Optimising the use of electronic medical records for large scale research in psychiatry.

The explosion and abundance of digital data could facilitate large-scale research for psychiatry and mental health. Research using so-called "real world data"-such as electronic medical/health records-can be resource-efficient, facilitate rapid hypothesis generation and testing, complement existing evidence (e.g. from trials and evidence-synthesis) and may enable a route to translate evidence into clinically effective, outcomes-driven care for patient populations that may be under-represented. However, the interpretation and processing of real-world data sources is complex because the clinically important 'signal' is often contained in both structured and unstructured (narrative or "free-text") data. Techniques for extracting meaningful information (signal) from unstructured text exist and have advanced the re-use of routinely collected clinical data, but these techniques require cautious evaluation. In this paper, we survey the opportunities, risks and progress made in the use of electronic medical record (real-world) data for psychiatric research.

Peril and Promise: Teens, Tech, and America's Mental Health Crisis.

Advances in Internet technologies have implications for the health and development of children and adolescents with potential for both beneficial and harmful outcomes. Similar technological advances also impact how psychiatrists deliver mental health care in clinical settings. Internet tech adds complexities to psychiatric practice in the form of electronic health records, patient portals, and virtual patient contact, which clinicians must understand and successfully incorporate into practice. Digital therapeutics and virtual mental health endeavors offer new treatment delivery options for patients and providers. Some have proven benefits, such as improved accessibility for patients, but all require provider expertise to utilize.

Blockchain en salud: transformando la seguridad y la gestión de datos clínicos / Blockchain in health: Transforming security and clinical data management

Introducción Los avances tecnológicos continúan transformando la sociedad, incluyendo el sector de la salud. La naturaleza descentralizada y verificable de la tecnología blockchain presenta un gran potencial para abordar desafíos actuales en la gestión de datos sanitarios. Discusión Este artículo indaga sobre cómo la adopción generalizada de blockchain se enfrenta a importantes desafíos y barreras que deben abordarse, como la falta de regulación, la complejidad técnica, la salvaguarda de la privacidad y los costos tanto económicos como tecnológicos. La colaboración entre profesionales médicos, tecnólogos y legisladores es esencial para establecer un marco normativo sólido y una capacitación adecuada. Conclusión La tecnología blockchain tiene potencial de revolucionar la gestión de datos en el sector de la salud, mejorando la calidad de la atención médica, empoderando a los usuarios y fomentando la compartición segura de datos. Es necesario un cambio cultural y regulatorio, junto a más evidencia, para concluir sus ventajas frente a las alternativas tecnológicas existentes. (AU)

Introduction Technological advances continue to transform society, including the health sector. The decentralized and verifiable nature of blockchain technology presents great potential for addressing current challenges in healthcare data management. Discussion This article reports on how the generalized adoption of blockchain faces important challenges and barriers that must be addressed, such as the lack of regulation, technical complexity, safeguarding privacy, and economic and technological costs. Collaboration between medical professionals, technologists and legislators is essential to establish a solid regulatory framework and adequate training. Conclusion Blockchain technology has the potential to revolutionize data management in the healthcare sector, improving the quality of medical care, empowering users, and promoting the secure sharing of data, but an important cultural change is needed, along with more evidence, to reveal its advantages in front of the existing technological alternative. (AU)

Ease and practicability of the 2017 classification of periodontal diseases and conditions: a study of dental electronic health records.

BACKGROUND: A new classification for Periodontal and Peri-implant Diseases and Conditions was introduced in the 2017 World Workshop. In the past the 1999 Armitage Classification was commonly used in practice. This study aimed to assess the ease and practicability of retroactively diagnosing a subset of patients formerly diagnosed using the 1999 AAP/CDC classification with the 2017 AAP/EFP disease classification. METHODS: A random subset of 10% of all patients referred over a 7-year period (2011-2018) to the Post-Doctoral Periodontics Clinic at Columbia University College of Dental Medicine were reviewed by accessing the Electronic Health Records (EHRs) on axiUm. Patients diagnosed with periodontal disease based on the 1999 AAP/CDC classification (including chronic and aggressive Periodontitis) were reclassified using the 2017 classification (stage: I, II, III and grade: A, B, C). RESULTS: A sample of 336 patient records were examined. 132 were diagnosed with gingivitis, and 204 with periodontitis. Of these 204 patients, 68 (33.3%) were diagnosed with aggressive and 136 (66.7%) with chronic periodontitis. Patients diagnosed with aggressive periodontitis, 10% were reclassified as stage II, 47% as stage III, and 43% as stage IV periodontitis, and 100% were reclassified as grade C. Among patients with chronic periodontitis, 7% were reclassified as stage I, 65% as stage II, 21% as stage III, and 7% as stage IV; 11% of these were reclassified as grade A, 63% grade B, and 26% grade C. CONCLUSIONS: The majority of those originally diagnosed with aggressive (90%) and chronic (80%) periodontitis were reclassified as either molar/incisor pattern stage III grade C or stage IV grade C periodontitis, and stage II or III periodontitis, respectively. The study demonstrated that it is practical to retroactively reassign a diagnosis according to the new 2017 classification using available information included in dental EHRs.

Improving Sexual History Documentation in Teenagers.

BACKGROUND AND OBJECTIVES: Teen access to sexual health care is essential. The 21st Century Cures Act mandates that most electronic health information be shared with patients; no standard exists for how to meet this mandate for teens and their proxy caregivers. Our confidential shared teen sexual history (SexHx) section, which is not note-based, allows clinicians to easily find information, promotes clinical decision support, and protects privacy. Nevertheless, significant variability existed in SexHx section usage, SexHx documentation, and teen note-sharing practices. For teens (aged 12-17) admitted to the Pediatric Hospital Medicine service, we aim to increase the use of the SexHx section by 10% and increase History and Physical notes (H&Ps) shared with teens by 5% over 12 months. METHODS: Quality improvement methodology and tools were used to conduct a barrier analysis and implement a series of interventions, which included education, training, and electronic health record clinical decision support. Statistical process control charts were used to examine the impact of the interventions. RESULTS: At baseline, from April to July 2021, sexual activity was documented or reviewed in the SexHx section for 56% of teen patients. Over the intervention period, the center line shifted to 72%. At baseline, 76% of teen H&Ps were shared with patients. The percentage of H&Ps shared revealed a center-line shift to 81% throughout the intervention period. CONCLUSIONS: The shared teen SexHx section is an innovative tool for capturing sensitive patient history discretely. We demonstrated increased and sustained SexHx section use and H&P note-sharing in this quality improvement initiative.

Electronic Health Record-Based Algorithm for Monitoring Respiratory Virus-Like Illness.

Viral respiratory illness surveillance has traditionally focused on single pathogens (e.g., influenza) and required fever to identify influenza-like illness (ILI). We developed an automated system applying both laboratory test and syndrome criteria to electronic health records from 3 practice groups in Massachusetts, USA, to monitor trends in respiratory viral-like illness (RAVIOLI) across multiple pathogens. We identified RAVIOLI syndrome using diagnosis codes associated with respiratory viral testing or positive respiratory viral assays or fever. After retrospectively applying RAVIOLI criteria to electronic health records, we observed annual winter peaks during 2015-2019, predominantly caused by influenza, followed by cyclic peaks corresponding to SARS-CoV-2 surges during 2020-2024, spikes in RSV in mid-2021 and late 2022, and recrudescent influenza in late 2022 and 2023. RAVIOLI rates were higher and fluctuations more pronounced compared with traditional ILI surveillance. RAVIOLI broadens the scope, granularity, sensitivity, and specificity of respiratory viral illness surveillance compared with traditional ILI surveillance.

Redefining Health Care Data Interoperability: Empirical Exploration of Large Language Models in Information Exchange.

BACKGROUND: Efficient data exchange and health care interoperability are impeded by medical records often being in nonstandardized or unstructured natural language format. Advanced language models, such as large language models (LLMs), may help overcome current challenges in information exchange. OBJECTIVE: This study aims to evaluate the capability of LLMs in transforming and transferring health care data to support interoperability. METHODS: Using data from the Medical Information Mart for Intensive Care III and UK Biobank, the study conducted 3 experiments. Experiment 1 assessed the accuracy of transforming structured laboratory results into unstructured format. Experiment 2 explored the conversion of diagnostic codes between the coding frameworks of the ICD-9-CM (International Classification of Diseases, Ninth Revision, Clinical Modification), and Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT) using a traditional mapping table and a text-based approach facilitated by the LLM ChatGPT. Experiment 3 focused on extracting targeted information from unstructured records that included comprehensive clinical information (discharge notes). RESULTS: The text-based approach showed a high conversion accuracy in transforming laboratory results (experiment 1) and an enhanced consistency in diagnostic code conversion, particularly for frequently used diagnostic names, compared with the traditional mapping approach (experiment 2). In experiment 3, the LLM showed a positive predictive value of 87.2% in extracting generic drug names. CONCLUSIONS: This study highlighted the potential role of LLMs in significantly improving health care data interoperability, demonstrated by their high accuracy and efficiency in data transformation and exchange. The LLMs hold vast potential for enhancing medical data exchange without complex standardization for medical terms and data structure.

Physicians in ACOs report greater documentation burden.

OBJECTIVES: First, to analyze the relationship between value-based payment (VBP) program participation and documentation burden among office-based physicians. Second, to analyze the relationship between specific VBP programs (eg, accountable care organizations [ACOs]) and documentation burden. STUDY DESIGN: Retrospective analyses of US office-based physicians in 2019 and 2021. METHODS: We used cross-sectional data from the National Electronic Health Records Survey to measure VBP program participation and our outcomes of reported electronic health record (EHR) documentation burden. We used ordinary least squares regression models adjusting for physician and practice characteristics to estimate the relationship between participation in any VBP program and EHR burden outcomes. We also estimated the relationship between participation in 6 distinct VBP programs and our outcomes to decompose the aggregate relationship into program-specific estimates. RESULTS: In adjusted analyses, participation in any VBP program was associated with 10.5% greater probability of reporting more than 1 hour per day of after-hours documentation time (P = .01), which corresponded to an estimated additional 11 minutes per day (P = .03). Program-specific estimates illustrated that ACO participation drove the aggregate relationship, with ACO participants reporting greater after-hours documentation time (18 additional minutes per day; P < .001), more difficulty documenting (30.6% more likely; P < .001), and more inappropriateness of time spent documenting (21.7% more likely; P < .001). CONCLUSIONS: Office-based physicians participating in ACOs report greater documentation burden across several measures; the same is not true for other VBP programs. Although many ACOs relax documentation requirements for reimbursement, documentation for quality reporting and risk adjustment may lead to a net increase in burden, especially for physicians exposed to numerous programs and payers.

Prediction of aneurysmal subarachnoid hemorrhage in comparison with other stroke types using routine care data.

Aneurysmal subarachnoid hemorrhage (aSAH) can be prevented by early detection and treatment of intracranial aneurysms in high-risk individuals. We investigated whether individuals at high risk of aSAH in the general population can be identified by developing an aSAH prediction model with electronic health records (EHR) data. To assess the aSAH model's relative performance, we additionally developed prediction models for acute ischemic stroke (AIS) and intracerebral hemorrhage (ICH) and compared the discriminative performance of the models. We included individuals aged ≥35 years without history of stroke from a Dutch routine care database (years 2007-2020) and defined outcomes aSAH, AIS and ICH using International Classification of Diseases (ICD) codes. Potential predictors included sociodemographic data, diagnoses, medications, and blood measurements. We cross-validated a Cox proportional hazards model with an elastic net penalty on derivation cohorts and reported the c-statistic and 10-year calibration on validation cohorts. We examined 1,040,855 individuals (mean age 54.6 years, 50.9% women) for a total of 10,173,170 person-years (median 11 years). 17,465 stroke events occurred during follow-up: 723 aSAH, 14,659 AIS, and 2,083 ICH. The aSAH model's c-statistic was 0.61 (95%CI 0.57-0.65), which was lower than the c-statistic of the AIS (0.77, 95%CI 0.77-0.78) and ICH models (0.77, 95%CI 0.75-0.78). All models were well-calibrated. The aSAH model identified 19 predictors, of which the 10 strongest included age, female sex, population density, socioeconomic status, oral contraceptive use, gastroenterological complaints, obstructive airway medication, epilepsy, childbirth complications, and smoking. Discriminative performance of the aSAH prediction model was moderate, while it was good for the AIS and ICH models. We conclude that it is currently not feasible to accurately identify individuals at increased risk for aSAH using EHR data.

Mapping the distribution of Lyme disease at a mid-Atlantic site in the United States using electronic health data.

Lyme disease is a spatially heterogeneous tick-borne infection, with approximately 85% of US cases concentrated in the mid-Atlantic and northeastern states. Surveillance for Lyme disease and its causative agent, including public health case reporting and entomologic surveillance, is necessary to understand its endemic range, but currently used case detection methods have limitations. To evaluate an alternative approach to Lyme disease surveillance, we have performed a geospatial analysis of Lyme disease cases from the Johns Hopkins Health System in Maryland. We used two sources of cases: a) individuals with both a positive test for Lyme disease and a contemporaneous diagnostic code consistent with a Lyme disease-related syndrome; and b) individuals referred for a Lyme disease evaluation who were adjudicated to have Lyme disease. Controls were individuals from the referral cohort judged not to have Lyme disease. Residential address data were available for all cases and controls. We used a hierarchical Bayesian model with a smoothing function for a coordinate location to evaluate the probability of Lyme disease within 100 km of Johns Hopkins Hospital. We found that the probability of Lyme disease was greatest in the north and west of Baltimore, and the local probability that a subject would have Lyme disease varied by as much as 30-fold. Adjustment for demographic and ecological variables partially attenuated the spatial gradient. Our study supports the suitability of electronic medical record data for the retrospective surveillance of Lyme disease.

A Taxonomy for Health Information Systems.

The health sector is highly digitized, which is enabling the collection of vast quantities of electronic data about health and well-being. These data are collected by a diverse array of information and communication technologies, including systems used by health care organizations, consumer and community sources such as information collected on the web, and passively collected data from technologies such as wearables and devices. Understanding the breadth of IT that collect these data and how it can be actioned is a challenge for the significant portion of the digital health workforce that interact with health data as part of their duties but are not for informatics experts. This viewpoint aims to present a taxonomy categorizing common information and communication technologies that collect electronic data. An initial classification of key information systems collecting electronic health data was undertaken via a rapid review of the literature. Subsequently, a purposeful search of the scholarly and gray literature was undertaken to extract key information about the systems within each category to generate definitions of the systems and describe the strengths and limitations of these systems.

Use of Natural Language Understanding to Facilitate Surgical De-Escalation of Axillary Staging in Patients With Breast Cancer.

PURPOSE: Natural language understanding (NLU) may be particularly well equipped for enhanced data capture from the electronic health record given its examination of both content-driven and context-driven extraction. METHODS: We developed and applied a NLU model to examine rates of pathological node positivity (pN+) and rates of lymphedema to determine whether omission of routine axillary staging could be extended to younger patients with estrogen receptor-positive (ER+)/cN0 disease. RESULTS: We found that rates of pN+ and arm lymphedema were similar between patients age 55-69 years and ≥70 years, with rates of lymphedema exceeding rates of pN+ for clinical stage T1c and smaller disease. CONCLUSION: Data from our NLU model suggest that omission of sentinel lymph node biopsy might be extended beyond Choosing Wisely recommendations, limited to those older than 70 years and to all postmenopausal women with early-stage ER+/cN0 disease. These data support the recently reported SOUND trial results and provide additional granularity to facilitate surgical de-escalation.

Identification of mycoplasma pneumonia in children based on fusion of multi-modal clinical free-text description and structured test data.

Mycoplasma pneumonia may lead to hospitalizations and pose life-threatening risks in children. The automated identification of mycoplasma pneumonia from electronic medical records holds significant potential for improving the efficiency of hospital resource allocation. In this study, we proposed a novel method for identifying mycoplasma pneumonia by integrating multi-modal features derived from both free-text descriptions and structured test data in electronic medical records. Our approach begins with the extraction of free-text and structured data from clinical records through a systematic preprocessing pipeline. Subsequently, we employ a pre-trained transformer language model to extract features from the free-text, while multiple additive regression trees are used to transform features from the structured data. An attention-based fusion mechanism is then applied to integrate these multi-modal features for effective classification. We validated our method using clinic records of 7157 patients, retrospectively collected for training and testing purposes. The experimental results demonstrate that our proposed multi-modal fusion approach achieves significant improvements over other methods across four key performance metrics.

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.

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.

Towards proactive palliative care in oncology: developing an explainable EHR-based machine learning model for mortality risk prediction.

BACKGROUND: Ex-ante identification of the last year in life facilitates a proactive palliative approach. Machine learning models trained on electronic health records (EHR) demonstrate promising performance in cancer prognostication. However, gaps in literature include incomplete reporting of model performance, inadequate alignment of model formulation with implementation use-case, and insufficient explainability hindering trust and adoption in clinical settings. Hence, we aim to develop an explainable machine learning EHR-based model that prompts palliative care processes by predicting for 365-day mortality risk among patients with advanced cancer within an outpatient setting. METHODS: Our cohort consisted of 5,926 adults diagnosed with Stage 3 or 4 solid organ cancer between July 1, 2017, and June 30, 2020 and receiving ambulatory cancer care within a tertiary center. The classification problem was modelled using Extreme Gradient Boosting (XGBoost) and aligned to our envisioned use-case: "Given a prediction point that corresponds to an outpatient cancer encounter, predict for mortality within 365-days from prediction point, using EHR data up to 365-days prior." The model was trained with 75% of the dataset (n = 39,416 outpatient encounters) and validated on a 25% hold-out dataset (n = 13,122 outpatient encounters). To explain model outputs, we used Shapley Additive Explanations (SHAP) values. Clinical characteristics, laboratory tests and treatment data were used to train the model. Performance was evaluated using area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC), while model calibration was assessed using the Brier score. RESULTS: In total, 17,149 of the 52,538 prediction points (32.6%) had a mortality event within the 365-day prediction window. The model demonstrated an AUROC of 0.861 (95% CI 0.856-0.867) and AUPRC of 0.771. The Brier score was 0.147, indicating slight overestimations of mortality risk. Explanatory diagrams utilizing SHAP values allowed visualization of feature impacts on predictions at both the global and individual levels. CONCLUSION: Our machine learning model demonstrated good discrimination and precision-recall in predicting 365-day mortality risk among individuals with advanced cancer. It has the potential to provide personalized mortality predictions and facilitate earlier integration of palliative care.

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.

User-Centered Framework for Implementation of Technology (UFIT): Development of an Integrated Framework for Designing Clinical Decision Support Tools Packaged With Tailored Implementation Strategies.

BACKGROUND: Electronic health record-based clinical decision support (CDS) tools can facilitate the adoption of evidence into practice. Yet, the impact of CDS beyond single-site implementation is often limited by dissemination and implementation barriers related to site- and user-specific variation in workflows and behaviors. The translation of evidence-based CDS from initial development to implementation in heterogeneous environments requires a framework that assures careful balancing of fidelity to core functional elements with adaptations to ensure compatibility with new contexts. OBJECTIVE: This study aims to develop and apply a framework to guide tailoring and implementing CDS across diverse clinical settings. METHODS: In preparation for a multisite trial implementing CDS for pediatric overweight or obesity in primary care, we developed the User-Centered Framework for Implementation of Technology (UFIT), a framework that integrates principles from user-centered design (UCD), human factors/ergonomics theories, and implementation science to guide both CDS adaptation and tailoring of related implementation strategies. Our transdisciplinary study team conducted semistructured interviews with pediatric primary care clinicians and a diverse group of stakeholders from 3 health systems in the northeastern, midwestern, and southeastern United States to inform and apply the framework for our formative evaluation. RESULTS: We conducted 41 qualitative interviews with primary care clinicians (n=21) and other stakeholders (n=20). Our workflow analysis found 3 primary ways in which clinicians interact with the electronic health record during primary care well-child visits identifying opportunities for decision support. Additionally, we identified differences in practice patterns across contexts necessitating a multiprong design approach to support a variety of workflows, user needs, preferences, and implementation strategies. CONCLUSIONS: UFIT integrates theories and guidance from UCD, human factors/ergonomics, and implementation science to promote fit with local contexts for optimal outcomes. The components of UFIT were used to guide the development of Improving Pediatric Obesity Practice Using Prompts, an integrated package comprising CDS for obesity or overweight treatment with tailored implementation strategies. TRIAL REGISTRATION: ClinicalTrials.gov NCT05627011; https://clinicaltrials.gov/study/NCT05627011.

Life, death, and statins: association of statin prescriptions and survival in older general practice patients.

AIMS: This study serves as an exemplar to demonstrate the scalability of a research approach using survival analysis applied to general practice electronic health record data from multiple sites. Collection of these data, the subsequent analysis, and the preparation of practice-specific reports were performed using a bespoke distributed data collection and analysis software tool. BACKGROUND: Statins are a very commonly prescribed medication, yet there is a paucity of evidence for their benefits in older patients. We examine the relationship between statin prescriptions for general practice patients over 75 and all-cause mortality. METHODS: We carried out a retrospective cohort study using survival analysis applied to data extracted from the electronic health records of five Australian general practices. FINDINGS: The data from 8025 patients were analysed. The median duration of follow-up was 6.48 years. Overall, 52 015 patient-years of data were examined, and the outcome of death from any cause was measured in 1657 patients (21%), with the remainder being censored. Adjusted all-cause mortality was similar for participants not prescribed statins versus those who were (HR 1.05, 95% CI 0.92-1.20, P = 0.46), except for patients with diabetes for whom all-cause mortality was increased (HR = 1.29, 95% CI: 1.00-1.68, P = 0.05). In contrast, adjusted all-cause mortality was significantly lower for patients deprescribed statins compared to those who were prescribed statins (HR 0.81, 95% CI 0.70-0.93, P < 0.001), including among females (HR = 0.75, 95% CI: 0.61-0.91, P < 0.001) and participants treated for secondary prevention (HR = 0.72, 95% CI: 0.60-0.86, P < 0.001). This study demonstrated the scalability of a research approach using survival analysis applied to general practice electronic health record data from multiple sites. We found no evidence of increased mortality due to statin-deprescribing decisions in primary care.