Linking Patient Encounters across Primary and Ancillary Electronic Health Record Systems: A Comparison of Two Approaches.

Background: To achieve scientific goals, researchers often require integration of data from a primary electronic health record (EHR) system and one or more ancillary EHR systems used during the same patient care encounter. Although studies have demonstrated approaches for linking patient identity records across different EHR systems, little is known about linking patient encounter records across primary and ancillary EHR systems. Objectives: We compared a patients-first approach versus an encounters-first approach for linking patient encounter records across multiple EHR systems. Methods: We conducted a retrospective observational study of 348,904 patients with 533,283 encounters from 2010 to 2020 across our institution's primary EHR system and an ancillary EHR system used in perioperative settings. For the patients-first approach and the encounters-first approach, we measured the number of patient and encounter links created as well as runtime. Results: While the patients-first approach linked 43% of patients and 49% of encounters, the encounters-first approach linked 98% of patients and 100% of encounters. The encounters-first approach was 20 times faster than the patients-first approach for linking patients and 33% slower for linking encounters. Conclusion: Findings suggest that common patient and encounter identifiers shared among EHR systems via automated interfaces may be clinically useful but not "research-ready" and thus require an encounters-first linkage approach to enable secondary use for scientific purposes. Based on our search, this study is among the first to demonstrate approaches for linking patient encounters across multiple EHR systems. Enterprise data warehouse for research efforts elsewhere may benefit from an encounters-first approach.

Leveraging generative AI for clinical evidence synthesis needs to ensure trustworthiness.

Evidence-based medicine promises to improve the quality of healthcare by empowering medical decisions and practices with the best available evidence. The rapid growth of medical evidence, which can be obtained from various sources, poses a challenge in collecting, appraising, and synthesizing the evidential information. Recent advancements in generative AI, exemplified by large language models, hold promise in facilitating the arduous task. However, developing accountable, fair, and inclusive models remains a complicated undertaking. In this perspective, we discuss the trustworthiness of generative AI in the context of automated summarization of medical evidence.

Implementation of a commercial federated network of electronic health record data to enable sponsor-initiated clinical trials at an academic medical center.

BACKGROUND: A commercial federated network called TriNetX has connected electronic health record (EHR) data from academic medical centers (AMCs) with biopharmaceutical sponsors in a privacy-preserving manner to promote sponsor-initiated clinical trials. Little is known about how AMCs have implemented TriNetX to support clinical trials. FINDINGS: At our AMC over a six-year period, TriNetX integrated into existing institutional workflows enabled 402 requests for sponsor-initiated clinical trials, 14 % (n = 56) of which local investigators expressed interest in conducting. Although clinical trials administrators indicated TriNetX yielded unique study opportunities, measurement of impact of institutional participation in the network was challenging due to lack of a common trial identifier shared across TriNetX, sponsor, and our institution. CONCLUSION: To the best of our knowledge, this study is among the first to describe integration of a federated network of EHR data into institutional workflows for sponsor-initiated clinical trials. This case report may inform efforts at other institutions.

Comparing automated vs. manual data collection for COVID-specific medications from electronic health records.

INTRODUCTION: Data extraction from electronic health record (EHR) systems occurs through manual abstraction, automated extraction, or a combination of both. While each method has its strengths and weaknesses, both are necessary for retrospective observational research as well as sudden clinical events, like the COVID-19 pandemic. Assessing the strengths, weaknesses, and potentials of these methods is important to continue to understand optimal approaches to extracting clinical data. We set out to assess automated and manual techniques for collecting medication use data in patients with COVID-19 to inform future observational studies that extract data from the electronic health record (EHR). MATERIALS AND METHODS: For 4,123 COVID-positive patients hospitalized and/or seen in the emergency department at an academic medical center between 03/03/2020 and 05/15/2020, we compared medication use data of 25 medications or drug classes collected through manual abstraction and automated extraction from the EHR. Quantitatively, we assessed concordance using Cohen's kappa to measure interrater reliability, and qualitatively, we audited observed discrepancies to determine causes of inconsistencies. RESULTS: For the 16 inpatient medications, 11 (69%) demonstrated moderate or better agreement; 7 of those demonstrated strong or almost perfect agreement. For 9 outpatient medications, 3 (33%) demonstrated moderate agreement, but none achieved strong or almost perfect agreement. We audited 12% of all discrepancies (716/5,790) and, in those audited, observed three principal categories of error: human error in manual abstraction (26%), errors in the extract-transform-load (ETL) or mapping of the automated extraction (41%), and abstraction-query mismatch (33%). CONCLUSION: Our findings suggest many inpatient medications can be collected reliably through automated extraction, especially when abstraction instructions are designed with data architecture in mind. We discuss quality issues, concerns, and improvements for institutions to consider when crafting an approach. During crises, institutions must decide how to allocate limited resources. We show that automated extraction of medications is feasible and make recommendations on how to improve future iterations.

An architecture for research computing in health to support clinical and translational investigators with electronic patient data.

OBJECTIVE: Obtaining electronic patient data, especially from electronic health record (EHR) systems, for clinical and translational research is difficult. Multiple research informatics systems exist but navigating the numerous applications can be challenging for scientists. This article describes Architecture for Research Computing in Health (ARCH), our institution's approach for matching investigators with tools and services for obtaining electronic patient data. MATERIALS AND METHODS: Supporting the spectrum of studies from populations to individuals, ARCH delivers a breadth of scientific functions-including but not limited to cohort discovery, electronic data capture, and multi-institutional data sharing-that manifest in specific systems-such as i2b2, REDCap, and PCORnet. Through a consultative process, ARCH staff align investigators with tools with respect to study design, data sources, and cost. Although most ARCH services are available free of charge, advanced engagements require fee for service. RESULTS: Since 2016 at Weill Cornell Medicine, ARCH has supported over 1200 unique investigators through more than 4177 consultations. Notably, ARCH infrastructure enabled critical coronavirus disease 2019 response activities for research and patient care. DISCUSSION: ARCH has provided a technical, regulatory, financial, and educational framework to support the biomedical research enterprise with electronic patient data. Collaboration among informaticians, biostatisticians, and clinicians has been critical to rapid generation and analysis of EHR data. CONCLUSION: A suite of tools and services, ARCH helps match investigators with informatics systems to reduce time to science. ARCH has facilitated research at Weill Cornell Medicine and may provide a model for informatics and research leaders to support scientists elsewhere.

ReCiter: An open source, identity-driven, authorship prediction algorithm optimized for academic institutions.

Academic institutions need to maintain publication lists for thousands of faculty and other scholars. Automated tools are essential to minimize the need for direct feedback from the scholars themselves who are practically unable to commit necessary effort to keep the data accurate. In relying exclusively on clustering techniques, author disambiguation applications fail to satisfy key use cases of academic institutions. Algorithms can perfectly group together a set of publications authored by a common individual, but, for them to be useful to an academic institution, they need to programmatically and recurrently map articles to thousands of scholars of interest en masse. Consistent with a savvy librarian's approach for generating a scholar's list of publications, identity-driven authorship prediction is the process of using information about a scholar to quantify the likelihood that person wrote certain articles. ReCiter is an application that attempts to do exactly that. ReCiter uses institutionally-maintained identity data such as name of department and year of terminal degree to predict which articles a given scholar has authored. To compute the overall score for a given candidate article from PubMed (and, optionally, Scopus), ReCiter uses: up to 12 types of commonly available, identity data; whether other members of a cluster have been accepted or rejected by a user; and the average score of a cluster. In addition, ReCiter provides scoring and qualitative evidence supporting why particular articles are suggested. This context and confidence scoring allows curators to more accurately provide feedback on behalf of scholars. To help users to more efficiently curate publication lists, we used a support vector machine analysis to optimize the scoring of the ReCiter algorithm. In our analysis of a diverse test group of 500 scholars at an academic private medical center, ReCiter correctly predicted 98% of their publications in PubMed.

Ten principles for data sharing and commercialization.

Digital medical records have enabled us to employ clinical data in many new and innovative ways. However, these advances have brought with them a complex set of demands for healthcare institutions regarding data sharing with topics such as data ownership, the loss of privacy, and the protection of the intellectual property. The lack of clear guidance from government entities often creates conflicting messages about data policy, leaving institutions to develop guidelines themselves. Through discussions with multiple stakeholders at various institutions, we have generated a set of guidelines with 10 key principles to guide the responsible and appropriate use and sharing of clinical data for the purposes of care and discovery. Industry, universities, and healthcare institutions can build upon these guidelines toward creating a responsible, ethical, and practical response to data sharing.

A Method to Improve Availability and Quality of Patient Race Data in an Electronic Health Record System.

BACKGROUND: Although federal regulations mandate documentation of structured race data according to Office of Management and Budget (OMB) categories in electronic health record (EHR) systems, many institutions have reported gaps in EHR race data that hinder secondary use for population-level research focused on underserved populations. When evaluating race data available for research purposes, we found our institution's enterprise EHR contained structured race data for only 51% (1.6 million) of patients. OBJECTIVES: We seek to improve the availability and quality of structured race data available to researchers by integrating values from multiple local sources. METHODS: To address the deficiency in race data availability, we implemented a method to supplement OMB race values from four local sources-inpatient EHR, inpatient billing, natural language processing, and coded clinical observations. We evaluated this method by measuring race data availability and data quality with respect to completeness, concordance, and plausibility. RESULTS: The supplementation method improved race data availability in the enterprise EHR up to 10% for some minority groups and 4% overall. We identified structured OMB race values for more than 142,000 patients, nearly a third of whom were from racial minority groups. Our data quality evaluation indicated that the supplemented race values improved completeness in the enterprise EHR, originated from sources in agreement with the enterprise EHR, and were unbiased to the enterprise EHR. CONCLUSION: Implementation of this method can successfully increase OMB race data availability, potentially enhancing accrual of patients from underserved populations to research studies.

Replacing Paper Informed Consent with Electronic Informed Consent for Research in Academic Medical Centers: A Scoping Review.

Although experts have identified benefits to replacing paper with electronic consent (eConsent) for research, a comprehensive understanding of strategies to overcome barriers to adoption is unknown. To address this gap, we performed a scoping review of the literature describing eConsent in academic medical centers. Of 69 studies that met inclusion criteria, 81% (n=56) addressed ethical, legal, and social issues; 67% (n=46) described user interface/user experience considerations; 39% (n=27) compared electronic versus paper approaches; 33% (n=23) discussed approaches to enterprise scalability; and 25% (n=17) described changes to consent elections. Findings indicate a lack of a leading commercial eConsent vendor, as articles described a myriad of homegrown systems and extensions of vendor EHR patient portals. Opportunities appear to exist for researchers and commercial software vendors to develop eConsent approaches that address the five critical areas identified in this review.

Evaluation of a REDCap-based Workflow for Supporting Federal Guidance for Electronic Informed Consent.

Adoption of electronic informed consent (eConsent) for research remains low despite evidence of improved patient comprehension, usability, and workflow processes compared to paper. At our institution, we implemented an eConsent workflow using REDCap, a widely used electronic data capture system. The goal of this study was to evaluate the extent to which the REDCap eConsent solution adhered to federal guidance for eConsent. Of 29 requirements derived from sixteen recommendations from the United States Office for Human Research Protections (OHRP) and Food and Drug Administration (FDA), the REDCap eConsent solution supported 24 (86%). To the best of our knowledge, this is among the first studies to evaluate an eConsent approach's support for federal guidance. Findings suggest use of REDCap may help other institutions overcome barriers to eConsent adoption, and that OHRP and FDA expand guidance to recommend eConsent solutions integrate with enterprise clinical and research information systems.

Implementation of Informatics to Support the NIH All of Us Research Program in a Healthcare Provider Organization.

The NIH All of Us Research Program, a national effort to collect biospecimens and health data for over one million participants from across the United States, requires participating healthcare provider organizations (HPOs) to use informatics tools maintained by the NIH to manage participant consent, biospecimen processing, physical measurements, and other workflows. HPOs also maintain distinct workflows for handling overlapping tasks within their individual aegis, which do not necessarily achieve seamless interoperability with NIH-maintained cloud-based systems. At our HPO, we implemented informatics to address gaps in enrollment workflows and hardware, clinical workflow integration, patient engagement, laboratory support, and study team reporting. In this case report we detail our approach to inform efforts at other institutions for the NIH All of Us Research Program and other studies.

A Method for Integrating Healthcare Provider Organization and Research Sponsor Systems and Workflows to Support Large-Scale Studies.

Healthcare provider organizations (HPOs) increasingly participate in large-scale research efforts sponsored by external organizations that require use of consent management systems that may not integrate seamlessly with local workflows. The resulting inefficiency can hinder the ability of HPOs to participate in studies. To overcome this challenge, we developed a method using REDCap, a widely adopted electronic data capture system, and novel middleware that can potentially generalize to other settings. In this paper, we describe the method, illustrate its use to support the NIHAll of Us Research Program and PCORI ADAPTABLE studies at our HPO, and encourage other HPOs to test replicability of the method to facilitate similar research efforts. Code is available on GitHub at https://github.com/wcmc-research-informatics/.

A scalable method for supporting multiple patient cohort discovery projects using i2b2.

Although i2b2, a popular platform for patient cohort discovery using electronic health record (EHR) data, can support multiple projects specific to individual disease areas or research interests, the standard approach for doing so duplicates data across projects, requiring additional disk space and processing time, which limits scalability. To address this deficiency, we developed a novel approach that stored data in a single i2b2 fact table and used structured query language (SQL) views to access data for specific projects. Compared to the standard approach, the view-based approach reduced required disk space by 59% and extract-transfer-load (ETL) time by 46%, without substantially impacting query performance. The view-based approach has enabled scalability of multiple i2b2 projects and generalized to another data model at our institution. Other institutions may benefit from this approach, code of which is available on GitHub (https://github.com/wcmc-research-informatics/super-i2b2).

Design and Implementation of a Secure Computing Environment for Analysis of Sensitive Data at an Academic Medical Center.

Academic medical centers need to make sensitive data from electronic health records, payer claims, genomic pipelines, and other sources available for analytical and educational purposes while ensuring privacy and security. Although many studies have described warehouses for collecting biomedical data, few studies have described secure computing environments for analysis of sensitive data. This case report describes the Weill Cornell Medicine Data Core with respect to user access, data controls, hardware, software, audit, and financial considerations. In the 2.5 years since launch, the Data Core has supported more than 200 faculty, staff, and students across nearly 60 research and education projects. Other institutions may benefit from adopting elements of the approach, including tools available on Github, for balancing access with privacy and security.

Secondary Use of Patients' Electronic Records (SUPER): An Approach for Meeting Specific Data Needs of Clinical and Translational Researchers.

Academic medical centers commonly approach secondary use of electronic health record (EHR) data by implementing centralized clinical data warehouses (CDWs). However, CDWs require extensive resources to model data dimensions and harmonize clinical terminology, which can hinder effective support of the specific and varied data needs of investigators. We hypothesized that an approach that aggregates raw data from source systems, ignores initial modeling typical of CDWs, and transforms raw data for specific research purposes would meet investigator needs. The approach has successfully enabled multiple tools that provide utility to the institutional research enterprise. To our knowledge, this is the first complete description of a methodology for electronic patient data acquisition and provisioning that ignores data harmonization at the time of initial storage in favor of downstream transformation to address specific research questions and applications.

Effects of an e-Prescribing interface redesign on rates of generic drug prescribing: exploiting default options.

OBJECTIVE: Increasing the use of generic medications could help control medical costs. However, educational interventions have limited impact on prescriber behavior, and e-prescribing alerts are associated with high override rates and alert fatigue. Our objective was to evaluate the effect of a less intrusive intervention, a redesign of an e-prescribing interface that provides default options intended to "nudge" prescribers towards prescribing generic drugs. METHODS: This retrospective cohort study in an academic ambulatory multispecialty practice assessed the effects of customizing an e-prescribing interface to substitute generic equivalents for brand-name medications during order entry and allow a one-click override to order the brand-name medication. RESULTS: Among drugs with generic equivalents, the proportion of generic drugs prescribed more than doubled after the interface redesign, rising abruptly from 39.7% to 95.9% (a 56.2% increase; 95% confidence interval, 56.0-56.4%; P < .001). Before the redesign, generic drug prescribing rates varied by therapeutic class, with rates as low as 8.6% for genitourinary products and 15.7% for neuromuscular drugs. After the redesign, generic drug prescribing rates for all but four therapeutic classes were above 90%: endocrine drugs, neuromuscular drugs, nutritional products, and miscellaneous products. DISCUSSION: Changing the default option in an e-prescribing interface in an ambulatory care setting was followed by large and sustained increases in the proportion of generic drugs prescribed at the practice. CONCLUSIONS: Default options in health information technology exert a powerful effect on user behavior, an effect that can be leveraged to optimize decision making.

Patient activation and use of an electronic patient portal.

PRIMARY OBJECTIVE: Electronic patient portals give patients access to personal medical data, potentially creating opportunities to improve knowledge, self-efficacy, and engagement in healthcare. The combination of knowledge, self-efficacy, and engagement has been termed activation. Our objective was to assess the relationship between patient activation and outpatient use of a patient portal. RESEARCH DESIGN: Survey. METHODS AND PROCEDURES: A telephone survey was conducted with 180 patients who had been given access to a portal, 113 of whom used it and 67 of whom did not. The validated patient activation measure (PAM) was administered along with questions about demographics and behaviors. RESULTS: Portal users were no different from nonusers in patient activation. Portal users did have higher education level and more frequent Internet use, and were more likely to have precisely 2 prescription medications than to have more or fewer. CONCLUSION: Patients who chose to use an electronic patient portal were not more highly activated than nonusers, although they were more educated and more likely to be Internet users.

Implementing a Clinical Research Management System: One Institution's Successful Approach Following Previous Failures.

Clinical research management systems (CRMSs) can facilitate research billing compliance and clinician awareness of study activities when integrated with practice management and electronic health record systems. However, adoption of CRMSs remains low, and optimal approaches to implementation are unknown. This case report describes one institution's successful approach to organization, technology, and workflow for CRMS implementation following previous failures. Critical factors for CRMS success included organizational commitment to clinical research, a dedicated research information technology unit, integration of research data across disparate systems, and centralized system usage workflows. In contrast, previous failed approaches at the institution lacked a mandate and mechanism for change, received support as a business rather than research activity, maintained data in separate systems, and relied on inconsistent distributed system usage workflows. To our knowledge, this case report is the first to describe CRMS implementation success and failures, which can assist practitioners and academic evaluators.

Promoting generic medication prescribing by order interface redesign: small change, large impact.

Prescription drugs are a significant component of the ever increasing health care costs. We describe the effects on generic medication prescribing behavior achieved through redesign of the order entry interface of our institutions ambulatory electronic health record. The redesign involved custom programming that automatically substituted brand medications with their generic equivalents and only allowed continuation with the brand medication if the clinician made an extra mouse click selecting "dispense as written". We conducted a before-after retrospective study around the time of the redesign and witnessed a net 36.9% percentage increase in the number of generic medications prescribed.

Physician productivity and the ambulatory EHR in a large academic multi-specialty physician group.

PURPOSE: The impact of the ambulatory electronic health record (EHR) on physician productivity is poorly understood. Fear of productivity loss remains a major concern for practitioners and health care delivery organizations and inhibits system adoption. This study describes the changes in physician productivity after the implementation of a commercially available ambulatory EHR system in a large academic multi-specialty physician group. METHODS: Weill Cornell faculty members implemented on the EpicCare (Epic Systems) EHR between 2001 and 2007 were identified as potential study participants. Monthly visit volume, charges, and work relative value units (wRVUs) were compared pre and post each provider's EHR implementation go-live date. Practitioners who lacked at least 6 months of pre- and post-implementation visit volume and charge data were excluded. Practitioners who did not meet pre-determined system proficiency metrics were additionally identified and became the basis of a non-adopter comparison group. RESULTS: 203 physicians met criteria for the analysis. The eligible providers were divided into an adopter and non-adopter cohort based on system proficiency benchmarks. Those practitioners who adopted the EHR had a statistically significant increase in average monthly patient visit volume of 9 visits per provider per month. The non-adopter cohort's visit volume was statistically unchanged. Both the EHR adopters and non-adopters had statistically significant increases (22% and 16% respectively) in average monthly charges in the post-implementation period. Average monthly wRVUs were statistically unchanged in the non-adopter cohort, but showed a positive and statistically significant increase of 12 wRVUs per provider per month for the adopter group. The EHR adoption group showed an incremental increase in productivity once practitioners achieved 6 or more months experience with the EHR, consistent with a "ramp-up" period. A multivariable regression model did not reveal any association between the post-EHR implementation change in wRVUs and several potential confounding variables, including baseline provider average monthly visit volume and wRVUs, date of system adoption, and specialty categorization. CONCLUSION: Provider productivity, as measured by patient visit volume, charges, and wRVUs modestly increased for a cohort of multi-specialty providers that adopted a commercially available ambulatory EHR. The productivity gain appeared to become even more pronounced after several months of system experience. This objective data may help persuade apprehensive practitioners that EHR adoption need not harm productivity. The baseline differences in productivity metrics for the adopters and non-adopters in our study suggest that there are fundamental differences in these groups. Further characterizing these differences may help predict EHR adoption success and guide future implementation strategies.