Search

Three years after the start of the SARS-CoV-2 virus (COVID-19) pandemic, its effects continue to affect society and COVID-19 vaccination campaigns continue to be a topic of controversy and inconsistent practice. After experiencing spikes in COVID-19 cases, our University of California Davis Health Division of Hospital Medicine sought to understand the reasons underlying the low COVID-19 vaccination rates in our county and find approaches to improve the number of vaccinations among adults admitted to the inpatient setting. This quality improvement project aimed to increase COVID-19 primary and booster vaccine efforts through a multi-pronged approach of increased collaboration with specialised staff and optimisation of use of our electronic health record system.Our key interventions focused on developing a visual reminder of COVID-19 vaccine status using the functionality of our electronic medical record (EMR), standardising documentation of COVID-19 vaccine status and enhancing team-based vaccination discussions through team huddles and partnering with inpatient care coordinators. While our grassroots approach enhanced COVID-19 vaccination rates in the inpatient setting and had additional benefits such as increased collaboration among teams, system-level efforts often made a greater impact at our healthcare centre. For other institutions interested in increasing COVID-19 vaccination rates, our top three recommendations include integrating vaccination into pre-existing workflows, optimising EMR functionality and increasing vaccine accessibility in the inpatient setting.

BackgroundElectronic health record (EHR)-based patient messages can contribute to burnout. Messages with a negative tone are particularly challenging to address. In this perspective, we describe our initial evaluation of large language model (LLM)-generated responses to negative EHR patient messages and contend that using LLMs to generate initial drafts may be feasible, although refinement will be needed.MethodsA retrospective sample (n = 50) of negative patient messages was extracted from a health system EHR, de-identified, and inputted into an LLM (ChatGPT). Qualitative analyses were conducted to compare LLM responses to actual care team responses.ResultsSome LLM-generated draft responses varied from human responses in relational connection, informational content, and recommendations for next steps. Occasionally, the LLM draft responses could have potentially escalated emotionally charged conversations.ConclusionFurther work is needed to optimize the use of LLMs for responding to negative patient messages in the EHR.

OBJECTIVES: This study sought to capture current digital health company experiences integrating with electronic health records (EHRs), given new federally regulated standards-based application programming interface (API) policies. MATERIALS AND METHODS: We developed and fielded a survey among companies that develop solutions enabling human interaction with an EHR API. The survey was developed by the University of California San Francisco in collaboration with the Office of the National Coordinator for Health Information Technology, the California Health Care Foundation, and ScaleHealth. The instrument contained questions pertaining to experiences with API integrations, barriers faced during API integrations, and API-relevant policy efforts. RESULTS: About 73% of companies reported current or previous use of a standards-based EHR API in production. About 57% of respondents indicated using both standards-based and proprietary APIs to integrate with an EHR, and 24% worked about equally with both APIs. Most companies reported use of the Fast Healthcare Interoperability Resources standard. Companies reported that standards-based APIs required on average less burden than proprietary APIs to establish and maintain. However, companies face barriers to adopting standards-based APIs, including high fees, lack of realistic clinical testing data, and lack of data elements of interest or value. DISCUSSION: The industry is moving toward the use of standardized APIs to streamline data exchange, with a majority of digital health companies using standards-based APIs to integrate with EHRs. However, barriers persist. CONCLUSION: A large portion of digital health companies use standards-based APIs to interoperate with EHRs. Continuing to improve the resources for digital health companies to find, test, connect, and use these APIs without special effort will be crucial to ensure future technology robustness and durability.

OBJECTIVE: Recent clinical guidelines for sepsis management emphasize immediate antibiotic initiation for suspected septic shock. Though hypotension is a high-risk marker of sepsis severity, prior studies have not considered the precise timing of hypotension in relation to antibiotic initiation and how clinical characteristics and outcomes may differ. Our objective was to evaluate antibiotic initiation in relation to hypotension to characterize differences in sepsis presentation and outcomes in patients with suspected septic shock. METHODS: Adults presenting to the emergency department (ED) June 2012-December 2018 diagnosed with sepsis (Sepsis-III electronic health record [EHR] criteria) and hypotension (non-resolving for ≥30 min, systolic blood pressure 60 min after (late) treatment. RESULTS: Among 2219 patients, 55% received early treatment, 13% immediate, and 32% late. The late subgroup often presented to the ED with hypotension (median 0 min) but received antibiotics a median of 191 min post-ED presentation. Clinical characteristics notable for this subgroup included higher prevalence of heart failure and liver disease (p

The overarching goal of the third scientific oral health symposium was to introduce the concept of a learning health system to the dental community and to identify and discuss cutting-edge research and strategies using data for improving the quality of dental care and patient safety. Conference participants included clinically active dentists, dental researchers, quality improvement experts, informaticians, insurers, EHR vendors/developers, and members of dental professional organizations and dental service organizations. This report summarizes the main outputs of the third annual OpenWide conference held in Houston, Texas, on October 12, 2022, as an affiliated meeting of the American Dental Association (ADA) 2022 annual conference.

OBJECTIVES: Although guidelines recommend antiviral therapy for outpatients with COVID-19 who are at high risk of progressing to severe conditions, such as older adults, many patients do not receive appropriate treatment. Little is known, however, about the physician factors associated with the prescription of guideline-recommended antiviral therapy for patients with COVID-19. DESIGN: A cross-sectional study. SETTING: Data including outpatient visits in primary care clinics in Japan from April to August 2023. PARTICIPANTS: We analysed 30 953 outpatients aged ≥65 years treated with COVID-19 (mean (SD) age, 75.0 (7.6) years; 17 652 women (57.0%)) in 1394 primary care clinics. OUTCOME MEASURES: The primary outcome was the prescription of guideline-recommended antivirals (ie, nirmatrelvir-ritonavir or molnupiravir), adjusted for patient characteristics, months of visits and regions. RESULTS: Antiviral prescriptions were concentrated among a small proportion of physicians; for example, the top 10% of physicians that had the largest number of nirmatrelvir-ritonavir prescriptions accounted for 92.4% of all nirmatrelvir-ritonavir prescriptions. After adjusting for potential confounders, physicians with higher patient volumes were more likely to prescribe guideline-recommended antivirals to their patients (adjusted OR (aOR) for high vs low volume, 1.76; 95% CI 1.31 to 2.38; adjusted p

Identification of Alzheimer's disease (AD) onset risk can facilitate interventions before irreversible disease progression. We demonstrate that electronic health records from the University of California, San Francisco, followed by knowledge networks (for example, SPOKE) allow for (1) prediction of AD onset and (2) prioritization of biological hypotheses, and (3) contextualization of sex dimorphism. We trained random forest models and predicted AD onset on a cohort of 749 individuals with AD and 250,545 controls with a mean area under the receiver operating characteristic of 0.72 (7 years prior) to 0.81 (1 day prior). We further harnessed matched cohort models to identify conditions with predictive power before AD onset. Knowledge networks highlight shared genes between multiple top predictors and AD (for example, APOE, ACTB, IL6 and INS). Genetic colocalization analysis supports AD association with hyperlipidemia at the APOE locus, as well as a stronger female AD association with osteoporosis at a locus near MS4A6A. We therefore show how clinical data can be utilized for early AD prediction and identification of personalized biological hypotheses.

Advance care planning (ACP) has been recognized as crucial by patients, families, and clinicians; however, different definitions and measurements have led to inconsistencies in practice and mixed evidence in the literature. This narrative review explores ACPs evolution, innovations, and outcomes using thematic analysis to synthesize data from randomized controlled trials, reviews, and editorials. Key findings include (1) ACP has evolved over the past several decades from a sole focus on code status and advance directive (AD) forms to a continuum of care planning over the life course focused on tailored preparation for patients and surrogate decision-makers and (2) ACP measurement has evolved from traditional outcome metrics, such as AD completion, to a comprehensive outcomes framework that includes behavior change theory, systems, implementation science, and a focus on surrogate outcomes. Since the recent development of an ACP consensus definition and outcomes framework, high-quality trials have reported mainly positive outcomes for interventions, especially for surrogates, which aligns with the patient desire to relieve decision-making burden for loved ones. Additionally, measurement of clinically meaningful ACP information, including documented goals of care discussions, is increasingly being integrated into electronic health records (EHR), and emerging, real-time assessments and natural language processing are enhancing ACP evaluation. To make things easier for patients, families, and care teams, clinicians and researchers can use and disseminate these evolved definitions; provide patients validated, easy-to-use tools that prime patients for conversations and decrease health disparities; use easy-to-access clinician training and simple scripts for interdisciplinary team members; and document patients values and preferences in the medical record to capture clinically meaningful ACP so this information is available at the point of care. Future efforts should focus on efficient implementation, expanded reimbursement options, and seamless integration of EHR documentation to ensure ACPs continued evolution to better serve patients and their care partners.

PurposeUse of the electronic health record (EHR) has motivated the need for data standardization. A gap in knowledge exists regarding variations in existing terminologies for defining diabetic retinopathy (DR) cohorts. This study aimed to review the literature and analyze variations regarding codified definitions of DR.DesignLiterature review and quantitative analysis.SubjectsPublished manuscripts.MethodsFour graders reviewed PubMed and Google Scholar for peer-reviewed studies. Studies were included if they used codified definitions of DR (e.g., billing codes). Data elements such as author names, publication year, purpose, data set type, and DR definitions were manually extracted. Each study was reviewed by ≥ 2 authors to validate inclusion eligibility. Quantitative analyses of the codified definitions were then performed to characterize the variation between DR cohort definitions.Main outcome measuresNumber of studies included and numeric counts of billing codes used to define codified cohorts.ResultsIn total, 43 studies met the inclusion criteria. Half of the included studies used datasets based on structured EHR data (i.e., data registries, institutional EHR review), and half used claims data. All but 1 of the studies used billing codes such as the International Classification of Diseases 9th or 10th edition (ICD-9 or ICD-10), either alone or in addition to another terminology for defining disease. Of the 27 included studies that used ICD-9 and the 20 studies that used ICD-10 codes, the most common codes used pertained to the full spectrum of DR severity. Diabetic retinopathy complications (e.g., vitreous hemorrhage) were also used to define some DR cohorts.ConclusionsSubstantial variations exist among codified definitions for DR cohorts within retrospective studies. Variable definitions may limit generalizability and reproducibility of retrospective studies. More work is needed to standardize disease cohorts.Financial disclosuresProprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

OBJECTIVE: Map multimorbidity-weighted index (MWI) conditions to International Classification of Diseases, 10th Revision (ICD-10), expand the conditions and codes to develop a new ICD-10-coded MWI (MWI-ICD10) and updated MWI-ICD9, and assess their consistency. DESIGN: Population-based retrospective cohort. SETTING: Large medical centre between 2013 and 2017. PARTICIPANTS: Adults ≥18 years old with encounters in each of 4 years (2013, 2014, 2016, 2017). MAIN OUTCOME MEASURES: MWI conditions mapped to ICD-10 codes, and additional conditions and codes added to produce a new MWI-ICD10 and updated MWI-ICD9. We compared the prevalence of ICD-coded MWI conditions within the ICD-9 era (2013-2014), within the ICD-10 era (2016-2017) and across the ICD-9-ICD-10 transition in 2015 (washout period) among adults present in both sets of comparison years. We computed the prevalence and change in prevalence of conditions when using MWI-ICD10 versus MWI-ICD9. RESULTS: 88 175 adults met inclusion criteria. Participants were 60.8% female, 50.5% white, with mean age 54.7±17.3 years and baseline MWI-ICD9 4.47±6.02 (range 0-64.33). Of 94 conditions, 65 had

BACKGROUND: Despite efforts to view electronic health records (EHR) data through an equity lens, crucial contextual information regarding patients social environments remains limited. Integrating EHR data and Geographic Information Systems (GIS) technology can give deeper insights into the relationships between patients social environments, health outcomes, and geographic factors. This study aims to identify regions with the fastest and slowest access to outpatient physical therapy services using bivariate choropleth maps to provide contextual insights that may contribute to health disparity in access. METHODS: This was a retrospective cohort study of patients access timelines for the first visit to outpatient physical therapy services (n = 10,363). The three timelines evaluated were (1) referral-to-scheduled appointment time, (2) scheduled appointment to first visit time, and (3) referral to first visit time. Hot and coldspot analyses (CI 95%) determined the fastest and slowest access times with patient-level characteristics and bivariate choropleth maps that were developed to visualize associations between access patterns and disadvantaged areas using Area Deprivation Index scores. Data were collected between January 1, 2016 and January 1, 2020. EHR data were geocoded via GIS technology to calculate geospatial statistics (Gi∗ statistic from ArcGIS Pro) in an urban area. RESULTS: Statistically significant differences were found for all three access timelines between coldspot (i.e., fast access group) and hotspot (i.e., slow access group) comparisons (p

ObjectiveExisting research on social determinants of health (SDoH) predominantly focuses on physician notes and structured data within electronic medical records. This study posits that social work notes are an untapped, potentially rich source for SDoH information. We hypothesize that clinical notes recorded by social workers, whose role is to ameliorate social and economic factors, might provide a complementary information source of data on SDoH compared to physician notes, which primarily concentrate on medical diagnoses and treatments. We aimed to use word frequency analysis and topic modeling to identify prevalent terms and robust topics of discussion within a large cohort of social work notes including both outpatient and in-patient consultations.Materials and methodsWe retrieved a diverse, deidentified corpus of 0.95 million clinical social work notes from 181 644 patients at the University of California, San Francisco. We conducted word frequency analysis related to ICD-10 chapters to identify prevalent terms within the notes. We then applied Latent Dirichlet Allocation (LDA) topic modeling analysis to characterize this corpus and identify potential topics of discussion, which was further stratified by note types and disease groups.ResultsWord frequency analysis primarily identified medical-related terms associated with specific ICD10 chapters, though it also detected some subtle SDoH terms. In contrast, the LDA topic modeling analysis extracted 11 topics explicitly related to social determinants of health risk factors, such as financial status, abuse history, social support, risk of death, and mental health. The topic modeling approach effectively demonstrated variations between different types of social work notes and across patients with different types of diseases or conditions.DiscussionOur findings highlight LDA topic modeling's effectiveness in extracting SDoH-related themes and capturing variations in social work notes, demonstrating its potential for informing targeted interventions for at-risk populations.ConclusionSocial work notes offer a wealth of unique and valuable information on an individual's SDoH. These notes present consistent and meaningful topics of discussion that can be effectively analyzed and utilized to improve patient care and inform targeted interventions for at-risk populations.

ImportanceProcedural proficiency is a core competency for graduate medical education; however, procedural reporting often relies on manual workflows that are duplicative and generate data whose validity and accuracy are difficult to assess. Failure to accurately gather these data can impede learner progression, delay procedures, and negatively impact patient safety.ObjectiveTo examine accuracy and procedure logging completeness of a system that extracts procedural data from an electronic health record system and uploads these data securely to an application used by many residency programs for accreditation.Design, setting, and participantsThis quality improvement study of all emergency medicine resident physicians at University of California, San Diego Health was performed from May 23, 2023, to June 25, 2023.ExposuresAutomated system for procedure data extraction and upload to a residency management software application.Main outcomes and measuresThe number of procedures captured by the automated system when running silently compared with manually logged procedures in the same timeframe, as well as accuracy of the data upload.ResultsForty-seven residents participated in the initial silent assessment of the extraction component of the system. During a 1-year period (May 23, 2022, to May 7, 2023), 4291 procedures were manually logged by residents, compared with 7617 procedures captured by the automated system during the same period, representing a 78% increase. During assessment of the upload component of the system (May 8, 2023, to June 25, 2023), a total of 1353 procedures and patient encounters were evaluated, with the system operating with a sensitivity of 97.4%, specificity of 100%, and overall accuracy of 99.5%.Conclusions and relevanceIn this quality improvement study of emergency medicine resident physicians, an automated system demonstrated that reliance on self-reported procedure logging resulted in significant procedural underreporting compared with the use of data obtained at the point of performance. Additionally, this system afforded a degree of reliability and validity heretofore absent from the usual after-the-fact procedure logging workflows while using a novel application programming interface-based approach. To our knowledge, this system constitutes the first generalizable implementation of an automated solution to a problem that has existed in graduate medical education for decades.

BackgroundElectronic health record (EHR)-based clinical decision support is a scalable way to help standardize clinical care. Clinical decision support systems have not been extensively investigated in cirrhosis management. Human-centered design (HCD) is an approach that engages with potential users in intervention development. In this study, we applied HCD to design the features and interface for a clinical decision support system for cirrhosis management, called CirrhosisRx.MethodsWe conducted technical feasibility assessments to construct a visual blueprint that outlines the basic features of the interface. We then convened collaborative-design workshops with generalist and specialist clinicians. We elicited current workflows for cirrhosis management, assessed gaps in existing EHR systems, evaluated potential features, and refined the design prototype for CirrhosisRx. At the conclusion of each workshop, we analyzed recordings and transcripts.ResultsWorkshop feedback showed that the aggregation of relevant clinical data into 6 cirrhosis decompensation domains (defined as common inpatient clinical scenarios) was the most important feature. Automatic inference of clinical events from EHR data, such as gastrointestinal bleeding from hemoglobin changes, was not accepted due to accuracy concerns. Visualizations for risk stratification scores were deemed not necessary. Lastly, the HCD co-design workshops allowed us to identify the target user population (generalists).ConclusionsThis is one of the first applications of HCD to design the features and interface for an electronic intervention for cirrhosis management. The HCD process altered features, modified the design interface, and likely improved CirrhosisRx's overall usability. The finalized design for CirrhosisRx proceeded to development and production and will be tested for effectiveness in a pragmatic randomized controlled trial. This work provides a model for the creation of other EHR-based interventions in hepatology care.

Background: Several clinical trials support the efficacy of music therapy (MT) for improving outcomes in hospitalized patients, but few studies have evaluated the real-world delivery and integration of MT across multiple medical centers. This article describes the rationale, design, and population characteristics of a retrospective study examining the delivery and integration of MT within a large health system. Methods: A retrospective electronic health record (EHR) review was conducted of hospitalized patients seen by and/or referred to MT between January 2017 and July 2020. MT was provided across ten medical centers, including an academic medical center, a freestanding cancer center, and eight community hospitals. Discrete demographic, clinical, and MT treatment and referral characteristics were extracted from the EHR, cleaned, and organized using regular expressions functions, and they were summarized using descriptive statistics. Results: The MT team (average 11.6 clinical fulltime equivalent staff/year) provided 14,261 sessions to 7378 patients across 9091 hospitalizations. Patients were predominantly female (63.7%), White (54.3%) or Black/African American (44.0%), 63.7 ± 18.5 years of age at admission, and insured under Medicare (51.1%), Medicaid (18.1%), or private insurance (14.2%). Patients hospitalizations (median length of stay: 5 days) were primarily for cardiovascular (11.8%), respiratory (9.9%), or musculoskeletal (8.9%) conditions. Overall, 39.4% of patients hospital admissions included a mental health diagnosis, and 15.4% were referred to palliative care. Patients were referred by physicians (34.7%), nurses (29.4%), or advanced practice providers (24.7%) for coping (32.0%), anxiety reduction (20.4%), or pain management (10.1%). Therapists provided sessions to patients discharged from medical/surgical (74.5%), oncology (18.4%), or intensive care (5.8%) units. Conclusions: This retrospective study indicates that MT can be integrated across a large health system for addressing the needs of socioeconomically diverse patients. However, future research is needed to assess MTs impact on health care utilization (i.e., length of stay and rates of readmission) and immediate patient-reported outcomes.

The use of a stratified psychiatry approach that combines electronic health records (EHR) data with machine learning (ML) is one potentially fruitful path toward rapidly improving precision treatment in clinical practice. This strategy, however, requires confronting pervasive methodological flaws as well as deficiencies in transparency and reporting in the current conduct of ML-based studies for treatment prediction. EHR data shares many of the same data quality issues as other types of data used in ML prediction, plus some unique challenges. To fully leverage EHR datas power for patient stratification, increased attention to data quality and collection of patient-reported outcome data is needed.

BACKGROUND: Prescribing non-opioid pain medications, such as non-steroidal anti-inflammatory (NSAIDs) medications, has been shown to reduce pain and decrease opioid use, but it is unclear how to effectively encourage multimodal pain medication prescribing for hospitalised patients. Therefore, the aim of this study is to evaluate the effect of prechecking non-opioid pain medication orders on clinician prescribing of NSAIDs among hospitalised adults. METHODS: This was a cluster randomised controlled trial of adult (≥18 years) hospitalised patients admitted to three hospital sites under one quaternary hospital system in the USA from 2 March 2022 to 3 March 2023. A multimodal pain order panel was embedded in the admission order set, with NSAIDs prechecked in the intervention group. The intervention group could uncheck the NSAID order. The control group had access to the same NSAID order. The primary outcome was an increase in NSAID ordering. Secondary outcomes include NSAID administration, inpatient pain scores and opioid use and prescribing and relevant clinical harms including acute kidney injury, new gastrointestinal bleed and in-hospital death. RESULTS: Overall, 1049 clinicians were randomised. The study included 6239 patients for a total of 9595 encounters. Both NSAID ordering (36 vs 43%, p

OBJECTIVES: Low-value esophagogastroduodenoscopies (EGDs) for uncomplicated gastro-oesophageal reflux disease (GERD) can harm patients and raise patient and payer costs. We developed an electronic health record (EHR) eMeasure to detect low-value EGDs. DESIGN: Retrospective cohort of 518 adult patients diagnosed with GERD who underwent initial EGD between 1 January 2019 and 31 December 2019. SETTING: Outpatient primary care and gastroenterology clinics at a large, urban, academic health centre. PARTICIPANTS: Adult primary care patients at the University of California Los Angeles who underwent initial EGD for GERD in 2019. MAIN OUTCOME MEASURES: EGD appropriateness criteria were based on the American College of Gastroenterology 2012 guidelines. An initial EGD was considered low-value if it lacked a documented guideline-based indication, including alarm symptoms (eg, iron-deficiency anaemia); failure of an 8-week proton pump inhibitor trial or elevated Barretts oesophagus risk. We performed manual chart review on a random sample of 204 patients as a gold standard of the eMeasures validity. We estimated EGD costs using Medicare physician and facility fee rates. RESULTS: Among 518 initial EGDs performed (mean age 53 years; 54% female), the eMeasure identified 81 (16%) as low-value. The eMeasures sensitivity was 42% (95% CI 22 to 61) and specificity was 93% (95% CI 89 to 96). Stratifying across clinics, 62 (74.6%) low-value EGDs originated from 2 (12.5%) out of 16 clinics. Total cost for 81 low-value EGDs was approximately US$75 573, including US$14 985 in patients out-of-pocket costs. CONCLUSIONS: We developed a highly specific eMeasure that showed that low-value EGDs occurred frequently in our healthcare system and were concentrated in a minority of clinics. These results can inform future QI efforts at our institution, such as best practice alerts for the ordering physician. Moreover, this open-source eMeasure has a much broader potential impact, as it can be integrated into any EHR and improve medical decision-making at the point of care.

Background  The surgical burden in low- and middle-income countries (LMICs) as reported by the number of surgical cases per capita is great. To improve global health and help address this burden, there has been a rise in surgical outreach to LMICs. In high-income countries, an electronic health record (EHR) is used to document and communicate data critical to the quality of care and patient safety. Despite this, there is little guidance or precedence on the data elements or processes for utilizing an EHR on outreach trips. We validated data elements and process steps for utilizing an EHR for hand surgery outreach trips. Methods  We conducted a literature review to identify data elements collected during surgical outreach trips. A future-state process map for the collection and documentation of data elements within an EHR was developed through literature review and semistructured interviews with experts in global outreach. An expert consortium completed a modified RAND/University of California at Los Angeles Delphi process to evaluate the importance and feasibility of each data element and process step. Results  In total, 65 data elements (e.g., date of birth) and 24 process steps (e.g., surgical site marking) were validated for use in an EHR for hand surgery outreach trips to LMICs. Conclusion  This validated portfolio of data elements/process steps can serve as the foundation for pilot testing of an EHR to document and communicate critical patient data on hand surgery outreach trips. Utilization of an EHR during outreach trips to LMICs may serve to improve the safety and quality of care provided. The validated data elements/process steps can serve as a guide for EHR development and implementation of other surgical specialties.

MOTIVATION: Methods for concept recognition (CR) in clinical texts have largely been tested on abstracts or articles from the medical literature. However, texts from electronic health records (EHRs) frequently contain spelling errors, abbreviations, and other nonstandard ways of representing clinical concepts. RESULTS: Here, we present a method inspired by the BLAST algorithm for biosequence alignment that screens texts for potential matches on the basis of matching k-mer counts and scores candidates based on conformance to typical patterns of spelling errors derived from 2.9 million clinical notes. Our method, the Term-BLAST-like alignment tool (TBLAT) leverages a gold standard corpus for typographical errors to implement a sequence alignment-inspired method for efficient entity linkage. We present a comprehensive experimental comparison of TBLAT with five widely used tools. Experimental results show an increase of 10% in recall on scientific publications and 20% increase in recall on EHR records (when compared against the next best method), hence supporting a significant enhancement of the entity linking task. The method can be used stand-alone or as a complement to existing approaches. AVAILABILITY AND IMPLEMENTATION: Fenominal is a Java library that implements TBLAT for named CR of Human Phenotype Ontology terms and is available at https://github.com/monarch-initiative/fenominal under the GNU General Public License v3.0.

Background and aimsNonalcoholic fatty liver disease (NAFLD) has reached pandemic proportions. Early detection can identify at-risk patients who can be linked to hepatology care. The vibration-controlled transient elastography (VCTE) controlled attenuation parameter (CAP) is biopsy validated to diagnose hepatic steatosis (HS). We aimed to develop a novel clinical predictive algorithm for HS using the CAP score at a Veterans' Affairs hospital.MethodsWe identified 403 patients in the Greater Los Angeles VA Healthcare System with valid VCTEs during 1/2018-6/2020. Patients with alcohol-associated liver disease, genotype 3 hepatitis C, any malignancies, or liver transplantation were excluded. Linear regression was used to identify predictors of NAFLD. To identify a CAP threshold for HS detection, receiver operating characteristic analysis was applied using liver biopsy, MRI, and ultrasound as the gold standards.ResultsThe cohort was racially/ethnically diverse (26% Black/African American; 20% Hispanic). Significant positive predictors of elevated CAP score included diabetes, cholesterol, triglycerides, BMI, and self-identifying as Hispanic. Our predictions of CAP scores using this model strongly correlated (r = 0.61, p

PurposeWidespread electronic health record adoption has generated a large volume of data and emphasized the need for standardized terminology to describe clinical concepts. Here, we undertook a systematic concept coverage analysis to determine the representation of clinical concepts in ophthalmic infection and ophthalmic trauma among standardized medical terminologies, including the Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT), the International Classification of Diseases (ICD) version 10 with clinical modifications (ICD-10-CM), and ICD version 11 (ICD-11).DesignExtraction of concepts related to ophthalmic infection and ophthalmic trauma and structured search in terminology browsers.Data sourcesThe American Academy of Ophthalmology Basic and Clinical Science Course (BCSC), SNOMED-CT, and ICD-10-CM terminologies from the Observational Health Data Sciences and Informatics Athena browser, and the ICD-11 terminology browser.MethodsConcepts pertaining to ophthalmic infection and ophthalmic trauma were extracted from the 2022 BCSC free text and index terms. We searched terminology browsers to identify corresponding codes and classified the extent of semantic alignment as equal, wide, narrow, or unmatched in each terminology. The overlap of equal concepts in each terminology was represented in a Venn diagram.Main outcome measuresProportions of clinical concepts with corresponding codes at various levels of semantic alignment.ResultsA total of 443 concepts were identified: 304 concepts related to ophthalmic infection and 139 concepts related to ophthalmic trauma. The SNOMED-CT had the highest proportion of equal coverage, with 82.0% (249 of 304) among concepts related to ophthalmic infection and 82.0% (115 of 139) among concepts related to ophthalmic trauma. Across all concepts, 28% (124 of 443) were classified as equal in ICD-10-CM and 52.8% (234 of 443) were classified as equal in ICD-11.ConclusionsThe SNOMED-CT had significantly better semantic alignment than ICD-10-CM and ICD-11 for ophthalmic infections and ophthalmic trauma. This demonstrates opportunity for continuing advancement of representation of ophthalmic concepts in standardized medical terminologies.

BackgroundLung cancer screening (LCS) is recommended for individuals at high risk due to age and smoking history after a shared decision-making conversation. However, little is known about best strategies for incorporating shared decision-making, especially in a busy primary care setting.ObjectiveTo develop a novel tool, Lung Cancer Assessment of Risk and Education (LungCARE) to guide LCS decisions among eligible primary care patients.DesignPilot cluster randomized controlled trial of LungCARE versus usual care.ParticipantsPatients of providers in a university primary care clinic, who met criteria for LCS.InterventionProviders were randomized to LungCARE intervention or control. LungCARE participants completed a computer tablet-based video assessment of lung cancer educational needs in the waiting room prior to a primary care visit. Patient and provider both received a summary handout of patient concerns and responses.Main measuresAll eligible patients completed baseline interviews by telephone. One week after the index visit, participants completed a follow-up telephone survey that assessed patient-physician discussion of LCS, referral to and scheduling of LCS, as well as LCS knowledge and acceptability of LungCARE. Two months after index visit, we reviewed patients' electronic health records (EHRs) for evidence of a shared decision-making conversation and referral to and receipt of LCS.Key resultsA total of 66 participants completed baseline and follow-up visits (34: LungCARE; 32: usual care). Mean age was 65.9 (± 6.0). Based on EHR review, compared to usual care, LungCARE participants were more likely to have discussed LCS with their physicians (56% vs 25%; p = 0.04) and to be referred to LCS (44% vs 13%; p

BACKGROUND: For patients who may permanently or temporarily lose their ability to communicate preferences, advance care planning is a critical mechanism to guide medical decision-making but is currently underused among surgical patients. METHODS: A resident-led quality improvement project, including education and performance measurement, was conducted on an emergency general surgery service to increase the completion of inpatient advance care planning notes using a specialized template in the electronic health record. Advance care planning documentation was defined as either preadmission advance care planning documentation (eg, advance directive) or inpatient advance care planning (use of the electronic health record template). Data from patients admitted to the emergency general surgery service for 12+ hours were analyzed, and baseline data (July 2020 to June 2021) were compared with data from the intervention period (July 2021 to June 2022). The chart review evaluated the content of the inpatient advance care planning documentation from the intervention period. RESULTS: The frequency of inpatient advance care planning documentation increased (9.3%, n = 56 to 16.6%, n = 92, P < .001) with a greater contribution of inpatient advance care planning notes by the surgery team (16.7% to 55.4%) in the intervention period. Content analysis indicated that 79.0% of inpatient advance care planning notes listed preferences for life-sustaining therapy, 78.3% listed surrogacy, 57.3% listed overall health goals, and 50.3% listed treatment goals specific to the surgical encounter. CONCLUSION: Although a resident-led quality improvement project contributed to greater adoption of standardized inpatient advance care planning documentation on an emergency general surgery service, progress was slow, and integration into standard work was not achieved. Future efforts are needed to better understand the integration of essential advance care planning elements into workflows and to establish inclusive educational programming to prepare all team members for conducting and documenting advance care planning conversations.

OBJECTIVE: Physicians of all specialties experienced unprecedented stressors during the COVID-19 pandemic, exacerbating preexisting burnout. We examine burnouts association with perceived and actionable electronic health record (EHR) workload factors and personal, professional, and organizational characteristics with the goal of identifying levers that can be targeted to address burnout. MATERIALS AND METHODS: Survey of physicians of all specialties in an academic health center, using a standard measure of burnout, self-reported EHR work stress, and EHR-based work assessed by the number of messages regarding prescription reauthorization and use of a staff pool to triage messages. Descriptive and multivariable regression analyses examined the relationship among burnout, perceived EHR work stress, and actionable EHR work factors. RESULTS: Of 1038 eligible physicians, 627 responded (60% response rate), 49.8% reported burnout symptoms. Logistic regression analysis suggests that higher odds of burnout are associated with physicians feeling higher level of EHR stress (odds ratio [OR], 1.15; 95% confidence interval [CI], 1.07-1.25), having more prescription reauthorization messages (OR, 1.23; 95% CI, 1.04-1.47), not feeling valued (OR, 3.38; 95% CI, 1.69-7.22) or aligned in values with clinic leaders (OR, 2.81; 95% CI, 1.87-4.27), in medical practice for ≤15 years (OR, 2.57; 95% CI, 1.63-4.12), and sleeping for

BACKGROUND: An effective and scalable information retrieval (IR) system plays a crucial role in enabling clinicians and researchers to harness the valuable information present in electronic health records. In a previous study, we developed a prototype medical IR system, which incorporated a semantically based query recommendation (SBQR) feature. The system was evaluated empirically and demonstrated high perceived performance by end users. To delve deeper into the factors contributing to this perceived performance, we conducted a follow-up study using query log analysis. OBJECTIVE: One of the primary challenges faced in IR is that users often have limited knowledge regarding their specific information needs. Consequently, an IR system, particularly its user interface, needs to be thoughtfully designed to assist users through the iterative process of refining their queries as they encounter relevant documents during their search. To address these challenges, we incorporated query recommendation into our Electronic Medical Record Search Engine (EMERSE), drawing inspiration from the success of similar features in modern IR systems for general purposes. METHODS: The query log data analyzed in this study were collected during our previous experimental study, where we developed EMERSE with the SBQR feature. We implemented a logging mechanism to capture user query behaviors and the output of the IR system (retrieved documents). In this analysis, we compared the initial query entered by users with the query formulated with the assistance of the SBQR. By examining the results of this comparison, we could examine whether the use of SBQR helped in constructing improved queries that differed from the original ones. RESULTS: Our findings revealed that the first query entered without SBQR and the final query with SBQR assistance were highly similar (Jaccard similarity coefficient=0.77). This suggests that the perceived positive performance of the system was primarily attributed to the automatic query expansion facilitated by the SBQR rather than users manually manipulating their queries. In addition, through entropy analysis, we observed that search results converged in scenarios of moderate difficulty, and the degree of convergence correlated strongly with the perceived system performance. CONCLUSIONS: The study demonstrated the potential contribution of the SBQR in shaping participants positive perceptions of system performance, contingent upon the difficulty of the search scenario. Medical IR systems should therefore consider incorporating an SBQR as a user-controlled option or a semiautomated feature. Future work entails redesigning the experiment in a more controlled manner and conducting multisite studies to demonstrate the effectiveness of EMERSE with SBQR for patient cohort identification. By further exploring and validating these findings, we can enhance the usability and functionality of medical IR systems in real-world settings.

BACKGROUND: Whether the accuracy of the phenotype ascribed to patients in electronic health records (EHRs) is associated with variation in prognosis and care provision is unknown. We investigated this for heart failure (HF, characterised as HF with preserved ejection fraction [HFpEF], HF with reduced ejection fraction [HFrEF] and unspecified HF). METHODS: We included individuals aged 16 years and older with a new diagnosis of HF between January 2, 1998 and February 28, 2022 from linked primary and secondary care records in the Clinical Practice Research Datalink in England. We investigated the provision of guideline-recommended diagnostic investigations and pharmacological treatments. The primary outcome was a composite of HF hospitalisation or all-cause death, and secondary outcomes were time to HF hospitalisation, all-cause death and death from cardiovascular causes. We used Kaplan-Meier curves and log rank tests to compare survival across HF phenotypes and adjusted for potential confounders in Cox proportional hazards regression analyses. FINDINGS: Of a cohort of 95,262 individuals, 1271 (1.3%) were recorded as having HFpEF, 10,793 (11.3%) as HFrEF and 83,198 (87.3%) as unspecified HF. Individuals recorded as unspecified HF were older with a higher prevalence of dementia. Unspecified HF, compared to patients with a recorded HF phenotype, were less likely to receive specialist assessment, echocardiography or natriuretic peptide testing in the peri-diagnostic period, or receive angiotensin-converting enzyme inhibitors, beta blockers or mineralocorticoid receptor antagonists up to 12 months after diagnosis (risk ratios compared to HFrEF, 0.64, 95% CI 0.63-0.64; 0.59, 0.58-0.60; 0.57, 0.55-0.59; respectively) and had significantly worse outcomes (adjusted hazard ratios compared to HFrEF, HF hospitalisation and death 1.66, 95% CI 1.59-1.74; all-cause mortality 2.00, 1.90-2.10; cardiovascular death 1.77, 1.65-1.90). INTERPRETATION: Our findings suggested that absence of specification of HF phenotype in routine EHRs is inversely associated with clinical investigations, treatments and survival, representing an actionable target to mitigate prognostic and health resource burden. FUNDING: Japan Research Foundation for Healthy Aging and British Heart Foundation.

Aim: To evaluate treatment effects of eteplirsen among patients with Duchenne muscular dystrophy. Methods: Using real-world claims and electronic medical record data, this retrospective comparative analysis assessed eteplirsen-treated and control cohorts matched by age, disease progression state, and pre-index period healthcare resource utilization. Poisson regression was used to evaluate eteplirsen effects on healthcare resource utilization outcomes. Results: Eteplirsen was associated with statistically significant reductions in rates of hospital encounters (31%), emergency room visits (31%), need for pulmonary management (33%), cardiac management (21%), tracheostomy (86%), and assisted ventilation (39%) versus the control group. Other assessed outcomes favored eteplirsen numerically but did not all reach statistical significance. Conclusion: Eteplirsen-treated patients had reduced rates of multiple healthcare resource utilization measures versus matched controls.

Objective: Individual unsupervised home-based exercise programs can enhance muscle strength, physical function, gait, and balance in older adults. However, the effectiveness of such programs may be limited by the lack of supervision. This study aims to verify the effectiveness of individual unsupervised home-based programs, compare the effects of individual unsupervised home-based to supervised programs, and verify the influence of supervision over individual unsupervised home-based programs on the physical function of older adults. Methods: A systematic literature search was performed in four electronic databases, and the trials involved randomized controlled comparing the home-based programs to supervised, control groups, or home-based + supervised evaluating the muscle strength, physical function, gait, and balance in older adults. Results: Eleven studies met the inclusion criteria. The meta-analysis revealed no differences between home-based program versus supervised program in gait, mobility, and balance, revealing a trend of significance to supervised program on strength (standardized mean difference [SMD] = 0.27, p =.05). The analysis revealed effects in mobility (SMD = 0.40, p =.003), balance (SMD = 0.58, p =.0002), and muscle strength (SMD = 0.36, p =.02) favoring home-based program versus control group. Significant effects between home-based program versus home-based + supervised program were observed in balance (SMD = 0.74, p =.002) and muscle strength (SMD = 0.58, p =.01) in favor of home-based + supervised program. Conclusion: Home-based programs effectively improve older adults' physical function compared with control groups. However, supervised programs were more effective for muscle strength. [ABSTRACT FROM AUTHOR]

We examine how hospital cost efficiency can improve because of knowledge spillover effects, arising from the experiences of members of a healthcare system. We identify two types of functional technology networks—that we term repository-type and workflow-type networks—which are defined by the adoption patterns of different types of electronic health record (EHR) systems application functions by hospitals in a health system. Additionally, we examine how structural features of these two network types—namely, network centrality and interconnectedness—impact such knowledge spillover effects. By analyzing seven years of panel data for 1,420 U.S. hospitals across 216 health systems, and data obtained from surveys of healthcare experts, we found that greater centrality of nodes in repository-type networks enhances the influence of partners' experiences on cost efficiency but has the opposite effect in workflow-type networks, while greater interconnectedness of workflow-type networks enhances the influence of partners' experiences on cost efficiency. Interestingly, we found the opposite effect in repository-type networks, where greater interconnectedness diminishes the influence of partners' experiences on cost efficiency. We discuss reasons for this surprising and counterintuitive finding. Overall, our results demonstrate that at least in healthcare settings where information technologies enable knowledge-sharing networks to be formed, benefits can accrue through knowledge spillover effects; moreover, variations across functional categories of technologies adopted in a network context can result in different technology-network structures with differential impacts on hospital cost efficiency because of their influence on knowledge spillover at both the hospital and system levels. Not only can these results explain mixed findings in the literature that has been dominated by a focus on cost efficiency outcomes dependent solely on the EHR adoption choices of individual hospitals, but they also provide the basis for guidelines for a network-based perspective of technology adoption. We discuss other possible settings to which our empirical findings can be reasonably generalized, including supply chain technologies and the Internet of Things. [ABSTRACT FROM AUTHOR]

Objective: To explore the feasibility of screening for type 2 diabetes (T2DM) and the prevalence of adult patients seen in a dental clinic at risk for T2DM based on American Diabetes Association (ADA) diabetes risk test (DRT) scores and point of care hemoglobin A1C (A1C) values. Method and materials: This was a cross-sectional analysis of data from adults 18 to 89 years old seen in an academic dental clinic between November 2019 and April 2022 without prior history of diabetes. Clinical and demographic data were obtained from electronic health records and odontograms. Frequency distributions, chi-square, and Mann-Whitney U tests were used for data analysis. Results: Of the 13,519 patients whose data were included, 54.7% (n = 7,389) were women. Of those with race and ethnicity data, 53.6% (n = 2,871) were white, 40.2% (n = 2,153) were African American, and 29.5% (n = 1,559) were Hispanic/Latino. Mean ± SD age was 47.0 + 17.0 years; mean ± SD body mass index was 28.0 ± 6.1 kg/m2. Thirty-five percent (n = 4,774) had diabetes risk test scores reflecting T2DM risk. Those at risk were more likely to be older, male, and overweight/obese than those not at risk (P < .001). Of those at risk who consented to a point of care A1C (9.8%, n = 470), 40.2% (n = 189) had values consistent with dysglycemia (A1C = 5.7%); 34.9% (n = 164) reflecting prediabetes (A1C = 5.7% to 6.4%) and 5.3% (n = 25) diabetes (A1C = 6.5%). Conclusions: Diabetes screening in a dental clinic identified that over one-third of adults without T2DM were at risk based on diabetes risk test scores. Of those who had point of care A1Cs conducted; 40% had dysglycemia. Diabetes screening in an academic dental clinic can help identify patients at risk for T2DM. [ABSTRACT FROM AUTHOR]

Objective: To assemble and characterize an electronic health record (EHR) dataset for a large cohort of US military Veterans diagnosed with ALS (Amyotrophic Lateral Sclerosis). Methods: An EHR dataset for 19,662 Veterans diagnosed with ALS between January 1, 2000 to December 31, 2020 was compiled from the Veterans Health Administration (VHA) EHR database by a query for ICD9 diagnosis (335.20) or ICD10 diagnosis (G12.21) for Amyotrophic Lateral Sclerosis. Results: The cohort is predominantly male (98.94%) and white (72.37%) with a median age at disease onset of 68 years and median survival from the date of diagnosis of 590 days. With the designation of ALS as a compensable illness in 2009, there was a subsequent increase in the number of Veterans diagnosed per year in the VHA, but no change in median survival. The cohort included a greater-than-expected proportion of individuals whose branch of service at the time of separation was the Army. Conclusions: The composition of the cohort reflects the VHA population who are at greatest risk for ALS. The greater than expected proportion of individuals whose branch of service at the time of separation was the Army suggests the possibility of a branch-specific risk factor for ALS.

INTRODUCTION: Vaccine safety surveillance commonly includes a serial testing approach with a sensitive method for signal generation and specific method for signal validation. The extent to which serial testing in real-world studies improves or hinders overall performance in terms of sensitivity and specificity remains unknown. METHODS: We assessed the overall performance of serial testing using three administrative claims and one electronic health record database. We compared type I and II errors before and after empirical calibration for historical comparator, self-controlled case series (SCCS), and the serial combination of those designs against six vaccine exposure groups with 93 negative control and 279 imputed positive control outcomes. RESULTS: The historical comparator design mostly had fewer type II errors than SCCS. SCCS had fewer type I errors than the historical comparator. Before empirical calibration, the serial combination increased specificity and decreased sensitivity. Type II errors mostly exceeded 50%. After empirical calibration, type I errors returned to nominal; sensitivity was lowest when the methods were combined. CONCLUSION: While serial combination produced fewer false-positive signals compared with the most specific method, it generated more false-negative signals compared with the most sensitive method. Using a historical comparator design followed by an SCCS analysis yielded decreased sensitivity in evaluating safety signals relative to a one-stage SCCS approach. While the current use of serial testing in vaccine surveillance may provide a practical paradigm for signal identification and triage, single epidemiological designs should be explored as valuable approaches to detecting signals.

BACKGROUND: Timely discharge to post-acute care (PAC) settings, such as skilled nursing facilities, requires early identification of eligible patients. We sought to develop and internally validate a model which predicts a patients likelihood of requiring PAC based on information obtained in the first 24 h of hospitalization. METHODS: This was a retrospective observational cohort study. We collected clinical data and commonly used nursing assessments from the electronic health record (EHR) for all adult inpatient admissions at our academic tertiary care center from September 1, 2017 to August 1, 2018. We performed a multivariable logistic regression to develop the model from the derivation cohort of the available records. We then evaluated the capability of the model to predict discharge destination on an internal validation cohort. RESULTS: Age (adjusted odds ratio [AOR], 1.04 [per year]; 95% Confidence Interval [CI], 1.03 to 1.04), admission to the intensive care unit (AOR, 1.51; 95% CI, 1.27 to 1.79), admission from the emergency department (AOR, 1.53; 95% CI, 1.31 to 1.78), more home medication prescriptions (AOR, 1.06 [per medication count increase]; 95% CI 1.05 to 1.07), and higher Morse fall risk scores at admission (AOR, 1.03 [per unit increase]; 95% CI 1.02 to 1.03) were independently associated with higher likelihood of being discharged to PAC facility. The c-statistic of the model derived from the primary analysis was 0.875, and the model predicted the correct discharge destination in 81.2% of the validation cases. CONCLUSIONS: A model that utilizes baseline clinical factors and risk assessments has excellent model performance in predicting discharge to a PAC facility.

ObjectiveOccupational health (OH) documentation has traditionally been separate from health system electronic health records (EHRs), but this can create patient safety and care continuity challenges. Herein, we describe outcomes and challenges of such integration including how one health system managed compliance with laws, regulations, and ethical principles concerning digital privacy.MethodsOccupational health integration with the enterprise EHR at the University of California San Diego Health was started in June 2021 and completed in December 2021.ResultsIntegrating with the enterprise EHR allowed for a secure telehealth system, faster visit times, digitization of questionnaires medical clearance forms, and improved reporting capabilities.ConclusionsIntegration and interoperability are fundamental building blocks to any OH EHR solution and will allow for evaluation of worker population trends, and targeted interventions to improve worker health status.

ObjectivesAs the real-world electronic health record (EHR) data continue to grow exponentially, novel methodologies involving artificial intelligence (AI) are becoming increasingly applied to enable efficient data-driven learning and, ultimately, to advance healthcare. Our objective is to provide readers with an understanding of evolving computational methods and help in deciding on methods to pursue.Target audienceThe sheer diversity of existing methods presents a challenge for health scientists who are beginning to apply computational methods to their research. Therefore, this tutorial is aimed at scientists working with EHR data who are early entrants into the field of applying AI methodologies.ScopeThis manuscript describes the diverse and growing AI research approaches in healthcare data science and categorizes them into 2 distinct paradigms, the bottom-up and top-down paradigms to provide health scientists venturing into artificial intelligent research with an understanding of the evolving computational methods and help in deciding on methods to pursue through the lens of real-world healthcare data.

IntroductionWe evaluated real-world outcomes in patients with advanced ovarian cancer (AOC) based on their cumulative risk profile and maintenance therapy (MT) status following first-line (1L) treatment.MethodsThis retrospective observational study of a nationwide electronic health record-derived de-identified database included adult patients diagnosed with stage III/IV OC from January 1, 2011 to February 28, 2021, who received 1L therapy and had ≥ 12 weeks of follow-up after the index date (end of 1L therapy). Patients were grouped according to whether they received MT or active surveillance (AS) following 1L treatment and by the cumulative number of risk factors (RF) present (stage IV disease; no surgery/treated with neoadjuvant therapy and interval debulking surgery; had postoperative visible residual disease; and had BRCA wild-type disease/unknown BRCA status). Time to next treatment (TTNT) and overall survival (OS) were assessed with a cloning and inverse probability of censoring (IPC)-weighted Kaplan-Meier method.ResultsAmong 1920 patients, 22.2% received MT and 77.8% received AS. Median IPC-weighted TTNT and OS were 13.3 months (95% CI 11.7-15.8) and 39.1 months (95% CI 32.5-48.6) in the MT cohort, respectively, and 8.6 months (95% CI 8.0-9.5) and 38.4 months (95% CI 36.4-41.0) in the AS cohort, respectively. Almost all patients had ≥ 1 RF (MT 95.3%; AS 96.7%). Median IPC-weighted TTNT was shorter among patients with more RF in both cohorts (MT: 1 RF, 19.3 months, 95% CI 13.5-37.8; 2 RF, 17.2 months, 95% CI 12.8-20.2; 3 RF, 11.0 months, 95% CI 8.2-13.8; 4 RF, 7.0 months, 95% CI 6.2-8.8; AS: 1 RF, 17.7 months, 95% CI 13.5-22.3; 2 RF, 10.2 months, 95% CI 9.1-11.5; 3 RF, 6.5 months, 95% CI 5.8-7.4; 4 RF, 4.1 months, 95% CI 3.5-4.5).ConclusionRegardless of RF number, MT was associated with longer TTNT in real-world patients with AOC.

BackgroundAlzheimer's Disease (AD) is a complex clinical phenotype with unprecedented social and economic tolls on an ageing global population. Real-world data (RWD) from electronic health records (EHRs) offer opportunities to accelerate precision drug development and scale epidemiological research on AD. A precise characterization of AD cohorts is needed to address the noise abundant in RWD.MethodsWe conducted a retrospective cohort study to develop and test computational models for AD cohort identification using clinical data from 8 Massachusetts healthcare systems. We mined temporal representations from EHR data using the transitive sequential pattern mining algorithm (tSPM) to train and validate our models. We then tested our models against a held-out test set from a review of medical records to adjudicate the presence of AD. We trained two classes of Machine Learning models, using Gradient Boosting Machine (GBM), to compare the utility of AD diagnosis records versus the tSPM temporal representations (comprising sequences of diagnosis and medication observations) from electronic medical records for characterizing AD cohorts.FindingsIn a group of 4985 patients, we identified 219 tSPM temporal representations (i.e., transitive sequences) of medical records for constructing the best classification models. The models with sequential features improved AD classification by a magnitude of 3-16 percent over the use of AD diagnosis codes alone. The computed cohort included 663 patients, 35 of whom had no record of AD. Six groups of tSPM sequences were identified for characterizing the AD cohorts.InterpretationWe present sequential patterns of diagnosis and medication codes from electronic medical records, as digital markers of Alzheimer's Disease. Classification algorithms developed on sequential patterns can replace standard features from EHRs to enrich phenotype modelling.FundingNational Institutes of Health: the National Institute on Aging (RF1AG074372) and the National Institute of Allergy and Infectious Diseases (R01AI165535).

IntroductionAdverse drug events (ADEs) are associated with poor outcomes and increased costs but may be prevented with prediction tools. With the National Institute of Health All of Us (AoU) database, we employed machine learning (ML) to predict selective serotonin reuptake inhibitor (SSRI)-associated bleeding.MethodsThe AoU program, beginning in 05/2018, continues to recruit ≥ 18 years old individuals across the United States. Participants completed surveys and consented to contribute electronic health record (EHR) for research. Using the EHR, we determined participants who were exposed to SSRIs (citalopram, escitalopram, fluoxetine, fluvoxamine, paroxetine, sertraline, vortioxetine). Features (n = 88) were selected with clinicians' input and comprised sociodemographic, lifestyle, comorbidities, and medication use information. We identified bleeding events with validated EHR algorithms and applied logistic regression, decision tree, random forest, and extreme gradient boost to predict bleeding during SSRI exposure. We assessed model performance with area under the receiver operating characteristic curve statistic (AUC) and defined clinically significant features as resulting in > 0.01 decline in AUC after removal from the model, in three of four ML models.ResultsThere were 10,362 participants exposed to SSRIs, with 9.6% experiencing a bleeding event during SSRI exposure. For each SSRI, performance across all four ML models was relatively consistent. AUCs from the best models ranged 0.632-0.698. Clinically significant features included health literacy for escitalopram, and bleeding history and socioeconomic status for all SSRIs.ConclusionsWe demonstrated feasibility of predicting ADEs using ML. Incorporating genomic features and drug interactions with deep learning models may improve ADE prediction.

ObjectiveWe performed a scoping review of algorithms using electronic health record (EHR) data to identify patients with Alzheimer's disease and related dementias (ADRD), to advance their use in research and clinical care.Materials and methodsStarting with a previous scoping review of EHR phenotypes, we performed a cumulative update (April 2020 through March 1, 2023) using Pubmed, PheKB, and expert review with exclusive focus on ADRD identification. We included algorithms using EHR data alone or in combination with non-EHR data and characterized whether they identified patients at high risk of or with a current diagnosis of ADRD.ResultsFor our cumulative focused update, we reviewed 271 titles meeting our search criteria, 49 abstracts, and 26 full text papers. We identified 8 articles from the original systematic review, 8 from our new search, and 4 recommended by an expert. We identified 20 papers describing 19 unique EHR phenotypes for ADRD: 7 algorithms identifying patients with diagnosed dementia and 12 algorithms identifying patients at high risk of dementia that prioritize sensitivity over specificity. Reference standards range from only using other EHR data to in-person cognitive screening.ConclusionA variety of EHR-based phenotypes are available for use in identifying populations with or at high-risk of developing ADRD. This review provides comparative detail to aid in choosing the best algorithm for research, clinical care, and population health projects based on the use case and available data. Future research may further improve the design and use of algorithms by considering EHR data provenance.

Primary care physicians (PCPs) play an indispensable role in providing comprehensive care and referring patients for specialty care and other medical services. As the COVID-19 outbreak disrupts patient access to care, understanding the quality of primary care is critical at this unprecedented moment to support patients with complex medical needs in the primary care setting and inform policymakers to redesign our primary care system. The traditional way of collecting information from patient surveys is time-consuming and costly, and novel data collection and analysis methods are needed. In this review paper, we describe the existing algorithms and metrics that use the real-world data to qualify and quantify primary care, including the identification of an individual's likely PCP (identification of plurality provider and major provider), assessment of process quality (for example, appropriate-care-model composite measures), and continuity and regularity of care index (including the interval index, variance index and relative variance index), and highlight the strength and limitation of real world data from electronic health records (EHRs) and claims data in determining the quality of PCP care. The EHR audits facilitate assessing the quality of the workflow process and clinical appropriateness of primary care practices. With extensive and diverse records, administrative claims data can provide reliable information as it assesses primary care quality through coded information from different providers or networks. The use of EHRs and administrative claims data may be a cost-effective analytic strategy for evaluating the quality of primary care.