Healthcare utilization patterns of individuals with depression after national policy to increase the mental health workforce in primary care: a data linkage study.

BACKGROUND: The deployment of the mental health nurse, an additional healthcare provider for individuals in need of mental healthcare in Dutch general practices, was expected to substitute treatments from general practitioners and providers in basic and specialized mental healthcare (psychologists, psychotherapists, psychiatrists, etc.). The goal of this study was to investigate the extent to which the degree of mental health nurse deployment in general practices is associated with healthcare utilization patterns of individuals with depression. METHODS: We combined national health insurers' claims data with electronic health records from general practices. Healthcare utilization patterns of individuals with depression between 2014 and 2019 (N = 31,873) were analysed. The changes in the proportion of individuals treated after depression onset were assessed in association with the degree of mental health nurse deployment in general practices. RESULTS: The proportion of individuals with depression treated by the GP, in basic and specialized mental healthcare was lower in individuals in practices with high mental health nurse deployment. While the association between mental health nurse deployment and consultation in basic mental healthcare was smaller for individuals who depleted their deductibles, the association was still significant. Treatment volume of general practitioners was also lower in practices with higher levels of mental health nurse deployment. CONCLUSION: Individuals receiving care at a general practice with a higher degree of mental health nurse deployment have lower odds of being treated by mental healthcare providers in other healthcare settings. More research is needed to evaluate to what extent substitution of care from specialized mental healthcare towards general practices might be associated with waiting times for specialized mental healthcare.

Association of Patient Portal Messaging with Survival Among Radiation Oncology Patients.

OBJECTIVE: The shift to electronic health records has led to both patient portal messaging and large amounts of digital, real-world data for research. The objective of this study was to examine the association between portal messaging and survival among radiation oncology patients, using real-world data. METHODS: This retrospective cohort study included patients at least 21 years old and seen by radiation oncology providers between January 14, 2014 and April 23, 2023 at XXXX. We developed Cox proportional hazards models for the outcome of death and examined factors associated with portal messaging using logistic regression models. RESULTS: Among 25,367 patients, the median age was 64 (interquartile range, 54-72), 13,175 (52%) were White, and 14,389 (57%) were male. Overall, as the first message in a thread, 8,986 (35%) patients sent messages to radiation oncology providers and 4,218 (17%) patients were sent messages from radiation oncology providers. Patients with head and neck or genitourinary malignancies were more likely than those with other diagnoses to send portal messages to and be sent portal messages from radiation oncology providers. Both patients sending portal messages to radiation oncology providers (Hazard Ratio [HR], 0.90; 95% Confidence Interval [CI], 0.84-0.96; P=0.001) and patients being sent messages from radiation oncology providers (HR, 0.77; CI, 0.70-0.84; P<0.001) as the first message in a thread were associated with survival after adjusting for socioeconomic, disease, and treatment characteristics. There were disparities among patients sending portal messages to radiation oncology providers including for Black versus White patients (Odds Ratio [OR], 0.60; CI, 0.51-0.69; P<0.001) and for Medicaid versus Medicare patients (OR, 0.70; CI, 0.62-0.79; P<0.001). There were also disparities among patients being sent portal messages by radiation oncology providers including for Black versus White patients (OR, 0.77; CI, 0.64-0.91; P=0.003), for Medicaid versus Medicare patients (OR, 0.76; CI, 0.65-0.89; P<0.001), and for patients with female versus male providers (OR, 1.47; CI 1.34-1.62; P<0.001). CONCLUSIONS: Sending portal messages to and being sent portal messages from radiation oncology providers were associated with better survival. Future studies should elucidate how best to support patient and provider engagement.

Development of a multimodal geomarker pipeline to assess the impact of social, economic, and environmental factors on pediatric health outcomes.

OBJECTIVES: We sought to create a computational pipeline for attaching geomarkers, contextual or geographic measures that influence or predict health, to electronic health records at scale, including developing a tool for matching addresses to parcels to assess the impact of housing characteristics on pediatric health. MATERIALS AND METHODS: We created a geomarker pipeline to link residential addresses from hospital admissions at Cincinnati Children's Hospital Medical Center (CCHMC) between July 2016 and June 2022 to place-based data. Linkage methods included by date of admission, geocoding to census tract, street range geocoding, and probabilistic address matching. We assessed 4 methods for probabilistic address matching. RESULTS: We characterized 124 244 hospitalizations experienced by 69 842 children admitted to CCHMC. Of the 55 684 hospitalizations with residential addresses in Hamilton County, Ohio, all were matched to 7 temporal geomarkers, 97% were matched to 79 census tract-level geomarkers and 13 point-level geomarkers, and 75% were matched to 16 parcel-level geomarkers. Parcel-level geomarkers were linked using our exact address matching tool developed using the best-performing linkage method. DISCUSSION: Our multimodal geomarker pipeline provides a reproducible framework for attaching place-based data to health data while maintaining data privacy. This framework can be applied to other populations and in other regions. We also created a tool for address matching that democratizes parcel-level data to advance precision population health efforts. CONCLUSION: We created an open framework for multimodal geomarker assessment by harmonizing and linking a set of over 100 geomarkers to hospitalization data, enabling assessment of links between geomarkers and hospital admissions.

Addressing bias in preterm birth research: The role of advanced imputation techniques for missing race and ethnicity in perinatal health data.

OBJECTIVES: To evaluate the effectiveness of Bayesian Improved Surname Geocoding (BISG) and Bayesian Improved First Name Surname Geocoding (BIFSG) in estimating race and ethnicity, and how they influence odds ratios for preterm birth. METHODS: We analyzed hospital birth admission electronic health records (EHR) data (N = 9985). We created two simulation sets with 40 % of race and ethnicity data missing randomly or more likely for non-Hispanic black birthing people who had preterm birth. We calculated C-statistics to evaluate how accurately BISG and BIFSG estimate race and ethnicity. We examined the association between race and ethnicity and preterm birth using logistic regression and reported odds ratios (OR). RESULTS: BISG and BIFSG showed high accuracy for most racial and ethnic categories (C-statistics = 0.94-0.97, 95 % confidence intervals [CI] = 0.92-0.97). When race and ethnicity were not missing at random, BISG (OR = 1.25, CI = 0.97-1.62) and BIFSG (OR = 1.38, CI = 1.08-1.76) resulted in positive estimates mirroring the true association (OR = 1.68, CI = 1.34-2.09) for Non-Hispanic Black birthing people, while traditional methods showed contrasting estimates (Complete case OR = 0.62, CI = 0.41-0.94; multiple imputation OR = 0.63, CI = 0.40-0.98). CONCLUSIONS: BISG and BIFSG accurately estimate missing race and ethnicity in perinatal EHR data, decreasing bias in preterm birth research, and are recommended over traditional methods to reduce potential bias.

Clinician Barriers to Ordering Pulmonary Function Tests for Adults with Suspected Asthma.

BACKGROUND: Primary care clinicians do not adhere to national and international guidelines recommending pulmonary function testing (PFTs) in patients with suspected asthma. Little is known about why that occurs. Our objective was to assess clinician focused barriers to ordering PFTs. METHODS: An internet-based 11-item survey of primary care clinicians at a large safety-net institution was conducted between August 2021 and November 2021. This survey assessed barriers and possible electronic health record (EHR) solutions to ordering PFTs. One of the survey questions contained an open-ended question about barriers which was analyzed qualitatively. RESULTS: The survey response rate was 59% (117/200). The top 3 reported barriers included beliefs that testing will not change management, distance to testing site, and the physical effort it takes to complete testing. Clinicians were in favor of an EHR intervention to prompt them to order PFTs. Responses to the open-ended question also conveyed that objective testing does not change management. DISCUSSION: PFTs improve diagnostic accuracy and reduce inappropriate therapies. Of the barriers we identified, the most modifiable is to educate clinicians about how PFTs can change management. That in conjunction with an EHR prompt, which clinicians approved of, may lead to guideline congruent and improved quality in asthma care.

Variable Impact of Medical Scribes on Physician Electronic Health Record Documentation Practices: A Quantitative Analysis Across a Large, Integrated Health-System.

BACKGROUND: Medical scribes have been utilized to reduce electronic health record (EHR) associated documentation burden. Although evidence suggests benefits to scribes, no large-scale studies have quantitatively evaluated scribe impact on physician documentation across clinical settings. This study aimed to evaluate the effect of scribes on physician EHR documentation behaviors and performance. METHODS: This retrospective cohort study used EHR audit log data from a large academic health system to evaluate clinical documentation for all ambulatory encounters between January 2014 and December 2019 to evaluate the effect of scribes on physician documentation behaviors. Scribe services were provided on a first-come, first-served basis on physician request. Based on a physician's scribe use, encounters were grouped into 3 categories: never using a scribe, prescribe (before scribe use), or using a scribe. Outcomes included chart closure time, the proportion of delinquent charts, and charts closed after-hours. RESULTS: Three hundred ninety-five physicians (23% scribe users) across 29 medical subspecialties, encompassing 1,132,487 encounters, were included in the analysis. At baseline, scribe users had higher chart closure time, delinquent charts, and after-hours documentation than physicians who never used scribes. Among scribe users, the difference in outcome measures postscribe compared with baseline varied, and using a scribe rarely resulted in outcome measures approaching a range similar to the performance levels of nonusing physicians. In addition, there was variability in outcome measures across medical specialties and within similar subspecialties. CONCLUSION: Although scribes may improve documentation efficiency among some physicians, not all will improve EHR-related documentation practices. Different strategies may help to optimize documentation behaviors of physician-scribe dyads and maximize outcomes of scribe implementation.

To weight or not to weight? The effect of selection bias in 3 large electronic health record-linked biobanks and recommendations for practice.

OBJECTIVES: To develop recommendations regarding the use of weights to reduce selection bias for commonly performed analyses using electronic health record (EHR)-linked biobank data. MATERIALS AND METHODS: We mapped diagnosis (ICD code) data to standardized phecodes from 3 EHR-linked biobanks with varying recruitment strategies: All of Us (AOU; n = 244 071), Michigan Genomics Initiative (MGI; n = 81 243), and UK Biobank (UKB; n = 401 167). Using 2019 National Health Interview Survey data, we constructed selection weights for AOU and MGI to represent the US adult population more. We used weights previously developed for UKB to represent the UKB-eligible population. We conducted 4 common analyses comparing unweighted and weighted results. RESULTS: For AOU and MGI, estimated phecode prevalences decreased after weighting (weighted-unweighted median phecode prevalence ratio [MPR]: 0.82 and 0.61), while UKB estimates increased (MPR: 1.06). Weighting minimally impacted latent phenome dimensionality estimation. Comparing weighted versus unweighted phenome-wide association study for colorectal cancer, the strongest associations remained unaltered, with considerable overlap in significant hits. Weighting affected the estimated log-odds ratio for sex and colorectal cancer to align more closely with national registry-based estimates. DISCUSSION: Weighting had a limited impact on dimensionality estimation and large-scale hypothesis testing but impacted prevalence and association estimation. When interested in estimating effect size, specific signals from untargeted association analyses should be followed up by weighted analysis. CONCLUSION: EHR-linked biobanks should report recruitment and selection mechanisms and provide selection weights with defined target populations. Researchers should consider their intended estimands, specify source and target populations, and weight EHR-linked biobank analyses accordingly.

Comparing penalization methods for linear models on large observational health data.

OBJECTIVE: This study evaluates regularization variants in logistic regression (L1, L2, ElasticNet, Adaptive L1, Adaptive ElasticNet, Broken adaptive ridge [BAR], and Iterative hard thresholding [IHT]) for discrimination and calibration performance, focusing on both internal and external validation. MATERIALS AND METHODS: We use data from 5 US claims and electronic health record databases and develop models for various outcomes in a major depressive disorder patient population. We externally validate all models in the other databases. We use a train-test split of 75%/25% and evaluate performance with discrimination and calibration. Statistical analysis for difference in performance uses Friedman's test and critical difference diagrams. RESULTS: Of the 840 models we develop, L1 and ElasticNet emerge as superior in both internal and external discrimination, with a notable AUC difference. BAR and IHT show the best internal calibration, without a clear external calibration leader. ElasticNet typically has larger model sizes than L1. Methods like IHT and BAR, while slightly less discriminative, significantly reduce model complexity. CONCLUSION: L1 and ElasticNet offer the best discriminative performance in logistic regression for healthcare predictions, maintaining robustness across validations. For simpler, more interpretable models, L0-based methods (IHT and BAR) are advantageous, providing greater parsimony and calibration with fewer features. This study aids in selecting suitable regularization techniques for healthcare prediction models, balancing performance, complexity, and interpretability.

Coding of Childhood Psychiatric and Neurodevelopmental Disorders in Electronic Health Records of a Large Integrated Health Care System: Validation Study.

Background: Mental, emotional, and behavioral disorders are chronic pediatric conditions, and their prevalence has been on the rise over recent decades. Affected children have long-term health sequelae and a decline in health-related quality of life. Due to the lack of a validated database for pharmacoepidemiological research on selected mental, emotional, and behavioral disorders, there is uncertainty in their reported prevalence in the literature. objectives: We aimed to evaluate the accuracy of coding related to pediatric mental, emotional, and behavioral disorders in a large integrated health care system's electronic health records (EHRs) and compare the coding quality before and after the implementation of the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) coding as well as before and after the COVID-19 pandemic. Methods: Medical records of 1200 member children aged 2-17 years with at least 1 clinical visit before the COVID-19 pandemic (January 1, 2012, to December 31, 2014, the ICD-9-CM coding period; and January 1, 2017, to December 31, 2019, the ICD-10-CM coding period) and after the COVID-19 pandemic (January 1, 2021, to December 31, 2022) were selected with stratified random sampling from EHRs for chart review. Two trained research associates reviewed the EHRs for all potential cases of autism spectrum disorder (ASD), attention-deficit hyperactivity disorder (ADHD), major depression disorder (MDD), anxiety disorder (AD), and disruptive behavior disorders (DBD) in children during the study period. Children were considered cases only if there was a mention of any one of the conditions (yes for diagnosis) in the electronic chart during the corresponding time period. The validity of diagnosis codes was evaluated by directly comparing them with the gold standard of chart abstraction using sensitivity, specificity, positive predictive value, negative predictive value, the summary statistics of the F-score, and Youden J statistic. κ statistic for interrater reliability among the 2 abstractors was calculated. Results: The overall agreement between the identification of mental, behavioral, and emotional conditions using diagnosis codes compared to medical record abstraction was strong and similar across the ICD-9-CM and ICD-10-CM coding periods as well as during the prepandemic and pandemic time periods. The performance of AD coding, while strong, was relatively lower compared to the other conditions. The weighted sensitivity, specificity, positive predictive value, and negative predictive value for each of the 5 conditions were as follows: 100%, 100%, 99.2%, and 100%, respectively, for ASD; 100%, 99.9%, 99.2%, and 100%, respectively, for ADHD; 100%, 100%, 100%, and 100%, respectively for DBD; 87.7%, 100%, 100%, and 99.2%, respectively, for AD; and 100%, 100%, 99.2%, and 100%, respectively, for MDD. The F-score and Youden J statistic ranged between 87.7% and 100%. The overall agreement between abstractors was almost perfect (κ=95%). Conclusions: Diagnostic codes are quite reliable for identifying selected childhood mental, behavioral, and emotional conditions. The findings remained similar during the pandemic and after the implementation of the ICD-10-CM coding in the EHR system.

Data-Driven Exploration of National Health Service Talking Therapies Care Pathways Using Process Mining: Retrospective Cohort Study.

BACKGROUND: The National Health Service (NHS) Talking Therapies program treats people with common mental health problems in England according to "stepped care," in which lower-intensity interventions are offered in the first instance, where clinically appropriate. Limited resources and pressure to achieve service standards mean that program providers are exploring all opportunities to evaluate and improve the flow of patients through their service. Existing research has found variation in clinical performance and stepped care implementation across sites and has identified associations between service delivery and patient outcomes. Process mining offers a data-driven approach to analyzing and evaluating health care processes and systems, enabling comparison of presumed models of service delivery and their actual implementation in practice. The value and utility of applying process mining to NHS Talking Therapies data for the analysis of care pathways have not been studied. OBJECTIVE: A better understanding of systems of service delivery will support improvements and planned program expansion. Therefore, this study aims to demonstrate the value and utility of applying process mining to NHS Talking Therapies care pathways using electronic health records. METHODS: Routine collection of a wide variety of data regarding activity and patient outcomes underpins the Talking Therapies program. In our study, anonymized individual patient referral records from two sites over a 2-year period were analyzed using process mining to visualize the care pathway process by mapping the care pathway and identifying common pathway routes. RESULTS: Process mining enabled the identification and visualization of patient flows directly from routinely collected data. These visualizations illustrated waiting periods and identified potential bottlenecks, such as the wait for higher-intensity cognitive behavioral therapy (CBT) at site 1. Furthermore, we observed that patients discharged from treatment waiting lists appeared to experience longer wait durations than those who started treatment. Process mining allowed analysis of treatment pathways, showing that patients commonly experienced treatment routes that involved either low- or high-intensity interventions alone. Of the most common routes, >5 times as many patients experienced direct access to high-intensity treatment rather than stepped care. Overall, 3.32% (site 1: 1507/45,401) and 4.19% (site 2: 527/12,590) of all patients experienced stepped care. CONCLUSIONS: Our findings demonstrate how process mining can be applied to Talking Therapies care pathways to evaluate pathway performance, explore relationships among performance issues, and highlight systemic issues, such as stepped care being relatively uncommon within a stepped care system. Integration of process mining capability into routine monitoring will enable NHS Talking Therapies service stakeholders to explore such issues from a process perspective. These insights will provide value to services by identifying areas for service improvement, providing evidence for capacity planning decisions, and facilitating better quality analysis into how health systems can affect patient outcomes.

Forecasting acute kidney injury and resource utilization in ICU patients using longitudinal, multimodal models.

BACKGROUND: Advances in artificial intelligence (AI) have realized the potential of revolutionizing healthcare, such as predicting disease progression via longitudinal inspection of Electronic Health Records (EHRs) and lab tests from patients admitted to Intensive Care Units (ICU). Although substantial literature exists addressing broad subjects, including the prediction of mortality, length-of-stay, and readmission, studies focusing on forecasting Acute Kidney Injury (AKI), specifically dialysis anticipation like Continuous Renal Replacement Therapy (CRRT) are scarce. The technicality of how to implement AI remains elusive. OBJECTIVE: This study aims to elucidate the important factors and methods that are required to develop effective predictive models of AKI and CRRT for patients admitted to ICU, using EHRs in the Medical Information Mart for Intensive Care (MIMIC) database. METHODS: We conducted a comprehensive comparative analysis of established predictive models, considering both time-series measurements and clinical notes from MIMIC-IV databases. Subsequently, we proposed a novel multi-modal model which integrates embeddings of top-performing unimodal models, including Long Short-Term Memory (LSTM) and BioMedBERT, and leverages both unstructured clinical notes and structured time series measurements derived from EHRs to enable the early prediction of AKI and CRRT. RESULTS: Our multimodal model achieved a lead time of at least 12 h ahead of clinical manifestation, with an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.888 for AKI and 0.997 for CRRT, as well as an Area Under the Precision Recall Curve (AUPRC) of 0.727 for AKI and 0.840 for CRRT, respectively, which significantly outperformed the baseline models. Additionally, we performed a SHapley Additive exPlanation (SHAP) analysis using the expected gradients algorithm, which highlighted important, previously underappreciated predictive features for AKI and CRRT. CONCLUSION: Our study revealed the importance and the technicality of applying longitudinal, multimodal modeling to improve early prediction of AKI and CRRT, offering insights for timely interventions. The performance and interpretability of our model indicate its potential for further assessment towards clinical applications, to ultimately optimize AKI management and enhance patient outcomes.

Using electronic health records to evaluate the adherence to cervical cancer prevention guidelines: A cross-sectional study.

OBJECTIVE: The fight against cervical cancer requires effective screening together with optimal and on-time treatment along the care continuum. We examined the impact of cervical cancer testing and treatment guidelines on testing practices, and follow-up adherence to guidelines. METHODS: Data from Estonian electronic health records and healthcare provision claims for 50,702 women was used. The annual rates of PAP tests, HPV tests and colposcopies during two guideline periods (2nd version 2012-2014 vs 3rd version 2016-2019) were compared. To assess the adherence to guidelines, the subjects were classified as adherent, over- or undertested based on the timing of the appropriate follow-up test. RESULTS: The number of PAP tests decreased and HPV tests increased during the 3rd guideline period (p < 0.01). During the 3rd guideline period, among 21-29-year-old women, the adherence to guidelines ranged from 38.7% (44.4…50.1) for ASC-US to 73.4% (62.6…84.3) for HSIL and among 30-59-year-old from 49.0% (45.9…52.2) for ASC-US to 65.7% (58.8…72.7) for ASCH. The highest rate of undertested women was for ASC-US (21-29y: 25.7%; 30-59y: 21.9%). The rates of over-tested women remained below 12% for all cervical pathologies observed. There were 55.2% (95% CI 49.7…60.8) of 21-24-year-olds and 57.1% (95% CI 53.6…60.6) of 25-29-year-old women who received HPV test not adherent to guidelines. CONCLUSIONS: Our findings highlighted some shortcomings in guideline adherence, especially among women under 30. The insights gained from this study help to improve the quality of care and, thus, reduce cervical cancer incidence and mortality.

Asynchronous Versus Synchronous Screening for Depression and Suicidality in a Primary Health Care System: Quality Improvement Study.

Background: Despite being a debilitating, costly, and potentially life-threatening condition, depression is often underdiagnosed and undertreated. Previsit Patient Health Questionnaire-9 (PHQ-9) may help primary care health systems identify symptoms of severe depression and prevent suicide through early intervention. Little is known about the impact of previsit web-based PHQ-9 on patient care and safety. Objective: We aimed to investigate differences among patient characteristics and provider clinical responses for patients who complete a web-based (asynchronous) versus in-clinic (synchronous) PHQ-9. Methods: This quality improvement study was conducted at 33 clinic sites across 2 health systems in Northern California from November 1, 2020, to May 31, 2021, and evaluated 1683 (0.9% of total PHQs completed) records of patients endorsing thoughts that they would be better off dead or of self-harm (question 9 in the PHQ-9) following the implementation of a depression screening program that included automated electronic previsit PHQ-9 distribution. Patient demographics and providers' clinical response (suicide risk assessment, triage nurse connection, medication management, electronic consultation with psychiatrist, and referral to social worker or psychiatrist) were compared for patients with asynchronous versus synchronous PHQ-9 completion. Results: Of the 1683 patients (female: n=1071, 63.7%; non-Hispanic: n=1293, 76.8%; White: n=831, 49.4%), Hispanic and Latino patients were 40% less likely to complete a PHQ-9 asynchronously (odds ratio [OR] 0.6, 95% CI 0.45-0.8; P<.001). Patients with Medicare insurance were 36% (OR 0.64, 95% CI 0.51-0.79) less likely to complete a PHQ-9 asynchronously than patients with private insurance. Those with moderate to severe depression were 1.61 times more likely (95% CI 1.21-2.15; P=.001) to complete a PHQ-9 asynchronously than those with no or mild symptoms. Patients who completed a PHQ-9 asynchronously were twice as likely to complete a Columbia-Suicide Severity Rating Scale (OR 2.41, 95% CI 1.89-3.06; P<.001) and 77% less likely to receive a referral to psychiatry (OR 0.23, 95% CI 0.16-0.34; P<.001). Those who endorsed question 9 "more than half the days" (OR 1.62, 95% CI 1.06-2.48) and "nearly every day" (OR 2.38, 95% CI 1.38-4.12) were more likely to receive a referral to psychiatry than those who endorsed question 9 "several days" (P=.002). Conclusions: Shifting depression screening from in-clinic to previsit led to a dramatic increase in PHQ-9 completion without sacrificing patient safety. Asynchronous PHQ-9 can decrease workload on frontline clinical team members, increase patient self-reporting, and elicit more intentional clinical responses from providers. Observed disparities will inform future improvement efforts.

Risk equations for prosthetic joint infections (PJIs) in UK: a retrospective study using the Clinical Practice Research Datalink (CPRD) AURUM and GOLD databases.

BACKGROUND: Prosthetic joint infections (PJIs) are a serious negative outcome of arthroplasty with incidence of about 1%. Risk of PJI could depend on local treatment policies and guidelines; no UK-specific risk scoring is currently available. OBJECTIVE: To determine a risk quantification model for the development of PJI using electronic health records. DESIGN: Records in Clinical Practice Research Datalink (CPRD) GOLD and AURUM of patients undergoing hip or knee arthroplasty between January 2007 and December 2014, with linkage to Hospital Episode Statistics and Office of National Statistics, were obtained. Cohorts' characteristics and risk equations through parametric models were developed and compared between the two databases. Pooled cohort risk equations were determined for the UK population and simplified through stepwise selection. RESULTS: After applying the inclusion/exclusion criteria, 174 905 joints (1021 developed PJI) were identified in CPRD AURUM and 48 419 joints (228 developed PJI) in CPRD GOLD. Patients undergoing hip or knee arthroplasty in both databases exhibited different sociodemographic characteristics and medical/drug history. However, the quantification of the impact of such covariates (coefficients of parametric models fitted to the survival curves) on the risk of PJI between the two cohorts was not statistically significant. The log-normal model fitted to the pooled cohorts after stepwise selection had a C-statistic >0.7. CONCLUSIONS: The risk prediction tool developed here could help prevent PJI through identifying modifiable risk factors pre-surgery and identifying the patients most likely to benefit from close monitoring/preventive actions. As derived from the UK population, such tool will help the National Health Service reduce the impact of PJI on its resources and patient lives.

Insomnia symptom prevalence in England: a comparison of cross-sectional self-reported data and primary care records in the UK Biobank.

OBJECTIVES: We aimed to use a large dataset to compare self-reported and primary care measures of insomnia symptom prevalence in England and establish whether they identify participants with similar characteristics. DESIGN: Cross-sectional study with linked electronic health records (EHRs). SETTING: Primary care in England. PARTICIPANTS: 163 748 UK Biobank participants in England (aged 38-71 at baseline) with linked primary care EHRs. OUTCOME MEASURES: We compared the percentage of those self-reporting 'usually' having insomnia symptoms at UK Biobank baseline assessment (2006-2010) to those with a Read code for insomnia symptoms in their primary care records prior to baseline. We stratified prevalence in both groups by sociodemographic, lifestyle, sleep and health characteristics. RESULTS: We found that 29% of the sample self-reported having insomnia symptoms, while only 6% had a Read code for insomnia symptoms in their primary care records. Only 10% of self-reported cases had an insomnia symptom Read code, while 49% of primary care cases self-reported having insomnia symptoms. In both primary care and self-reported data, prevalence of insomnia symptom cases was highest in females, older participants and those with the lowest household incomes. However, while snorers and risk takers were more likely to be a primary care case, they were less likely to self-report insomnia symptoms than non-snorers and non-risk takers. CONCLUSIONS: Only a small proportion of individuals experiencing insomnia symptoms have an insomnia symptom Read code in their primary care record. However, primary care data do provide a clinically meaningful measure of insomnia prevalence. In addition, the sociodemographic characteristics of people attending primary care with insomnia were consistent with those with self-reported insomnia, thus primary care records are a valuable data source for studying risk factors for insomnia. Further studies should replicate our findings in other populations and examine ways to increase discussions about sleep health in primary care.

Postpartum hemorrhage in electronic health records: risk factors at admission and in-hospital occurrence.

Objective: Postpartum hemorrhage (PPH) is the leading cause of maternal death globally. Therefore, prevention strategies have been created. The study aimed to evaluate the occurrence of PPH and its risk factors after implementing a risk stratification at admission in a teaching hospital. Methods: A retrospective cohort involving a database of SISMATER® electronic medical record. Classification in low, medium, or high risk for PPH was performed through data filled out by the obstetrician-assistant. PPH frequency was calculated, compared among these groups and associated with the risk factors. Results: The prevalence of PPH was 6.8%, 131 among 1,936 women. Sixty-eight (51.9%) of them occurred in the high-risk group, 30 (22.9%) in the medium-risk and 33 (25.2%) in the low-risk group. The adjusted-odds ratio (OR) for PPH were analyzed using a confidence interval (95% CI) and was significantly higher in who presented multiple pregnancy (OR 2.88, 95% CI 1.28 to 6.49), active bleeding on admission (OR 6.12, 95% CI 1.20 to 4.65), non-cephalic presentation (OR 2.36, 95% CI 1.20 to 4.65), retained placenta (OR 9.39, 95% CI 2.90 to 30.46) and placental abruption (OR 6.95, 95% CI 2.06 to 23.48). Vaginal delivery figured out as a protective factor (OR 0.58, 95% CI 0.34 to 0.98). Conclusion: Prediction of PPH is still a challenge since its unpredictable factor arrangements. The fact that the analysis did not demonstrate a relationship between risk category and frequency of PPH could be attributable to the efficacy of the strategy: Women classified as "high-risk" received adequate medical care, consequently.

Validation and clinical discovery demonstration of breast cancer data from a real-world data extraction platform.

Objective: To validate and demonstrate the clinical discovery utility of a novel patient-mediated, medical record collection and data extraction platform developed to improve access and utilization of real-world clinical data. Materials and Methods: Clinical variables were extracted from the medical records of 1011 consented patients with breast cancer. To validate the extracted data, case report forms completed using the structured data output of the platform were compared to manual chart review for 50 randomly-selected patients with metastatic breast cancer. To demonstrate the platform's clinical discovery utility, we identified 194 patients with early-stage clinical data who went on to develop distant metastases and utilized the platform-extracted data to assess associations between time to distant metastasis (TDM) and early-stage tumor histology, molecular type, and germline BRCA status. Results: The platform-extracted data for the validation cohort had 97.6% precision (91.98%-100% by variable type) and 81.48% recall (58.15%-95.00% by variable type) compared to manual chart review. In our discovery cohort, the shortest TDM was significantly associated with metaplastic (739.0 days) and inflammatory histologies (1005.8 days), HR-/HER2- molecular types (1187.4 days), and positive BRCA status (1042.5 days) as compared to other histologies, molecular types, and negative BRCA status, respectively. Multivariable analyses did not produce statistically significant results. Discussion: The precision and recall of platform-extracted clinical data are reported, although specificity could not be assessed. The data can generate clinically-relevant insights. Conclusion: The structured real-world data produced by a novel patient-mediated, medical record-extraction platform are reliable and can power clinical discovery.

The role and limitations of electronic medical records versus patient interviews for determining symptoms, underlying comorbidities, and medication usage for patients with COVID-19.

Electronic medical records (EMR) are important for rapidly compiling information to determine disease characteristics (e.g., symptoms) and risk factors (e.g., 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 usage among COVID-19 patients collected from EMR and patient interviews were compared using overall agreement (same answer in EMR and interview), reported agreement (yes answer in both EMR and interview among those who reported yes in either), and Kappa statistics. Overall, patients reported more symptoms in interviews than in EMR abstractions. Overall agreement was high (≥50% for 20/23 symptoms), but only subjective fever and dyspnea had reported agreement of ≥50%. Kappa statistics for symptoms were generally low. Reported medical conditions had greater agreement with all condition categories (10/10) having ≥50% overall agreement and half (5/10) having ≥50% reported agreement. More non-prescription medications were reported in interviews than in EMR abstractions leading to low reported agreement (28%). Discordance was observed for symptoms, medical history, and medication usage between EMR abstractions and patient interviews. Investigations utilizing EMR to describe clinical characteristics and identify risk factors should consider the potential for incomplete data, particularly for symptoms and medications.

Biomedical blockchain with practical implementations and quantitative evaluations: a systematic review.

OBJECTIVE: Blockchain has emerged as a potential data-sharing structure in healthcare because of its decentralization, immutability, and traceability. However, its use in the biomedical domain is yet to be investigated comprehensively, especially from the aspects of implementation and evaluation, by existing blockchain literature reviews. To address this, our review assesses blockchain applications implemented in practice and evaluated with quantitative metrics. MATERIALS AND METHODS: This systematic review adapts the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework to review biomedical blockchain papers published by August 2023 from 3 databases. Blockchain application, implementation, and evaluation metrics were collected and summarized. RESULTS: Following screening, 11 articles were included in this review. Articles spanned a range of biomedical applications including COVID-19 medical data sharing, decentralized internet of things (IoT) data storage, clinical trial management, biomedical certificate storage, electronic health record (EHR) data sharing, and distributed predictive model generation. Only one article demonstrated blockchain deployment at a medical facility. DISCUSSION: Ethereum was the most common blockchain platform. All but one implementation was developed with private network permissions. Also, 8 articles contained storage speed metrics and 6 contained query speed metrics. However, inconsistencies in presented metrics and the small number of articles included limit technological comparisons with each other. CONCLUSION: While blockchain demonstrates feasibility for adoption in healthcare, it is not as popular as currently existing technologies for biomedical data management. Addressing implementation and evaluation factors will better showcase blockchain's practical benefits, enabling blockchain to have a significant impact on the health sector.