screening-detected nodules) or among patients for whom a scan was performed for another reason (incidental nodules). Although the vast majority of lung nodules are not lung cancer, it is important to have evidence-based, standardized approaches to the evaluation and management of a lung nodule. The primary aims of lung nodule management are to diagnose lung cancer while it is still in an early stage and to avoid unnecessary procedures and other harms.

Transforming the oncology data paradigm by creating, capturing, and retrieving structured cancer data at the point of care: A Mayo Clinic pilot.

INTRODUCTION: Structured data capture requires defined languages such as minimal Common Oncology Data Elements (mCODE). This pilot assessed the feasibility of capturing 5 mCODE categories (stage, disease status, performance status (PS), intent of therapy and intent to change therapy). METHODS: A tool (SmartPhrase) using existing and custom structured data elements was Built to capture 4 data categories (disease status, PS, intent of therapy and intent to change therapy) typically documented as free-text within notes. Existing functionality for stage was supported by the Build. Participant survey data, presence of data (per encounter), and time in chart were collected prior to go-live and repeat timepoints. The anticipated outcome was capture of >50% sustained over time without undue burden. RESULTS: Pre-intervention (5-weeks before go-live), participants had 1390 encounters (1207 patients). The median percent capture across all participants was 32% for stage; no structured data was available for other categories pre-intervention. During a 6-month pilot with 14 participants across three sites, 4995 encounters (3071 patients) occurred. The median percent capture across all participants and all post-intervention months increased to 64% for stage and 81%-82% for the other data categories post-intervention. No increase in participant time in chart was noted. Participants reported that data were meaningful to capture. CONCLUSIONS: Structured data can be captured (1) in real-time, (2) sustained over time without (3) undue provider burden using note-based tools. Our system is expanding the pilot, with integration of these data into clinical decision support, practice dashboards and potential for clinical trial matching.

Electronic health record-enabled routine assessment of medication adherence after solid organ transplantation: the time is now.

Medication nonadherence after solid organ transplantation is recognized as an important impediment to long-term graft survival. Yet, assessment of adherence is often not part of routine care. In this Personal Viewpoint, we call for the transplant community to consider implementing a systematic process to screen and assess medication adherence. We believe acceptable tools are available to support integrating adherence assessments into the electronic health record. Creating a standard assessment can be done efficiently and cost-effectively if we come together as a community. More importantly, such monitoring can improve outcomes and strengthen provider-patient relationships. We further discuss the practical challenges and potential rebuttals to our position.

Host genetic effects in pneumonia.

Given the coronavirus disease 2019 (COVID-19) pandemic, investigations into host susceptibility to infectious diseases and downstream sequelae have never been more relevant. Pneumonia is a lung disease that can cause respiratory failure and hypoxia and is a common complication of infectious diseases, including COVID-19. Few genome-wide association studies (GWASs) of host susceptibility and severity of pneumonia have been conducted. We performed GWASs of pneumonia susceptibility and severity in the Vanderbilt University biobank (BioVU) with linked electronic health records (EHRs), including Illumina Expanded Multi-Ethnic Global Array (MEGAEX)-genotyped European ancestry (EA, n= 69,819) and African ancestry (AA, n = 15,603) individuals. Two regions of large effect were identified: the CFTR locus in EA (rs113827944; OR = 1.84, p value = 1.2 × 10-36) and HBB in AA (rs334 [p.Glu7Val]; OR = 1.63, p value = 3.5 × 10-13). Mutations in these genes cause cystic fibrosis (CF) and sickle cell disease (SCD), respectively. After removing individuals diagnosed with CF and SCD, we assessed heterozygosity effects at our lead variants. Further GWASs after removing individuals with CF uncovered an additional association in R3HCC1L (rs10786398; OR = 1.22, p value = 3.5 × 10-8), which was replicated in two independent datasets: UK Biobank (n = 459,741) and 7,985 non-overlapping BioVU subjects, who are genotyped on arrays other than MEGAEX. This variant was also validated in GWASs of COVID-19 hospitalization and lung function. Our results highlight the importance of the host genome in infectious disease susceptibility and severity and offer crucial insight into genetic effects that could potentially influence severity of COVID-19 sequelae.

An Automated Electronic Health Record Score to Estimate Length of Stay and Readmission in Patients Undergoing Radical Cystectomy for Bladder Cancer.

PURPOSE: Patients treated with radical cystectomy experience a high rate of postoperative complications and frequent hospital readmissions. We sought to explore the utility of the Care Assessment Need (CAN) score, derived from electronic health data, to estimate the risk of these adverse clinical outcomes, thereby aiding patient counseling and informed treatment decision-making. MATERIALS AND METHODS: We retrospectively examined data from 982 patients with bladder cancer who underwent radical cystectomy between 2013 and 2018 within the national Veterans Health Administration system. We tested for associations between the preoperative CAN score and length of stay, discharge location, and readmission rates. RESULTS: We observed a correlation between higher CAN scores and longer hospital stays (adjusted relative risk = 1.03 [95% CI: 1.02-1.05]). An increased CAN score was also linked to greater odds of discharge to a skilled nursing facility or death (adjusted odds ratio = 1.16 [95% CI: 1.06-1.26]). Furthermore, the score was associated with hospital readmission at both 30 and 90 days postdischarge (adjusted HR = 1.03 [95% CI: 1.00-1.07] and 1.04 [95% CI: 1.00-1.07], respectively). CONCLUSIONS: The CAN score is associated with length of hospital stay, discharge to a skilled nursing facility, and readmission within 30 and 90 days after radical cystectomy. These findings highlight the potential of health care systems leveraging electronic health records for automatically calculating multidimensional tools, such as the CAN score, to identify patients at risk of adverse clinical outcomes after radical cystectomy.

Comparison of Clinical Decision Support Tools to Improve Pediatric Lipid Screening.

OBJECTIVE: To test whether different clinical decision support tools increase clinician orders and patient completions relative to standard practice and each other. STUDY DESIGN: A pragmatic, patient-randomized clinical trial in the electronic health record was conducted between October 2019 and April 2020 at Geisinger Health System in Pennsylvania, with 4 arms: care gap-a passive listing recommending screening; alert-a panel promoting and enabling lipid screen orders; both; and a standard practice-no guideline-based notification-control arm. Data were analyzed for 13 346 9- to 11-year-old patients seen within Geisinger primary care, cardiology, urgent care, or nutrition clinics, or who had an endocrinology visit. Principal outcomes were lipid screening orders by clinicians and completions by patients within 1 week of orders. RESULTS: Active (care gap and/or alert) vs control arm patients were significantly more likely (P < .05) to have lipid screening tests ordered and completed, with ORs ranging from 1.67 (95% CI 1.28-2.19) to 5.73 (95% CI 4.46-7.36) for orders and 1.54 (95% CI 1.04-2.27) to 2.90 (95% CI 2.02-4.15) for completions. Alerts, with or without care gaps listed, outperformed care gaps alone on orders, with odds ratios ranging from 2.92 (95% CI 2.32-3.66) to 3.43 (95% CI 2.73-4.29). CONCLUSIONS: Electronic alerts can increase lipid screening orders and completions, suggesting clinical decision support can improve guideline-concordant screening. The study also highlights electronic record-based patient randomization as a way to determine relative effectiveness of support tools. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04118348.

An electronic health record (EHR)-based risk calculator can predict fractures comparably to FRAX: a proof-of-concept study.

Information in the electronic health record (EHR), such as diagnoses, vital signs, utilization, medications, and laboratory values, may predict fractures well without the need to verbally ascertain risk factors. In our study, as a proof of concept, we developed and internally validated a fracture risk calculator using only information in the EHR. PURPOSE: Fracture risk calculators, such as the Fracture Risk Assessment Tool, or FRAX, typically lie outside the clinician workflow. Conversely, the electronic health record (EHR) is at the center of the clinical workflow, and many variables in the EHR could predict fractures without having to verbally ascertain FRAX risk factors. We sought to evaluate the utility of EHR variables to predict fractures and, as a proof of concept, to create an EHR-based fracture risk model. METHODS: Routine clinical data from 24,189 subjects presenting to primary care from 2010 to 2018 was utilized. Major osteoporotic fractures (MOFs) were captured by physician diagnosis codes. Data was split into training (n = 18,141) and test sets (n = 6048). We fit Cox regression models for candidate risk factors in the training set, and then created a global model using a backward stepwise approach. We then applied the model to the test set and compared the discrimination and calibration to FRAX. RESULTS: We found variables related to vital signs, utilization, diagnoses, medications, and laboratory values to be associated with incident MOF. Our final model included 19 variables, including age, BMI, Parkinson's disease, chronic kidney disease, and albumin levels. When applied to the test set, we found the discrimination (AUC 0.73 vs. 0.70, p = 0.08) and calibration were comparable to FRAX. CONCLUSION: Routinely collected data in EHR systems can generate adequate fracture predictions without the need to verbally ascertain fracture risk factors. In the future, this could allow for automated fracture prediction at the point of care to improve osteoporosis screening and treatment rates.

Measuring Documentation Burden in Healthcare.

BACKGROUND: The enactment of the Health Information Technology for Economic and Clinical Health Act and the wide adoption of electronic health record (EHR) systems have ushered in increasing documentation burden, frequently cited as a key factor affecting the work experience of healthcare professionals and a contributor to burnout. This systematic review aims to identify and characterize measures of documentation burden. METHODS: We integrated discussions with Key Informants and a comprehensive search of the literature, including MEDLINE, Embase, Scopus, and gray literature published between 2010 and 2023. Data were narratively and thematically synthesized. RESULTS: We identified 135 articles about measuring documentation burden. We classified measures into 11 categories: overall time spent in EHR, activities related to clinical documentation, inbox management, time spent in clinical review, time spent in orders, work outside work/after hours, administrative tasks (billing and insurance related), fragmentation of workflow, measures of efficiency, EHR activity rate, and usability. The most common source of data for most measures was EHR usage logs. Direct tracking such as through time-motion analysis was fairly uncommon. Measures were developed and applied across various settings and populations, with physicians and nurses in the USA being the most frequently represented healthcare professionals. Evidence of validity of these measures was limited and incomplete. Data on the appropriateness of measures in terms of scalability, feasibility, or equity across various contexts were limited. The physician perspective was the most robustly captured and prominently focused on increased stress and burnout. DISCUSSION: Numerous measures for documentation burden are available and have been tested in a variety of settings and contexts. However, most are one-dimensional, do not capture various domains of this construct, and lack robust validity evidence. This report serves as a call to action highlighting an urgent need for measure development that represents diverse clinical contexts and support future interventions.

Improving Identification of Patients Experiencing Homelessness in the Electronic Health Record: A Curated Registry Approach.

BACKGROUND: Identification of persons experiencing homelessness (PEH) within healthcare systems is critical to facilitate patient and population-level interventions to address health inequities. OBJECTIVE: We created an enhanced electronic health record (EHR) registry to improve identification of PEH within a safety net healthcare system. DESIGN: We compared patients identified as experiencing homelessness in 2021, stratified by method of identification (i.e., through registration data sources versus through new EHR registry criteria). MAIN MEASURES: Sociodemographic and clinical characteristics, healthcare utilization, engagement with homeless service providers, and mortality. KEY RESULTS: In total, 10,896 patients met the registry definition of a PEH; 30% more than identified through standard registration processes; 78% were identified through only one data source. Compared with those identified only through registration data, PEH identified through new registry criteria were more likely to be female (42% vs. 25%, p < 0.001), Hispanic/Latinx or Black/African American (30% versus 25% and 25% vs. 18%, p < 0.0001), and Medicaid or Medicare beneficiaries (74% vs. 67% and 16% vs.10%, respectively, p < 0.0001). New data sources also identified a higher proportion of patients: at extremes of age (16% < 18 years and 9% ≥ 65 years vs. 2% and 5%, respectively, p < 0.0001), with increased clinical risk (31% with CRG 6-9 vs. 18%, p < 0.0001), and with a mental health diagnosis (56% vs. 42%, p < 0.0001), and a lower proportion of patients with a substance use diagnosis (39% vs. 54%, p < 0.0001) or criminal justice involvement (8% vs. 15%, p < 0.0001). Newly identified patients were more likely to be engaged in primary care (OR 2.03, 95% CI 1.83-2.26) but less likely to be engaged with homeless service providers (OR 0.70, 95% CI 0.63-0.77). CONCLUSIONS: Commonly utilized methods of identifying PEH within healthcare systems may underestimate the population and introduce reporting biases. Recognizing alternate identification methods may more comprehensively and inclusively identify PEH for intervention.