Building and maintaining trust in clinical decision support: Recommendations from the Patient‐Centered CDS Learning Network

Knowledge artifacts in digital repositories for clinical decision support (CDS) can promote the use of CDS in clinical practice. However, stakeholders will benefit from knowing which they can trust before adopting artifacts from knowledge repositories. We discuss our investigation into trust for knowledge artifacts and repositories by the Patient‐Centered CDS Learning Network’s Trust Framework Working Group (TFWG). The TFWG identified 12 actors (eg, vendors, clinicians, and policy makers) within a CDS ecosystem who each may play a meaningful role in prioritizing, authoring, implementing, or evaluating CDS and developed 33 recommendations distributed across nine “trust attributes.” The trust attributes and recommendations represent a range of considerations such as the “Competency” of knowledge artifact engineers and the “Organizational Capacity” of institutions that develop and implement CDS. The TFWG findings highlight an initial effort to make trust explicit and embedded within CDS knowledge artifacts and repositories and thus more broadly accepted and used.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154962/1/lrh210208.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154962/2/lrh210208_am.pd

Measuring agreement between decision support reminders: the cloud vs. the local expert

Background: A cloud-based clinical decision support system (CDSS) was implemented to remotely provide evidence-based guideline reminders in support of preventative health. Following implementation, we measured the agreement between preventive care reminders generated by an existing, local CDSS and the new, cloud-based CDSS operating on the same patient visit data. Methods: Electronic health record data for the same set of patients seen in primary care were sent to both the cloud-based web service and local CDSS. The clinical reminders returned by both services were captured for analysis. Cohen’s Kappa coefficient was calculated to compare the two sets of reminders. Kappa statistics were further adjusted for prevalence and bias due to the potential effects of bias in the CDS logic and prevalence in the relative small sample of patients. Results: The cloud-based CDSS generated 965 clinical reminders for 405 patient visits over 3 months. The local CDSS returned 889 reminders for the same patient visit data. When adjusted for prevalence and bias, observed agreement varied by reminder from 0.33 (95% CI 0.24 – 0.42) to 0.99 (95% CI 0.97 – 1.00) and demonstrated almost perfect agreement for 7 of the 11 reminders. Conclusions: Preventive care reminders delivered by two disparate CDS systems show substantial agreement. Subtle differences in rule logic and terminology mapping appear to account for much of the discordance. Cloud-based CDSS therefore show promise, opening the door for future development and implementation in support of health care providers with limited resources for knowledge management of complex logic and rules

The Value of Information Technology-Enabled Diabetes Management

Reviews different technologies used in diabetes disease management, as well as the costs, benefits, and quality implications of technology-enabled diabetes management programs in the United States

A first step towards translating evidence into practice: heart failure in a community practice-based research network

Objective To determine the validity of an electronic health record (EHR) in the identification of patients with left ventricular dysfunction in a primary care setting. Design A cross-sectional study. Setting Nine clinics participating from the Providence Research Network (PRN) comprising 75 physicians serving approximately 200 000 patients. All clinics utilise the Logician™ EHR for all patient care activities. Patients The study included all PRN patients with an active chart. Interventions All patients with a heart failure diagnosis in the problem list were identified by database query. Left ventricular ejection fraction (LVEF) data were identified through query of local cardiology and hospital echocardiography databases. Additional LVEF data were sought in a manual search of paper charts. Measurements and main results To determine the problem list coding accuracy for a heart failure (HF) diagnosis we evaluated sensitivity, positive predictive value and related derived statistical measures using documented LVEF as the ‘gold standard’.Of 205 755 active PRN patients, 1731 were identified with a problem list entry of HF. Based on comparison with documented LVEF, the sensitivity for problem list entry was 43.9% and 54.4% when HF was defined as an LVEF ≤55% and ≤40%, respectively. Conclusion The validity of an EHR problem list entry of HF was poor. The problem list validity could be enhanced through reconciliation with other data sources. Inaccurate EHR problem lists may have clinical consequences, including underprescribing of beneficial therapies

Using a Service Oriented Architecture Approach to Clinical Decision Support: Performance Results from Two CDS Consortium Demonstrations

The Clinical Decision Support Consortium has completed two demonstration trials involving a web service for the execution of clinical decision support (CDS) rules in one or more electronic health record (EHR) systems. The initial trial ran in a local EHR at Partners HealthCare. A second EHR site, associated with Wishard Memorial Hospital, Indianapolis, IN, was added in the second trial. Data were gathered during each 6 month period and analyzed to assess performance, reliability, and response time in the form of means and standard deviations for all technical components of the service, including assembling and preparation of input data. The mean service call time for each period was just over 2 seconds. In this paper we report on the findings and analysis to date while describing the areas for further analysis and optimization as we continue to expand our use of a Services Oriented Architecture approach for CDS across multiple institutions

Criteria for assessing high-priority drug-drug interactions for clinical decision support in electronic health records

Background: High override rates for drug-drug interaction (DDI) alerts in electronic health records (EHRs) result in the potentially dangerous consequence of providers ignoring clinically significant alerts. Lack of uniformity of criteria for determining the severity or validity of these interactions often results in discrepancies in how these are evaluated. The purpose of this study was to identify a set of criteria for assessing DDIs that should be used for the generation of clinical decision support (CDS) alerts in EHRs. Methods: We conducted a 20-year systematic literature review of MEDLINE and EMBASE to identify characteristics of high-priority DDIs. These criteria were validated by an expert panel consisting of medication knowledge base vendors, EHR vendors, in-house knowledge base developers from academic medical centers, and both federal and private agencies involved in the regulation of medication use. Results: Forty-four articles met the inclusion criteria for assessing characteristics of high-priority DDIs. The panel considered five criteria to be most important when assessing an interaction- Severity, Probability, Clinical Implications of the interaction, Patient characteristics, and the Evidence supporting the interaction. In addition, the panel identified barriers and considerations for being able to utilize these criteria in medication knowledge bases used by EHRs. Conclusions: A multi-dimensional approach is needed to understanding the importance of an interaction for inclusion in medication knowledge bases for the purpose of CDS alerting. The criteria identified in this study can serve as a first step towards a uniform approach in assessing which interactions are critical and warrant interruption of a provider’s workflow

Summary of second annual MCBK public meeting: Mobilizing Computable Biomedical Knowledge—A movement to accelerate translation of knowledge into action

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.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154970/1/lrh2-sup-0001-supinfo.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154970/2/lrh210222.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154970/3/lrh210222_am.pd