Are You Using the Right Electronic Health Record?

With the enactment of the Health Information Technology for Economic and Clinical Health (HITECH) Act in 2009, hospitals and physician practices across the country converted from a system of paper recordkeeping to fully integrated electronic health records (EHR).1, 2 With financial incentives in hand, there was a rush to market to acquire and implement these systems. Fast-forward 10 years, and it is apparent that the EHR space has significantly evolved in technology, processes, and policies.3 These changes should make organizations examine their EHR and organizational models and consider if they are using the best EHR to meet their organizational needs for the next 20 years. The National Institutes of Health (NIH) Clinical Center (CC) implemented its EHR in 2004 and, recognizing all of the new participants, technologies, and the advancement of clinical research needs since then, made the decision to embark on a comprehensive business case analysis to evaluate the best solution to meet the CC's and NIH's needs over the next 20 years. The goal was to answer this question: "Given the evolution of the EHR market, is the CC on the best platform to meet its needs now and in the future?"

THE EVOLUTION OF INFORMATION TECHNOLOGY GOVERNANCE AT THE NIH CLINICAL CENTER.

An information technology governance (ITG) program has helped the National Institutes of Health (NIH) Clinical Center (CC) in the implementation of many systems and has guided the organization through the maturity of its project management methodology. The NIHCC Department of Clinical Research Informatics (DCRI) maintains an electronic health record (EHR) called the clinical research information system (CRIS) along with many clinical information systems (CIS) and research information systems, supporting approximately 3,200 users. ITG involves establishing processes to guide the review, selection, implementation, management, and setting of the IT strategy representing the business owners, stakeholders, and IT.1 Research conducted by Levstek, Hovelja, and Pucihar2 identified that different organizations may need different ITG structures, frameworks, and strategies. The path to achieving strong ITG is a continuous journey. This paper reviews the evolution of the NIHCC IT governance strategy.

Unveiling the silent threat among us: leveraging health information technology in the search for asymptomatic COVID 19 healthcare workers.

Assuring the safety of both patients and healthcare workers (HCWs) in hospitals has been the primary focus of every healthcare organization during the COVID 19 pandemic. This article discusses the NIH Clinical Center's interdisciplinary approach to deploying an organizational Asymptomatic Staff Testing System.

Pharmacogenomics Implementation at the National Institutes of Health Clinical Center.

The National Institutes of Health Clinical Center (NIH CC) is the largest hospital in the United States devoted entirely to clinical research, with a highly diverse spectrum of patients. Patient safety and clinical quality are major goals of the hospital, and therapy is often complicated by multiple cotherapies and comorbidities. To this end, we implemented a pharmacogenomics program in 2 phases. In the first phase, we implemented genotyping for HLA-A and HLA-B gene variations with clinical decision support (CDS) for abacavir, carbamazepine, and allopurinol. In the second phase, we implemented genotyping for drug-metabolizing enzymes and transporters: SLCO1B1 for CDS of simvastatin and TPMT for CDS of mercaptopurine, azathioprine, and thioguanine. The purpose of this review is to describe the implementation process, which involves clinical, laboratory, informatics, and policy decisions pertinent to the NIH CC.

Integrating pharmacogenetic information and clinical decision support into the electronic health record.

Pharmacogenetics (PG) examines gene variations for drug disposition, response, or toxicity. At the National Institutes of Health Clinical Center (NIH CC), a multidepartment Pharmacogenetics Testing Implementation Committee (PGTIC) was established to develop clinical decision support (CDS) algorithms for abacavir, carbamazepine, and allopurinol, medications for which human leukocyte antigen (HLA) variants predict severe hypersensitivity reactions. Providing PG CDS in the electronic health record (EHR) during order entry could prevent adverse drug events. Medical Logic Module (MLM) programming was used to implement PG CDS in our EHR. The MLM checks to see if an HLA sequence-based gene test is ordered. A message regarding test status (result present, absent, pending, or test not ordered) is displayed on the order form, and the MLM determines if the prescriber can place the order, place it but require an over-ride reason, or be blocked from placing the order. Since implementation, more than 725 medication orders have been placed for over 230 patients by 154 different prescribers for the three drugs included in our PG program. Prescribers commonly used an over-ride reason when placing the order mainly because patients had been receiving the drug without reaction before implementation of the CDS program. Successful incorporation of PG CDS into the NIH CC EHR required a coordinated, interdisciplinary effort to ensure smooth activation and a positive effect on patient care. Prescribers have adapted to using the CDS and have ordered PG testing as a direct result of the implementation.

Interpreting nurses' responses to clinical documentation alerts.

OBJECTIVE: To understand how nurses respond to alerts that detect attempts to enter into electronic health records patient weights that vary significantly from previously recorded weights. METHODS: Examination of subsequent patient weights to determine if the alerts were true positive (TP) or false positive (FP), and whether nurses overrode alerts, changed their entry or quit without storing a value. RESULTS: Alerts occurred 2.74%, with 41.9% TP and 58.1% FP. Nurses overrode 30.3% of TP and 97.3% of FP alerts. CONCLUSIONS: The alert has an acceptable FP rate and does not appear to cause nurses to change entries to satisfy the alert. The alert improves recording of patient weights.