Electronic Health Record Research Research Articles

Trends and Patterns in Electronic Health Record Research (1991-2022): A Bibliometric Analysis of Australian Literature.

Electronic Heath Records (EHRs) play vital roles in facilitating streamlined service provision and governance across the Australian health system. Given the recent challenges due to the COVID-19 pandemic, an ageing population, health workforce silos, and growing inefficiencies in traditional systems, a detailed historical analysis of the use of EHR research in Australia is necessary. The aim of this study is to examine the trends and patterns in EHR research in Australia over the past three decades by employing bibliometric methods. A total of 951 articles published in 443 sources were included in the bibliometric analysis. The annual growth rate of EHR research in Australia was about 17.1%. Since 2022, the main trending topics in EHR research were COVID-19, opioid usage, and natural language processing. A thematic analysis indicated aged care, clinical decision support systems, cardiovascular disease, drug allergy, and adverse drug reaction as the "hot" themes in EHR research in Australia. This study reveals a significant uptrend in EHR research in Australia, highlighting the evolving intellectual and collaborative landscape of this interdisciplinary field. The data also provide guidance for policymakers and funding institutions in terms of the most significant contributions and key fields of research while also holding public interest.

Preexisting Psychiatric Conditions as Risk Factors for Diagnosed Long COVID-19 Syndrome Within Aggregated Electronic Health Record Data.

This study aimed to investigate the frequency of long COVID diagnosis among patients infected with severe acute respiratory syndrome coronavirus 2 with preexisting psychiatric conditions versus those without preexisting psychiatric conditions. The TriNetX Analytics platform, an aggregated electronic health record research network containing the deidentified electronic health record data of more than 90 million patients, was queried for patients who were diagnosed with COVID-19 infection based on International Classifications of Disease, Tenth Revision codes. Patients were stratified based on their preexisting psychiatric conditions, and new diagnoses of long COVID were recorded and reported as the primary outcome. Among 1,180,948 patients previously diagnosed with COVID-19, 17,990 patients (1.52%) were diagnosed with long COVID based on the newly implemented International Classifications of Disease, Tenth Revision code "U09: post-COVID-19 condition." After propensity score matching, patients with any preexisting psychiatric diagnosis had a 1.52 (95% confidence interval [CI] = 1.47-1.58) times greater prevalence of diagnosed long COVID within 180 days of infection than patients without preexisting psychiatric diagnoses. Patients with diagnosed anxiety disorders (relative risk [RR] = 1.64; 95% CI = 1.57-1.71), mood disorders (RR = 1.65; 95% CI = 1.57-1.72), bipolar disorder (RR = 1.37; 95% CI = 1.21-1.54), major depressive disorder (RR = 1.69; 95% CI = 1.56-1.83), psychotic disorders (RR = 1.23; 95% CI = 1.06-1.44), and substance use disorders (RR = 1.28; 95% CI = 1.22-1.36) had higher risks for long COVID diagnoses when compared with patients without preexisting psychiatric illness at the time of diagnosis. Multiple preexisting psychiatric diagnoses are associated with an increased risk of being diagnosed with long COVID after COVID-19 infection.

Proxy measures for the assessment of psychotic and affective symptoms in studies using electronic health records.

There is a lack of standardised psychometric data in electronic health record (EHR)-based research. Proxy measures of symptom severity based on patients' clinical records may be useful surrogates in mental health EHR research. This study aimed to validate proxy tools for the short versions of the Positive and Negative Syndrome Scale (PANSS-6), Young Mania Rating Scale (YMRS-6) and Montgomery-Åsberg Depression Rating Scale (MADRS-6). A cross-sectional, multicentre study was conducted in a sample of 116 patients with first-episode psychosis from 12 public hospitals in Spain. Concordance between PANSS-6, YMRS-6 and MADRS-6 scores and their respective proxies was evaluated based on information from EHR clinical notes, using a variety of statistical procedures, including multivariate tests to adjust for potential confounders. Bootstrapping techniques were used for internal validation, and an independent cohort from the Treatment and Early Intervention in Psychosis Program (TIPP-Lausanne, Switzerland) for external validation. The proxy versions correlated strongly with their respective standardised scales (partial correlations ranged from 0.75 to 0.84) and had good accuracy and discriminatory power in distinguishing between patients in and not in remission (percentage of patients correctly classified ranged from 83.9 to 91.4% and bootstrapped optimism-corrected area under the receiver operating characteristic curve ranged from 0.76 to 0.89), with high interrater reliability (intraclass correlation coefficient of 0.81). The findings remained robust in the external validation data-set. The proxy instruments proposed for assessing psychotic and affective symptoms by reviewing EHR provide a feasible and reliable alternative to traditional structured psychometric procedures, and a promising methodology for real-world practice settings.

Ophthalmology procedure trends in the United States during the COVID-19 pandemic.

To evaluate the relationship between the COVID-19 pandemic and ophthalmic procedural volume. A retrospective cohort study using TriNetX, a federated electronic health record's research network was done. Monthly Current Procedural Terminology-specific volumes per healthcare organization were clustered chronologically to calculate average volumes into 3-month seasons to calculate average procedural volumes. An aggregate of the total pandemic period (March 2020-August 2021) was compared to corresponding figures in pre-pandemic timeframes. Intravitreal injections were the most prevalent procedure in this time period with 320,106 occurrences. Phacoemulsification cataract surgery was the second most prevalent (N = 176,095) procedure. From March 2020 to August 2021, a mean pandemic volume of 266.7 (SD = 15) was observed, a 5% decrease (p < 0.05) in procedures compared to the pre-pandemic mean of 280.8 (SD = 26.1). Spring 2020 exhibited the sharpest seasonal decrease in procedural volume (- 88%). The largest count of statistically significant increases in procedure volume was in Spring 2021 (+ 18%). The aggregate mean volume per HCO showed significant decreases for 11 out of 17 procedures in the 12month March 2020-February 2021 timeframe and significant decreases for 10 out of 17 procedures over the 18-month March 2020-August 2021 pandemic period. This study highlights the relative inverse relationship between COVID-19 cases and ophthalmic procedure volume in America. Quantifying ophthalmic procedure trends is important in retrospectively assessing surgical disruptions and prospectively accommodating delayed surgeries. Furthermore, awareness of these trends could help ophthalmologists prepare should similar disruptions occur in the setting of future pandemics or national disasters.

Increased risk of osteomyelitis and septic arthritis in osteogenesis imperfecta.

We investigated the prevalence of osteomyelitis and septic arthritis in individuals with osteogenesis imperfecta (OI) as compared to the general population. We performed a retrospective cohort study utilizing the TriNetX Analytics platform, a federated, aggregated electronic health record (EHR) research network containing national, deidentified EHR data. We queried patients with OI, based on encounter diagnoses. Patients in this group with any occurrence of osteomyelitis or septic arthritis were recorded. A control cohort was established to compare the prevalence in patients without OI. Of 8444 individuals with OI, 433 (5.13%) had encounter diagnoses for osteomyelitis and 61 (0.72%) had encounter diagnoses for septic arthritis. In comparison, of 79 176 436 patients without OI, 352 009 (0.44%) had encounter diagnoses for osteomyelitis, while 106 647 (0.13%) had encounter diagnoses for septic arthritis. The relative risk for osteomyelitis in OI patients was 11.53 (95% CI: 10.52-12.64), while the relative risk for septic arthritis was 5.36 (95% CI: 4.18-6.89). The relative risk for osteomyelitis in pediatric OI patients was 30.55 (95% CI: 24.35-38.28). To our knowledge, this is the first study investigating musculoskeletal infections in patients with OI, as well as the first to report the overall prevalence in the general population. Clinicians may benefit from a high index of suspicion for musculoskeletal infections in OI patients with corresponding symptoms. Further study is warranted to investigate if modifications to conventional diagnostic pathways and criteria are valuable in this population. Level of evidence: Retrospective Cohort Study - Level II.

Barriers to Adoption of Tailored Drug-Drug Interaction Clinical Decision Support.

Despite the benefits of the tailored drug-drug interaction (DDI) alerts and the broad dissemination strategy, the uptake of our tailored DDI alert algorithms that are enhanced with patient-specific and context-specific factors has been limited. The goal of the study was to examine barriers and health care system dynamics related to implementing tailored DDI alerts and identify the factors that would drive optimization and improvement of DDI alerts. We employed a qualitative research approach, conducting interviews with a participant interview guide framed based on Proctor's taxonomy of implementation outcomes and informed by the Theoretical Domains Framework. Participants included pharmacists with informatics roles within hospitals, chief medical informatics officers, and associate medical informatics directors/officers. Our data analysis was informed by the technique used in grounded theory analysis, and the reporting of open coding results was based on a modified version of the Safety-Related Electronic Health Record Research Reporting Framework. Our analysis generated 15 barriers, and we mapped the interconnections of these barriers, which clustered around three entities (i.e., users, organizations, and technical stakeholders). Our findings revealed that misaligned interests regarding DDI alert performance and misaligned expectations regarding DDI alert optimizations among these entities within health care organizations could result in system inertia in implementing tailored DDI alerts. Health care organizations primarily determine the implementation and optimization of DDI alerts, and it is essential to identify and demonstrate value metrics that health care organizations prioritize to enable tailored DDI alert implementation. This could be achieved via a multifaceted approach, such as partnering with health care organizations that have the capacity to adopt tailored DDI alerts and identifying specialists who know users' needs, liaise with organizations and vendors, and facilitate technical stakeholders' work. In the future, researchers can adopt the systematic approach to study tailored DDI implementation problems from other system perspectives (e.g., the vendors' system).

Parkinson's disease diagnosis codes are insufficiently accurate for electronic health record research and differ by race

BackgroundThere are no evidence-based guidelines for data cleaning of electronic health record (EHR) databases in Parkinson's disease (PD). Previous filtering criteria have primarily used the 9th International Statistical Classification of Diseases and Related Health Problems (ICD) with variable accuracy for true PD cases. Prior studies have not excluded atypical or drug-induced parkinsonism, and little is known about differences in accuracy by race. ObjectiveTo determine if excluding parkinsonism diagnoses improves accuracy of ICD-9 and -10 PD diagnosis codes. MethodsWe included ≥2 instances of an ICD-9 and/or −10 code for PD. We removed any records with at least one code indicating atypical or drug-induced parkinsonism first in all races, and then in Non-Hispanic White and Black patients. We manually reviewed 100 randomly selected charts per group before and after filtering, and performed a test of proportion (null hypothesis 0.5) for confirmed PD. Results5633 records had ≥2 instances of a PD code. 2833 remained after filtering. The rate of true PD cases was low before and after filtering to remove parkinsonism codes (0.55 vs. 0.51, p = 0.84). Accuracy was lowest in Black patients before filtering (0.48, p = 0.69), but filtering had a greater (though modest) impact on accuracy (0.68, p < 0.001). ConclusionsThere was inadequate accuracy of PD diagnosis codes in the largest study of ICD-9 and -10 codes. Accuracy was lowest in Black patients but improved the most with removing other parkinsonism codes. This highlights the limitations of using current real-world EHR data in PD research and need for further study.

Population-wide analysis of hospital laboratory tests to assess seasonal variation and temporal reference interval modification

We identified mortality-, age-, and sex-associated differences in relation to reference intervals (RIs) for laboratory tests in population-wide data from nearly 2 million hospital patients in Denmark and comprising more than 300 million measurements. A low-parameter mathematical wave-based modification method was developed to adjust for dietary and environment influences during the year. The resulting mathematical fit allowed for improved association rates between re-classified abnormal laboratory tests, patient diagnoses, and mortality. The study highlights the need for seasonally modified RIs and presents an approach that has the potential to reduce over- and underdiagnosis, affecting both physician-patient interactions and electronic health record research as a whole.

Refining Vendor-Defined Measures to Accurately Quantify EHR Workload Outside Time Scheduled With Patients.

Accurately quantifying clinician time spent on electronic health record (EHR) activities outside the time scheduled with patients is critical for understanding occupational stress associated with ambulatory clinic environments. We make 3 recommendations regarding EHR workload measures that are intended to capture time working in the EHR outside time scheduled with patients, formally defined as work outside of work (WOW): (1) separate all time working in the EHR outside of time scheduled with patients from time working in the EHR during time scheduled with patients, (2) do not exclude any time before or after scheduled time with patients, and (3) encourage the EHR vendor and research communities to develop and standardize validated, vendor-agnostic methods for measuring active EHR use. Attributing all EHR work outside time scheduled with patients to WOW, regardless of when it occurs, will produce an objective and standardized measure better suited for use in efforts to reduce burnout, set policy, and facilitate research.

Risk of New Retinal Vascular Occlusion After mRNA COVID-19 Vaccination Within Aggregated Electronic Health Record Data

New-onset retinal vascular occlusion (RVO) occurring acutely after messenger RNA (mRNA) COVID-19 vaccination has been described in recent literature. Because RVO can cause vision loss or blindness, an epidemiologic investigation evaluating this potential association is of great importance to public health. To investigate how often patients are diagnosed with new RVO acutely after the mRNA COVID-19 vaccine compared with influenza and tetanus, diphtheria, pertussis (Tdap) vaccines. A retrospective population-based cohort design using the TriNetX Analytics platform, a federated, aggregated electronic health record (EHR) research network containing the deidentified EHR data of more than 103 million patients, was used to examine aggregate EHR data. Data were collected and analyzed on October 20, 2022. Data on patients within the TriNetX Analytics platform were searched for the presence of vaccination Common Procedural Technology codes, and instances of newly diagnosed RVO within 21 days of vaccination were recorded and reported. Propensity score matching based on demographic characteristics (age, sex, race and ethnicity) and comorbidities (diabetes, hypertension, and hyperlipidemia) was performed between vaccination groups for evaluation of relative risks (RRs). The appearance of a new-encounter diagnosis of RVO within 21 days of the mRNA COVID-19 vaccination was the primary outcome. Historical comparison cohorts of patients receiving influenza and Tdap vaccinations allowed for evaluation of the RRs for RVO. Of 3 108 829 patients (mean [SD] age at vaccination, 50.7 [20.4] years; 56.4% women) who received the mRNA COVID-19 vaccine, 104 (0.003%; 95% CI, 0.003%-0.004%) patients had a new diagnosis of RVO within 21 days of vaccination. After propensity score matching, the RR for new RVO diagnosis after the first dose of COVID-19 vaccination was not significantly different from that after influenza (RR, 0.74; 95% CI, 0.54-1.01) or Tdap (RR, 0.78; 95% CI, 0.44-1.38) vaccinations, but was greater when compared with the second dose of the COVID-19 vaccination (RR, 2.25; 95% CI, 1.33-3.81). The findings of this study suggest that RVO diagnosed acutely after mRNA COVID-19 vaccination occurs extremely rarely at rates similar to those of 2 different historically used vaccinations, the influenza and Tdap vaccines. No evidence suggesting an association between the mRNA COVID-19 vaccination and newly diagnosed RVO was found.

Assessing clinical site readiness for electronic health record (EHR)-to-electronic data capture (EDC) automated data collection

BackgroundeSource software is used to automatically copy a patient's electronic health record data into a clinical study's electronic case report form. However, there is little evidence to assist sponsors in identifying the best sites for multi-center eSource studies. MethodsWe developed an eSource site readiness survey. The survey was administered to principal investigators, clinical research coordinators, and chief research information officers at Pediatric Trial Network sites. ResultsA total of 61 respondents were included in this study (clinical research coordinator, 22; principal investigator, 20; and chief research information officer, 19). Clinical research coordinators and principal investigators ranked medication administration, medication orders, laboratory, medical history, and vital signs data as having the highest priority for automation. While most organizations used some electronic health record research functions (clinical research coordinator, 77%; principal investigator, 75%; and chief research information officer, 89%), only 21% of sites were using Fast Healthcare Interoperability Resources standards to exchange patient data with other institutions. Respondents generally gave lower readiness for change ratings to organizations that did not have a separate research information technology group and where researchers practiced in hospitals not operated by their medical schools. ConclusionsSite readiness to participate in eSource studies is not merely a technical problem. While technical capabilities are important, organizational priorities, structure, and the site's support of clinical research functions are equally important considerations.

Inpatient Constipation Among Migraine Patients Prescribed Anti-calcitonin Gene-Related Peptide Monoclonal Antibodies and Standard of Care Antiepileptic Drugs: A Retrospective Cohort Study in a United States Electronic Health Record Database.

Erenumab, an anti-calcitonin gene-related peptide (CGRP) receptor monoclonal antibody (mAb), was approved by the US Food and Drug Administration in May 2018. Constipation with serious complications was added to the Warning and Precautions section in the erenumab Prescribing Information in October 2019 after events were observed during post-marketing surveillance. We aimed to assess and compare the risk of inpatient constipation, and, separately, inpatient constipation with serious complications, among patients with migraine treated with CGRP mAbs and standard of care antiepileptic drugs (AEDs). Within Optum's Electronic Health Record Research Database, patients with migraine who initiated erenumab, other CGRP mAbs, and AEDs were identified from May 2018 through March 2020. Erenumab initiators were propensity score-matched separately to initiators of other CGRP mAbs and AEDs. Incident inpatient constipation events, and serious complications, were identified using multiple risk windows for outcome assessment (30-, 60-, 90-day risk windows, and all available follow-up). Odds ratios (ORs) were calculated comparing inpatient constipation risk among matched erenumab initiators relative to comparators. We identified 17,902 erenumab, 13,404 other CGRP mAb, and 49,497 AED initiators who met study criteria. Among matched initiators, the risk of inpatient constipation was 0.46% (95% confidence interval (CI) 0.35-0.60) for erenumab and 0.44% (95%CI 0.33-0.58) for other CGRP mAbs within the 90-day risk window, with a corresponding OR of 1.06 (95%CI 0.72-1.55). Among matched erenumab and AED initiators, inpatient constipation risk was 0.53% (95%CI 0.42-0.66) and 0.76% (95%CI 0.62-0.92), respectively, and the OR was 0.69 (95%CI 0.51-0.94). Few serious complications were observed. Patients initiating erenumab had similar risk of inpatient constipation within 90days of treatment initiation versus patients initiating other CGRP mAbs, and lower risk versus patients initiating AEDs. These findings provide context to events observed during post-marketing surveillance.

Data Quality in Electronic Health Record Research: An Approach for Validation and Quantitative Bias Analysis for Imperfectly Ascertained Health Outcomes Via Diagnostic Codes.

It is incumbent upon all researchers who use the electronic health record (EHR), including data scientists, to understand the quality of such data. EHR data may be subject to measurement error or misclassification that have the potential to bias results, unless one applies the available computational techniques specifically created for this problem. In this article, we begin with a discussion of data-quality issues in the EHR focusing on health outcomes. We review the concepts of sensitivity, specificity, positive and negative predictive values, and demonstrate how the imperfect classification of a dichotomous outcome variable can bias an analysis, both in terms of prevalence of the outcome, and relative risk of the outcome under one treatment regime (aka exposure) compared to another. This is then followed by a description of a generalizable approach to probabilistic (quantitative) bias analysis using a combination of regression estimation of the parameters that relate the true and observed data and application of these estimates to adjust the prevalence and relative risk that may have existed if there was no misclassification. We describe bias analysis that accounts for both random and systematic errors and highlight its limitations. We then motivate a case study with the goal of validating the accuracy of a health outcome, chronic infection with hepatitis C virus, derived from a diagnostic code in the EHR. Finally, we demonstrate our approaches on the case study and conclude by summarizing the literature on outcome misclassification and quantitative bias analysis.

Efficient Evaluation of Prediction Rules in Semi-Supervised Settings under Stratified Sampling.

In many contemporary applications, large amounts of unlabeled data are readily available while labeled examples are limited. There has been substantial interest in semi-supervised learning (SSL) which aims to leverage unlabeled data to improve estimation or prediction. However, current SSL literature focuses primarily on settings where labeled data is selected uniformly at random from the population of interest. Stratified sampling, while posing additional analytical challenges, is highly applicable to many real world problems. Moreover, no SSL methods currently exist for estimating the prediction performance of a fitted model when the labeled data is not selected uniformly at random. In this paper, we propose a two-step SSL procedure for evaluating a prediction rule derived from a working binary regression model based on the Brier score and overall misclassification rate under stratified sampling. In step I, we impute the missing labels via weighted regression with nonlinear basis functions to account for stratified sampling and to improve efficiency. In step II, we augment the initial imputations to ensure the consistency of the resulting estimators regardless of the specification of the prediction model or the imputation model. The final estimator is then obtained with the augmented imputations. We provide asymptotic theory and numerical studies illustrating that our proposals outperform their supervised counterparts in terms of efficiency gain. Our methods are motivated by electronic health record (EHR) research and validated with a real data analysis of an EHR-based study of diabetic neuropathy.

MedicalForms: Integrated Management of Semantics for Electronic Health Record Systems and Research Platforms

(1) Background: Clinical information modeling tools are software instruments designed to support the definition of semantic structures able to be implemented in health information systems. Based on the analysis of existing tools, this research developed a tool that proposes new approaches to promoting clinician involvement and supporting information modeling processes through mechanisms that ensure governance, information consistency and consensus building. (2) Method: This research developed the MedicalForms system, which is based on the requirements identified in both a Delphi study about tool requirements and the ISO/TS 13972 specifications. (3) Results: This system allows the management of projects, information structures and implementable forms related to clinical documentation. Users can easily define clinical documents in collaboration with the rest of the professionals in their team by being able to reuse previously defined forms, terminologies and information structures. The system is able to export the defined forms as interoperable specifications or as several implementable form formats compatible with multiple open source EHR systems and research platforms. End user perception of this tool was evaluated through the Technology Acceptance Questionnaire with satisfactory results. Finally, the system was applied to develop 12 research registries and 2 clinical trial research forms, 3 mobile applications and 1 decision support system.

Cohort design and natural language processing to reduce bias in electronic health records research

Electronic health record (EHR) datasets are statistically powerful but are subject to ascertainment bias and missingness. Using the Mass General Brigham multi-institutional EHR, we approximated a community-based cohort by sampling patients receiving longitudinal primary care between 2001-2018 (Community Care Cohort Project [C3PO], n = 520,868). We utilized natural language processing (NLP) to recover vital signs from unstructured notes. We assessed the validity of C3PO by deploying established risk models for myocardial infarction/stroke and atrial fibrillation. We then compared C3PO to Convenience Samples including all individuals from the same EHR with complete data, but without a longitudinal primary care requirement. NLP reduced the missingness of vital signs by 31%. NLP-recovered vital signs were highly correlated with values derived from structured fields (Pearson r range 0.95–0.99). Atrial fibrillation and myocardial infarction/stroke incidence were lower and risk models were better calibrated in C3PO as opposed to the Convenience Samples (calibration error range for myocardial infarction/stroke: 0.012–0.030 in C3PO vs. 0.028–0.046 in Convenience Samples; calibration error for atrial fibrillation 0.028 in C3PO vs. 0.036 in Convenience Samples). Sampling patients receiving regular primary care and using NLP to recover missing data may reduce bias and maximize generalizability of EHR research.

Abstract P098: Feasibility Of Training Primary Care Staff To Conduct Weight Maintenance Coaching Through The Electronic Health Record During Routine Clinical Work: Initial Results From The Maintain Prime Study

Introduction: Obesity is a significant health problem in the US. Sustainable interventions are needed to support longer-term maintenance after weight loss. We designed and tested a successful electronic health record (EHR)-based weight maintenance intervention with coaching delivered by research staff. Our next step is to determine whether coaching can be done by primary care staff during routine clinical work. We address 3 feasibility questions for using staff as coaches: coach recruitment, efficiency and effectiveness of coach training, and workflow integration and burden. Methods: MAINTAIN PRIME is an RCT comparing EHR-based coaching plus tracking vs tracking alone for primary care patients with recent weight loss. Implementation in 2 pilot clinics included: 1) approval of clinic leaders; 2) advertising to clinic staff for volunteers; 3) development and delivery of a self-paced coach training program; 4) coaching pilot patients for one month. Training (with a $400 one-time stipend) consisted of: 1) in-person orientation; 2) six core interactive online learning modules; 3) in-person review session; and 4) hands-on EHR tool training. Topics included weight management and behavior change, online communication, and structure of coaching messages. We obtained quantitative and qualitative feedback for both training and pilot coaching experience. Results: Recruitment: All clinical leaders (n=9) agreed to staff participation. Thirteen (10 medical assistants (MAs), 2 nurses, and 1 care manager) volunteered, out of 96 total clinical staff. Training : Six (46%) staff completed training. Reported confidence was very high (4.6/5) after training and the likelihood of recommending training to others was moderate (6.8/10). A common theme to improve training was to consolidate; we eliminated in-person review and added case studies to online modules. Staff found hands-on EHR tool training and research team support most valuable components of training. Coaching Experience: Integration of coaching into routine clinic work varied extensively, with some coaching throughout the day, and others saving it for end of day or once a week. All reported that coaching was easy to do with tools provided and took between 10-20 minutes/day. Most believed that they could coach between 10 to 20 patients at a time. Confidence levels were high (5.8/7) while coaching and belief in usefulness of coaching was very high (6.4/7). Of the 6 coaches trained, 3 elected to continue coaching after pilot conclusion. Conclusions: Coaching by primary care staff appears to be feasible with clinical leaders supporting staff engagement and staff coaching during routine clinical work. The training program was well received and reported to be effective by coaches both right after the training and during coaching. The relatively low volunteer levels and coach attrition may be barriers and further incentives may need to be considered.

A New Era in Pharmacovigilance: Toward Real-World Data and Digital Monitoring.

Adverse drug reactions (ADRs) are a major concern for patients, clinicians, and regulatory agencies. The discovery of serious ADRs leading to substantial morbidity and mortality has resulted in mandatory phase IV clinical trials, black box warnings, and withdrawal of drugs from the market. Real‐world data, data collected during routine clinical care, is being adopted by innovators, regulators, payors, and providers to inform decision making throughout the product life cycle. We outline several different approaches to modern pharmacovigilance, including spontaneous reporting databases, electronic health record monitoring and research frameworks, social media surveillance, and the use of digital devices. Some of these platforms are well‐established while others are still emerging or experimental. We highlight both the potential opportunity, as well as the existing challenges within these pharmacovigilance systems that have already begun to impact the drug development process, as well as the landscape of postmarket drug safety monitoring. Further research and investment into different and complementary pharmacovigilance systems is needed to ensure the continued safety of pharmacotherapy.

Assessing an Electronic Health Record research platform for identification of clinical trial participants

Electronic health records (EHR) are a potential resource for identification of clinical trial participants. We evaluated how accurately a commercially available EHR Research Platform, InSite, is able to identify potential trial participants from the EHR system of a large tertiary care hospital. Patient counts were compared with results obtained in a conventional manual search performed for a reference study that investigated the associations of atrial fibrillation (AF) and cerebrovascular incidents. The Clinical Data Warehouse (CDW) of Turku University Hospital was used to verify the capabilities of the EHR Research Platform.The EHR query resulted in a larger patient count than the manual query (EHR Research Platform 5859 patients, manual selection 2166 patients). This was due to the different search logic and some exclusion criteria that were not addressable in structured digital format. The EHR Research Platform (5859 patients) and the CDW search (5840 patients) employed the same search logic. The temporal relationship between the two diagnoses could be identified when they were available in structured format and the time difference was longer than a single hospital visit.Searching for patients with the EHR Research Platform can help to identify potential trial participants from a hospital's EHR system by limiting the number of records to be manually reviewed. EHR query tools can best be utilized in trials where the selection criteria are expressed in structured digital format.

A Sociotechnical Framework for Safety-Related Electronic Health Record Research Reporting: The SAFER Reporting Framework.

Electronic health record (EHR)-based interventions to improve patient safety are complex and sensitive to who, what, where, why, when, and how they are delivered. Success or failure depends not only on the characteristics and behaviors of individuals who are targeted by an intervention, but also on the technical characteristics of the intervention and the culture and environment of the health system that implements it. Current reporting guidelines do not capture the complexity of sociotechnical factors (technical and nontechnical factors, such as workflow and organizational issues) that confound or influence these interventions. This article proposes a methodological reporting framework for EHR interventions targeting patient safety and builds on an 8-dimension sociotechnical model previously developed by the authors for design, development, implementation, use, and evaluation of health information technology. The Safety-related EHR Research (SAFER) Reporting Framework enables reporting of patient safety-focused EHR-based interventions while accounting for the multifaceted, dynamic sociotechnical context affecting intervention implementation, effectiveness, and generalizability. As an example, an EHR-based intervention to improve communication and timely follow-up of subcritical abnormal test results to operationalize the framework is presented. For each dimension, reporting should include what sociotechnical changes were made to implement an EHR-related intervention to improve patient safety, why the intervention did or did not lead to safety improvements, and how this intervention can be applied or exported to other health care organizations. A foundational list of research and reporting recommendations to address implementation, effectiveness, and generalizability of EHR-based interventions needed to effectively reduce preventable patient harm is provided. The SAFER Reporting Framework is not meant to replace previous research reporting guidelines, but rather provides a sociotechnical adjunct that complements their use.