Long-Term Outcomes Associated With ß-Lactam Allergies.

Importance: ß-lactam (BL) allergies are the most common drug allergy worldwide, but most are reported in error. BL allergies are also well-established risk factors for adverse drug events and antibiotic-resistant infections during inpatient health care encounters, but the understanding of the long-term outcomes of patients with BL allergies remains limited. Objective: To evaluate the long-term clinical outcomes of patients with BL allergies. Design, Setting, and Participants: This longitudinal retrospective cohort study was conducted at a single regional health care system in western Pennsylvania. Electronic health records were analyzed for patients who had an index encounter with a diagnosis of sepsis, pneumonia, or urinary tract infection between 2007 and 2008. Patients were followed-up until death or the end of 2018. Data analysis was performed from January 2022 to January 2024. Exposure: The presence of any BL class antibiotic in the allergy section of a patient's electronic health record, evaluated at the earliest occurring observed health care encounter. Main Outcomes and Measures: The primary outcome was all-cause mortality, derived from the Social Security Death Index. Secondary outcomes were defined using laboratory and microbiology results and included infection with methicillin-resistant Staphylococcus aureus (MRSA), Clostridium difficile, or vancomycin-resistant Enterococcus (VRE) and severity and occurrence of acute kidney injury (AKI). Generalized estimating equations with a patient-level panel variable and time exposure offset were used to evaluate the odds of occurrence of each outcome between allergy groups. Results: A total of 20 092 patients (mean [SD] age, 62.9 [19.7] years; 12 231 female [60.9%]), of whom 4211 (21.0%) had BL documented allergy and 15 881 (79.0%) did not, met the inclusion criteria. A total of 3513 patients (17.5%) were Black, 15 358 (76.4%) were White, and 1221 (6.0%) were another race. Using generalized estimating equations, documented BL allergies were not significantly associated with the odds of mortality (odds ratio [OR], 1.02; 95% CI, 0.96-1.09). BL allergies were associated with increased odds of MRSA infection (OR, 1.44; 95% CI, 1.36-1.53), VRE infection (OR, 1.18; 95% CI, 1.05-1.32), and the pooled rate of the 3 evaluated antibiotic-resistant infections (OR, 1.33; 95% CI, 1.30-1.36) but were not associated with C difficile infection (OR, 1.04; 95% CI, 0.94-1.16), stage 2 and 3 AKI (OR, 1.02; 95% CI, 0.96-1.10), or stage 3 AKI (OR, 1.06; 95% CI, 0.98-1.14). Conclusions and Relevance: Documented BL allergies were not associated with the long-term odds of mortality but were associated with antibiotic-resistant infections. Health systems should emphasize accurate allergy documentation and reduce unnecessary BL avoidance.

Characteristics of a transgender and gender-diverse patient population in Utah: Use of electronic health records to advance clinical and health equity research.

Transgender and gender-diverse (TGD) people, individuals whose gender identity differs from their sex assigned at birth, face unique challenges in accessing gender-affirming care and often experience disparities in a variety of health outcomes. Clinical research on TGD health is limited by a lack of standardization on how to best identify these individuals. The objective of this retrospective cohort analysis was to accurately identify and describe TGD adults and their use of gender-affirming care from 2003-2023 in a healthcare system in Utah, United States. International Classification of Disease (ICD)-9 and 10 codes and surgical procedure codes, along with sexual orientation and gender identity data were used to develop a dataset of 4,587 TGD adults. During this time frame, 2,985 adults received gender-affirming hormone therapy (GAHT) and/or gender-affirming surgery (GAS) within one healthcare system. There was no significant difference in race or ethnicity between TGD adults who received GAHT and/or GAS compared to TGD adults who did not receive such care. TGD adults who received GAHT and/or GAS were more likely to have commercial insurance coverage, and adults from rural communities were underrepresented. Patients seeking estradiol-based GAHT tended to be older than those seeking testosterone-based GAHT. The first GAS occurred in 2013, and uptake of GAS have doubled since 2018. This study provides a methodology to identify and examine TGD patients in other health systems and offers insights into emerging trends and access to gender-affirming care.

The impact of hospitalization on inappropriate prescribing and polypharmacy in older patients: A descriptive cross-sectional study.

BACKGROUND: Polypharmacy and the use of potentially inappropriate medications (PIMs) in older individuals are widespread phenomena that are associated with an increase in morbidity and mortality. The Beers Criteria is a tool that helps to identify patients that are prescribed with PIMs, thereby reducing the risk of associated harm. Amongst other populations, the criteria identify drugs that should not be used by the majority of older patients. AIM: Determining the proportion of older inpatients who were discharged from hospitalization with polypharmacy (a prescription for more than seven drugs), or with a PIM as defined by the Beers Criteria. METHODS: A descriptive cross-sectional study based on patients aged 65 and over who were hospitalized in the years 2019-2021 in the internal medicine, orthopedic and surgical wards at a medium-size hospital. Demographic information and details about drug treatment were collected from the electronic patient records system. Patients who died during hospitalization were excluded from the study group. MAIN OUTCOME MEASURES: The proportion of inpatients with polypharmacy or a PIM as part of their regular prescription, at the time of admission and at discharge. RESULTS: 49 564 patients were included in the study cohort. At discharge, 19% of the patients were given a prescription for a PIM, with a small but significant decrease compared with the rate admission (22.1%). At discharge, 42.8% of patients had polypharmacy, representing a small but significant increase compared with the rate on admission (40.6%). CONCLUSIONS: The study demonstrated high baseline rates of PIM prescription and polypharmacy. Hospitalization was associated with a decrease in PIM prescription and an increase in polypharmacy. This highlights the importance of medication review during admission to reduce the potential risk to older adults from polypharmacy and PIM prescription.

Iraqi Population Trusts in Electronic Healthcare Records: A Cross-sectional Study.

This study investigated the perceptions of Iraqi patients regarding Electronic Healthcare Records (EHRs) in terms of trust and propensity to share and exchange medical and personal information and data within the healthcare ecosystem. During the period of April to June 2022, a researcher-assisted questionnaire was disseminated to adult Iraqi patients attending public or private healthcare facilities in a subset of Iraqi governorates. Data collection was followed by descriptive and inferential analyses. In total, 552 respondents filled out the questionnaire. The findings revealed that 71.6% of respondents were conversant with EHRs and trusted them as data collection and storage systems. In addition, 10% of respondents did not want their EHRs to be shared between healthcare professionals and institutions. However, only 3.6% of participants were willing to share all of their personal information with healthcare professionals. Female respondents were considerably more willing to share their full names with healthcare professionals than male respondents, despite the society's reputation for conservatism. The findings of this study highlighted the necessity of tailoring initiatives to enhance patients' trust in EHRs and their interactions with healthcare professionals other than medical physicians.

Implementation and use of electronic patient records in the Brazilian Air Force: a cross-sectional study1.

OBJECTIVE: The objective was to analyze the implementation and use of the electronic patient record in the health services of the Brazilian Air Force. METHODS: This is a cross-sectional study carried out with 234 physicians, between March and May 2021. The data collection instrument was sent by email. The electronic patient record was implemented in the Air Force approximately 3 years ago (64.5%), and about 81% of the physicians received training to operate it. RESULTS: The most common records involve data related to consultations (90.1%) and interviews with physical examination (67.1%). Physicians cited that information storage (75.6%), agility, and feasibility of recording (55.1%) were the main advantages of the electronic patient record. As disadvantages, problems in electronic equipment (69.7%) and system errors (65%) were reported. Most participants considered that the implementation had a positive impact on work dynamics (75.6%) and productivity (66.7%), mainly regarding the components "Work processes" (57.3%) and "Amount of carried out activities" (21.4%). Keeping records was significantly associated with the job position (p<0.001), type of unit (p=0.008), time of implementation (p<0.001), and participation in training (p=0.028). CONCLUSION: The implementation of the electronic patient record in the Air Force was recently done, and just over half of the physicians were trained prior to the implementation. The tool is considered compatible with work processes and has a positive effect on productivity.

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.

Identification of patients' smoking status using an explainable AI approach: a Danish electronic health records case study.

BACKGROUND: Smoking is a critical risk factor responsible for over eight million annual deaths worldwide. It is essential to obtain information on smoking habits to advance research and implement preventive measures such as screening of high-risk individuals. In most countries, including Denmark, smoking habits are not systematically recorded and at best documented within unstructured free-text segments of electronic health records (EHRs). This would require researchers and clinicians to manually navigate through extensive amounts of unstructured data, which is one of the main reasons that smoking habits are rarely integrated into larger studies. Our aim is to develop machine learning models to classify patients' smoking status from their EHRs. METHODS: This study proposes an efficient natural language processing (NLP) pipeline capable of classifying patients' smoking status and providing explanations for the decisions. The proposed NLP pipeline comprises four distinct components, which are; (1) considering preprocessing techniques to address abbreviations, punctuation, and other textual irregularities, (2) four cutting-edge feature extraction techniques, i.e. Embedding, BERT, Word2Vec, and Count Vectorizer, employed to extract the optimal features, (3) utilization of a Stacking-based Ensemble (SE) model and a Convolutional Long Short-Term Memory Neural Network (CNN-LSTM) for the identification of smoking status, and (4) application of a local interpretable model-agnostic explanation to explain the decisions rendered by the detection models. The EHRs of 23,132 patients with suspected lung cancer were collected from the Region of Southern Denmark during the period 1/1/2009-31/12/2018. A medical professional annotated the data into 'Smoker' and 'Non-Smoker' with further classifications as 'Active-Smoker', 'Former-Smoker', and 'Never-Smoker'. Subsequently, the annotated dataset was used for the development of binary and multiclass classification models. An extensive comparison was conducted of the detection performance across various model architectures. RESULTS: The results of experimental validation confirm the consistency among the models. However, for binary classification, BERT method with CNN-LSTM architecture outperformed other models by achieving precision, recall, and F1-scores between 97% and 99% for both Never-Smokers and Active-Smokers. In multiclass classification, the Embedding technique with CNN-LSTM architecture yielded the most favorable results in class-specific evaluations, with equal performance measures of 97% for Never-Smoker and measures in the range of 86 to 89% for Active-Smoker and 91-92% for Never-Smoker. CONCLUSION: Our proposed NLP pipeline achieved a high level of classification performance. In addition, we presented the explanation of the decision made by the best performing detection model. Future work will expand the model's capabilities to analyze longer notes and a broader range of categories to maximize its utility in further research and screening applications.

Predicting patients with dementia most at risk of needing psychiatric in-patient or enhanced community care using routinely collected clinical data: retrospective multi-site cohort study.

BACKGROUND: Dementia is a common and progressive condition whose prevalence is growing worldwide. It is challenging for healthcare systems to provide continuity in clinical services for all patients from diagnosis to death. AIMS: To test whether individuals who are most likely to need enhanced care later in the disease course can be identified at the point of diagnosis, thus allowing the targeted intervention. METHOD: We used clinical information collected routinely in de-identified electronic patient records from two UK National Health Service (NHS) trusts to identify at diagnosis which individuals were at increased risk of needing enhanced care (psychiatric in-patient or intensive (crisis) community care). RESULTS: We examined the records of a total of 25 326 patients with dementia. A minority (16% in the Cambridgeshire trust and 2.4% in the London trust) needed enhanced care. Patients who needed enhanced care differed from those who did not in age, cognitive test scores and Health of the Nation Outcome Scale scores. Logistic regression discriminated risk, with an area under the receiver operating characteristic curve (AUROC) of up to 0.78 after 1 year and 0.74 after 4 years. We were able to confirm the validity of the approach in two trusts that differed widely in the populations they serve. CONCLUSIONS: It is possible to identify, at the time of diagnosis of dementia, individuals most likely to need enhanced care later in the disease course. This permits the development of targeted clinical interventions for this high-risk group.

Electronic health records and e-prescribing in Australia: An exploration of technological utilisation in Australian community pharmacies.

OBJECTIVE: This study aimed to assess the utilisation, benefits, and challenges associated with Electronic Health Records (EHR) and e-prescribing systems in Australian Community Pharmacies, focusing on their integration into daily practice and the impacts on operational efficiency, while also gathering qualitative insights from community pharmacists. METHODS: A mixed-methods online survey was carried out among community pharmacists throughout Australia to assess the utilisation of EHR and e-prescribing systems, including the benefits and challenges associated with their use. Data was analysed based on pharmacists' age, gender, and practice location (metropolitan vs. regional). The chi-square test was applied to examine the relationship between these demographic factors and the utilisation and operational challenges of EHR and e-prescribing systems. RESULTS: The survey engaged 120 Australian community pharmacists. Of the participants, 67 % reported usability and efficiency issues with EHR systems. Regarding e-prescribing, 58 % of pharmacists faced delays due to slow software performance, while 42 % encountered errors in data transmission. Despite these challenges, the benefits of e-prescribing were evident, with 79 % of respondents noting the elimination of illegible prescriptions and 40 % observing a reduction in their workload. Issues with prescription quantity discrepancies and the reprinting process were highlighted, indicating areas for improvement in workflow and system usability. The analysis revealed no significant statistical relationship between the utilisation and challenges of EHR and e-prescribing systems with the demographic variables of age, gender and location (p > 0.05), emphasising the necessity for healthcare solutions that address the needs of all pharmacists regardless of specific demographic segments. CONCLUSION: In Australian community pharmacies, EHR and e-prescribing may enhance patient care but come with challenges such as data completeness, technical issues, and usability concerns. Implementing successful integration relies on user-centric design, standardised practices, and robust infrastructure. While demanding for pharmacists, the digital transition improves efficiency and quality of care. Ensuring user-friendly tools is crucial for the smooth utilisation of digital health.

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.

The Role of Telehealth in Improving Care Connections and Outcomes for Community Health Center Patients with Diabetes.

INTRODUCTION: Does telehealth decrease health disparities by improving connections to care or simply result in new barriers for vulnerable populations who often lack access to technology? This study aims to better understand the role of telehealth and social determinants of health in improving care connections and outcomes for Community Health Center patients with diabetes. METHODS: This retrospective analysis of Electronic Health Record (EHR) data examined the relationship between telehealth utilization and glycemic control and consistency of connection to the health care team ("connectivity"). EHR data were collected from 20 Community Health Centers from July 1, 2019 through December 31, 2021. Descriptive statistics were calculated, and multivariable linear regression was used to assess the associations between telehealth use and engagement in care and glycemic control. RESULTS: The adjusted analysis found positive, statistically significant associations between telehealth use and each of the 2 primary outcomes. Telehealth use was associated with 0.89 additional months of hemoglobin A1c (HbA1c) control (95% confidence interval [CI], 0.73 to 1.04) and 4.49 additional months of connection to care (95% CI, 4.27 to 4.70). DISCUSSION: The demonstrated increased engagement in primary care for telehealth users is significant and encouraging as Community Health Center populations are at greater risk of lapses in care and loss to follow up. CONCLUSIONS: Telehealth can be a highly effective, patient-centered form of care for people with diabetes. Telehealth can play a critical role in keeping vulnerable patients with diabetes connected to their care team and involved in care and may be an important tool for reducing health disparities.

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.

The Ability of Primary Care Practices to Measure and Report on Care Quality.

BACKGROUND: Creating useful clinical quality measure (CQM) reports in a busy primary care practice is known to depend on the capability of the electronic health record (EHR). Two other domains may also contribute: supportive leadership to prioritize the work and commit the necessary resources, and individuals with the necessary health information technology (IT) skills to do so. Here we describe the results of an assessment of the above 3 domains and their associations with successful CQM reporting during an initiative to improve smaller primary care practices' cardiovascular disease CQMs. METHODS: The study took place within an AHRQ EvidenceNOW initiative of external support for smaller practices across Washington, Oregon and Idaho. Practice facilitators who provided this support completed an assessment of the 3 domains previously described for each of their assigned practices. Practices submitted 3 CQMs to the study team: appropriate aspirin prescribing, use of statins when indicated, blood pressure control, and tobacco screening/cessation. RESULTS: Practices with advanced EHR reporting capability were more likely to report 2 or more CQMs. Only one-third of practices were "advanced" in this domain, and this domain had the highest proportion of practices (39.1%) assessed as "basic." The presence of advanced leadership or advanced skills did not appreciably increase the proportion of practices that reported 2 or more CQMs. CONCLUSIONS: Our findings support previous reports of limited EHR reporting capabilities within smaller practices but extend these findings by demonstrating that practices with advanced capabilities in this domain are more likely to produce CQM reports.

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.

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.

Using a general practice research database to assess the spatio-temporal COVID-19 risk.

BACKGROUND: In Flanders, general practitioners (GPs) were among the first ones to collect data regarding COVID-19 cases. Intego is a GPs' morbidity registry in primary care with data collected from the electronic medical records from a sample of general practices. The Intego database contain elaborate information regarding patient characteristics, such as comorbidities. At the national level, the Belgian Public Health Institute (Sciensano) recorded all test-confirmed COVID-19 cases, but without other patient characteristics. METHODS: Spatio and spatio-temporal analyses were used to analyse the spread of COVID-19 incidence at two levels of spatial aggregation: the municipality and the health sector levels. Our study goal was to compare spatio-temporal modelling results based on the Intego and Sciensano data, in order to see whether the Intego database is capable of detecting epidemiological trends similar to those in the Sciensano data. Comparable results would allow researchers to use these Intego data, and their wealth of patient information, to model COVID-19-related processes. RESULTS: The two data sources provided comparable results. Being a male decreased the odds of having COVID-19 disease. The odds for the age categories (17,35], (35,65] and (65,110] of being a confirmed COVID-19 case were significantly higher than the odds for the age category [0,17]. In the Intego data, having one of the following comorbidities, i.e., chronic kidney disease, heart and vascular disease, and diabetes, was significantly associated with being a COVID-19 case, increasing the odds of being diagnosed with COVID-19. CONCLUSION: We were able to show how an alternative data source, the Intego data, can be used in a pandemic situation. We consider our findings useful for public health officials who plan intervention strategies aimed at monitor and control disease outbreaks such as that of COVID-19.

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.