Factors Influencing Data Quality in Electronic Health Record Systems in 50 Health Facilities in Rwanda and the Role of Clinical Alerts: Cross-Sectional Observational Study.

BACKGROUND: Electronic health records (EHRs) play an increasingly important role in delivering HIV care in low- and middle-income countries. The data collected are used for direct clinical care, quality improvement, program monitoring, public health interventions, and research. Despite widespread EHR use for HIV care in African countries, challenges remain, especially in collecting high-quality data. OBJECTIVE: We aimed to assess data completeness, accuracy, and timeliness compared to paper-based records, and factors influencing data quality in a large-scale EHR deployment in Rwanda. METHODS: We randomly selected 50 health facilities (HFs) using OpenMRS, an EHR system that supports HIV care in Rwanda, and performed a data quality evaluation. All HFs were part of a larger randomized controlled trial, with 25 HFs receiving an enhanced EHR with clinical decision support systems. Trained data collectors visited the 50 HFs to collect 28 variables from the paper charts and the EHR system using the Open Data Kit app. We measured data completeness, timeliness, and the degree of matching of the data in paper and EHR records, and calculated concordance scores. Factors potentially affecting data quality were drawn from a previous survey of users in the 50 HFs. RESULTS: We randomly selected 3467 patient records, reviewing both paper and EHR copies (194,152 total data items). Data completeness was >85% threshold for all data elements except viral load (VL) results, second-line, and third-line drug regimens. Matching scores for data values were close to or >85% threshold, except for dates, particularly for drug pickups and VL. The mean data concordance was 10.2 (SD 1.28) for 15 (68%) variables. HF and user factors (eg, years of EHR use, technology experience, EHR availability and uptime, and intervention status) were tested for correlation with data quality measures. EHR system availability and uptime was positively correlated with concordance, whereas users' experience with technology was negatively correlated with concordance. The alerts for missing VL results implemented at 11 intervention HFs showed clear evidence of improving timeliness and completeness of initially low matching of VL results in the EHRs and paper records (11.9%-26.7%; P<.001). Similar effects were seen on the completeness of the recording of medication pickups (18.7%-32.6%; P<.001). CONCLUSIONS: The EHR records in the 50 HFs generally had high levels of completeness except for VL results. Matching results were close to or >85% threshold for nondate variables. Higher EHR stability and uptime, and alerts for entering VL both strongly improved data quality. Most data were considered fit for purpose, but more regular data quality assessments, training, and technical improvements in EHR forms, data reports, and alerts are recommended. The application of quality improvement techniques described in this study should benefit a wide range of HFs and data uses for clinical care, public health, and disease surveillance.

Experiences from patients in mental healthcare accessing their electronic health records: results from a cross-national survey in Estonia, Finland, Norway, and Sweden.

BACKGROUND: Patients' online record access (ORA) enables patients to read and use their health data through online digital solutions. One such solution, patient-accessible electronic health records (PAEHRs) have been implemented in Estonia, Finland, Norway, and Sweden. While accumulated research has pointed to many potential benefits of ORA, its application in mental healthcare (MHC) continues to be contested. The present study aimed to describe MHC users' overall experiences with national PAEHR services. METHODS: The study analysed the MHC-part of the NORDeHEALTH 2022 Patient Survey, a large-scale multi-country survey. The survey consisted of 45 questions, including demographic variables and questions related to users' experiences with ORA. We focused on the questions concerning positive experiences (benefits), negative experiences (errors, omissions, offence), and breaches of security and privacy. Participants were included in this analysis if they reported receiving mental healthcare within the past two years. Descriptive statistics were used to summarise data, and percentages were calculated on available data. RESULTS: 6,157 respondents were included. In line with previous research, almost half (45%) reported very positive experiences with ORA. A majority in each country also reported improved trust (at least 69%) and communication (at least 71%) with healthcare providers. One-third (29.5%) reported very negative experiences with ORA. In total, half of the respondents (47.9%) found errors and a third (35.5%) found omissions in their medical documentation. One-third (34.8%) of all respondents also reported being offended by the content. When errors or omissions were identified, about half (46.5%) reported that they took no action. There seems to be differences in how patients experience errors, omissions, and missing information between the countries. A small proportion reported instances where family or others demanded access to their records (3.1%), and about one in ten (10.7%) noted that unauthorised individuals had seen their health information. CONCLUSIONS: Overall, MHC patients reported more positive experiences than negative, but a large portion of respondents reported problems with the content of the PAEHR. Further research on best practice in implementation of ORA in MHC is therefore needed, to ensure that all patients may reap the benefits while limiting potential negative consequences.

Automating incidence and prevalence analysis in open cohorts.

MOTIVATION: Data is increasingly used for improvement and research in public health, especially administrative data such as that collected in electronic health records. Patients enter and exit these typically open-cohort datasets non-uniformly; this can render simple questions about incidence and prevalence time-consuming and with unnecessary variation between analyses. We therefore developed methods to automate analysis of incidence and prevalence in open cohort datasets, to improve transparency, productivity and reproducibility of analyses. IMPLEMENTATION: We provide both a code-free set of rules for incidence and prevalence that can be applied to any open cohort, and a python Command Line Interface implementation of these rules requiring python 3.9 or later. GENERAL FEATURES: The Command Line Interface is used to calculate incidence and point prevalence time series from open cohort data. The ruleset can be used in developing other implementations or can be rearranged to form other analytical questions such as period prevalence. AVAILABILITY: The command line interface is freely available from https://github.com/THINKINGGroup/analogy_publication .

Are all cancer survivors included when using electronically administered patient reported outcomes in primary healthcare cancer rehabilitation? A cross-sectional study.

BACKGROUND: Patient reported outcomes (PROs) are being used frequently in clinical practice. PROs often serve several purposes, such as increasing patient involvement, assessing health status, and monitoring and improving the quality-of-care at an aggregated level. However, the lack of representative PRO-data may have implications for all these purposes. This study aims to assess the association of non-administration of (not sending an electronic invite to PRO) and non-response to (not responding to PRO) electronically administered PROs with social inequality in a primary healthcare cancer rehabilitation setting. Furthermore, it examines whether the workflows surrounding PRO have an impact on non-administration and non-response. METHODS: This is a cross sectional study using routinely collected data from electronic health records and registers including cancer survivors (CSs) over 18 years booked for an initial consultation in a primary healthcare cancer rehabilitation setting using PROs for systematic health status assessment. During the study period two different PRO platforms were used, each associated with different workflows. Non-administration and non-response rates were calculated for sociodemographic characteristics for each PRO platform. Crude and adjusted odds ratios were calculated using univariate and multivariate logistic regression. RESULTS: In total, 1868 (platform 1) and 1446 (platform 2) CSCSs were booked for an initial consultation. Of these, 233 (12.5%) (platform 1) and 283 (19.6%) (platform 2) were not sent a PRO (non-administration). Among those who received a PRO, 157 (9.6%) on platform 1 and 140 (12.0%) on platform 2 did not respond (non-response). Non-administration of and non-response to PROs were significantly associated with lower socioeconomic status. Moreover, the workflows surrounding PROs seem to have an impact on non-inclusion in and non-response to PROs. CONCLUSIONS: Non-administration of and non-response to PROs in clinical practice is associated with determinants of social inequality. Clinical workflows and the PRO platforms used may potentially worsen this inequality. It is important to consider these implications when using PROs at both the individual and aggregated levels. A key aspect of implementing PROs in clinical practice is the ongoing focus on representativeness, including a focus on monitoring PRO administration and response.

Disparities and Biases in Food Insecurity Screening Among Admitted Children.

BACKGROUND AND OBJECTIVES: Food insecurity (FI) has increasingly become a focus for hospitalized patients. The best methods for screening practices, particularly in hospitalized children, are unknown. The purpose of the study was to evaluate results of an electronic medical record (EMR) embedded, brief screening tool for FI among inpatients. METHODS: This was a cross-sectional study from August 2020 to September 2022 for all children admitted to a quaternary children's hospital. Primary outcomes were proportion of those screened for FI and those identified to have a positive screen. FI was evaluated by The Hunger Vital Sign, a validated 2-question screen verbally obtained in the nursing intake form in the EMR. Covariates include demographic variables of age, sex, race, ethnicity, primary language, and insurance. Statistical analyses including all univariate outcome and bivariate comparisons were performed with SAS 9.4. RESULTS: There were 31 553 patient encounters with 81.7% screened for FI. Patients had a median age of 6.3 years, were mostly male (54.2%), White (60.6%), non-Hispanic (92.7%), English-speaking (94.3%), and had government insurance (79.8%). Younger (0-2 years), non-White, and noninsured patients were all screened significantly less often for FI (all P < .001). A total of 3.4% were identified as having FI. Patients who were older, non-White, Hispanic, non-English speaking, and had nonprivate insurance had higher FI (all P < .001). CONCLUSIONS: Despite the use of an EMR screening tool intended to be universal, we found variation in how we screen for FI. At times, we missed those who would benefit the most from intervention, and thus it may be subject to implementation bias.

Using electronic health records for clinical pharmacology research: Challenges and considerations.

Electronic health records (EHRs) contain a vast array of phenotypic data on large numbers of individuals, often collected over decades. Due to the wealth of information, EHR data have emerged as a powerful resource to make first discoveries and identify disparities in our healthcare system. While the number of EHR-based studies has exploded in recent years, most of these studies are directed at associations with disease rather than pharmacotherapeutic outcomes, such as drug response or adverse drug reactions. This is largely due to challenges specific to deriving drug-related phenotypes from the EHR. There is great potential for EHR-based discovery in clinical pharmacology research, and there is a critical need to address specific challenges related to accurate and reproducible derivation of drug-related phenotypes from the EHR. This review provides a detailed evaluation of challenges and considerations for deriving drug-related data from EHRs. We provide an examination of EHR-based computable phenotypes and discuss cutting-edge approaches to map medication information for clinical pharmacology research, including medication-based computable phenotypes and natural language processing. We also discuss additional considerations such as data structure, heterogeneity and missing data, rare phenotypes, and diversity within the EHR. By further understanding the complexities associated with conducting clinical pharmacology research using EHR-based data, investigators will be better equipped to design thoughtful studies with more reproducible results. Progress in utilizing EHRs for clinical pharmacology research should lead to significant advances in our ability to understand differential drug response and predict adverse drug reactions.

A Scalable and Extensible Logical Data Model of Electronic Health Record Audit Logs for Temporal Data Mining (RNteract): Model Conceptualization and Formulation.

BACKGROUND: Increased workload, including workload related to electronic health record (EHR) documentation, is reported as a main contributor to nurse burnout and adversely affects patient safety and nurse satisfaction. Traditional methods for workload analysis are either administrative measures (such as the nurse-patient ratio) that do not represent actual nursing care or are subjective and limited to snapshots of care (eg, time-motion studies). Observing care and testing workflow changes in real time can be obstructive to clinical care. An examination of EHR interactions using EHR audit logs could provide a scalable, unobtrusive way to quantify the nursing workload, at least to the extent that nursing work is represented in EHR documentation. EHR audit logs are extremely complex; however, simple analytical methods cannot discover complex temporal patterns, requiring use of state-of-the-art temporal data-mining approaches. To effectively use these approaches, it is necessary to structure the raw audit logs into a consistent and scalable logical data model that can be consumed by machine learning (ML) algorithms. OBJECTIVE: We aimed to conceptualize a logical data model for nurse-EHR interactions that would support the future development of temporal ML models based on EHR audit log data. METHODS: We conducted a preliminary review of EHR audit logs to understand the types of nursing-specific data captured. Using concepts derived from the literature and our previous experience studying temporal patterns in biomedical data, we formulated a logical data model that can describe nurse-EHR interactions, the nurse-intrinsic and situational characteristics that may influence those interactions, and outcomes of relevance to the nursing workload in a scalable and extensible manner. RESULTS: We describe the data structure and concepts from EHR audit log data associated with nursing workload as a logical data model named RNteract. We conceptually demonstrate how using this logical data model could support temporal unsupervised ML and state-of-the-art artificial intelligence (AI) methods for predictive modeling. CONCLUSIONS: The RNteract logical data model appears capable of supporting a variety of AI-based systems and should be generalizable to any type of EHR system or health care setting. Quantitatively identifying and analyzing temporal patterns of nurse-EHR interactions is foundational for developing interventions that support the nursing documentation workload and address nurse burnout.

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.

Preparing to Address Social Determinants of Health (SDOH): Approaches to Clinic Transformation.

CONTEXT: Pennsylvanians' health is influenced by numerous social determinants of health (SDOH). Integrating SDOH data into electronic health records (EHRs) is critical to identifying health disparities, informing public health policies, and devising interventions. Nevertheless, challenges remain in its implementation within clinical settings. In 2018, the Pennsylvania Department of Health (PADOH) received the Centers for Disease Control and Prevention's DP18-1815 "Improving the Health of Americans Through Prevention and Management of Diabetes and Heart Disease and Stroke" grant to strengthen SDOH data integration in Pennsylvania practices. IMPLEMENTATION: Quality Insights was contracted by PADOH to provide training tailored to each practice's readiness, an International Classification of Diseases, Tenth Revision (ICD-10) guide for SDOH, Continuing Medical Education on SDOH topics, and introduced the PRAPARE toolkit to streamline SDOH data integration and address disparities. Dissemination efforts included a podcast highlighting success stories and lessons learned from practices. From 2019 to 2022, Quality Insights and the University of Pittsburgh Evaluation Institute for Public Health (Pitt evaluation team) executed a mixed-methods evaluation. FINDINGS: During 2019-2022, Quality Insights supported 100 Pennsylvania practices in integrating SDOH data into EHR systems. Before COVID-19, 82.8% actively collected SDOH data, predominantly using PRAPARE tool (62.7%) and SDOH ICD-10 codes (80.4%). Amidst COVID-19, these statistics shifted to 65.1%, 45.2%, and 42.7%, respectively. Notably, the pandemic highlighted the importance of SDOH assessment and catalyzed some practices' utilization of SDOH data. Progress was evident among practices, with additional contribution to other DP18-1815 objectives. The main challenge was the variable understanding, utilization, and capability of handling SDOH data across practices. Effective strategies involved adaptable EHR systems, persistent efforts by Quality Insights, and the presence of change champions within practices. DISCUSSION: The COVID-19 pandemic strained staffing in many practices, impeding SDOH data integration into EHRs. Addressing the diverse understanding and use of SDOH data requires standardized training and procedures. Customized support and sustained engagement by facilitating organizations are paramount in ensuring practices' efficient SDOH data collection and integration.

MSGene: a multistate model using genetic risk and the electronic health record applied to lifetime risk of coronary artery disease.

Coronary artery disease (CAD) is the leading cause of death among adults worldwide. Accurate risk stratification can support optimal lifetime prevention. Current methods lack the ability to incorporate new information throughout the life course or to combine innate genetic risk factors with acquired lifetime risk. We designed a general multistate model (MSGene) to estimate age-specific transitions across 10 cardiometabolic states, dependent on clinical covariates and a CAD polygenic risk score. This model is designed to handle longitudinal data over the lifetime to address this unmet need and support clinical decision-making. We analyze longitudinal data from 480,638 UK Biobank participants and compared predicted lifetime risk with the 30-year Framingham risk score. MSGene improves discrimination (C-index 0.71 vs 0.66), age of high-risk detection (C-index 0.73 vs 0.52), and overall prediction (RMSE 1.1% vs 10.9%), in held-out data. We also use MSGene to refine estimates of lifetime absolute risk reduction from statin initiation. Our findings underscore our multistate model's potential public health value for accurate lifetime CAD risk estimation using clinical factors and increasingly available genetics toward earlier more effective prevention.

Doubly robust estimation and sensitivity analysis for marginal structural quantile models.

The marginal structure quantile model (MSQM) provides a unique lens to understand the causal effect of a time-varying treatment on the full distribution of potential outcomes. Under the semiparametric framework, we derive the efficiency influence function for the MSQM, from which a new doubly robust estimator is proposed for point estimation and inference. We show that the doubly robust estimator is consistent if either of the models associated with treatment assignment or the potential outcome distributions is correctly specified, and is semiparametric efficient if both models are correct. To implement the doubly robust MSQM estimator, we propose to solve a smoothed estimating equation to facilitate efficient computation of the point and variance estimates. In addition, we develop a confounding function approach to investigate the sensitivity of several MSQM estimators when the sequential ignorability assumption is violated. Extensive simulations are conducted to examine the finite-sample performance characteristics of the proposed methods. We apply the proposed methods to the Yale New Haven Health System Electronic Health Record data to study the effect of antihypertensive medications to patients with severe hypertension and assess the robustness of the findings to unmeasured baseline and time-varying confounding.

Severe psychiatric disorders are associated with increased risk of dementia.

BACKGROUND: Individuals with psychiatric disorders have an increased risk of developing dementia. Most cross-sectional studies suffer from selection bias, underdiagnosis and poor population representation, while there is only limited evidence from longitudinal studies on the role of anxiety, bipolar and psychotic disorders. Electronic health records (EHRs) permit large cohorts to be followed across the lifespan and include a wide range of diagnostic information. OBJECTIVE: To assess the association between four groups of psychiatric disorders (schizophrenia, bipolar disorder/mania, depression and anxiety) with dementia in two large population-based samples with EHR. METHODS: Using EHR on nearly 1 million adult individuals in Wales, and from 228 937 UK Biobank participants, we studied the relationships between schizophrenia, mania/bipolar disorder, depression, anxiety and subsequent risk of dementia. FINDINGS: In Secure Anonymised Information Linkage, there was a steep increase in the incidence of a first diagnosis of psychiatric disorder in the years prior to the diagnosis of dementia, reaching a peak in the year prior to dementia diagnosis for all psychiatric diagnoses. Psychiatric disorders, except anxiety, were highly significantly associated with a subsequent diagnosis of dementia: HRs=2.87, 2.80, 1.63 for schizophrenia, mania/bipolar disorder and depression, respectively. A similar pattern was found in the UK Biobank (HRs=4.46, 3.65, 2.39, respectively) and anxiety was also associated with dementia (HR=1.34). Increased risk of dementia was observed for all ages at onset of psychiatric diagnoses when these were divided into 10-year bins. CONCLUSIONS: Psychiatric disorders are associated with an increased risk of subsequent dementia, with a greater risk of more severe disorders. CLINICAL IMPLICATIONS: A late onset of psychiatric disorders should alert clinicians of possible incipient dementia.

Effect of integration of prescription drug monitoring program data in the electronic health record on queries by primary care providers.

Objective: To evaluate the impact of PDMP integration in the EHR on provider query rates within twelve primary care clinics in one academic medical center. Methods: Using linked data from the EHR and state PDMP program, we evaluated changes in PDMP query rates using a stepped-wedge observational design where integration was implemented in three waves (four clinics per wave) over a five-month period (May, July, September 2019). Multivariable negative binomial general estimating equations (GEE) models assessed changes in PDMP query rates, overall and across several provider and clinic-level subgroups. Results: Among 206 providers in PDMP integrated clinics, the average number of queries per provider per month increased significantly from 1.43 (95% CI 1.07 - 1.91) pre-integration to 3.94 (95% CI 2.96 - 5.24) post-integration, a 2.74-fold increase (95% CI 2.11 to 3.59; p < .0001). Those in the lowest quartile of PDMP use pre-integration increased 36.8-fold (95% CI 16.91 - 79.95) after integration, significantly more than other pre-integration PDMP use quartiles. Conclusions: Integration of the PDMP in the EHR significantly increased the use of the PDMP overall and across all studied subgroups. PDMP use increased to a greater degree among providers with lower PDMP use pre-integration.

Identifying subtypes of type 2 diabetes mellitus with machine learning: development, internal validation, prognostic validation and medication burden in linked electronic health records in 420 448 individuals.

INTRODUCTION: None of the studies of type 2 diabetes (T2D) subtyping to date have used linked population-level data for incident and prevalent T2D, incorporating a diverse set of variables, explainable methods for cluster characterization, or adhered to an established framework. We aimed to develop and validate machine learning (ML)-informed subtypes for type 2 diabetes mellitus (T2D) using nationally representative data. RESEARCH DESIGN AND METHODS: In population-based electronic health records (2006-2020; Clinical Practice Research Datalink) in individuals ≥18 years with incident T2D (n=420 448), we included factors (n=3787), including demography, history, examination, biomarkers and medications. Using a published framework, we identified subtypes through nine unsupervised ML methods (K-means, K-means++, K-mode, K-prototype, mini-batch, agglomerative hierarchical clustering, Birch, Gaussian mixture models, and consensus clustering). We characterized clusters using intracluster distributions and explainable artificial intelligence (AI) techniques. We evaluated subtypes for (1) internal validity (within dataset; across methods); (2) prognostic validity (prediction for 5-year all-cause mortality, hospitalization and new chronic diseases); and (3) medication burden. RESULTS: Development: We identified four T2D subtypes: metabolic, early onset, late onset and cardiometabolic. Internal validity: Subtypes were predicted with high accuracy (F1 score >0.98). Prognostic validity: 5-year all-cause mortality, hospitalization, new chronic disease incidence and medication burden differed across T2D subtypes. Compared with the metabolic subtype, 5-year risks of mortality and hospitalization in incident T2D were highest in late-onset subtype (HR 1.95, 1.85-2.05 and 1.66, 1.58-1.75) and lowest in early-onset subtype (1.18, 1.11-1.27 and 0.85, 0.80-0.90). Incidence of chronic diseases was highest in late-onset subtype and lowest in early-onset subtype. Medications: Compared with the metabolic subtype, after adjusting for age, sex, and pre-T2D medications, late-onset subtype (1.31, 1.28-1.35) and early-onset subtype (0.83, 0.81-0.85) were most and least likely, respectively, to be prescribed medications within 5 years following T2D onset. CONCLUSIONS: In the largest study using ML to date in incident T2D, we identified four distinct subtypes, with potential future implications for etiology, therapeutics, and risk prediction.

Use of electronic health record data to examine administrations of pro re nata analgesics during hip fracture post-acute care.

PURPOSE: Medications prescribed to older adults in US skilled nursing facilities (SNF) and administrations of pro re nata (PRN) "as needed" medications are unobservable in Medicare insurance claims. There is an ongoing deficit in our understanding of medication use during post-acute care. Using SNF electronic health record (EHR) datasets, including medication orders and barcode medication administration records, we described patterns of PRN analgesic prescribing and administrations among SNF residents with hip fracture. METHODS: Eligible participants resided in SNFs owned by 11 chains, had a diagnosis of hip fracture between January 1, 2018 to August 2, 2021, and received at least one administration of an analgesic medication in the 100 days after the hip fracture. We described the scheduling of analgesics, the proportion of available PRN doses administered, and the proportion of days with at least one PRN analgesic administration. RESULTS: Among 24 038 residents, 57.3% had orders for PRN acetaminophen, 67.4% PRN opioids, 4.2% PRN non-steroidal anti-inflammatory drugs, and 18.6% PRN combination products. The median proportion of available PRN doses administered per drug was 3%-50% and the median proportion of days where one or more doses of an ordered PRN analgesic was administered was 25%-75%. Results differed by analgesic class and the number of administrations ordered per day. CONCLUSIONS: EHRs can be leveraged to ascertain precise analgesic exposures during SNF stays. Future pharmacoepidemiology studies should consider linking SNF EHRs to insurance claims to construct a longitudinal history of medication use and healthcare utilization prior to and during episodes of SNF care.

Relationship between youth cardiometabolic health and physical activity in medical records.

BACKGROUND: Thers is limited research examining modifiable cardiometabolic risk factors with a single-item health behavior question obtained during a clinic visit. Such information could support clinicians in identifying patients at risk for adverse cardiometabolic health. We investigated if children meeting physical activity or screen time recommendations, collected during clinic visits, have better cardiometabolic health than children not meeting recommendations. We hypothesized that children meeting either recommendation would have fewer cardiometabolic risk factors. METHODS AND FINDINGS: This cross-sectional study used data from electronic medical records (EMRs) between January 1, 2013 through December 30, 2017 from children (2-18 years) with a well child visits and data for ≥1 cardiometabolic risk factor (i.e., systolic and diastolic blood pressure, glycated hemoglobin, alanine transaminase, high-density and low-density lipoprotein, total cholesterol, and/or triglycerides). Physical activity and screen time were patient/caregiver-reported. Analyses included EMRs from 63,676 well child visits by 30,698 unique patients (49.3% female; 41.7% Black, 31.5% Hispanic). Models that included data from all visits indicated children meeting physical activity recommendations had reduced risk for abnormal blood pressure (odds ratio [OR] = 0.91, 95%CI 0.86, 0.97; p = 0.002), glycated hemoglobin (OR = 0.83, 95%CI 0.75, 0.91; p = 0.00006), alanine transaminase (OR = 0.85, 95%CI 0.79, 0.92; p = 0.00001), high-density lipoprotein (OR = 0.88, 95%CI 0.82, 0.95; p = 0.0009), and triglyceride values (OR = 0.89, 95%CI 0.83, 0.96; p = 0.002). Meeting screen time recommendations was not associated with abnormal cardiometabolic risk factors. CONCLUSION: Collecting information on reported adherence to meeting physical activity recommendations can provide clinicians with additional information to identify patients with a higher risk of adverse cardiometabolic health.

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.

Improving quality of care by standardising patient data collection in electronic medical records in an oncology department in Spain.

BACKGROUND: Evaluation of quality of care in oncology is key in ensuring patients receive adequate treatment. American Society of Clinical Oncology's (ASCO) Quality Oncology Practice Initiative (QOPI) Certification Program (QCP) is an international initiative that evaluates quality of care in outpatient oncology practices. METHODS: We retrospectively reviewed free-text electronic medical records from patients with breast cancer (BR), colorectal cancer (CRC) or non-small cell lung cancer (NSCLC). In a baseline measurement, high scores were obtained for the nine disease-specific measures of QCP Track (2021 version had 26 measures); thus, they were not further analysed. We evaluated two sets of measures: the remaining 17 QCP Track measures, as well as these plus other 17 measures selected by us (combined measures). Review of data from 58 patients (26 BR; 18 CRC; 14 NSCLC) seen in June 2021 revealed low overall quality scores (OQS)-below ASCO's 75% threshold-for QCP Track measures (46%) and combined measures (58%). We developed a plan to improve OQS and monitored the impact of the intervention by abstracting data at subsequent time points. RESULTS: We evaluated potential causes for the low OQS and developed a plan to improve it over time by educating oncologists at our hospital on the importance of improving collection of measures and highlighting the goal of applying for QOPI certification. We conducted seven plan-do-study-act cycles and evaluated the scores at seven subsequent data abstraction time points from November 2021 to December 2022, reviewing 404 patients (199 BR; 114 CRC; 91 NSCLC). All measures were improved. Four months after the intervention, OQS surpassed the quality threshold and was maintained for 10 months until the end of the study (range, 78-87% for QCP Track measures; 78-86% for combined measures). CONCLUSIONS: We developed an easy-to-implement intervention that achieved a fast improvement in OQS, enabling our Medical Oncology Department to aim for QOPI certification.

10-year multimorbidity patterns among people with and without rheumatic and musculoskeletal diseases: an observational cohort study using linked electronic health records from Wales, UK.

OBJECTIVES: To compare the patterns of multimorbidity between people with and without rheumatic and musculoskeletal diseases (RMDs) and to describe how these patterns change by age and sex over time, between 2010 and 2019. PARTICIPANTS: 103 426 people with RMDs and 2.9 million comparators registered in 395 Wales general practices (GPs). Each patient with an RMD aged 0-100 years between January 2010 and December 2019 registered in Clinical Practice Research Welsh practices was matched with up to five comparators without an RMD, based on age, gender and GP code. PRIMARY OUTCOME MEASURES: The prevalence of 29 Elixhauser-defined comorbidities in people with RMDs and comparators categorised by age, gender and GP practices. Conditional logistic regression models were fitted to calculate differences (OR, 95% CI) in associations with comorbidities between cohorts. RESULTS: The most prevalent comorbidities were cardiovascular risk factors, hypertension and diabetes. Having an RMD diagnosis was associated with a significantly higher odds for many conditions including deficiency anaemia (OR 1.39, 95% CI (1.32 to 1.46)), hypothyroidism (OR 1.34, 95% CI (1.19 to 1.50)), pulmonary circulation disorders (OR 1.39, 95% CI 1.12 to 1.73) diabetes (OR 1.17, 95% CI (1.11 to 1.23)) and fluid and electrolyte disorders (OR 1.27, 95% CI (1.17 to 1.38)). RMDs have a higher proportion of multimorbidity (two or more conditions in addition to the RMD) compared with non-RMD group (81% and 73%, respectively in 2019) and the mean number of comorbidities was higher in women from the age of 25 and 50 in men than in non-RMDs group. CONCLUSION: People with RMDs are approximately 1.5 times as likely to have multimorbidity as the general population and provide a high-risk group for targeted intervention studies. The individuals with RMDs experience a greater load of coexisting health conditions, which tend to manifest at earlier ages. This phenomenon is particularly pronounced among women. Additionally, there is an under-reporting of comorbidities in individuals with RMDs.