A genome-wide Association study of the Count of Codeine prescriptions.

Opioid prescription records in existing electronic health record (EHR) databases are a potentially useful, high-fidelity data source for opioid use-related risk phenotyping in genetic analyses. Prescriptions for codeine derived from EHR records were used as targeting traits by screening 16 million patient-level medication records. Genome-wide association analyses were then conducted to identify genomic loci and candidate genes associated with different count patterns of codeine prescriptions. Both low- and high-prescription counts were captured by developing 8 types of phenotypes with selected ranges of prescription numbers to reflect potentially different levels of opioid risk severity. We identified one significant locus associated with low-count codeine prescriptions (1, 2 or 3 prescriptions), while up to 7 loci were identified for higher counts (≥ 4, ≥ 5, ≥6, or ≥ 7 prescriptions), with a strong overlap across different thresholds. We identified 9 significant genomic loci with all-count phenotype. Further, using the polygenic risk approach, we identified a significant correlation (Tau = 0.67, p = 0.01) between an externally derived polygenic risk score for opioid use disorder and numbers of codeine prescriptions. As a proof-of-concept study, our research provides a novel and generalizable phenotyping pipeline for the genomic study of opioid-related risk traits.

Utilising primary care electronic health records to deliver the ALABAMA randomised controlled trial of penicillin allergy assessment.

BACKGROUND: Use of electronic health records (EHR) to provide real-world data for research is established, but using EHR to deliver randomised controlled trials (RCTs) more efficiently is less developed. The Allergy AntiBiotics And Microbial resistAnce (ALABAMA) RCT evaluated a penicillin allergy assessment pathway versus usual clinical care in a UK primary care setting. The aim of this paper is to describe how EHRs were used to facilitate efficient delivery of a large-scale randomised trial of a complex intervention embracing efficient participant identification, supporting minimising GP workload, providing accurate post-intervention EHR updates of allergy status, and facilitating participant follow up and outcome data collection. The generalisability of the EHR approach and health economic implications of EHR in clinical trials will be reported in the main ALABAMA trial cost-effectiveness analysis. METHODS: A descriptive account of the adaptation of functionality within SystmOne used to deliver/facilitate multiple trial processes from participant identification to outcome data collection. RESULTS: An ALABAMA organisation group within SystmOne was established which allowed sharing of trial functions/materials developed centrally by the research team. The 'ALABAMA unit' within SystmOne was also created and provided a secure efficient environment to access participants' EHR data. Processes of referring consented participants, allocating them to a trial arm, and assigning specific functions to the intervention arm were developed by adapting tools such as templates, reports, and protocols which were already available in SystmOne as well as pathways to facilitate allergy de-labelling processes and data retrieval for trial outcome analysis. CONCLUSIONS: ALABAMA is one of the first RCTs to utilise SystmOne EHR functionality and data across the RCT delivery, demonstrating feasibility and applicability to other primary care RCTs. TRIAL REGISTRATION: ClinicalTrials.gov: NCT04108637, registered 05/03/2019. ISRCTN: ISRCTN20579216.

The Utility and Implications of Ambient Scribes in Primary Care.

Ambient scribe technology, utilizing large language models, represents an opportunity for addressing several current pain points in the delivery of primary care. We explore the evolution of ambient scribes and their current use in primary care. We discuss the suitability of primary care for ambient scribe integration, considering the varied nature of patient presentations and the emphasis on comprehensive care. We also propose the stages of maturation in the use of ambient scribes in primary care and their impact on care delivery. Finally, we call for focused research on safety, bias, patient impact, and privacy in ambient scribe technology, emphasizing the need for early training and education of health care providers in artificial intelligence and digital health tools.

Clinical Use of Mental Health Digital Therapeutics in a Large Health Care Delivery System: Retrospective Patient Cohort Study and Provider Survey.

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

Unlocking the Potential of Secondary Data for Public Health Research: Retrospective Study With a Novel Clinical Platform.

BACKGROUND: Clinical routine data derived from university hospitals hold immense value for health-related research on large cohorts. However, using secondary data for hypothesis testing necessitates adherence to scientific, legal (such as the General Data Protection Regulation, federal and state protection legislations), technical, and administrative requirements. This process is intricate, time-consuming, and susceptible to errors. OBJECTIVE: This study aims to develop a platform that enables clinicians to use current real-world data for testing research and evaluate advantages and limitations at a large university medical center (542,944 patients in 2022). METHODS: We identified requirements from clinical practitioners, conceptualized and implemented a platform based on the existing components, and assessed its applicability in clinical reality quantitatively and qualitatively. RESULTS: The proposed platform was established at the University Medical Center Hamburg-Eppendorf and made 639 forms encompassing 10,629 data elements accessible to all resident scientists and clinicians. Every day, the number of patients rises, and parts of their electronic health records are made accessible through the platform. Qualitatively, we were able to conduct a retrospective analysis of Parkinson disease over 777 patients, where we provide additional evidence for a significantly higher proportion of action tremors in patients with rest tremors (340/777, 43.8%) compared with those without rest tremors (255/777, 32.8%), as determined by a chi-square test (P<.001). Quantitatively, our findings demonstrate increased user engagement within the last 90 days, underscoring clinicians' increasing adoption of the platform in their regular research activities. Notably, the platform facilitated the retrieval of clinical data from 600,000 patients, emphasizing its substantial added value. CONCLUSIONS: This study demonstrates the feasibility of simplifying the use of clinical data to enhance exploration and sustainability in scientific research. The proposed platform emerges as a potential technological and legal framework for other medical centers, providing them with the means to unlock untapped potential within their routine data.

Harnessing the Power of Complementarity Between Smart Tracking Technology and Associated Health Information Technologies: Longitudinal Study.

BACKGROUND: Smart tracking technology (STT) that was applied for clinical use has the potential to reduce 30-day all-cause readmission risk through streamlining clinical workflows with improved accuracy, mobility, and efficiency. However, previously published literature has inadequately addressed the joint effects of STT for clinical use and its complementary health ITs (HITs) in this context. Furthermore, while previous studies have discussed the symbiotic and pooled complementarity effects among different HITs, there is a lack of evidence-based research specifically examining the complementarity effects between STT for clinical use and other relevant HITs. OBJECTIVE: Through a complementarity theory lens, this study aims to examine the joint effects of STT for clinical use and 3 relevant HITs on 30-day all-cause readmission risk. These HITs are STT for supply chain management, mobile IT, and health information exchange (HIE). Specifically, this study examines whether the pooled complementarity effect exists between STT for clinical use and STT for supply chain management, and whether symbiotic complementarity effects exist between STT for clinical use and mobile IT and between STT for clinical use and HIE. METHODS: This study uses a longitudinal in-patient dataset, including 879,122 in-patient hospital admissions for 347,949 patients in 61 hospitals located in Florida and New York in the United States, from 2014 to 2015. Logistic regression was applied to assess the effect of HITs on readmission risks. Time and hospital fixed effects were controlled in the regression model. Robust standard errors (SEs) were used to account for potential heteroskedasticity. These errors were further clustered at the patient level to consider possible correlations within the patient groups. RESULTS: The interaction between STT for clinical use and STT for supply chain management, mobile IT, and HIE was negatively associated with 30-day readmission risk, with coefficients of -0.0352 (P=.003), -0.0520 (P<.001), and -0.0216 (P=.04), respectively. These results indicate that the pooled complementarity effect exists between STT for clinical use and STT for supply chain management, and symbiotic complementarity effects exist between STT for clinical use and mobile IT and between STT for clinical use and HIE. Furthermore, the joint effects of these HITs varied depending on the hospital affiliation and patients' disease types. CONCLUSIONS: Our results reveal that while individual HIT implementations have varying impacts on 30-day readmission risk, their joint effects are often associated with a reduction in 30-day readmission risk. This study substantially contributes to HIT value literature by quantifying the complementarity effects among 4 different types of HITs: STT for clinical use, STT for supply chain management, mobile IT, and HIE. It further offers practical implications for hospitals to maximize the benefits of their complementary HITs in reducing the 30-day readmission risk in their respective care scenarios.

Addressing Information Biases Within Electronic Health Record Data to Improve the Examination of Epidemiologic Associations With Diabetes Prevalence Among Young Adults: Cross-Sectional Study.

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

An Automated Electronic Health Record Score to Estimate Length of Stay and Readmission in Patients Undergoing Radical Cystectomy for Bladder Cancer.

PURPOSE: Patients treated with radical cystectomy experience a high rate of postoperative complications and frequent hospital readmissions. We sought to explore the utility of the Care Assessment Needs (CAN) score, derived from electronic health data, to estimate the risk of these adverse clinical outcomes, thereby aiding patient counseling and informed treatment decision-making. MATERIALS AND METHODS: We retrospectively examined data from 982 bladder cancer patients who underwent radical cystectomy between 2013 to 2018 within the national Veterans Health Administration system. We tested for associations between the preoperative CAN score and length of stay, discharge location, and readmission rates. RESULTS: We observed a correlation between higher CAN scores and longer hospital stays (adjusted relative risk = 1.03 [95% CI: 1.02-1.05]). An increased CAN score was also linked to greater odds of discharge to a skilled nursing facility or death (adjusted odds ratio = 1.16 [95% CI: 1.06-1.26]). Furthermore, the score was associated with hospital readmission at both 30 and 90 days post-discharge (adjusted hazard ratio = 1.03 [95% CI: 1.00-1.07] and 1.04 [95% CI: 1.00-1.07], respectively). CONCLUSIONS: The CAN score is associated with the length of hospital stay, discharge to a skilled nursing facility, and readmission within 30 and 90 days following radical cystectomy. These findings highlight the potential of healthcare systems leveraging electronic health records for automatically calculating multi-dimensional tools, like the CAN score, to identify patients at risk of adverse clinical outcomes following radical cystectomy.

Latent space representation of electronic health records for clustering dialysis-associated kidney failure subtypes.

OBJECTIVE: Kidney failure manifests in various forms, from sudden occurrences such as Acute Kidney Injury (AKI) to progressive like Chronic Kidney Disease (CKD). Given its intricate nature, marked by overlapping comorbidities and clinical similarities-including treatment modalities like dialysis-we sought to design and validate an end-to-end framework for clustering kidney failure subtypes. MATERIALS AND METHODS: Our emphasis was on dialysis, utilizing a comprehensive dataset from the UK Biobank (UKB). We transformed raw Electronic Health Record (EHR) data into standardized matrices that incorporate patient demographics, clinical visit data, and the innovative feature of visit time-gaps. This matrix structure was achieved using a unique data cutting method. Latent space transformation was facilitated using a convolution autoencoder (ConvAE) model, which was then subjected to clustering using Principal Component Analysis (PCA) and K-means algorithms. RESULTS: Our transformation model effectively reduced data dimensionality, thereby accelerating computational processes. The derived latent space demonstrated remarkable clustering capacities. Through cluster analysis, two distinct groups were identified: CKD-majority (cluster 1) and a mixed group of non-CKD and some CKD subtypes (cluster 0). Cluster 1 exhibited notably low survival probability, suggesting it predominantly represented severe CKD. In contrast, cluster 0, with substantially higher survival probability, likely to include milder CKD forms and severe AKI. Our end-to-end framework effectively differentiates kidney failure subtypes using the UKB dataset, offering potential for nuanced therapeutic interventions. CONCLUSIONS: This innovative approach integrates diverse data sources, providing a holistic understanding of kidney failure, which is imperative for patient management and targeted therapeutic interventions.

Implications of Diagnosis Through a Machine Learning Algorithm on Management of People With Familial Hypercholesterolemia.

Background: Familial hypercholesterolemia (FH) is an underdiagnosed genetic condition that leads to premature cardiovascular disease. Flag, Identify, Network, and Deliver (FIND) FH is a machine learning algorithm (MLA) developed by the Family Heart Foundation that identifies high-risk individuals in the electronic medical record for targeted FH screening. Objectives: The purpose of this study was to characterize the FH diagnostic coding status of patients detected by a MLA screening and assess for correlations with patterns in medical management and cardiovascular outcomes. Methods: We applied the FIND FH MLA to a retrospective, cross-sectional cohort within one large academic medical center. Individual patient charts were manually reviewed and stratified by diagnosis status. Variables including baseline characteristics, medical history, family history, laboratory values, medications, and cardiovascular outcomes were compared across diagnosis status. Results: The MLA identified 471 patients over 5.5 years with a high probability for FH. 121 (26%) previously undiagnosed patients met criteria for having "likely FH." Those with established FH diagnoses (n = 32) had significantly more lipid panel monitoring, prescriptions for non-statin or combination lipid-lowering agents, visits with a cardiologist, and frequency of coronary artery calcium score (CACS) testing or lipoprotein(a) testing than undiagnosed patients with likely FH. The 2 groups had no significant differences in having had prior major adverse cardiovascular events. The remaining 318 patients were classified as having "suspected FH." Conclusions: These findings suggest that implementation of a MLA approach such as FIND FH may be feasible for identifying undiagnosed individuals living with FH, as well as addressing treatment disparities in this population at increased cardiovascular risk.

Applying Implementation Science to Advance Electronic Health Record-Driven Learning Health Systems: Case Studies, Challenges, and Recommendations.

With the widespread implementation of electronic health records (EHRs), there has been significant progress in developing learning health systems (LHSs) aimed at improving health and health care delivery through rapid and continuous knowledge generation and translation. To support LHSs in achieving these goals, implementation science (IS) and its frameworks are increasingly being leveraged to ensure that LHSs are feasible, rapid, iterative, reliable, reproducible, equitable, and sustainable. However, 6 key challenges limit the application of IS to EHR-driven LHSs: barriers to team science, limited IS experience, data and technology limitations, time and resource constraints, the appropriateness of certain IS approaches, and equity considerations. Using 3 case studies from diverse health settings and 1 IS framework, we illustrate these challenges faced by LHSs and offer solutions to overcome the bottlenecks in applying IS and utilizing EHRs, which often stymie LHS progress. We discuss the lessons learned and provide recommendations for future research and practice, including the need for more guidance on the practical application of IS methods and a renewed emphasis on generating and accessing inclusive data.

Use of ICD-10-CM Codes for Adverse Social Determinants of Health Across Health Systems.

OBJECTIVE: This study investigated ICD-10-CM codes for adverse social determinants of health (SDoH) across 12 U.S. health systems by using data from multiple health care encounter types for diverse patients covered by multiple payers. METHODS: The authors described documentation of 11 SDoH ICD-10-CM code categories (e.g., educational problems or social environmental problems) between 2016 and 2021; assessed changes over time by using chi-square tests for trend in proportions; compared documentation in 2021 by gender, age, race-ethnicity, and site with chi-square tests; and compared all patients' mental health outcomes in 2021 with those of patients with documented SDoH ICD-10-CM codes by using exact binomial tests and one-proportion z tests. RESULTS: Documentation of any SDoH ICD-10-CM code significantly increased, from 1.7% of patients in 2016 to 2.7% in 2021, as did that for all SDoH categories except educational problems. Documentation was often more prevalent among female patients and those of other or unknown gender than among male patients and among American Indian or Alaska Native, Black or African American, and Hispanic individuals than among those belonging to other race-ethnicity categories. More educational problems were documented for younger patients, and more social environmental problems were documented for older patients. Psychiatric diagnoses and emergency department visits and hospitalizations related to mental health were more common among patients with documented SDoH codes. CONCLUSIONS: SDoH ICD-10-CM code documentation was infrequent and differed by population subgroup. Differences may reflect documentation practices or true SDoH prevalence variation. Standardized SDoH documentation methods are needed in health care settings.

Extracting Critical Information from Unstructured Clinicians' Notes Data to Identify Dementia Severity Using a Rule-Based Approach: Feasibility Study.

BACKGROUND: The severity of Alzheimer disease and related dementias (ADRD) is rarely documented in structured data fields in electronic health records (EHRs). Although this information is important for clinical monitoring and decision-making, it is often undocumented or "hidden" in unstructured text fields and not readily available for clinicians to act upon. OBJECTIVE: We aimed to assess the feasibility and potential bias in using keywords and rule-based matching for obtaining information about the severity of ADRD from EHR data. METHODS: We used EHR data from a large academic health care system that included patients with a primary discharge diagnosis of ADRD based on ICD-9 (International Classification of Diseases, Ninth Revision) and ICD-10 (International Statistical Classification of Diseases, Tenth Revision) codes between 2014 and 2019. We first assessed the presence of ADRD severity information and then the severity of ADRD in the EHR. Clinicians' notes were used to determine the severity of ADRD based on two criteria: (1) scores from the Mini Mental State Examination and Montreal Cognitive Assessment and (2) explicit terms for ADRD severity (eg, "mild dementia" and "advanced Alzheimer disease"). We compiled a list of common ADRD symptoms, cognitive test names, and disease severity terms, refining it iteratively based on previous literature and clinical expertise. Subsequently, we used rule-based matching in Python using standard open-source data analysis libraries to identify the context in which specific words or phrases were mentioned. We estimated the prevalence of documented ADRD severity and assessed the performance of our rule-based algorithm. RESULTS: We included 9115 eligible patients with over 65,000 notes from the providers. Overall, 22.93% (2090/9115) of patients were documented with mild ADRD, 20.87% (1902/9115) were documented with moderate or severe ADRD, and 56.20% (5123/9115) did not have any documentation of the severity of their ADRD. For the task of determining the presence of any ADRD severity information, our algorithm achieved an accuracy of >95%, specificity of >95%, sensitivity of >90%, and an F1-score of >83%. For the specific task of identifying the actual severity of ADRD, the algorithm performed well with an accuracy of >91%, specificity of >80%, sensitivity of >88%, and F1-score of >92%. Comparing patients with mild ADRD to those with more advanced ADRD, the latter group tended to contain older, more likely female, and Black patients, and having received their diagnoses in primary care or in-hospital settings. Relative to patients with undocumented ADRD severity, those with documented ADRD severity had a similar distribution in terms of sex, race, and rural or urban residence. CONCLUSIONS: Our study demonstrates the feasibility of using a rule-based matching algorithm to identify ADRD severity from unstructured EHR report data. However, it is essential to acknowledge potential biases arising from differences in documentation practices across various health care systems.

Flipping healthcare by including the patient perspective in integrated care pathway design: A scoping review.

BACKGROUND: Despite the recognized benefits of integrating patient perspectives into healthcare design and clinical decision support, theoretical approaches and standardized methods are lacking. Various strategies, such as developing pathways, have evolved to address these challenges. Previous research emphasized the need for a framework for care pathways that includes theoretical principles, extensive user involvement, and data from electronic health records to bridge the gap between different fields and disciplines. Standardizing the representation of the patient perspective could facilitate its sharing across healthcare organizations and domains and its integration into journal systems, shifting the balance of power from the provider to the patient. OBJECTIVES: This study aims to 1) Identify research approaches taken to develop patient-centred, integrated, care pathways supported by electronic health records 2) Propose a socio-technical framework for designing patient-centred care pathways across multiple healthcare levels that integrates the voice of the patient with the knowledge of the care provider and technological perspectives. METHODS: This study conducted a scoping review following the Joanna Briggs Institute guidelines and PRISMA-ScR protocol. The databases PubMed, Scopus, Web of Science, ProQuest, IEEE, and Google Scholar were searched using a key term search strategy including variations of patient-centred, integrated care, pathway, framework and model to identify relevant studies. Eligible articles included peer-reviewed literature documenting methodologies for mapping patient-centred, integrated care pathways in healthcare service design. RESULTS: This review summarizes the application of care pathway modelling practices across various areas of healthcare innovation. The search resulted in 410 studies, with 16 articles included after the full review and grey literature search. CONCLUSIONS: Our research illustrated incorporating patient perspectives into modelling care pathways and healthcare service design. Regardless of the medical domain, our methodology proposes an approach for modelling patient-centred, integrated care pathways across the care continuum, including using electronic health records to support the pathways.

DrugDoctor: enhancing drug recommendation in cold-start scenario via visit-level representation learning and training.

Medication recommendation is a crucial application of artificial intelligence in healthcare. Current methodologies mostly depend on patient-level longitudinal representation, which utilizes the entirety of historical electronic health records for making predictions. However, they tend to overlook a few key elements: (1) The need to analyze the impact of past medications on previous conditions. (2) Similarity in patient visits is more common than similarity in the complete medical histories of patients. (3) It is difficult to accurately represent patient-level longitudinal data due to the varying numbers of visits. To our knowledge, current models face difficulties in dealing with initial patient visits (i.e. in cold-start scenarios) which are common in clinical practice. This paper introduces DrugDoctor, an innovative drug recommendation model crafted to emulate the decision-making mechanics of human doctors. Unlike previous methods, DrugDoctor explores the visit-level relationship between prescriptions and diseases while considering the impact of past prescriptions on the patient's condition to provide more accurate recommendations. We design a plug-and-play block to effectively capture drug substructure-aware disease information and effectiveness-aware medication information, employing cross-attention and multi-head self-attention mechanisms. Furthermore, DrugDoctor adopts a fundamentally new visit-level training strategy, aligning more closely with the practices of doctors. Extensive experiments conducted on the MIMIC-III and MIMIC-IV datasets demonstrate that DrugDoctor outperforms 10 other state-of-the-art methods in terms of Jaccard, F1-score, and PRAUC. Moreover, DrugDoctor exhibits strong robustness in handling patients with varying numbers of visits and effectively tackles "cold-start" issues in medication combination recommendations.

Using the electronic health record to provide audit and feedback in medical student clerkships.

Objectives: This article focuses on the role of the electronic health record (EHR) to generate meaningful formative feedback for medical students in the clinical setting. Despite the scores of clinical data housed within the EHR, medical educators have only just begun to tap into this data to enhance student learning. Literature to-date has focused almost exclusively on resident education. Materials and Methods: Development of EHR auto-logging and triggered notifications are discussed as specific use cases in providing enhanced feedback for medical students. Results: By incorporating predictive and prescriptive analytics into the EHR, there is an opportunity to create powerful educational tools which may also support general clinical activity. Discussion: This article explores the possibilities of EHR as an educational resource. This serves as a call to action for educators and technology developers to work together on creating health record user-centric tools, acknowledging the ongoing work done to improve student-level attribution to patients. Conclusion: EHR analytics and tools present a novel approach to enhancing clinical clerkship education for medical students.

Association of a Novel Electronic Form for Preoperative Cardiac Risk Assessment With Reduction in Cardiac Consultations and Testing: Retrospective Cohort Study.

BACKGROUND: Preoperative cardiac risk assessment is an integral part of preoperative evaluation; however, there is significant variation among providers, leading to inappropriate referrals for cardiology consultation or excessive low-value cardiac testing. We implemented a novel electronic medical record (EMR) form in our preoperative clinics to decrease variation. OBJECTIVE: This study aimed to investigate the impact of the EMR form on the preoperative utilization of cardiology consultation and cardiac diagnostic testing (echocardiograms, stress tests, and cardiac catheterization) and evaluate postoperative outcomes. METHODS: A retrospective cohort study was conducted. Patients who underwent outpatient preoperative evaluation prior to an elective surgery over 2 years were divided into 2 cohorts: from July 1, 2021, to June 30, 2022 (pre-EMR form implementation), and from July 1, 2022, to June 30, 2023 (post-EMR form implementation). Demographics, comorbidities, resource utilization, and surgical characteristics were analyzed. Propensity score matching was used to adjust for differences between the 2 cohorts. The primary outcomes were the utilization of preoperative cardiology consultation, cardiac testing, and 30-day postoperative major adverse cardiac events (MACE). RESULTS: A total of 25,484 patients met the inclusion criteria. Propensity score matching yielded 11,645 well-matched pairs. The post-EMR form, matched cohort had lower cardiology consultation (pre-EMR form: n=2698, 23.2% vs post-EMR form: n=2088, 17.9%; P<.001) and echocardiogram (pre-EMR form: n=808, 6.9% vs post-EMR form: n=591, 5.1%; P<.001) utilization. There were no significant differences in the 30-day postoperative outcomes, including MACE (all P>.05). While patients with "possible indications" for cardiology consultation had higher MACE rates, the consultations did not reduce MACE risk. Most algorithm end points, except for active cardiac conditions, had MACE rates <1%. CONCLUSIONS: In this cohort study, preoperative cardiac risk assessment using a novel EMR form was associated with a significant decrease in cardiology consultation and testing utilization, with no adverse impact on postoperative outcomes. Adopting this approach may assist perioperative medicine clinicians and anesthesiologists in efficiently decreasing unnecessary preoperative resource utilization without compromising patient safety or quality of care.

Early Diagnosis of Hereditary Angioedema in Japan Based on a US Medical Dataset: Algorithm Development and Validation.

BACKGROUND: Hereditary angioedema (HAE), a rare genetic disease, induces acute attacks of swelling in various regions of the body. Its prevalence is estimated to be 1 in 50,000 people, with no reported bias among different ethnic groups. However, considering the estimated prevalence, the number of patients in Japan diagnosed with HAE remains approximately 1 in 250,000, which means that only 20% of potential HAE cases are identified. OBJECTIVE: This study aimed to develop an artificial intelligence (AI) model that can detect patients with suspected HAE using medical history data (medical claims, prescriptions, and electronic medical records [EMRs]) in the United States. We also aimed to validate the detection performance of the model for HAE cases using the Japanese dataset. METHODS: The HAE patient and control groups were identified using the US claims and EMR datasets. We analyzed the characteristics of the diagnostic history of patients with HAE and developed an AI model to predict the probability of HAE based on a generalized linear model and bootstrap method. The model was then applied to the EMR data of the Kyoto University Hospital to verify its applicability to the Japanese dataset. RESULTS: Precision and sensitivity were measured to validate the model performance. Using the comprehensive US dataset, the precision score was 2% in the initial model development step. Our model can screen out suspected patients, where 1 in 50 of these patients have HAE. In addition, in the validation step with Japanese EMR data, the precision score was 23.6%, which exceeded our expectations. We achieved a sensitivity score of 61.5% for the US dataset and 37.6% for the validation exercise using data from a single Japanese hospital. Overall, our model could predict patients with typical HAE symptoms. CONCLUSIONS: This study indicates that our AI model can detect HAE in patients with typical symptoms and is effective in Japanese data. However, further prospective clinical studies are required to investigate whether this model can be used to diagnose HAE.

Automated Identification of Postoperative Infections to Allow Prediction and Surveillance Based on Electronic Health Record Data: Scoping Review.

BACKGROUND: Postoperative infections remain a crucial challenge in health care, resulting in high morbidity, mortality, and costs. Accurate identification and labeling of patients with postoperative bacterial infections is crucial for developing prediction models, validating biomarkers, and implementing surveillance systems in clinical practice. OBJECTIVE: This scoping review aimed to explore methods for identifying patients with postoperative infections using electronic health record (EHR) data to go beyond the reference standard of manual chart review. METHODS: We performed a systematic search strategy across PubMed, Embase, Web of Science (Core Collection), the Cochrane Library, and Emcare (Ovid), targeting studies addressing the prediction and fully automated surveillance (ie, without manual check) of diverse bacterial infections in the postoperative setting. For prediction modeling studies, we assessed the labeling methods used, categorizing them as either manual or automated. We evaluated the different types of EHR data needed for the surveillance and labeling of postoperative infections, as well as the performance of fully automated surveillance systems compared with manual chart review. RESULTS: We identified 75 different methods and definitions used to identify patients with postoperative infections in studies published between 2003 and 2023. Manual labeling was the predominant method in prediction modeling research, 65% (49/75) of the identified methods use structured data, and 45% (34/75) use free text and clinical notes as one of their data sources. Fully automated surveillance systems should be used with caution because the reported positive predictive values are between 0.31 and 0.76. CONCLUSIONS: There is currently no evidence to support fully automated labeling and identification of patients with infections based solely on structured EHR data. Future research should focus on defining uniform definitions, as well as prioritizing the development of more scalable, automated methods for infection detection using structured EHR data.

The Use of Online Consultation Systems or Remote Consulting in England Characterized Through the Primary Care Health Records of 53 Million People in the OpenSAFELY Platform: Retrospective Cohort Study.

BACKGROUND: The National Health Service (NHS) Long Term Plan, published in 2019, committed to ensuring that every patient in England has the right to digital-first primary care by 2023-2024. The COVID-19 pandemic and infection prevention and control measures accelerated work by the NHS to enable and stimulate the use of online consultation (OC) systems across all practices for improved access to primary care. OBJECTIVE: We aimed to explore general practice coding activity associated with the use of OC systems in terms of trends, COVID-19 effect, variation, and quality. METHODS: With the approval of NHS England, the OpenSAFELY platform was used to query and analyze the in situ electronic health records of suppliers The Phoenix Partnership (TPP) and Egton Medical Information Systems, covering >53 million patients in >6400 practices, mainly in 2019-2020. Systematized Medical Nomenclature for Medicine-Clinical Terminology (SNOMED-CT) codes relevant to OC systems and written OCs were identified including eConsultation. Events were described by volumes and population rates, practice coverage, and trends before and after the COVID-19 pandemic. Variation was characterized among practices, by sociodemographics, and by clinical history of long-term conditions. RESULTS: Overall, 3,550,762 relevant coding events were found in practices using TPP, with the code eConsultation detected in 84.56% (2157/2551) of practices. Activity related to digital forms of interaction increased rapidly from March 2020, the onset of the pandemic; namely, in the second half of 2020, >9 monthly eConsultation coding events per 1000 registered population were registered compared to <1 a year prior. However, we found large variations among regions and practices: December 2020 saw the median practice have 0.9 coded instances per 1000 population compared to at least 36 for the highest decile of practices. On sociodemographics, the TPP cohort with OC instances, when compared (univariate analysis) to the cohort with general practitioner consultations, was more predominantly female (661,235/1,087,919, 60.78% vs 9,172,833/17,166,765, 53.43%), aged 18 to 40 years (349,162/1,080,589, 32.31% vs 4,295,711/17,000,942, 25.27%), White (730,389/1,087,919, 67.14% vs 10,887,858/17,166,765, 63.42%), and less deprived (167,889/1,068,887, 15.71% vs 3,376,403/16,867,074, 20.02%). Looking at the eConsultation code through multivariate analysis, it was more commonly recorded among patients with a history of asthma (adjusted odds ratio [aOR] 1.131, 95% CI 1.124-1.137), depression (aOR 1.144, 95% CI 1.138-1.151), or atrial fibrillation (aOR 1.119, 95% CI 1.099-1.139) when compared to other patients with general practitioner consultations, adjusted for long-term conditions, age, and gender. CONCLUSIONS: We successfully queried general practice coding activity relevant to the use of OC systems, showing increased adoption and key areas of variation during the pandemic at both sociodemographic and clinical levels. The work can be expanded to support monitoring of coding quality and underlying activity. This study suggests that large-scale impact evaluation studies can be implemented within the OpenSAFELY platform, namely looking at patient outcomes.