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Objective: To identify important barriers and facilitators relating to the feasibility of implementing clinical practice guidelines (CPGs) as clinical decision support (CDS)., Materials and Methods: We conducted a qualitative, thematic analysis of interviews from seven interviews with dyads (one clinical expert and one systems analyst) who discussed the feasibility of implementing 10 Choosing Wisely ® guidelines at their institutions. We conducted a content analysis to extract salient themes describing facilitators, challenges, and other feasibility considerations regarding implementing CPGs as CDS., Results: We identified five themes: concern about data quality impacts implementation planning; the availability of data in a computable format is a primary factor for implementation feasibility; customized strategies are needed to mitigate uncertainty and ambiguity when translating CPGs to an electronic health record-based tool; misalignment of expected CDS with pre-existing clinical workflows impact implementation; and individual level factors of end-users must be considered when selecting and implementing CDS tools., Discussion: The themes reveal several considerations for CPG as CDS implementations regarding data quality, knowledge representation, and sociotechnical issues. Guideline authors should be aware that using CDS to implement CPGs is becoming increasingly popular and should consider providing clear guidelines to aid implementation. The complex nature of CPG as CDS implementation necessitates a unified effort to overcome these challenges., Conclusion: Our analysis highlights the importance of cooperation and co-development of standards, strategies, and infrastructure to address the difficulties of implementing CPGs as CDS. The complex interactions between the concepts revealed in the interviews necessitates the need that such work should not be conducted in silos. We also implore that implementers disseminate their experiences., (© The Author(s) 2021. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

Clinical decision support interventions, such as alerts and reminders, can improve clinician compliance with practice guidelines and patient outcomes. Alerts that trigger at inappropriate times are often dismissed by clinicians, reducing desired actions rather than increasing them. A set of nursing-specific alerts related to influenza screening and vaccination were optimized so that they would "trigger" less often but function adequately to maintain institutional flu vaccination compliance. We analyzed the current triggering criteria for six flu vaccine-related alerts and asked nurse end users for suggestions to increase specificity. Using the "five rights" (of clinical decision support) as a framework, alerts were redesigned to address user needs. New alerts were tested and implemented and their activity compared in two different flu seasons, preoptimization and postoptimization. The redesigned alerts resulted in fewer alerts per encounter (P < .0001), less dismissals of alerts (P < .0001), and a 2.8% point improvement in compliance rates for flu vaccine screening, documentation, and administration. A focus group confirmed that the redesign improved workflow, but some nurses thought they still triggered too often. The five rights model can support improvements in alert design and outcomes., (Copyright © 2020 Wolters Kluwer Health, Inc. All rights reserved.)

The volume of biomedical knowledge is growing exponentially and much of this knowledge is represented in computer executable formats, such as models, algorithms and programmatic code. There is a growing need to apply this knowledge to improve health in Learning Health Systems, health delivery organizations, and other settings. However, most organizations do not yet have the infrastructure required to consume and apply computable knowledge, and national policies and standards adoption are not sufficient to ensure that it is discoverable and used safely and fairly, nor is there widespread experience in the process of knowledge implementation as clinical decision support. The Mobilizing Computable Biomedical Knowledge (MCBK) community formed in 2016 to address these needs. This report summarizes the main outputs of the Second Annual MCBK public meeting, which was held at the National Institutes of Health on July 18-19, 2019 and brought together over 150 participants from various domains to frame and address important dimensions for mobilizing CBK., Competing Interests: Blackford Middleton is employed by Apervita, Inc., which provides a platform and marketplace to democratize healthcare data and analytics. Leslie McIntosh is founder and CEO of Ripeta, LLC, a company providing reproducibility checks on scientific manuscripts. Gerald (Jerry) Perry served on the Board of Directors for the Association of Academic Health Sciences Libraries (AAHSL) during the period of work described in this article. AAHSL is one of the four professional societies of whose members were interviewed to develop targeted communications strategies for affinity professional societies, as a component of the Sustainability for Mobilization and Inclusion Work Group's efforts. Mr. Perry has no competing commercial interests. The other authors (Rachel Richesson, Bruce Bray, Christine Dymek, Robert Greenes, Leslie McIntosh, Jodyn Platt, and Christopher Shaffer) have no competing interests to declare., (© 2020 The Authors. Learning Health Systems published by Wiley Periodicals, Inc. on behalf of the University of Michigan.)

Clinical practice guidelines (CPGs) often serve as the knowledge base for clinical decision support (CDS). While CPGs are rigorously created by medical professional societies, the concepts in each guideline may not be sufficient for translation into CDS applications. In addition, clinicians' perceptions of these concepts may differ greatly, affecting the implementation and impact of CDS within an organization. Five guidelines developed by the American College of Emergency Physicians were systematically explored, generating fifty-one unique clinical concepts. These concepts were presented to two nurses and two physicians, whom were asked to assess and comment on the capture of each clinical concept in the electronic health record (EHR) and the subsequent availability of the data for CDS. Nurses and physicians showed differing perceptions of data availability. These differing perceptions may influence an organizational approach to developing and implementing CDS, potentially informing our understanding of why CDS may not achieve the intended impact.

The purpose of this project was to standardize the referral and documentation process for diabetes education in patients with type 2 diabetes. The goal was to refer all eligible patients with type 2 diabetes into diabetes education. A standardized template within the clinical note was created to capture if the patients had ever received diabetes education. Use of this template by the clinician improved documentation about diabetes education and increased referrals into diabetes education programs. The findings of this project can be applied to other primary care clinics to help increase the historically low referrals rates into diabetes education.

Pragmatic clinical trials (PCTs) are research investigations embedded in health care settings designed to increase the efficiency of research and its relevance to clinical practice. The Health Care Systems Research Collaboratory, initiated by the National Institutes of Health Common Fund in 2010, is a pioneering cooperative aimed at identifying and overcoming operational challenges to pragmatic research. Drawing from our experience, we present 4 broad categories of informatics-related challenges: (1) using clinical data for research, (2) integrating data from heterogeneous systems, (3) using electronic health records to support intervention delivery or health system change, and (4) assessing and improving data capture to define study populations and outcomes. These challenges impact the validity, reliability, and integrity of PCTs. Achieving the full potential of PCTs and a learning health system will require meaningful partnerships between health system leadership and operations, and federally driven standards and policies to ensure that future electronic health record systems have the flexibility to support research., (© The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Disparate data must be represented in a common format to enable comparison across multiple institutions and facilitate Big Data science. Nursing assessments represent a rich source of information. However, a lack of agreement regarding essential concepts and standardized terminology prevent their use for Big Data science in the current state. The purpose of this study was to align a minimum set of physiological nursing assessment data elements with national standardized coding systems. Six institutions shared their 100 most common electronic health record nursing assessment data elements. From these, a set of distinct elements was mapped to nationally recognized Logical Observations Identifiers Names and Codes (LOINC®) and Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT®) standards. We identified 137 observation names (55% new to LOINC), and 348 observation values (20% new to SNOMED CT) organized into 16 panels (72% new LOINC). This reference set can support the exchange of nursing information, facilitate multi-site research, and provide a framework for nursing data analysis.

A fundamental premise of scientific research is that it should be reproducible. However, the specific requirements for reproducibility of research using electronic health record (EHR) data have not been sufficiently articulated. There is no guidance for researchers about how to assess a given project and identify provisions for reproducibility. We analyze three different clinical research initiatives that use EHR data in order to define a set of requirements to reproduce the research using the original or other datasets. We identify specific project features that drive these requirements. The resulting framework will support the much-needed discussion of strategies to ensure the reproducibility of research that uses data from EHRs.

Objective: The combination of phenomic data from electronic health records (EHR) and clinical data repositories with dense biological data has enabled genomic and pharmacogenomic discovery, a first step toward precision medicine. Computational methods for the identification of clinical phenotypes from EHR data will advance our understanding of disease risk and drug response, and support the practice of precision medicine on a national scale., Methods: Based on our experience within three national research networks, we summarize the broad approaches to clinical phenotyping and highlight the important role of these networks in the progression of high-throughput phenotyping and precision medicine. We provide supporting literature in the form of a non-systematic review., Results: The practice of clinical phenotyping is evolving to meet the growing demand for scalable, portable, and data driven methods and tools. The resources required for traditional phenotyping algorithms from expert defined rules are significant. In contrast, machine learning approaches that rely on data patterns will require fewer clinical domain experts and resources., Conclusions: Machine learning approaches that generate phenotype definitions from patient features and clinical profiles will result in truly computational phenotypes, derived from data rather than experts. Research networks and phenotype developers should cooperate to develop methods, collaboration platforms, and data standards that will enable computational phenotyping and truly modernize biomedical research and precision medicine., (Copyright © 2016 Elsevier B.V. All rights reserved.)

Preference cards, the nexus of an efficient OR, help ensure that OR personnel have what they need to safely and efficiently care for surgical patients. In June 2013, a new electronic health record (EHR) was implemented in the ORs of the Duke University Health System, Durham, North Carolina. Although it was an integrated system, the EHR disrupted supply management in the OR, which affected the availability of correct resources. The project team performed a process-improvement project using root cause analysis (RCA) to identify the factors that led to the disruption in the preference card management process for orthopedic surgical procedures. Team members identified that a chief cause of resource disruption was the mistiming of interface messages that created the pick lists and requisition forms. Based on the results of the RCA, the project team developed and implemented recommendations for process improvement., (Copyright © 2016 AORN, Inc. Published by Elsevier Inc. All rights reserved.)

Clinical decision support tools in electronic health records have demonstrated improvement with process measures and clinician performance, predominantly for providers. Clinical decision support tools could improve patient fall risk identification and prevention plans, a common concern for nursing. This quality-improvement project used clinical decision support to improve the rate of nurse compliance with documented fall risk assessments and, for patients at high risk, fall prevention plans of care in 16 adult inpatient units. Preintervention and postintervention data were compared using quarterly audits, retrospective chart review, safety reports, and falls and falls-with-injury rates. Documentation of fall risk assessments on the 16 units improved significantly according to quarterly audit data (P = .05), whereas documentation of the plans of care did not. Retrospective chart review on two units indicated improvement for admission fall risk assessment (P = .05) and a decrease in the documentation of the shift plan of care (P = .01); one unit had a statistically significant decrease in documentation of plans of care on admission (P = .00). Examination of safety reports for patients who fell showed all patients before and after clinical decision support had fall risk assessments documented. Falls and falls with injury did not change significantly before and after clinical decision support intervention.

Medication exposure is an important variable in virtually all clinical research, yet there is great variation in how the data are collected, coded, and analyzed. Coding and classification systems for medication data are heterogeneous in structure, and there is little guidance for implementing them, especially in large research networks and multi-site trials. Current practices for handling medication data in clinical trials have emerged from the requirements and limitations of paper-based data collection, but there are now many electronic tools to enable the collection and analysis of medication data. This paper reviews approaches to coding medication data in multi-site research contexts, and proposes a framework for the classification, reporting, and analysis of medication data. The framework can be used to develop tools for classifying medications in coded data sets to support context appropriate, explicit, and reproducible data analyses by researchers and secondary users in virtually all clinical research domains., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Clinical and epidemiological researchers across all medical specialties need tools and knowledge representations to support the classification, aggregation, and analysis of medication data. The Veterans Affairs National Drug File Reference Terminology (NDF-RT) is a named standard for classifying medications. We describe our experience applying NDF-RT to aggregate RxNorm-encoded medications that were collected from an international cohort of over 8,000 children. We detail the researchers' analysis objectives and subsequent requirements for a drug classification representation, and assess the ability of NDF-RT to provide classes that are meaningful to pediatric researchers. In addition, we explore the completeness of RxNorm - NDF-RT mappings (i.e., the coverage of NDF-RT) for this sample of pediatric medications. We conclude that NDF-RT is sufficient to address the knowledge representation needs for this research study, though only a small subset of NDF-RT is needed for research analyses. Researchers from all domains would benefit from tools for easily extracting a set of relevant classes from the NDF-RT knowledge structure.

Strategies to enhance recruitment are useful in clinical research network settings. Our network data center maintains a Contact Registry of patients who express a willingness to be contacted to enroll in clinical studies. An automated system generates periodic and customized communications to notify registrants of potential studies and network events. The majority of these communications are sent by email, although the system also supports postal communications. A database tracks the sending of all communications and facilitates reports of registry activity.