TA-RNN: an attention-based time-aware recurrent neural network architecture for electronic health records.

MOTIVATION: Electronic health records (EHRs) represent a comprehensive resource of a patient's medical history. EHRs are essential for utilizing advanced technologies such as deep learning (DL), enabling healthcare providers to analyze extensive data, extract valuable insights, and make precise and data-driven clinical decisions. DL methods such as recurrent neural networks (RNN) have been utilized to analyze EHR to model disease progression and predict diagnosis. However, these methods do not address some inherent irregularities in EHR data such as irregular time intervals between clinical visits. Furthermore, most DL models are not interpretable. In this study, we propose two interpretable DL architectures based on RNN, namely time-aware RNN (TA-RNN) and TA-RNN-autoencoder (TA-RNN-AE) to predict patient's clinical outcome in EHR at the next visit and multiple visits ahead, respectively. To mitigate the impact of irregular time intervals, we propose incorporating time embedding of the elapsed times between visits. For interpretability, we propose employing a dual-level attention mechanism that operates between visits and features within each visit. RESULTS: The results of the experiments conducted on Alzheimer's Disease Neuroimaging Initiative (ADNI) and National Alzheimer's Coordinating Center (NACC) datasets indicated the superior performance of proposed models for predicting Alzheimer's Disease (AD) compared to state-of-the-art and baseline approaches based on F2 and sensitivity. Additionally, TA-RNN showed superior performance on the Medical Information Mart for Intensive Care (MIMIC-III) dataset for mortality prediction. In our ablation study, we observed enhanced predictive performance by incorporating time embedding and attention mechanisms. Finally, investigating attention weights helped identify influential visits and features in predictions. AVAILABILITY AND IMPLEMENTATION: https://github.com/bozdaglab/TA-RNN.

A Nordic Perspective on Patient Online Record Access and the European Health Data Space.

The Nordic countries are, together with the United States, forerunners in online record access (ORA), which has now become widespread. The importance of accessible and structured health data has also been highlighted by policy makers internationally. To ensure the full realization of ORA's potential in the short and long term, there is a pressing need to study ORA from a cross-disciplinary, clinical, humanistic, and social sciences perspective that looks beyond strictly technical aspects. In this viewpoint paper, we explore the policy changes in the European Health Data Space (EHDS) proposal to advance ORA across the European Union, informed by our research in a Nordic-led project that carries out the first of its kind, large-scale international investigation of patients' ORA-NORDeHEALTH (Nordic eHealth for Patients: Benchmarking and Developing for the Future). We argue that the EHDS proposal will pave the way for patients to access and control third-party access to their electronic health records. In our analysis of the proposal, we have identified five key principles for ORA: (1) the right to access, (2) proxy access, (3) patient input of their own data, (4) error and omission rectification, and (5) access control. ORA implementation today is fragmented throughout Europe, and the EHDS proposal aims to ensure all European citizens have equal online access to their health data. However, we argue that in order to implement the EHDS, we need more research evidence on the key ORA principles we have identified in our analysis. Results from the NORDeHEALTH project provide some of that evidence, but we have also identified important knowledge gaps that still need further exploration.

Multimodal learning system integrating electronic medical records and hysteroscopic images for reproductive outcome prediction and risk stratification of endometrial injury: a multicenter diagnostic study.

OBJECTIVE: To develop a multimodal learning application system that integrates electronic medical records (EMR) and hysteroscopic images for reproductive outcome prediction and risk stratification of patients with intrauterine adhesions (IUAs) resulting from endometrial injuries. MATERIALS AND METHODS: EMR and 5014 revisited hysteroscopic images of 753 post hysteroscopic adhesiolysis patients from the multicenter IUA database we established were randomly allocated to training, validation, and test datasets. The respective datasets were used for model development, tuning, and testing of the multimodal learning application. MobilenetV3 was employed for image feature extraction, and XGBoost for EMR and image feature ensemble learning. The performance of the application was compared against the single-modal approaches (EMR or hysteroscopic images), DeepSurv and ElasticNet models, along with the clinical scoring systems. The primary outcome was the 1-year conception prediction accuracy, and the secondary outcome was the assisted reproductive technology (ART) benefit ratio after risk stratification. RESULTS: The multimodal learning system exhibited superior performance in predicting conception within 1-year, achieving areas under the curves of 0.967 (95% CI: 0.950-0.985), 0.936 (95% CI: 0.883-0.989), and 0.965 (95% CI: 0.935-0.994) in the training, validation, and test datasets, respectively, surpassing single-modal approaches, other models and clinical scoring systems (all P<0.05). The application of the model operated seamlessly on the hysteroscopic platform, with an average analysis time of 3.7±0.8 s per patient. By employing the application's conception probability-based risk stratification, mid-high-risk patients demonstrated a significant ART benefit (odds ratio=6, 95% CI: 1.27-27.8, P=0.02), while low-risk patients exhibited good natural conception potential, with no significant increase in conception rates from ART treatment (P=1). CONCLUSIONS: The multimodal learning system using hysteroscopic images and EMR demonstrates promise in accurately predicting the natural conception of patients with IUAs and providing effective postoperative stratification, potentially contributing to ART triage after IUA procedures.

Minimal Common Oncology Data Elements Genomics Pilot Project: Enhancing Oncology Research Through Electronic Health Record Interoperability at Vanderbilt University Medical Center.

PURPOSE: The expanding presence of the electronic health record (EHR) underscores the necessity for improved interoperability. To test the interoperability within the field of oncology research, our team at Vanderbilt University Medical Center (VUMC) enabled our Epic-based EHR to be compatible with the Minimal Common Oncology Data Elements (mCODE), which is a Fast Healthcare Interoperability Resources (FHIR)-based consensus data standard created to facilitate the transmission of EHRs for patients with cancer. METHODS: Our approach used an extract, transform, load tool for converting EHR data from the VUMC Epic Clarity database into mCODE-compatible profiles. We established a sandbox environment on Microsoft Azure for data migration, deployed a FHIR server to handle application programming interface (API) requests, and mapped VUMC data to align with mCODE structures. In addition, we constructed a web application to demonstrate the practical use of mCODE profiles in health care. RESULTS: We developed an end-to-end pipeline that converted EHR data into mCODE-compliant profiles, as well as a web application that visualizes genomic data and provides cancer risk assessments. Despite the complexities of aligning traditional EHR databases with mCODE standards and the limitations of FHIR APIs in supporting advanced statistical methodologies, this project successfully demonstrates the practical integration of mCODE standards into existing health care infrastructures. CONCLUSION: This study provides a proof of concept for the interoperability of mCODE within a major health care institution's EHR system, highlighting both the potential and the current limitations of FHIR APIs in supporting complex data analysis for oncology research.

Patient Portals Fail to Collect Structured Information About Who Else is Involved in a Person's Care.

The US health care delivery system does not systematically engage or support family or friend care partners. Meanwhile, the uptake and familiarity of portals to personal health information are increasing among patients. Technology innovations, such as shared access to the portal, use separate identity credentials to differentiate between patients and care partners. Although not well-known, or commonly used, shared access allows patients to identify who they do and do not want to be involved in their care. However, the processes for patients to grant shared access to portals are often limited or so onerous that interested patients and care partners often circumvent the process entirely. As a result, the vast majority of care partners resort to accessing portals using a patient's identity credentials-a "do-it-yourself" solution in conflict with a health systems' legal responsibility to protect patient privacy and autonomy. The personal narratives in this viewpoint (shared by permission) elaborate on quantitative studies and provide first-person snapshots of challenges faced by patients and families as they attempt to gain or grant shared access during crucial moments in their lives. As digital modalities increase patient roles in health care interactions, so does the importance of making shared access work for all stakeholders involved-patients, clinicians, and care partners. Electronic health record vendors must recognize that both patients and care partners are important users of their products, and health care organizations must acknowledge and support the critical contributions of care partners as distinct from patients.

Data-driven prediction of continuous renal replacement therapy survival.

Continuous renal replacement therapy (CRRT) is a form of dialysis prescribed to severely ill patients who cannot tolerate regular hemodialysis. However, as the patients are typically very ill to begin with, there is always uncertainty whether they will survive during or after CRRT treatment. Because of outcome uncertainty, a large percentage of patients treated with CRRT do not survive, utilizing scarce resources and raising false hope in patients and their families. To address these issues, we present a machine learning-based algorithm to predict short-term survival in patients being initiated on CRRT. We use information extracted from electronic health records from patients who were placed on CRRT at multiple institutions to train a model that predicts CRRT survival outcome; on a held-out test set, the model achieves an area under the receiver operating curve of 0.848 (CI = 0.822-0.870). Feature importance, error, and subgroup analyses provide insight into bias and relevant features for model prediction. Overall, we demonstrate the potential for predictive machine learning models to assist clinicians in alleviating the uncertainty of CRRT patient survival outcomes, with opportunities for future improvement through further data collection and advanced modeling.

Addressing label noise for electronic health records: insights from computer vision for tabular data.

The analysis of extensive electronic health records (EHR) datasets often calls for automated solutions, with machine learning (ML) techniques, including deep learning (DL), taking a lead role. One common task involves categorizing EHR data into predefined groups. However, the vulnerability of EHRs to noise and errors stemming from data collection processes, as well as potential human labeling errors, poses a significant risk. This risk is particularly prominent during the training of DL models, where the possibility of overfitting to noisy labels can have serious repercussions in healthcare. Despite the well-documented existence of label noise in EHR data, few studies have tackled this challenge within the EHR domain. Our work addresses this gap by adapting computer vision (CV) algorithms to mitigate the impact of label noise in DL models trained on EHR data. Notably, it remains uncertain whether CV methods, when applied to the EHR domain, will prove effective, given the substantial divergence between the two domains. We present empirical evidence demonstrating that these methods, whether used individually or in combination, can substantially enhance model performance when applied to EHR data, especially in the presence of noisy/incorrect labels. We validate our methods and underscore their practical utility in real-world EHR data, specifically in the context of COVID-19 diagnosis. Our study highlights the effectiveness of CV methods in the EHR domain, making a valuable contribution to the advancement of healthcare analytics and research.

Dissecting heritability, environmental risk, and air pollution causal effects using > 50 million individuals in MarketScan.

Large national-level electronic health record (EHR) datasets offer new opportunities for disentangling the role of genes and environment through deep phenotype information and approximate pedigree structures. Here we use the approximate geographical locations of patients as a proxy for spatially correlated community-level environmental risk factors. We develop a spatial mixed linear effect (SMILE) model that incorporates both genetics and environmental contribution. We extract EHR and geographical locations from 257,620 nuclear families and compile 1083 disease outcome measurements from the MarketScan dataset. We augment the EHR with publicly available environmental data, including levels of particulate matter 2.5 (PM2.5), nitrogen dioxide (NO2), climate, and sociodemographic data. We refine the estimates of genetic heritability and quantify community-level environmental contributions. We also use wind speed and direction as instrumental variables to assess the causal effects of air pollution. In total, we find PM2.5 or NO2 have statistically significant causal effects on 135 diseases, including respiratory, musculoskeletal, digestive, metabolic, and sleep disorders, where PM2.5 and NO2 tend to affect biologically distinct disease categories. These analyses showcase several robust strategies for jointly modeling genetic and environmental effects on disease risk using large EHR datasets and will benefit upcoming biobank studies in the era of precision medicine.

A simple electronic medical record-based predictors of illness severity in sepsis (sepsis) score.

OBJECTIVE: Current scores for predicting sepsis outcomes are limited by generalizability, complexity, and electronic medical record (EMR) integration. Here, we validate a simple EMR-based score for sepsis outcomes in a large multi-centre cohort. DESIGN: A simple electronic medical record-based predictor of illness severity in sepsis (SEPSIS) score was developed (4 additive lab-based predictors) using a population-based retrospective cohort study. SETTING: Internal medicine services across four academic teaching hospitals in Toronto, Canada from April 2010-March 2015 (primary cohort) and 2015-2019 (secondary cohort). PATIENTS: We identified patients admitted with sepsis based upon receipt of antibiotics and positive cultures. MEASUREMENTS AND MAIN RESULTS: The primary outcome was in-hospital mortality and secondary outcomes were ICU admission at 72 hours, and hospital length of stay (LOS). We calculated the area under the receiver operating curve (AUROC) for the SEPSIS score, qSOFA, and NEWS2. We then evaluated the SEPSIS score in a secondary cohort (2015-2019) of hospitalized patients receiving antibiotics. Our primary cohort included 1,890 patients with a median age of 72 years (IQR: 56-83). 9% died during hospitalization, 18.6% were admitted to ICU, and mean LOS was 12.7 days (SD: 21.5). In the primary and secondary (2015-2019, 4811 patients) cohorts, the AUROCs of the SEPSIS score for predicting in-hospital mortality were 0.63 and 0.64 respectively, which were similar to NEWS2 (0.62 and 0.67) and qSOFA (0.62 and 0.68). AUROCs for predicting ICU admission at 72 hours, and length of stay > 14 days, were similar between scores, in the primary and secondary cohorts. All scores had comparable calibration for predicting mortality. CONCLUSIONS: An EMR-based SEPSIS score shows a similar ability to predict important clinical outcomes compared with other validated scores (qSOFA and NEWS2). Because of the SEPSIS score's simplicity, it may prove a useful tool for clinical and research applications.

End-to-end pseudonymization of fine-tuned clinical BERT models : Privacy preservation with maintained data utility.

Many state-of-the-art results in natural language processing (NLP) rely on large pre-trained language models (PLMs). These models consist of large amounts of parameters that are tuned using vast amounts of training data. These factors cause the models to memorize parts of their training data, making them vulnerable to various privacy attacks. This is cause for concern, especially when these models are applied in the clinical domain, where data are very sensitive. Training data pseudonymization is a privacy-preserving technique that aims to mitigate these problems. This technique automatically identifies and replaces sensitive entities with realistic but non-sensitive surrogates. Pseudonymization has yielded promising results in previous studies. However, no previous study has applied pseudonymization to both the pre-training data of PLMs and the fine-tuning data used to solve clinical NLP tasks. This study evaluates the effects on the predictive performance of end-to-end pseudonymization of Swedish clinical BERT models fine-tuned for five clinical NLP tasks. A large number of statistical tests are performed, revealing minimal harm to performance when using pseudonymized fine-tuning data. The results also find no deterioration from end-to-end pseudonymization of pre-training and fine-tuning data. These results demonstrate that pseudonymizing training data to reduce privacy risks can be done without harming data utility for training PLMs.

Development of a quantitative index system for evaluating the quality of electronic medical records in disease risk intelligent prediction.

OBJECTIVE: This study aimed to develop and validate a quantitative index system for evaluating the data quality of Electronic Medical Records (EMR) in disease risk prediction using Machine Learning (ML). MATERIALS AND METHODS: The index system was developed in four steps: (1) a preliminary index system was outlined based on literature review; (2) we utilized the Delphi method to structure the indicators at all levels; (3) the weights of these indicators were determined using the Analytic Hierarchy Process (AHP) method; and (4) the developed index system was empirically validated using real-world EMR data in a ML-based disease risk prediction task. RESULTS: The synthesis of review findings and the expert consultations led to the formulation of a three-level index system with four first-level, 11 second-level, and 33 third-level indicators. The weights of these indicators were obtained through the AHP method. Results from the empirical analysis illustrated a positive relationship between the scores assigned by the proposed index system and the predictive performances of the datasets. DISCUSSION: The proposed index system for evaluating EMR data quality is grounded in extensive literature analysis and expert consultation. Moreover, the system's high reliability and suitability has been affirmed through empirical validation. CONCLUSION: The novel index system offers a robust framework for assessing the quality and suitability of EMR data in ML-based disease risk predictions. It can serve as a guide in building EMR databases, improving EMR data quality control, and generating reliable real-world evidence.

EHR Smart Phrases Used as Enrollment Mechanism in Diabetes Self-Management Support Programs: Preliminary Outcomes.

ABSTRACT: Diabetes in the United States is increasing rapidly. Innovative strategies are needed for diabetes prevention and self-management. This study assessed the usability, acceptability, and awareness of an electronic health record (EHR) tool for referring patients to a community-based diabetes self-management support program. Mixed-methods approaches were used, using EHR data and key informant interviews to assess the implementation of this quality improvement (QI) process intervention. The implementation of a smart phrase tool within the EHR led to a substantial increase in referrals (773) to the Health Extension for Diabetes (HED) program. Clinical health care professionals have actively used the referral mechanism; they reported using smart phrases to increase efficiency in patient care. Lack of training and program awareness was identified as a barrier to adoption. Awareness of the HED program and .HEDREF smart phrase was limited, but improved with targeted QI and training interventions. The .HEDREF smart phrase demonstrated effectiveness in increasing patient referrals to the HED program, highlighting the potential of EHR tools to streamline documentation and promote patient engagement in diabetes self-management. Future research should focus on broader health care contexts, patient perspectives, and integration of technology for optimal patient outcomes.

Exploring the Feasibility of an Electronic Tool for Predicting Retention in HIV Care: Provider Perspectives.

Retention in care for people living with HIV (PLWH) is important for individual and population health. Preemptive identification of PLWH at high risk of lapsing in care may improve retention efforts. We surveyed providers at nine institutions throughout Chicago about their perspectives on using an electronic health record (EHR) tool to predict the risk of lapsing in care. Sixty-three percent (20/32) of providers reported currently assessing patients' risk for lapsing in care, and 91% (29/32) reported willingness to implement an EHR tool. When compared to those with other job roles, prescribers agreed (vs. neutral) that the tool would be less biased than personal judgment (OR 13.33, 95% CI 1.05, 169.56). Prescribers were also more likely to identify community health workers as persons who should deliver these interventions (OR 10.50, 95% CI 1.02, 108.58). Transportation, housing, substance use, and employment information were factors that providers wanted to be included in an EHR-based tool. Social workers were significantly more likely to indicate the inclusion of employment information as important (OR 10.50, 95% CI 1.11, 98.87) when compared to other participants. Acceptability of an EHR tool was high; future research should investigate barriers and evaluate the effectiveness of such a tool.

Characteristics of Older Adults with Alzheimer's Disease Who Were Hospitalized during the COVID-19 Pandemic: A Secondary Data Analysis.

We aim to investigate the relationships between the population characteristics of patients with Alzheimer's Disease (AD) and their Healthcare Utilization (HU) during the COVID-19 pandemic. Electronic health records (EHRs) were utilized. The study sample comprised those with ICD-10 codes G30.0, G30.1, G30.8, and G30.9 between 1 January 2020 and 31 December 2021. Pearson's correlation and multiple regression were used. The analysis utilized 1537 patient records with an average age of 82.20 years (SD = 7.71); 62.3% were female. Patients had an average of 1.64 hospitalizations (SD = 1.18) with an average length of stay (ALOS) of 7.45 days (SD = 9.13). Discharge dispositions were primarily home (55.1%) and nursing facilities (32.4%). Among patients with multiple hospitalizations, a negative correlation was observed between age and both ALOS (r = -0.1264, p = 0.0030) and number of hospitalizations (r = -0.1499, p = 0.0004). Predictors of longer ALOS included male gender (p = 0.0227), divorced or widowed (p = 0.0056), and the use of Medicare Advantage and other private insurance (p = 0.0178). Male gender (p = 0.0050) and Black race (p = 0.0069) were associated with a higher hospitalization frequency. We recommend future studies including the co-morbidities of AD patients, larger samples, and longitudinal data.

Frailty-aware surgical care: Validation of Hospital Frailty Risk Score (HFRS) in older surgical patients.

Introduction: Frailty has an important impact on the health outcomes of older patients, and frailty screening is recommended as part of perioperative evaluation. The Hospital Frailty Risk Score (HFRS) is a validated tool that highlights frailty risk using 109 International Classification of Diseases, 10th revision (ICD-10) codes. In this study, we aim to compare HFRS to the Charlson Comorbidity Index (CCI) and validate HFRS as a predictor of adverse outcomes in Asian patients admitted to surgical services. Method: A retrospective study of electronic health records (EHR) was undertaken in patients aged 65 years and above who were discharged from surgical services between 1 April 2022 to 31 July 2022. Patients were stratified into low (HFRS <5), interme-diate (HFRS 5-15) and high (HFRS >15) risk of frailty. Results: Those at high risk of frailty were older and more likely to be men. They were also likely to have more comorbidities and a higher CCI than those at low risk of frailty. High HFRS scores were associated with an increased risk of adverse outcomes, such as mortality, hospital length of stay (LOS) and 30-day readmission. When used in combination with CCI, there was better prediction of mortality at 90 and 270 days, and 30-day readmission. Conclusion: To our knowledge, this is the first validation of HFRS in Singapore in surgical patients and confirms that high-risk HFRS predicts long LOS (≥7days), increased unplanned hospital readmissions (both 30-day and 270-day) and increased mortality (inpatient, 10-day, 30-day, 90-day, 270-day) compared with those at low risk of frailty.

Return to Work After Breast Cancer Treatment: An Electronic Health Record-based Study in North Norway.

BACKGROUND/AIM: Breast cancer treatment may interfere with work ability. Previous return-to-work studies have often focused on participants who were invited to participate after treatment completion. Participation varied, resulting in potential selection bias. This is a health-record-based study evaluating data completeness, both at baseline and one year after diagnosis. Correlations between baseline variables and return to work were also analyzed. PATIENTS AND METHODS: This is a retrospective review of 150 relapse-free survivors treated in Nordland county between 2019 and 2022 (all-comers managed with different types of systemic treatment and surgery). Work status was assessed in the regional electronic patient record (EPR). A 65-years age cut-off was employed to define two subgroups. RESULTS: At diagnosis, occupational status was assessable in all 150 patients. Almost all patients older than 65 years of age were retired (79%) or on disability pension for previously diagnosed conditions (19%). Data completeness one year after diagnosis was imperfect, because the EPR did not contain required information in 19 survivors. The majority of those ≤65 years of age at diagnosis returned to work. Only 14 of 88 patients (16%) did not return to work. Postoperative nodal stage was the only significant predictive factor. Those with pN1-3 had a lower return rate (68%) than their counterparts with lower nodal stage. CONCLUSION: This pilot study highlights the utility and limitations of EPR-based research in a rural Norwegian setting, emphasizing the need for comprehensive, individualized interventions to support breast cancer survivors in returning to work. The findings underscore the importance of considering diverse sociodemographic and clinical factors, as well as the potential benefits of long-term, population-based studies to address these complex challenges.

Using 42 CFR part 2 revisions to integrate substance use disorder treatment information into electronic health records at a safety net health system.

BACKGROUND: Regulations put in place to protect the privacy of individuals receiving substance use disorder (SUD) treatment have resulted in an unintended consequence of siloed SUD treatment and referral information outside of the integrated electronic health record (EHR). Recent revisions to these regulations have opened the door to data integration, which creates opportunities for enhanced patient care and more efficient workflows. We report on the experience of one safety-net hospital system integrating SUD treatment data into the EHR. METHODS: SUD treatment and referral information was integrated from siloed systems into the EHR through the implementation of a referral order, treatment episode definition, and referral and episode-related tools for addiction therapists and other clinicians. Integration was evaluated by monitoring SUD treatment episode characteristics, patient characteristics, referral linkage, and treatment episode retention before and after integration. Satisfaction of end-users with the new tools was evaluated through a survey of addiction therapists. RESULTS: After integration, three more SUD treatment programs were represented in the EHR. This increased the number of patients that could be tracked as initiating SUD treatment by 250%, from 562 before to 1,411 after integration. After integration, overall referral linkage declined (74% vs. 48%) and treatment episode retention at 90-days was higher (45% vs. 74%). Addiction therapists appreciated the efficiency of having all SUD treatment information in the EHR but did not find that the tools provided a large time savings shortly after integration. CONCLUSIONS: Integration of SUD treatment program data into the EHR facilitated both care coordination in patient treatment and quality improvement initiatives for treatment programs. Referral linkage and retention rates were likely modified by a broader capture of patients and changed outcome definition criteria. Greater preparatory workflow analysis may decrease initial end-user burden. Integration of siloed data, made possible given revised regulations, is essential to an efficient hub-and-spoke model of care, which must standardize and coordinate patient care across multiple clinics and departments.

Leveraging Electronic Health Records to Assess Residential Mobility Among Veterans in the Veterans Health Administration.

BACKGROUND: Residential mobility, or a change in residence, can influence health care utilization and outcomes. Health systems can leverage their patients' residential addresses stored in their electronic health records (EHRs) to better understand the relationships among patients' residences, mobility, and health. The Veteran Health Administration (VHA), with a unique nationwide network of health care systems and integrated EHR, holds greater potential for examining these relationships. METHODS: We conducted a cross-sectional analysis to examine the association of sociodemographics, clinical conditions, and residential mobility. We defined residential mobility by the number of VHA EHR residential addresses identified for each patient in a 1-year period (1/1-12/31/2018), with 2 different addresses indicating one move. We used generalized logistic regression to model the relationship between a priori selected correlates and residential mobility as a multinomial outcome (0, 1, ≥2 moves). RESULTS: In our sample, 84.4% (n=3,803,475) veterans had no move, 13.0% (n=587,765) had 1 move, and 2.6% (n=117,680) had ≥2 moves. In the multivariable analyses, women had greater odds of moving [aOR=1.11 (95% CI: 1.10,1.12) 1 move; 1.27 (1.25,1.30) ≥2 moves] than men. Veterans with substance use disorders also had greater odds of moving [aOR=1.26 (1.24,1.28) 1 move; 1.77 (1.72,1.81) ≥2 moves]. DISCUSSION: Our study suggests about 16% of veterans seen at VHA had at least 1 residential move in 2018. VHA data can be a resource to examine relationships between place, residential mobility, and health.

Isolation forest-voting fusion-multioutput: A stroke risk classification method based on the multidimensional output of abnormal sample detection.

BACKGROUND AND OBJECTIVE: Stroke has become a major disease threatening the health of people around the world. It has the characteristics of high incidence, high fatality, and a high recurrence rate. At this stage, problems such as poor recognition accuracy of stroke screening based on electronic medical records and insufficient recognition of stroke risk levels exist. These problems occur because of the systematic errors of medical equipment and the characteristics of the collectors during the process of electronic medical record collection. Errors can also occur due to misreporting or underreporting by the collection personnel and the strong subjectivity of the evaluation indicators. METHODS: This paper proposes an isolation forest-voting fusion-multioutput algorithm model. First, the screening data are collected for numerical processing and normalization. The composite feature score index of this paper is used to analyze the importance of risk factors, and then, the isolation forest is used. The algorithm detects abnormal samples, uses the voting fusion algorithm proposed in this article to perform decision fusion prediction classification, and outputs multidimensional (risk factor importance score, abnormal sample label, risk level classification, and stroke prediction) results that can be used as auxiliary decision information by doctors and medical staff. RESULTS: The isolation forest-voting fusion-multioutput algorithm proposed in this article has five categories (zero risk, low risk, high risk, ischemic stroke (TIA), and hemorrhagic stroke (HE)). The average accuracy rate of stroke prediction reached 79.59 %. CONCLUSIONS: The isolation forest-voting fusion-multioutput algorithm model proposed in this paper can not only accurately identify the various categories of stroke risk levels and stroke prediction but can also output multidimensional auxiliary decision-making information to help medical staff make decisions, thereby greatly improving the screening efficiency.

Speed and Accuracy of Recording Medication History by Pharmacists in Secondary Emergency Rooms.

In Japan, only few hospitals have pharmacists in their secondary emergency rooms to record medication history and provide drug information in real time. In this study, we investigated the benefits of pharmacist intervention in secondary emergency rooms by comparing the time taken by the pharmacists and non-pharmacists in the emergency room to record the medication history in the electronic medical record and the accuracy of its content. The study period was from September 1 to September 30, 2022, and included patients who were transported to our hospital for emergency care between 9:00 and 16:30. We compared the time taken between the patient's arrival until the recording of their medication history and the accuracy of the record by the emergency room pharmacists and non-pharmacists (paramedics or medical clerks). The study included 58 patients whose medication histories were collected by pharmacists, and 11 patients whose histories were collected by non-pharmacists. For pharmacists, the median time to record medication history in the electronic medical record was 12 min, whereas for non-pharmacists, it was 19 min, which was significantly different (p=0.015). The pharmacists accurately recorded the medication history of 98.3% (57/58) of patients, whereas non-pharmacists accurately recorded it for only 54.5% (6/11) of patients, with a significant difference (p<0.01). We observed that in secondary emergency rooms, when pharmacists were responsible for recording the patients' medication histories, it resulted in rapid and accurate sharing of medication history.