Search

Background: While PTSD is commonly associated with multiple comorbidities, studies have yet to quantify the impact of these comorbidities on key clinical outcomes and HCRU. This study explored risks of emergency room (ER) visits, inpatient admissions (IA), suicidal ideation (SI), and treatment follow-up duration (FU), amongst PTSD patients with comorbid MDD and/or SUD., Methods: Using real-world data (RWD) generated by electronic health records accessed from the NeuroBlu database, a cohort of adolescent patients (12-17 yrs) was examined over a one-year study period following PTSD diagnosis., Results: 5794 patients were included in the cohort. Compared to patients with only PTSD (n = 3061), those with comorbid MDD (n = 1820) had greater odds of ER (4.5 times), IA (1.6 times), and FU (4.3 times). Those with comorbid SUD (n = 653) had greater odds of IA (4.5 times), shorter FU (34 days), and lower odds of ER (0.5 times). Both comorbidities (n = 260) had greater odds of ER (3.8 times), IA (2.6 times), SI (3.6 times), and shorter FU (12 days)., Limitations: These RWD had a high proportion of missingness. Health records of patients who changed service providers could not be accounted for in this study., Conclusions: Both MDD and SUD substantially elevated the risk of HCRU and suicidal ideation for PTSD patients., Competing Interests: Declaration of competing interest A. John Rush has received consulting fees from Compass Inc., Curbstone Consultant LLC, Emmes Corp., Evecxia Therapeutics, Inc., Holmusk Technologies, Inc., ICON, PLC, Johnson and Johnson (Janssen), Liva-Nova, MindStreet, Inc., Neurocrine Biosciences Inc., Otsuka-US;speaking fees from Liva-Nova, Johnson and Johnson (Janssen); and royalties from Wolters Kluwer Health, Guilford Press and the University of Texas Southwestern Medical Center, Dallas, TX (for the Inventory of Depressive Symptoms and its derivatives). He is also named co- inventor on two patents: U.S. Patent No. 7,795,033: Methods to Predict the Outcome of Treatment with Antidepressant Medication, Inventors: McMahon FJ, Laje G, Manji H, Rush AJ, Paddock S, Wilson AS; and U.S. Patent No. 7,906,283: Methods to Identify Patients at Risk of Developing Adverse Events During Treatment with Antidepressant Medication, Inventors:McMahon FJ, Laje G, Manji H, Rush AJ, Paddock S. Kelly Chan, Li Tong Low, Joshua Wong, and Sherwin Kuah are employees of Holmusk Technologies, Inc. when the research was undertaken., (Copyright © 2024. Published by Elsevier B.V.)

Purpose: Accurate identification of hepatic decompensation is essential for pharmacoepidemiologic research among patients with chronic liver disease., Methods: An algorithm using ≥ 1 inpatient or ≥ 2 outpatient International Classification of Diseases, 10th revision (ICD-10) codes for hepatic decompensation was developed in Veterans Health Administration data from October 2015 through July 2019. Medical records were reviewed by hepatologists to confirm cases. The positive predictive value (PPV) of the coding algorithm for confirmed hepatic decompensation was calculated., Results: Hepatic decompensation was confirmed in 149/185 records meeting the algorithm (PPV 81%; 95% CI, 70%, 90%). The most common hepatic decompensation diagnosis was ascites. Only 56% of confirmed cases had an accompanying diagnosis code for cirrhosis., Conclusions: Our ICD-10-based coding algorithm identified hepatic decompensation with high PPV in Veterans Health Administration data., (© 2024 John Wiley & Sons Ltd. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.)

Objective: Given the increasing proportion of patients and caregivers who use languages other than English (LOE) at our institution and across the U.S, we evaluated key workflow and outcome measures in our emergency department (ED) for patients and caregivers who use LOE., Methods: This was a retrospective, cross-sectional study of patients and caregivers who presented to a free-standing urban pediatric facility. We used electronic health record data (EHR) and interpreter usage log data for our analysis of language documentation, length of stay, and ED revisits. We assessed ED revisits within 72-h using a multivariable logistic regression model adjusting for whether a primary care provider (PCP) was listed in the EHR, whether discharge was close to or on the weekend, and insurance status. We restricted our analysis to low-acuity patient encounters (Emergency Severity Index (ESI) scores of 4 and 5) to limit confounding factors related to higher ESI scores., Results: We found that one in five patients and caregivers who use LOE had incorrect documentation of their language needs in the EHR. Using interpreter usage data to most accurately capture encounters using LOE, we found that patient encounters using LOE had a 38-min longer length of stay (LOS) and twice the odds of a 72-h ED revisit compared to encounters using English., Conclusion: These results highlight the need for better language documentation and understanding of factors contributing to extended stays and increased revisits for pediatric patients and caregivers who use LOE., Competing Interests: Declaration of competing interest The authors have no conflicts of interest to disclose related to this manuscript., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Objective: Symptoms are significant kind of phenotypes for managing and controlling of the burst of acute infectious diseases, such as COVID-19. Although patterns of symptom clusters and time series have been considered the high potential prediction factors for the prognosis of patients, the elaborated subtypes and their progression patterns based on symptom phenotypes related to the prognosis of COVID-19 patients still need be detected. This study aims to investigate patient subtypes and their progression patterns with distinct features of outcome and prognosis., Methods: This study included a total of 14,139 longitudinal electronic medical records (EMRs) obtained from four hospitals in Hubei Province, China, involving 2,683 individuals in the early stage of COVID-19 pandemic. A deep representation learning model was developed to help acquire the symptom profiles of patients. K-means clustering algorithm is used to divide them into distinct subtypes. Subsequently, symptom progression patterns were identified by considering the subtypes associated with patients upon admission and discharge. Furthermore, we used Fisher's test to identify significant clinical entities for each subtype., Results: Three distinct patient subtypes exhibiting specific symptoms and prognosis have been identified. Particularly, Subtype 0 includes 44.2% of the whole and is characterized by poor appetite, fatigue and sleep disorders; Subtype 1 includes 25.6% cases and is characterized by confusion, cough with bloody sputum, encopresis and urinary incontinence; Subtype 2 includes 30.2% cases and is characterized by dry cough and rhinorrhea. These three subtypes demonstrate significant disparities in prognosis, with the mortality rates of 4.72%, 8.59%, and 0.25% respectively. Furthermore, symptom cluster progression patterns showed that patients with Subtype 0 who manifest dark yellow urine, chest pain, etc. in the admission stage exhibit an elevated risk of transforming into the more severe subtypes with poor outcome, whereas those presenting with nausea and vomiting tend to incline towards entering the milder subtype., Conclusion: This study has proposed a clinical meaningful approach by utilizing the deep representation learning and real-world EMR data containing symptom phenotypes to identify the COVID-19 subtypes and their progression patterns. The results would be potentially useful to help improve the precise stratification and management of acute infectious diseases., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: Prediction of mortality is very important for care planning in hospitalized patients with dementia and artificial intelligence has the potential to serve as a solution; however, this issue remains unclear. Thus, this study was conducted to elucidate this matter., Methods: We identified 10,573 hospitalized patients aged ≥ 45 years with dementia from three hospitals between 2010 and 2020 for this study. Utilizing 44 feature variables extracted from electronic medical records, an artificial intelligence (AI) model was constructed to predict death during hospitalization. The data was randomly separated into 70 % training set and 30 % testing set. We compared predictive accuracy among six algorithms including logistic regression, random forest, extreme gradient boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), multilayer perceptron (MLP), and support vector machine (SVM). Additionally, another set of data collected in 2021 was used as the validation set to assess the performance of six algorithms., Results: The average age was 79.8 years, with females constituting 54.5 % of the sample. The in-hospital mortality rate was 6.7 %. LightGBM exhibited the highest area under the curve (0.991) for predicting mortality compared to other algorithms (XGBoost: 0.987, random forest: 0.985, logistic regression: 0.918, MLP: 0.898, SVM: 0.897). The accuracy, sensitivity, positive predictive value, and negative predictive value of LightGBM were 0.943, 0.944, 0.943, 0.542, and 0.996, respectively. Among the features in LightGBM, the three most important variables were the Glasgow Coma Scale, respiratory rate, and blood urea nitrogen. In the validation set, the area under the curve of LightGBM reached 0.753., Conclusions: The AI prediction model demonstrates strong accuracy in predicting in-hospital mortality among patients with dementia, suggesting its potential implementation to enhance future care quality., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: Assessing disease progression among patients with uncontrolled hypertension is important for identifying opportunities for intervention., Objective: We aim to develop and validate 2 models, one to predict sustained, uncontrolled hypertension (≥2 blood pressure [BP] readings ≥140/90 mm Hg or ≥1 BP reading ≥180/120 mm Hg) and one to predict hypertensive crisis (≥1 BP reading ≥180/120 mm Hg) within 1 year of an index visit (outpatient or ambulatory encounter in which an uncontrolled BP reading was recorded)., Methods: Data from 142,897 patients with uncontrolled hypertension within Atrium Health Greater Charlotte in 2018 were used. Electronic health record-based predictors were based on the 1-year period before a patient's index visit. The dataset was randomly split (80:20) into a training set and a validation set. In total, 4 machine learning frameworks were considered: L2-regularized logistic regression, multilayer perceptron, gradient boosting machines, and random forest. Model selection was performed with 10-fold cross-validation. The final models were assessed on discrimination (C-statistic), calibration (eg, integrated calibration index), and net benefit (with decision curve analysis). Additionally, internal-external cross-validation was performed at the county level to assess performance with new populations and summarized using random-effect meta-analyses., Results: In internal validation, the C-statistic and integrated calibration index were 0.72 (95% CI 0.71-0.72) and 0.015 (95% CI 0.012-0.020) for the sustained, uncontrolled hypertension model, and 0.81 (95% CI 0.79-0.82) and 0.009 (95% CI 0.007-0.011) for the hypertensive crisis model. The models had higher net benefit than the default policies (ie, treat-all and treat-none) across different decision thresholds. In internal-external cross-validation, the pooled performance was consistent with internal validation results; in particular, the pooled C-statistics were 0.70 (95% CI 0.69-0.71) and 0.79 (95% CI 0.78-0.81) for the sustained, uncontrolled hypertension model and hypertensive crisis model, respectively., Conclusions: An electronic health record-based model predicted hypertensive crisis reasonably well in internal and internal-external validations. The model can potentially be used to support population health surveillance and hypertension management. Further studies are needed to improve the ability to predict sustained, uncontrolled hypertension., (© Hieu Minh Nguyen, William Anderson, Shih-Hsiung Chou, Andrew McWilliams, Jing Zhao, Nicholas Pajewski, Yhenneko Taylor. Originally published in JMIR Medical Informatics (https://medinform.jmir.org).)

Background: Health information technology (health IT) has revolutionized health care in the United States through interoperable clinical care data exchange, e-prescribing, electronic public health reporting, and electronic patient access to health information., Objective: This study aims to examine progress in health IT adoption and its alignment with the Office of the Assistant Secretary for Technology Policy/Office of the National Coordinator for Health IT (ASTP's) mission to enhance health care through data access and exchange., Methods: This study leverages data on end users of health IT to capture trends in engagement in interoperable clinical care data exchange (ability to find, send, receive, and integrate information from outside organizations), e-prescribing, electronic public health reporting, and capabilities to enable patient access to electronic health information. Data were primarily sourced from the American Hospital Association Annual Survey IT Supplement (2008 to 2023), Surescripts e-prescribing use data (2008 to 2023), the National Cancer Institute's Health Information National Trends Survey (2014 to 2022), and the National Center for Health Statistics' National Electronic Health Records Survey (2009 to 2023)., Results: Since 2009, there has been a 10-fold increase in electronic health record (EHR) use among hospitals and a 5-fold increase among physicians. This enabled the interoperable exchange of electronic health information, e- prescribing, electronic public health data exchange, and the means for patients and their caregivers to access crucial personal health information digitally. As of 2023, 70% of hospitals are interoperable, with many providers integrated within EHR systems. Nearly all pharmacies and 92% of prescribers possess e-prescribing capabilities, an 85%-point increase since 2008. In 2013, 40% of hospitals and one-third of physicians allowed patients to view their online medical records. Patient access has improved, with 97% of hospitals and 65% of physicians possessing EHRs that enable patients to access their online medical records. As of 2022, three-fourths of individuals report being offered access to patient portals, and over half (57%) report engaging with their health information through their patient portal. Electronic public health reporting has also seen an increase, with most hospitals and physicians actively engaged in key reporting types., Conclusions: Federal incentives have contributed to the widespread adoption of EHRs and broad digitization in health care, while efforts to promote interoperability have encouraged collaboration across health care entities. As a result, interoperable clinical care data exchange, e-prescribing, electronic public health reporting, and patient access to health information have grown substantially over the past quarter century and have been shown to improve health care outcomes. However, interoperability hurdles, usability issues, data security concerns, and inequitable patient access persist. Addressing these issues will require collaborative efforts among stakeholders to promote data standardization, implement governance structures, and establish robust health information exchange networks., (©Wesley Barker, Wei Chang, Jordan Everson, Meghan Gabriel, Vaishali Patel, Chelsea Richwine, Catherine Strawley. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 28.10.2024.)

Background: Frailty is a widespread geriatric syndrome among older adults, including hospitalized older inpatients. Some countries use electronic frailty measurement tools to identify frailty at the primary care level, but this method has rarely been investigated during hospitalization in acute care hospitals. An electronic frailty measurement instrument based on population-based hospital electronic health records could effectively detect frailty, frailty-related problems, and complications as well be a clinical alert. Identifying frailty among older adults using existing patient health data would greatly aid the management and support of frailty identification and could provide a valuable public health instrument without additional costs., Objective: We aim to explore a data-driven frailty measurement instrument for older adult inpatients using data routinely collected at hospital admission and discharge., Methods: A retrospective electronic patient register study included inpatients aged ≥65 years admitted to and discharged from a public hospital between 2015 and 2017. A dataset of 53,690 hospitalizations was used to customize this data-driven frailty measurement instrument inspired by the Edmonton Frailty Scale developed by Rolfson et al. A 2-step hierarchical cluster procedure was applied to compute e-Frail-CH (Switzerland) scores at hospital admission and discharge. Prevalence, central tendency, comparative, and validation statistics were computed., Results: Mean patient age at admission was 78.4 (SD 7.9) years, with more women admitted (28,018/53,690, 52.18%) than men (25,672/53,690, 47.81%). Our 2-step hierarchical clustering approach computed 46,743 inputs of hospital admissions and 47,361 for discharges. Clustering solutions scored from 0.5 to 0.8 on a scale from 0 to 1. Patients considered frail comprised 42.02% (n=19,643) of admissions and 48.23% (n=22,845) of discharges. Within e-Frail-CH's 0-12 range, a score ≥6 indicated frailty. We found a statistically significant mean e-Frail-CH score change between hospital admission (5.3, SD 2.6) and discharge (5.75, SD 2.7; P<.001). Sensitivity and specificity cut point values were 0.82 and 0.88, respectively. The area under the receiver operating characteristic curve was 0.85. Comparing the e-Frail-CH instrument to the existing Functional Independence Measure (FIM) instrument, FIM scores indicating severe dependence equated to e-Frail-CH scores of ≥9, with a sensitivity and specificity of 0.97 and 0.88, respectively. The area under the receiver operating characteristic curve was 0.92. There was a strong negative association between e-Frail-CH scores at hospital discharge and FIM scores (r s =-0.844; P<.001)., Conclusions: An electronic frailty measurement instrument was constructed and validated using patient data routinely collected during hospitalization, especially at admission and discharge. The mean e-Frail-CH score was higher at discharge than at admission. The routine calculation of e-Frail-CH scores during hospitalization could provide very useful clinical alerts on the health trajectories of older adults and help select interventions for preventing or mitigating frailty., (©Boris Wernli, Henk Verloo, Armin von Gunten, Filipa Pereira. Originally published in JMIR Aging (https://aging.jmir.org), 28.10.2024.)

Background: United States military policies regarding service by transgender service members have shifted several times within the past decade. The relationships between policy changes and electronic health record documentation of gender dysphoria, a current and historic policy requisite for gender affirming care receipt, in active duty service members remain unknown., Methods: Bayesian estimator of abrupt change, seasonality, and trend models identified changepoints in the proportion of service members who had new and then historical medical record documentation consistent with gender dysphoria from January 2015 to August 2022. Changepoints were evaluated as they related to salient military policy-related events., Results: Approximately 3,853 active duty and activated National Guard or Reserve service members received a documented diagnosis corresponding to gender dysphoria from January 2015 to August 2022. Four significant changepoints were identified across both time series. Salient historical events that occurred during the changepoint periods were identified for contextualization., Conclusions: Clinical documentation of gender dysphoria oscillated with changes to policies and public statements by government leaders, which may in turn, impact military recruitment and retention. This study highlights the need for equitable policies that optimize the strength of a diverse military force. Equity-oriented monitoring is needed to continually examine the impact of military service policies on readiness and retention to support actionable, data-driven improvements to policies and their implementation., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

Background: Missing data in confounding variables present a frequent challenge in generating evidence using real-world data, including electronic health records (EHR). Our objective was to apply a recently published toolkit for characterizing missing data patterns and based on the toolkit results about likely missingness mechanisms, illustrate the decision-making process for analyses in an empirical case example., Methods: We utilized the Structural Missing Data Investigations (SMDI) toolkit to characterize missing data patterns in the context of a pharmacoepidemiology study comparing cardiovascular outcomes of initiating sodium-glucose-cotransporter-2 inhibitors (SGLT2i) and dipeptidyl peptidase-4 inhibitors (DPP-4i) among older adults. The study used a linked EHR-Medicare claims dataset from Duke Health patients (2015-2017), focusing on partially observed confounders from EHR data (HbA1c lab and body mass index [BMI] values). Our analysis incorporated SMDI's descriptive functions and diagnostic tests to explore missingness patterns and determine missingness mitigation approaches. We used findings from these investigations to inform estimation of adjusted hazard ratios comparing the two classes of medications., Results: High levels of missingness were noted for important confounding variables including HbA1c (63.6%) and BMI (16.5%). Diagnostic tests resulted in output that described: 1) the distributions of patient characteristics, exposure, and outcome between patients with or without an observed value of the partially observed covariate, 2) the ability to predict missingness based on observed covariates, and 3) estimate if the missingness of a partially observed covariate is differential with respect to the outcome. There was evidence that missingness could be sufficiently described using observed data, which allowed multiple imputation by chained equations using random forests to address missing confounder data in estimating treatment effects. Multiple imputation resulted in improved alignment of effect estimates with previous studies., Conclusions: We were able to demonstrate the practical application of the SMDI toolkit in a real-world setting. Application of the SMDI toolkit and the resulting insights of potential missingness patterns can inform the choice of appropriate analytic methods and increase transparency of research methods in handling missing data. This type of approach can inform analytic decision making and may increase our ability to generate evidence from real-world data., (© 2024. The Author(s).)

Background: Many hospitalised patients require rehabilitation during recovery from acute illness. We use routine data from Electronic Health Records (EHR) to report the quantity and intensity of rehabilitation required to achieve hospital discharge, comparing patients with and without COVID-19., Methods: We performed a retrospective cohort study of consecutive adults in whom COVID-19 testing was undertaken between March 2020 and August 2021 across three acute hospitals in Scotland. We defined rehabilitation contacts (physiotherapy, occupational therapy, dietetics and speech and language therapy) from timestamped EHR data and determined contact time from a linked workforce planning dataset. Our aim was to clarify rehabilitation required to achieve hospital discharge and so we excluded patients who died during their admission, and those who did not require rehabilitation (fewer than two specialist contacts). The primary outcome was total rehabilitation time. Secondary outcomes included the number of contacts, admission to first contact, and rehabilitation minutes per day. A multivariate regression analysis for identifying patient characteristics associated with rehabilitation time included age, sex, comorbidities, and socioeconomic status., Results: We included 11,591 consecutive unique patient admissions (76 [63,85] years old, 56% female), of which 651 (6%) were with COVID-19, and 10,940 (94%) were admissions with negative testing. There were 128,646 rehabilitation contacts. Patients with COVID-19 received more than double the rehabilitation time compared to those without (365 [165, 772] vs 170 [95, 350] mins, p<0.001), and this was delivered over more specialist contacts (12 [6, 25] vs 6 [3, 11], p<0.001). Admission to first rehabilitation contact was later in patients with COVID-19 (3 [1, 5] vs 2 [1, 4] days from admission). Overall, patients with COVID-19 received fewer minutes of rehabilitation per day of admission (14.1 [9.8, 18.7] vs 15.6 [10.6, 21.3], p<0.001). In our regression analyses, older age and COVID-19 were associated with increased rehabilitation time., Conclusions: Patients with COVID received more rehabilitation contact time than those without COVID, but this was delivered less intensively and was commenced later in an admission. Rehabilitation data derived from the EHR represents a novel measure of delivered hospital care., (© 2024. The Author(s).)

Background: Electronic health records (EHRs) commonly contain patient addresses that provide valuable data for geocoding and spatial analysis, enabling more comprehensive descriptions of individual patients for clinical purposes. Despite the widespread use of EHRs in clinical decision support and interventions, no systematic review has examined the extent to which spatial analysis is used to characterize patient phenotypes., Objective: This study reviews advanced spatial analyses that used individual-level health data from EHRs within the United States to characterize patient phenotypes., Methods: We systematically evaluated English-language, peer-reviewed studies from the PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar databases from inception to August 20, 2023, without imposing constraints on study design or specific health domains., Results: A substantial proportion of studies (>85%) were limited to geocoding or basic mapping without implementing advanced spatial statistical analysis, leaving only 49 studies that met the eligibility criteria. These studies used diverse spatial methods, with a predominant focus on clustering techniques, while spatiotemporal analysis (frequentist and Bayesian) and modeling were less common. A noteworthy surge (n=42, 86%) in publications was observed after 2017. The publications investigated a variety of adult and pediatric clinical areas, including infectious disease, endocrinology, and cardiology, using phenotypes defined over a range of data domains such as demographics, diagnoses, and visits. The primary health outcomes investigated were asthma, hypertension, and diabetes. Notably, patient phenotypes involving genomics, imaging, and notes were limited., Conclusions: This review underscores the growing interest in spatial analysis of EHR-derived data and highlights knowledge gaps in clinical health, phenotype domains, and spatial methodologies. We suggest that future research should focus on addressing these gaps and harnessing spatial analysis to enhance individual patient contexts and clinical decision support., (©Abolfazl Mollalo, Bashir Hamidi, Leslie A Lenert, Alexander V Alekseyenko. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 15.10.2024.)

Background: Studies have shown that patients have difficulty understanding medical jargon in electronic health record (EHR) notes, particularly patients with low health literacy. In creating the NoteAid dictionary of medical jargon for patients, a panel of medical experts selected terms they perceived as needing definitions for patients., Objective: This study aims to determine whether experts and laypeople agree on what constitutes medical jargon., Methods: Using an observational study design, we compared the ability of medical experts and laypeople to identify medical jargon in EHR notes. The laypeople were recruited from Amazon Mechanical Turk. Participants were shown 20 sentences from EHR notes, which contained 325 potential jargon terms as identified by the medical experts. We collected demographic information about the laypeople's age, sex, race or ethnicity, education, native language, and health literacy. Health literacy was measured with the Single Item Literacy Screener. Our evaluation metrics were the proportion of terms rated as jargon, sensitivity, specificity, Fleiss κ for agreement among medical experts and among laypeople, and the Kendall rank correlation statistic between the medical experts and laypeople. We performed subgroup analyses by layperson characteristics. We fit a beta regression model with a logit link to examine the association between layperson characteristics and whether a term was classified as jargon., Results: The average proportion of terms identified as jargon by the medical experts was 59% (1150/1950, 95% CI 56.1%-61.8%), and the average proportion of terms identified as jargon by the laypeople overall was 25.6% (22,480/87,750, 95% CI 25%-26.2%). There was good agreement among medical experts (Fleiss κ=0.781, 95% CI 0.753-0.809) and fair agreement among laypeople (Fleiss κ=0.590, 95% CI 0.589-0.591). The beta regression model had a pseudo-R 2 of 0.071, indicating that demographic characteristics explained very little of the variability in the proportion of terms identified as jargon by laypeople. Using laypeople's identification of jargon as the gold standard, the medical experts had high sensitivity (91.7%, 95% CI 90.1%-93.3%) and specificity (88.2%, 95% CI 86%-90.5%) in identifying jargon terms., Conclusions: To ensure coverage of possible jargon terms, the medical experts were loose in selecting terms for inclusion. Fair agreement among laypersons shows that this is needed, as there is a variety of opinions among laypersons about what is considered jargon. We showed that medical experts could accurately identify jargon terms for annotation that would be useful for laypeople., (©John P Lalor, David A Levy, Harmon S Jordan, Wen Hu, Jenni Kim Smirnova, Hong Yu. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 15.10.2024.)

Background: Optimising maintenance drug treatment selection for people with bipolar disorder is challenging. There is some evidence that clinical and demographic features may predict response to lithium. However, attempts to personalise treatment choice have been limited., Method: We aimed to determine if machine learning methods applied to electronic health records could predict differential response to lithium or olanzapine. From electronic United Kingdom primary care records, we extracted a cohort of individuals prescribed either lithium (19,106 individuals) or olanzapine (12,412) monotherapy. Machine learning models were used to predict successful monotherapy maintenance treatment, using 113 clinical and demographic variables, 8,017 (41.96%) lithium responders and 3,831 (30.87%) olanzapine responders., Results: We found a quantitative structural difference in that lithium maintenance responders were weakly predictable in our holdout sample, consisting of the 5% of patients with the most recent exposure. Age at first diagnosis, age at first treatment and the time between these were the most important variables in all models., Discussion: Even if we failed to predict successful monotherapy olanzapine treatment, and so to definitively separate lithium vs . olanzapine responders, the characterization of the two groups may be used for classification by proxy. This can, in turn, be useful for establishing maintenance therapy. The further exploration of machine learning methods on EHR data for drug treatment selection could in the future play a role for clinical decision support. Signals in the data encourage further experiments with larger datasets to definitively separate lithium vs . olanzapine responders., Competing Interests: Joseph F. Hayes has received consultancy fees from juli Health and Wellcome Trust. No other authors declare competing interests., (© 2024 Hayes et al.)

Rare genetic diseases (RGDs) affect a significant number of individuals, particularly in pediatric populations. This study investigates the efficacy of identifying RGD diagnoses through electronic health records (EHRs) and natural language processing (NLP) tools, and analyzes the prevalence of identified RGDs for potential underdiagnosis at Cincinnati Children's Hospital Medical Center (CCHMC). EHR data from 659,139 pediatric patients at CCHMC were utilized. Diagnoses corresponding to RGDs in Orphanet were identified using rule-based and machine learning-based NLP methods. Manual evaluation assessed the precision of the NLP strategies, with 100 diagnosis descriptions reviewed for each method. The rule-based method achieved a precision of 97.5% (95% CI: 91.5%, 99.4%), while the machine-learning-based method had a precision of 73.5% (95% CI: 63.6%, 81.6%). A manual chart review of 70 randomly selected patients with RGD diagnoses confirmed the diagnoses in 90.3% (95% CI: 82.0%, 95.2%) of cases. A total of 37,326 pediatric patients were identified with 977 RGD diagnoses based on the rule-based method, resulting in a prevalence of 5.66% in this population. While a majority of the disorders showed a higher prevalence at CCHMC compared with Orphanet, some diseases, such as 1p36 deletion syndrome, indicated potential underdiagnosis. Analyses further uncovered disparities in RGD prevalence and age of diagnosis across gender and racial groups. This study demonstrates the utility of employing EHR data with NLP tools to systematically investigate RGD diagnoses in large cohorts. The identified disparities underscore the need for enhanced approaches to guarantee timely and accurate diagnosis and management of pediatric RGDs., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Electronic medical records (EMRs) are important for rapidly compiling information to determine disease characteristics (eg, symptoms) and risk factors (eg, underlying comorbidities, medications) for disease-related outcomes. To assess EMR data accuracy, agreement between EMR abstractions and patient interviews was evaluated. Symptoms, medical history, and medication use among patients with COVID-19 collected from EMRs and patient interviews were compared using overall agreement (ie, same answer in EMR and interview), reported agreement (yes answer in both EMR and interview among those who reported yes in either), and κ statistics. Overall, patients reported more symptoms in interviews than in EMR abstractions. Overall agreement was high (≥50% for 20 of 23 symptoms), but only subjective fever and dyspnea had reported agreement of ≥50%. The κ statistics for symptoms were generally low. Reported medical conditions had greater agreement with all condition categories (n = 10 of 10) having ≥50% overall agreement and half (n = 5 of 10) having ≥50% reported agreement. More nonprescription medications were reported in interviews than in EMR abstractions, leading to low reported agreement (28%). Discordance was observed for symptoms, medical history, and medication use between EMR abstractions and patient interviews. Investigations using EMRs to describe clinical characteristics and identify risk factors should consider the potential for incomplete data, particularly for symptoms and medications., (Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health 2024.)

Background: This study aimed to develop and validate claims-based algorithms for identifying hospitalized patients with coronavirus disease 2019 (COVID-19) and disease severity., Methods: We used claims data including all patients at the National Center for Global and Medicine Hospital between January 1, 2020, and December 31, 2021. The claims-based algorithms for three statuses with COVID-19 (hospitalizations, moderate or higher status, and severe status) were developed using diagnosis codes (International Classification of Diseases, 10 th revision code: U07.1, B34.2) and relevant medical procedure code. True cases were determined using the COVID-19 inpatient registry and electronic health records. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for each algorithm at 6-month intervals., Results: Of the 75,711 total patients, the number of true cases was 1,192 for hospitalizations, 622 for moderate or higher status, and 55 for severe status. The diagnosis code-only algorithm for hospitalization had sensitivities 90.4% to 94.9% and PPVs 9.3% to 19.4%. Among the algorithms consisting of both diagnosis codes and procedure codes, high sensitivity and PPV were observed during the following periods: 93.9% and 97.1% for hospitalization (January-June 2021), 90.4% and 87.5% for moderate or higher status (July-December 2021), and 92.3% and 85.7% for severe status (July-December 2020), respectively. Almost all algorithms had specificities and NPVs of approximately 99%., Conclusion: The diagnosis code-only algorithm for COVID-19 hospitalization showed low validity throughout the study period. The algorithms for hospitalizations, moderate or higher status, and severe status with COVID-19, consisting of both diagnosis codes and procedure codes, showed high validity in some periods.

Background: While the number of digital therapeutics (DTx) has proliferated, there is little real-world research on the characteristics of providers recommending DTx, their recommendation behaviors, or the characteristics of patients receiving recommendations in the clinical setting., Objective: The aim of this study was to characterize the clinical and demographic characteristics of patients receiving DTx recommendations and describe provider characteristics and behaviors regarding DTx., Methods: This retrospective cohort study used electronic health record data from a large, integrated health care delivery system. Demographic and clinical characteristics of adult patients recommended versus not recommended DTx by a mental health provider between May 2020 and December 2021 were examined. A cross-sectional survey of mental health providers providing these recommendations was conducted in December 2022 to assess the characteristics of providers and recommendation behaviors related to DTx. Parametric and nonparametric tests were used to examine statistical significance between groups., Results: Of 335,250 patients with a mental health appointment, 53,546 (16%) received a DTx recommendation. Patients recommended to DTx were younger, were of Asian or Hispanic race or ethnicity, were female, were without medical comorbidities, and had commercial insurance compared to those without a DTx recommendation (P<.001). More patients receiving a DTx recommendation had anxiety or adjustment disorder diagnoses, but less had depression, bipolar, or psychotic disorder diagnoses (P<.001) versus matched controls not recommended to DTx. Overall, depression and anxiety symptom scores were lower in patients recommended to DTx compared to matched controls not receiving a recommendation, although female patients had a higher proportion of severe depression and anxiety scores compared to male patients. Provider survey results indicated a higher proportion of nonprescribers recommended DTx to patients compared to prescribers (P=.008). Of all providers, 29.4% (45/153) reported using the suggested internal electronic health record-based tools (eg, smart text) to recommend DTx, and of providers recommending DTx resources to patients, 64.1% (98/153) reported they follow up with patients to inquire on DTx benefits. Only 38.4% (58/151) of respondents report recommending specific DTx modules, and of those, 58.6% (34/58) report following up on the impact of these specific modules., Conclusions: DTx use in mental health was modest and varied by patient and provider characteristics. Providers do not appear to actively engage with these tools and integrate them into treatment plans. Providers, while expressing interest in potential benefits from DTx, may view DTx as a passive strategy to augment traditional treatment for select patients., (© Samuel J Ridout, Kathryn K Ridout, Teresa Y Lin, Cynthia I Campbell. Originally published in JMIR Mental Health (https://mental.jmir.org).)

Objective: We analyzed a natural language processing (NLP) toolkit's ability to classify unstructured EHR data by psychiatric diagnosis. Expertise can be a barrier to using NLP. We employed an NLP toolkit (CLARK) created to support studies led by investigators with a range of informatics knowledge. Methods: The EHR of 652 patients were manually reviewed to establish Depression and Substance Use Disorder (SUD) labeled datasets, which were split into training and evaluation datasets. We used CLARK to train depression and SUD classification models using training datasets; model performance was analyzed against evaluation datasets. Results: The depression model accurately classified 69% of records (sensitivity = 0.68, specificity = 0.70, F1 = 0.68). The SUD model accurately classified 84% of records (sensitivity = 0.56, specificity = 0.92, F1 = 0.57). Conclusion: The depression model performed a more balanced job, while the SUD model's high specificity was paired with a low sensitivity. NLP applications may be especially helpful when combined with a confidence threshold for manual review., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Importance: Suicide is a leading cause of death among young people. Accurate detection of self-injurious thoughts and behaviors (SITB) underpins equity in youth suicide prevention., Objectives: To compare methods of detecting SITB using structured electronic health information and measure algorithmic performance across demographics., Design, Setting, and Participants: This cross-sectional study used medical records among youths aged 6 to 17 years with at least 1 mental health-related emergency department (ED) visit in 2017 to 2019 to an academic health system in Southern California serving 787 000 unique individuals each year. Analyses were conducted between January and September 2023., Exposures: Multiexpert electronic health record review ascertained the presence of SITB using the Columbia Classification Algorithm of Suicide Assessment. Random forest classifiers with nested cross-validation were developed using (1) International Statistical Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes for nonfatal suicide attempt and self-harm and chief concern and (2) all available structured data, including diagnoses, medications, and laboratory tests., Main Outcome and Measures: Detection performance was assessed overall and stratified by age group, sex, and race and ethnicity., Results: The sample comprised 2702 unique youths with an MH-related ED visit (1384 youths who identified as female [51.2%]; 131 Asian [4.8%], 266 Black [9.8%], 719 Hispanic [26.6%], 1319 White [48.8%], and 233 other race [8.6%]; median [IQR] age, 14 [12-16] years), including 898 children and 1804 adolescents. Approximately half of visits were related to SITB (1286 visits [47.6%]). Sensitivity of SITB detection using only codes and chief concern varied by age group and increased until age 15 years (6-9 years: 59.3% [95% CI, 48.5%-69.5%]; 10-12 years: 69.0% [95% CI, 63.8%-73.9%]; 13-15 years: 88.4% [95% CI, 85.1%-91.2%]; 16-17 years: 83.1% [95% CI, 79.1%-86.6%]), while specificity remained constant. The area under the receiver operating characteristic curve (AUROC) was lower among preadolescents (0.841 [95% CI, 0.815-0.867]) and male (0.869 [95% CI, 0.848-0.890]), Black (0.859 [95% CI, 0.813-0.905]), and Hispanic (0.861 [95% CI, 0.831-0.891]) youths compared with adolescents (0.925 [95% CI, 0.912-0.938]), female youths (0.923 [95% CI, 0.909-0.937]), and youths of other races and ethnicities (eg, White: 0.901 [95% CI, 0.884-0.918]). Augmented classification (ie, using all available structured data) outperformed classification with codes and chief concern alone (AUROC, 0.975 [95% CI, 0.968-0.980] vs 0.894 [95% CI, 0.882-0.905]; P < .001)., Conclusions and Relevance: In this study, diagnostic codes and chief concern underestimated SITB prevalence, particularly among minoritized youths. These results suggest that priority on algorithmic fairness in suicide prevention strategies must extend to accurate detection of youths with suicide-related emergencies.

Objective: Electronic Health Records (EHRs) are now an integral part of health systems in middle and high-income countries despite recognized deficits in the digital competencies of Healthcare Professionals (HCPs). Therefore, we undertook a scoping review of factors influencing compliance with EHR data privacy policies. Methods: Seven databases revealed 27 relevant studies, covering a range of countries, professional groups, and research methods. The diverse nature of these factors meant that 18 separate theoretical frameworks representing technology-acceptance to behavioral psychology were used to interpret these. Results: The predominant factors influencing compliance with EHR data privacy policies included confidence and competence to comply, perceived ease of use, facilitatory environmental factors, perceived usefulness, fear that non-compliance would be detected and/or punished and the expectations of others. Conclusion: Human factors such as attitudes, social pressure, confidence, and perceived usefulness are as important as technical factors and must be addressed to improve compliance., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Background: Cardiovascular diseases are a common cause of death among patients with type 2 diabetes (T2DM). Major adverse cardiovascular event (MACE) risks can be significantly reduced under adequate glycemic control (GC). This study aims to identify factors that influence MACE risk among patients with T2DM, including Hemoglobin A1c variability score (HVS) and early use of MACE-preventive glucose-lowering medications (GLMs)., Methods: We conducted a longitudinal cohort study to retrospectively review electronic health records between 2011 and 2022. Patients with T2DM ≥18 years without previous stroke or acute myocardial infarction (AMI) were included. Cox regression was utilized to investigate MACE risk factors and compare MACE risk reduction associated with early use of MACE-preventive GLMs., Results: A total of 19 685 subjects were included, with 5431 having MACE, including 4453 strokes, 977 AMI, and 1 death. There were 11 123 subjects with good baseline GC. Subjects with good baseline GC had 0.837 (confidence interval [CI]: 0.782-0.895) times lower MACE risk than their counterpart. Subjects with a single MACE-preventive GLM at baseline with continuous use >365 days showed a decreased MACE hazard ratio (0.681; CI: 0.635-0.731). Among all MACE-preventive GLMs, semaglutide provided a more significant MACE-preventive effect., Conclusions: This study identified that GLM, early GC, and HVS are MACE determinants among patients with T2DM. Novel GLM, adequate GC, and reduction of HVS can benefit MACE outcomes., (© 2024 The Author(s). Journal of Diabetes published by Ruijin Hospital, Shanghai Jiaotong University School of Medicine and John Wiley & Sons Australia, Ltd.)

Pharmacogenomic (PGx) biomarkers integrated using machine learning can be embedded within the electronic health record (EHR) to provide clinicians with individualized predictions of drug treatment outcomes. Currently, however, drug alerts in the EHR are largely generic (not patient-specific) and contribute to increased clinician stress and burnout. Improving the usability of PGx alerts is an urgent need. Therefore, this work aimed to identify principles for optimal PGx alert design through a health-system-wide, mixed-methods study. Clinicians representing multiple practices and care settings (N = 1062) in urban, rural, and underserved regions were invited to complete an electronic survey comparing the usability of three drug alerts for citalopram, as a case study. Alert 1 contained a generic warning of pharmacogenomic effects on citalopram metabolism. Alerts 2 and 3 provided patient-specific predictions of citalopram efficacy with varying depth of information. Primary outcomes included the System's Usability Scale score (0-100 points) of each alert, the perceived impact of each alert on stress and decision-making, and clinicians' suggestions for alert improvement. Secondary outcomes included the assessment of alert preference by clinician age, practice type, and geographic setting. Qualitative information was captured to provide context to quantitative information. The final cohort comprised 305 geographically and clinically diverse clinicians. A simplified, individualized alert (Alert 2) was perceived as beneficial for decision-making and stress compared with a more detailed version (Alert 3) and the generic alert (Alert 1) regardless of age, practice type, or geographic setting. Findings emphasize the need for clinician-guided design of PGx alerts in the era of digital medicine., (© 2024 The Author(s). Clinical and Translational Science published by Wiley Periodicals LLC on behalf of American Society for Clinical Pharmacology and Therapeutics.)

Importance: Stigmatizing language in electronic health records (EHRs) can generate or reinforce negative stereotypes about patients from minoritized groups and erode their trust and experience. However, less is known about the use of such language to cast doubt on patient clinical history in hospital settings., Objective: To compare the prevalence of language expressing doubt about patient clinical history (eg, experiences and symptoms) in hospital admission notes by patient race and ethnicity., Design, Setting, and Participants: In this cohort study, natural language processing tools were applied to analyze 54 936 admission notes from 1249 clinicians about patients aged 18 years or older at admission and hospitalized at an academic health system in the Northeast US between January 1, 2018, and February 28, 2023. Data were analyzed from September 1, 2022, to July 31, 2023., Exposure: Patient race and ethnicity (non-Hispanic Black, non-Hispanic White, and racial and ethnic minoritized groups excluding non-Hispanic Black [includes multiple racial and ethnic groups, such as Hispanic and Asian]) as recorded in the EHR based on self-report or registrar determination., Main Outcome and Measure: Binary indicator for at least 1 term casting doubt on patient clinical history was defined using epistemic stance, a linguistic construct expressing a writer's degree of certainty in information. Terms were manually validated via iterative review of notes by the study team., Results: Among 56 325 admission notes (mean [SD] age of patients, 55.9 [19.0] years; 30 913 notes among female patients [54.9%]; 25 649 notes among non-Hispanic Black patients [45.5%], 26 442 notes among non-Hispanic White patients [46.9%], and 2985 notes among members of racial and ethnic minoritized groups excluding non-Hispanic Black patients [5.3%]), we analyzed 54 936 admission notes that had no missing data. Among all analyzed admission notes, 39 023 notes (71.0%) contained doubt language. Notes written about non-Hispanic Black patients had increased odds of containing at least 1 word or phrase of doubt (adjusted odds ratio, 1.21; 95% CI, 1.14-1.28; P < .001) compared with notes among non-Hispanic White patients. Compared with notes among non-Hispanic White patients, notes written about members of racial and ethnic minoritized groups excluding non-Hispanic Black patients had similar odds of containing at least 1 term of doubt., Conclusion and Relevance: In this study, language casting doubt on patient clinical history was more likely to be found in notes of non-Hispanic Black patients. These results suggest that with the implementation of policies allowing patients full access to their health records, examining clinical documentation standards may be associated with improved patient experience.

Purpose: The US Food and Drug Administration's Sentinel Innovation Center aimed to establish a query-ready, quality-checked distributed data network containing electronic health records (EHRs) linked with insurance claims data for at least 10 million individuals to expand the utility of real-world data for regulatory decision-making., Methods: In this report, we describe the resulting network, the Real-World Evidence Data Enterprise (RWE-DE), including data from two commercial EHR-claims linked assets collectively termed the Commercial Network covering 21 million lives, and four academic partner institutions collectively termed the Development Network covering 4.5 million lives., Results: We discuss provenance and completeness of the data converted in the Sentinel Common Data Model (SCDM), describe patient populations, and report on EHR-claims linkage characterization for all contributing data sources. Further, we introduce a standardized process to store free-text notes in the Development Network for efficient retrieval as needed., Conclusions: Finally, we outline typical use cases for the RWE-DE where it can broaden the reach of the types of questions that can be addressed by the Sentinel system., (© 2024 John Wiley & Sons Ltd.)

Objective: Faced with the challenges of differential diagnosis caused by the complex clinical manifestations and high pathological heterogeneity of pituitary adenomas, this study aims to construct a high-quality annotated corpus to characterize pituitary adenomas in clinical notes containing rich diagnosis and treatment information. Methods : A dataset from a pituitary adenomas neurosurgery treatment center of a tertiary first-class hospital in China was retrospectively collected. A semi-automatic corpus construction framework was designed. A total of 2000 documents containing 9430 sentences and 524,232 words were annotated, and the text corpus of pituitary adenomas (TCPA) was constructed and analyzed. Its potential application in large language models (LLMs) was explored through fine-tuning and prompting experiments. Results: TCPA had 4782 medical entities and 28,998 tokens, achieving good quality with the inter-annotator agreement value of 0.862-0.986. The LLMs experiments showed that TCPA can be used to automatically identify clinical information from free texts, and introducing instances with clinical characteristics can effectively reduce the need for training data, thereby reducing labor costs. Conclusion: This study characterized pituitary adenomas in clinical notes, and the proposed method were able to serve as references for relevant research in medical natural language scenarios with highly specialized language structure and terminology., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Purpose: Few studies have reported the agreement between medication information derived from ambulatory EHR data compared to administrative claims for high-cost specialty drugs. We used data from a national EHR-enabled registry, the Rheumatology Informatics System for Effectiveness (RISE), with linked Medicare claims in a population of patients with rheumatoid arthritis (RA) to investigate variations in agreement for different biologic disease-modifying agents (bDMARDs) between two data sources (RISE EHR data vs. Medicare claims), categorized by drug, route of administration, and patient insurance factors (dual eligibility)., Methods: Patients ≥ 65 years old, with ≥ 2 visits in RISE with RA ICD codes ≥ 30 days apart, and continuous enrollment in Medicare Parts B and D in 2017-2018 were included. We classified patients as bDMARD users or nonusers in Medicare claims or EHR data in 2018, and we calculated sensitivity, specificity, positive predicted value (PPV), and negative predicted value (NPV) of EHR data for identifying bDMARD users, using Medicare as the reference standard. We also calculated these metrics after stratifying by clinic-administered (Part B) versus. pharmacy-dispensed (Part D) bDMARDs and by patient dual-eligibility., Results: A total of 26 097 patients were included in the study. Using Medicare claims as the reference standard, EHR data had a sensitivity of 75.0%-90.8% for identifying patients with the same medication and route. PPV for Part B bDMARDs was higher compared with Part D bDMARDs (range 94.3%-97.3% vs. 51.0%-69.6%). We observed higher PPVs for Part D bDMARDs among patients who were dual-eligible (range 82.4%-95.1%)., Conclusion: The risk of misclassification of drug exposure based on EHR data sources alone is small for Medicare Part B bDMARDs but could be as high as 50% for Part D bDMARDs, in particular for patients who are not dually eligible for Medicare and Medicaid., (© 2024 The Author(s). Pharmacoepidemiology and Drug Safety published by John Wiley & Sons Ltd.)

Background: Electronic health records (EHRs) are increasingly used for epidemiologic research to advance public health practice. However, key variables are susceptible to missing data or misclassification within EHRs, including demographic information or disease status, which could affect the estimation of disease prevalence or risk factor associations., Objective: In this paper, we applied methods from the literature on missing data and causal inference to assess whether we could mitigate information biases when estimating measures of association between potential risk factors and diabetes among a patient population of New York City young adults., Methods: We estimated the odds ratio (OR) for diabetes by race or ethnicity and asthma status using EHR data from NYU Langone Health. Methods from the missing data and causal inference literature were then applied to assess the ability to control for misclassification of health outcomes in the EHR data. We compared EHR-based associations with associations observed from 2 national health surveys, the Behavioral Risk Factor Surveillance System (BRFSS) and the National Health and Nutrition Examination Survey, representing traditional public health surveillance systems., Results: Observed EHR-based associations between race or ethnicity and diabetes were comparable to health survey-based estimates, but the association between asthma and diabetes was significantly overestimated (OREHR 3.01, 95% CI 2.86-3.18 vs ORBRFSS 1.23, 95% CI 1.09-1.40). Missing data and causal inference methods reduced information biases in these estimates, yielding relative differences from traditional estimates below 50% (ORMissingData 1.79, 95% CI 1.67-1.92 and ORCausal 1.42, 95% CI 1.34-1.51)., Conclusions: Findings suggest that without bias adjustment, EHR analyses may yield biased measures of association, driven in part by subgroup differences in health care use. However, applying missing data or causal inference frameworks can help control for and, importantly, characterize residual information biases in these estimates., (© Sarah Conderino, Rebecca Anthopolos, Sandra S Albrecht, Shannon M Farley, Jasmin Divers, Andrea R Titus, Lorna E Thorpe. Originally published in JMIR Medical Informatics (https://medinform.jmir.org).)

The use of data derived from electronic health records (EHRs) to describe racial and ethnic health disparities is increasingly common, but there are challenges. While the number of patients covered by EHRs can be quite large, such patients may not be representative of a source population. One way to evaluate the extent of this limitation is by linking EHRs to an external source, in this case with the American Community Survey (ACS). Relying on a stratified random sample of about 200,000 patient records from a large, public, integrated health delivery system in North Carolina (2016-2019), we assess linkages to restricted ACS microdata (2001-2017) by race and ethnicity to understand the strengths and weaknesses of EHR-derived data for describing disparities. The results in this research note suggest that Black-White comparisons will benefit from standard adjustments (e.g., weighting procedures) but that misestimation of health disparities may arise for Hispanic patients because of differential coverage rates for this group., (Copyright © 2024 The Authors.)

Introduction: Medication nonadherence is common in systemic lupus erythematosus (SLE) and associated with morbidity and mortality. We explored the reliability of pharmacy data within the electronic medical record (EMR) to examine factors associated with nonadherence to SLE medications., Methods: We included patients with SLE who were prescribed ≥1 SLE medication for ≥90 days. We compared two datasets of pharmacy fill data, one within the EMR and another from the vendor who obtained this information from pharmacies and prescription benefit managers. Adherence was defined by medication possession ratio (MPR) ≥80%. In addition to MPR for each SLE medication, we evaluated the weighted-average MPR and the proportion of patients adherent to ≥1 SLE medication and to all SLE medications. We used logistic regression to examine factors associated with adherence., Results: Among 181 patients (median age 36, 96% female, 58% Black), 98% were prescribed hydroxychloroquine, 34% azathioprine, 33% mycophenolate, 18% methotrexate, and 7% belimumab. Among 1276 pharmacy records, 74% overlapped between linked EMR-pharmacy data and data obtained directly from the vendor. Only 9% were available from the vendor but not through linked EMR-pharmacy data. The weighted-average MPR was 57%; 45% were adherent to hydroxychloroquine, 46% to ≥1 SLE medication, and 32% to all SLE medications. Older age was associated with adherence in univariable and multivariable analyses., Discussion: Our study showed that obtaining linked EMR-pharmacy data is feasible with minimal missing data and can be leveraged in future adherence research. Younger patients were more likely to be nonadherent and may benefit from targeted intervention., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Background: Electronic health records (EHR) are frequently used for epidemiological research including drug utilisation studies in a defined population such as the population with knee osteoarthritis (KOA). We sought to describe the process of defining a cohort of patients with KOA from a large UK primary care database and estimate the annual incidence of diagnosed KOA between 2000 and 2015., Method: This was a retrospective study using data from the clinical practice research datalink (CPRD). CPRD is a large primary care longitudinal electronic medical records' database that contains anonymous records of patients from general practices across United Kingdom. Five different cohort definition strategies were applied including symptoms-based or diagnosis-based strategies or a combination of both. To validate results, the annual incidence of KOA was estimated and compared to published data., Results: The study defined 898,690 patients when symptoms-based strategy was applied, 137,541 patients when diagnosis based and 83,294 when a combination of both strategies were applied. The final cohort was defined using a diagnosis-based strategy that avoided overestimation (with symptoms-based definition) or underestimation (with a combination of symptoms and diagnosis). The incidence of KOA ranged from 1.33 per 1000 CPRD registrants in 2000, 1.76 in 2008 and 1.45 patients in 2015., Conclusion: This study logically/sensibly defined a cohort of patients with diagnosed KOA through the application of several strategies. This was an essential step to avoid subsequent over or underestimation of the prevalence of drug utilisation and the associated adverse clinical outcomes within primary care patients with KAO., (© 2024 The Author(s). Journal of Evaluation in Clinical Practice published by John Wiley & Sons Ltd.)

Background: This study aims to identify the common pathways of appendicectomy, the most common emergency surgery in Australia's public hospitals and any variations within a regional public health district in New South Wales, Australia., Methods: We analyzed the electronic medical records of 3,943 patients who underwent appendicectomy between January 2014 and July 2020 at 2 hospitals in the Illawarra Shoalhaven Local Health District, New South Wales, Australia, using the PM 2 approach for surgical pathway identification and subsequent statistical analyses., Results: Among 3,943 patients, 3,606 (91.5%) followed an 11-step main pathway: (1) emergency department admission, (2) surgery booking, (3) anesthesia start, (4) operating room entry, (5) surgery start, (6) surgery end, (7) anesthesia end, (8) operating room discharge, (9) postanesthesia care unit admission, (10) postanesthesia care unit discharge, and (11) hospital discharge. The median length of stay was 48.13 hours (interquartile range 32.74). The main pathway differed from either variation 1 (n = 246, 6.2%) or variation 2 (n = 30, 0.8%) only in the timing and location of anesthesia administration or conclusion. Variation 3 (n = 26, 0.7%) included patients who underwent appendicectomy twice, whereas variation 4 (n = 25, 0.6%) included patients booked for surgery before emergency department admission through community doctor referrals. Thirteen exceptional cases experienced combinations of the aforementioned pathways. The length of stay and phase durations varied between the main pathway and these variations., Conclusion: The appendicectomy pathway was largely standardized across the studied hospitals, with the location of anesthesia administration or conclusion affecting specific stages but not the overall length of stay. Only a complex 2-surgery pathway increased length of stay., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Atopic dermatitis (AD) is triggered by many environmental factors. We sought to determine the relationship between birth weight, infectious diseases, and AD. This retrospective cohort study analyzed data from the CGR Database for the period 2004 through 2015 in Taiwan. All diseases were classified using the International Classification of Disease codes. Logistic regression adjusted for birth weights and comorbidities were analyzed by SAS (version 9.4). P < .05 were considered statistically significant. In children with AD, bronchiolitis was significantly associated with the development of AD, whether the patients were aged < 2 years (odds ratio [OR] = 1.497; P = .014) or ≥ 2 years (OR = 1.882; P = .022). There was also no difference in the association between AD and different birth weights. We conclude that AD is associated with a previous history of bronchiolitis in children, regardless of age (less than or greater than 2 years)., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Background: Population-based studies describing the association between diabetes and increased risk of infection have largely been based in high-income countries. There is limited information describing the burden of infectious disease attributable to diabetes in low and middle-income countries. This study aimed to describe the burden and risk of infectious disease hospitalisation in people with diabetes compared to those without diabetes in northeastern Thailand., Methods: In a retrospective cohort study using electronic health record data for 2012-2018 for 3.8 million people aged ≥20 years in northeastern Thailand, hospitalisation rates for any infectious diseases (ICD-10 codes A00-B99) were estimated and negative binomial regression used to estimate rate ratios (RR) for the association between diabetes and infectious disease hospitalisation adjusted for age, sex and area of residence., Results: In this study, 164,177 people had a diagnosis of diabetes mellitus at any point over the study period. Infectious disease hospitalisation rates per 1000 person-years (95%CI) were 71.8 (70.9, 72.8), 27.7 (27.1, 28.3) and 7.5 (7.5, 7.5) for people with prevalent diabetes, incident diabetes and those without diabetes respectively. Diabetes was associated with a 4.6-fold higher risk of infectious disease hospitalisation (RR (95% CI) 4.59 (4.52, 4.66)). RRs for infectious disease hospitalisation were 3.38 (3.29, 3.47) for people with diabetes managed by lifestyle alone and 5.29 (5.20, 5.39) for people receiving prescriptions for diabetes drugs., Conclusions: In this Thai population, diabetes was associated with substantially increased risk of hospitalisation due to infectious diseases and people with diabetes who were on pharmacological treatment had a higher risk than those receiving lifestyle modification advice alone., (© 2024 Diabetes UK.)

Objectives: The US Agency for Healthcare Research and Quality, through the Evidence-based Practice Center (EPC) Program, aims to provide health system decision makers with the highest-quality evidence to inform clinical decisions. However, limitations in the literature may lead to inconclusive findings in EPC systematic reviews (SRs). The EPC Program conducted pilot projects to understand the feasibility, benefits, and challenges of utilizing health system data to augment SR findings to support confidence in healthcare decision-making based on real-world experiences., Study Design and Setting: Three contractors (each an EPC located at a different health system) selected a recently completed SR conducted by their center and identified an evidence gap that electronic health record (EHR) data might address. All pilot project topics addressed clinical questions as opposed to care delivery, care organization, or care disparities topics that are common in EPC reports. Topic areas addressed by each EPC included infantile epilepsy, migraine, and hip fracture. EPCs also tracked additional resources needed to conduct supplemental analyses. The workgroup met monthly in 2022-2023 to discuss challenges and lessons learned from the pilot projects., Results: Two supplemental data analyses filled an evidence gap identified in the SRs (raised certainty of evidence, improved applicability) and the third filled a health system knowledge gap. Project challenges fell under three themes: regulatory and logistical issues, data collection and analysis, and interpretation and presentation of findings. Limited ability to capture key clinical variables given inconsistent or missing data within the EHR was a major limitation. The workgroup found that conducting supplemental data analysis alongside an SR was feasible but adds considerable time and resources to the review process (estimated total hours to complete pilot projects ranged from 283 to 595 across EPCs), and that the increased effort and resources added limited incremental value., Conclusion: Supplementing existing SRs with analyses of EHR data is resource intensive and requires specialized skillsets throughout the process. While using EHR data for research has immense potential to generate real-world evidence and fill knowledge gaps, these data may not yet be ready for routine use alongside SRs., Competing Interests: Declaration of competing interest The authors have no relevant financial relationships to disclose., (Copyright © 2024. Published by Elsevier Inc.)

Background: The Veterans Health Administration (VA) implemented academic detailing (AD) to support safer opioid prescribing and overdose prevention initiatives., Methods: Patient-level data were extracted monthly from VA's electronic health record to evaluate whether AD implementation was associated with changes in all-cause mortality, opioid poisoning inpatient admissions, and opioid poisoning emergency department (ED) visits in an observational cohort of patients with long-term opioid prescriptions (≥45-day supply of opioids 6 months prior to a given month with ≤15 days between prescriptions). A single-group interrupted time series analysis using segmented logistic regression for mortality and Poisson regression for counts of inpatient admissions and ED visits was used to identify whether the level and slope of these outcomes changed in response to AD implementation., Results: Among 955 376 unique patients (19 431 241 person-months), there were 53 369 deaths (29 025 pre-AD; 24 344 post-AD), 1927 opioid poisoning inpatient admissions (610 pre-AD; 1317 post-AD), and 408 opioid poisoning ED visits (207 pre-AD; 201 post-AD). Immediately after AD implementation, there was a 5.8% reduction in the odds of all-cause mortality (95% confidence interval [CI]: 0.897, 0.990). However, patients had a significantly increased incidence rate of inpatient admissions for opioid poisoning immediately after AD implementation (incidence rate ratio = 1.523; 95% CI: 1.118, 2.077). No significant differences in ED visits for opioid poisoning were observed., Conclusions: AD was associated with decreased all-cause mortality but increased inpatient hospitalization for opioid poisoning among patients prescribed long-term opioids. Mechanisms via which AD's efforts influenced opioid-related outcomes should be explored., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Background: Lung cancer (LC) screening using low-dose computed tomography (CT) is recommended according to standard risk criteria or personalized risk calculators. Machine learning (ML) models that can predict disease risk are an emerging method in medicine for identifying hidden associations that are personally unique., Materials and Methods: Using the tree-based pipeline optimization tool (TPOT), we developed an ML-based model, which is an ensemble of the Random Forest and XGboost models, based on known risk factors for LC, as part of a larger trial for ML prediction using electronic medical records and chest CT. We used data from patients with LC vs. controls (1:2) of patients aged ≥ 35 years. We developed a model for all LC patients as well as for patients with and without a smoking background. We included age, gender, body mass index (BMI), smoking history, socioeconomic status (SES), history of chronic obstructive pulmonary disease (COPD)/emphysema/chronic bronchitis (CB), interstitial lung disease (ILD)/pulmonary fibrosis (PF), and family history of LC., Results: Of the 4076 patients, 1428 (35 %) were in the LC group and 2648 (65 %) were in the control group. For the entire study population, our model achieved an accuracy of 71.2 %, with a sensitivity of 69 % and a positive predictive value (PPV) of 74 %. Higher accuracy was achieved for the two subgroups. An accuracy of 74.8 % (sensitivity 72 %, PPV 76 %) and 73.0 % (sensitivity 76 %, PPV 72 %) was achieved for the smoking and never-smoking cohorts, respectively. For the entire population and smoker cohort, COPD/emphysema/CB were the most important contributors, followed by BMI and age, while in the never-smoking cohort, BMI, age and SES were the most important contributors., Conclusion: Known risk factors for LC could be used in ML models to modestly predict LC. Further studies are needed to confirm these results in new patients and to improve them., Competing Interests: Declaration of Competing Interest Dr. Shlomi was paid by the grant of this study for his role as a medical consulter. All other authors have nothing to declare., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Objective: To determine the incidence of patients presenting in pain to a large Australian inner-city emergency department (ED) using a clinical text deep learning algorithm., Materials and Methods: A fine-tuned, domain-specific, transformer-based clinical text deep learning model was used to interpret free-text nursing assessments in the electronic medical records of 235,789 adult presentations to the ED over a three-year period. The model classified presentations according to whether the patient had pain on arrival at the ED. Interrupted time series analysis was used to determine the incidence of pain in patients on arrival over time. We described the changes in the population characteristics and incidence of patients with pain on arrival occurring with the start of the Covid-19 pandemic., Results: 55.16% (95%CI 54.95%-55.36%) of all patients presenting to this ED had pain on arrival. There were differences in demographics and arrival and departure patterns between patients with and without pain. The Covid-19 pandemic initially precipitated a decrease followed by a sharp, sustained rise in pain on arrival, with concurrent changes to the population arriving in pain and their treatment., Discussion: Applying a clinical text deep learning model has successfully identified the incidence of pain on arrival. It represents an automated, reproducible mechanism to identify pain from routinely collected medical records. The description of this population and their treatment forms the basis of intervention to improve care for patients with pain. The combination of the clinical text deep learning models and interrupted time series analysis has reported on the effects of the Covid-19 pandemic on pain care in the ED, outlining a methodology to assess the impact of significant events or interventions on pain care in the ED., Conclusion: Applying a novel deep learning approach to identifying pain guides methodological approaches to evaluating pain care interventions in the ED, giving previously unavailable population-level insights., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

The use of narrative interventions in health care is an effective way to communicate connection between patients and clinicians. The electronic health record (EHR) is a primary mode of communicating patient information across clinical teams. Thus, incorporating a person-centered cocreated narrative with patients into the EHR is an opportunity to share a person's cultural values, beliefs, and preferences; provide connection; and foster positive patient-clinician interactions. This study of a person-centered narrative intervention was a randomized controlled trial to test intervention effects on the person's (patient) perceptions of the quality of communication with their nurses and their psychosocial and existential well-being. This article describes the clinical team's experiences and reach of the cocreated person-centered narrative integration into the patient's EHR. The data collected included (1) exit interviews (n = 14), (2) a usability survey (n = 8), and (3) data collected from the EHR for clinicians (n = 600) who accessed the uploaded narratives. Overall, the System Usability Scale and nurse participant's experiences provided confirmation that the person-centered narrative intervention was usable. There were also interprofessional groups of clinicians in the health care system who accessed the narratives. Future research should continue to identify core components and implementation strategies of EHR-integrated person-centered narratives in complex health care settings., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2024 by The Hospice and Palliative Nurses Association. All rights reserved.)

Medical health records comprise sensitive patient data for precise diagnosis and successive treatment. However, it must be stored and shared securely to protect data privacy. Generally, health records are kept on centralized servers, which raise the risk of security breaches and involve trust in a single authority that cannot efficiently defend data from internal attacks. Blockchain (BC) is extensively used in medical health records management because of its decentralized and tamper-proof properties. This work introduces a public-permissioned BC technology with a decentralized ledger (DL) to manage medical health records in the fog computing layer. The considered BC is decentralized and allows the transmission of records within the decentralized network of records. The data blocks are hashed using the SHA-256 hash algorithm. Especially, an Adaptive RSA Digital Signature Algorithm (ARSA-DS) is developed to prevent data tampering with medical health records in the fog computing layer. Moreover, an Ebola Search Optimization based Key Selection (ESO-KS) technique is employed to find the ideal key from the randomly generated keys to reduce processing time and increase overall efficiency. The proposed decentralized BC framework will help to preserve patient privacy and prevent the tampering of health records by attacks; moreover, it is efficient in terms of confidentiality, integrity, and availability.

Background and Aims: Utilization of electronic health records data to derive predictive indexes such as the electronic Child-Turcotte-Pugh (eCTP) Score can have significant utility in health care delivery. Within the records, CT scans contain phenotypic data which have significant prognostic value. However, data extractions have not traditionally been applied to imaging data. In this study, we used artificial intelligence to automate biomarker extraction from CT scans and examined the value of these features in improving risk prediction in patients with liver disease., Approach and Results: Using a regional liver disease cohort from the Veterans Health System, we retrieved administrative, laboratory, and clinical data for Veterans who had CT scans performed for any clinical indication between 2008 and 2014. Imaging biomarkers were automatically derived using the analytic morphomics platform. In all, 4614 patients were included. We found that the eCTP Score had a Concordance index of 0.64 for the prediction of overall mortality while the imaging-based model alone or with eCTP Score performed significantly better [Concordance index of 0.72 and 0.73 ( p <0.001)]. For the subset of patients without hepatic decompensation at baseline (n=4452), the Concordance index for predicting future decompensation was 0.67, 0.79, and 0.80 for eCTP Score, imaging alone, or combined, respectively., Conclusions: This proof of concept demonstrates that the potential of utilizing automated extraction of imaging features within CT scans either alone or in conjunction with classic health data can improve risk prediction in patients with chronic liver disease., (Copyright © 2023 American Association for the Study of Liver Diseases.)

Objectives: To evaluate the comparability of frailty assessment tools - the electronic frailty index (eFI), retrospective electronic frailty index (reFI), and clinical frailty scale (CFS) - in older residents of care facilities. Methods: Data from 813 individuals aged 65 or older, with frailty and co-morbidities, collected between 2022 and 2023, were analysed using various statistical methods. Results: The results showed significant differences in frailty classification among the tools: 78.3% were identified as moderately to severely frail by eFI, 59.6% by reFI, and 92.1% by CFS. Statistical tests confirmed significant differences ( p < .05) in their assessments, indicating variability in measurement methods. Discussion: This study advances the understanding of frailty assessment within aged-care settings, highlighting the differences in the efficacy of these assessment tools. It underscores the challenges in frailty assessments and emphasizes the need for continuous refinement of assessment methods to address the diverse facets of frailty in aged care., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Introduction: The incidence of Alzheimer's disease (AD) and obesity rise concomitantly. This study examined whether factors affecting metabolism, race/ethnicity, and sex are associated with AD development., Methods: The analyses included patients ≥ 65 years with AD diagnosis in six University of California hospitals between January 2012 and October 2023. The controls were race/ethnicity, sex, and age matched without dementia. Data analyses used the Cox proportional hazards model and machine learning (ML)., Results: Hispanic/Latino and Native Hawaiian/Pacific Islander, but not Black subjects, had increased AD risk compared to White subjects. Non-infectious hepatitis and alcohol abuse were significant hazards, and alcohol abuse had a greater impact on women than men. While underweight increased AD risk, overweight or obesity reduced risk. ML confirmed the importance of metabolic laboratory tests in predicting AD development., Discussion: The data stress the significance of metabolism in AD development and the need for racial/ethnic- and sex-specific preventive strategies., Highlights: Hispanics/Latinos and Native Hawaiians/Pacific Islanders show increased hazards of Alzheimer's disease (AD) compared to White subjects. Underweight individuals demonstrate a significantly higher hazard ratio for AD compared to those with normal body mass index. The association between obesity and AD hazard differs among racial groups, with elderly Asian subjects showing increased risk compared to White subjects. Alcohol consumption and non-infectious hepatitis are significant hazards for AD. Machine learning approaches highlight the potential of metabolic panels for AD prediction., (© 2024 The Author(s). Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

Background: Although some recent studies have examined the health of female Gulf War (GW) deployed and non-deployed GW era veterans, these all relied on self-report, which can be inaccurate and subject to recall bias. This study investigated the current health of GW deployed and non-deployed GW era female and male veterans using Veterans Health Administration (VHA) electronic health records (EHR). Methods: We performed a cohort study of deployed GW and non-deployed GW era veterans, identified from a list from the Defense Manpower Data Center (DMDC). We used the VA-Frailty Index (VA-FI), calculated with VHA administrative claims and EHR, as a proxy measure of current health. Results: We identified 402,869 veterans (351,496 GW deployed; 51,3373 non-deployed GW era; 38,555 female) in VHA databases. Deployed female veterans had the highest VA-FI (i.e., were frailest) despite being younger than deployed and non-deployed male veterans and non-deployed female veterans. Compared with deployed male veterans, deployed females were more likely to be pre-frail, mildly, and moderately frail. Health differences between deployed and non-deployed female veterans were more prominent among older (60+ years) than younger (<60 years) veterans. Conclusions: Mirroring reports from recent, smaller survey studies of users and non-users of VA health care, findings from this cohort study indicate that deployed female GW veterans who use VA health care are frailer and have more health deficits than non-deployed female GW era and deployed male GW veterans. Because deployed female GW veterans appear to have additional health care needs, this may warrant increased outreach from women's clinics at VA hospitals.

Background and Objectives: Suicide is a leading cause of death among youth in the United States. Pediatric emergency department visits for non-psychiatric concerns present an opportunity to identify youth at risk for suicidality. This quality improvement initiative was undertaken to ensure that those patients identified as low risk for suicide receive resources, bridging the gap between identifying at-risk youth and providing them with appropriate follow up mental health resources. The aim of this project was to increase the proportion of after visit summaries containing mental health resources by 25 % within 6 months for medical patients who are found to have non-acute low suicide risk and are discharged from the emergency department., Methods: The primary outcome measure was the proportion of discharged medical patients who screened positive for suicidal ideation and were determined to be at low risk for suicide who received mental health resources on discharge before and after intervention. A multidisciplinary team targeted the following 3 key drivers: 1) multidisciplinary engagement 2) training and education of providers and 3) health information technology. Plan, Do, Study and Act cycles included the following: 1) an educational campaign with regular multidisciplinary meetings, educational updates, and email reminders; 2). an electronic health record change; and 3) An individual report to providers., Results: After the intervention, the percentage of medical patients with low-risk suicidality being discharged with mental health resources increased by more than 70 %., Conclusions: A champion led multidisciplinary team, using PDSA methodology, can implement sustained improvements in mental health resource distribution., Competing Interests: Declaration of competing interest The authors have no potential conflicts of interest to disclose., (Copyright © 2024 Elsevier B.V. All rights reserved.)

We studied the association between non-osteoporotic fractures and future major osteoporotic fractures, using UK health records. Non-osteoporotic fractures were found to increase the risk of major osteoporotic fractures, although to a lesser extent than osteoporotic fractures. This highlights the importance of considering all previous fractures in assessing future fracture risk., Purpose: Previous studies demonstrated that osteoporotic fractures-minor and major-increase the risk for future major osteoporotic fractures; we test whether non-osteoporotic fractures are also associated with such increased risk., Methods: The study is a retrospective cohort study using UK primary care electronic health records. Exposure groups were defined according to fracture location prior to the year 2011 (index date): major, minor, and non-osteoporotic. The outcome of incident major osteoporotic fractures following the index date was compared between the exposure groups and the general population., Results: The general study population included 1,951,388 patients. The exposure groups included 39,931 patients with a prior major osteoporotic fracture, 19,397 with a prior minor osteoporotic fracture, and 50,115 patients with a prior non-osteoporotic fracture. The standardized Incidence Rate Ratio for future major osteoporotic fractures was 2.73 (95% confidence interval: 2.64-2.82), 2.43 (2.32-2.54), and 1.83 (1.74-1.92), respectively., Conclusion: Non-osteoporotic fractures are significantly associated with increased risk for future major osteoporotic fractures relative to the general population, yet to a lesser extent compared to major and minor osteoporotic fractures., (© 2024. The Author(s).)