Prognostic clinical decision support for pneumonia in the emergency department: A randomized trial.

BACKGROUND: Hospitalization rates for childhood pneumonia vary widely. Risk-based clinical decision support (CDS) interventions may reduce unwarranted variation. METHODS: We conducted a pragmatic randomized trial in two US pediatric emergency departments (EDs) comparing electronic health record (EHR)-integrated prognostic CDS versus usual care for promoting appropriate ED disposition in children (<18 years) with pneumonia. Encounters were randomized 1:1 to usual care versus custom CDS featuring a validated pneumonia severity score predicting risk for severe in-hospital outcomes. Clinicians retained full decision-making authority. The primary outcome was inappropriate ED disposition, defined as early transition to lower- or higher-level care. Safety and implementation outcomes were also evaluated. RESULTS: The study enrolled 536 encounters (269 usual care and 267 CDS). Baseline characteristics were similar across arms. Inappropriate disposition occurred in 3% of usual care encounters and 2% of CDS encounters (adjusted odds ratio: 0.99, 95% confidence interval: [0.32, 2.95]) Length of stay was also similar and adverse safety outcomes were uncommon in both arms. The tool's custom user interface and content were viewed as strengths by surveyed clinicians (>70% satisfied). Implementation barriers include intrinsic (e.g., reaching the right person at the right time) and extrinsic factors (i.e., global pandemic). CONCLUSIONS: EHR-based prognostic CDS did not improve ED disposition decisions for children with pneumonia. Although the intervention's content was favorably received, low subject accrual and workflow integration problems likely limited effectiveness. Clinical Trials Registration: NCT06033079.

Clinical decision support automates care gap detection among primary care patients with nonalcoholic fatty liver disease.

BACKGROUND: Although guidelines recommend primary care-driven management of NAFLD, workflow constraints hinder feasibility. Leveraging electronic health records to risk stratify patients proposes a scalable, workflow-integrated strategy. MATERIALS AND METHODS: We prospectively evaluated an electronic health record-embedded clinical decision support system's ability to risk stratify patients with NAFLD and detect gaps in care. Patients missing annual laboratory testing to calculate Fibrosis-4 Score (FIB-4) or those missing necessary linkage to further care were considered to have a gap in care. Linkage to care was defined as either referral for elastography-based testing or for consultation in hepatology clinic depending on clinical and biochemical characteristics. RESULTS: Patients with NAFLD often lacked annual screening labs within primary care settings (1129/2154; 52%). Linkage to care was low in all categories, with <3% of patients with abnormal FIB-4 undergoing further evaluation. DISCUSSION: Significant care gaps exist within primary care for screening and risk stratification of patients with NAFLD and can be efficiently addressed using electronic health record functionality.

Antibiotic clinical decision support for pneumonia in the ED: A randomized trial.

BACKGROUND: Electronic health record-based clinical decision support (CDS) is a promising antibiotic stewardship strategy. Few studies have evaluated the effectiveness of antibiotic CDS in the pediatric emergency department (ED). OBJECTIVE: To compare the effectiveness of antibiotic CDS vs. usual care for promoting guideline-concordant antibiotic prescribing for pneumonia in the pediatric ED. DESIGN: Pragmatic randomized clinical trial. SETTING AND PARTICIPANTS: Encounters for children (6 months-18 years) with pneumonia presenting to two tertiary care children s hospital EDs in the United States. INTERVENTION: CDS or usual care was randomly assigned during 4-week periods within each site. The CDS intervention provided antibiotic recommendations tailored to each encounter and in accordance with national guidelines. MAIN OUTCOME AND MEASURES: The primary outcome was exclusive guideline-concordant antibiotic prescribing within the first 24 h of care. Safety outcomes included time to first antibiotic order, encounter length of stay, delayed intensive care, and 3- and 7-day revisits. RESULTS: 1027 encounters were included, encompassing 478 randomized to usual care and 549 to CDS. Exclusive guideline-concordant prescribing did not differ at 24 h (CDS, 51.7% vs. usual care, 53.3%; odds ratio [OR] 0.94 [95% confidence interval [CI]: 0.73, 1.20]). In pre-specified stratified analyses, CDS was associated with guideline-concordant prescribing among encounters discharged from the ED (74.9% vs. 66.0%; OR 1.53 [95% CI: 1.01, 2.33]), but not among hospitalized encounters. Mean time to first antibiotic was shorter in the CDS group (3.0 vs 3.4 h; p = .024). There were no differences in safety outcomes. CONCLUSIONS: Effectiveness of ED-based antibiotic CDS was greatest among those discharged from the ED. Longitudinal interventions designed to target both ED and inpatient clinicians and to address common implementation challenges may enhance the effectiveness of CDS as a stewardship tool.

Impact of Updating Pharmacogenetic Results: Lessons Learned from the PREDICT Program.

Pharmacogenomic (PGx) evidence for selective serotonin reuptake inhibitors (SSRIs) continues to evolve. For sites offering testing, maintaining up-to-date interpretations and implementing new clinical decision support (CDS) driven by existing results creates practical and technical challenges. Vanderbilt University Medical Center initiated panel testing in 2010, added CYP2D6 testing in 2017, and released CDS for SSRIs in 2020. We systematically reinterpreted historic CYP2C19 and CYP2D6 genotypes to update phenotypes to current nomenclature and to launch provider CDS and patient-oriented content for SSRIs. Chart review was conducted to identify and recontact providers caring for patients with current SSRI therapy and new actionable recommendations. A total of 15,619 patients' PGx results were reprocessed. Of the non-deceased patients reprocessed, 21% (n = 3278) resulted in CYP2C19*1/*17 reinterpretations. Among 289 patients with an actionable recommendation and SSRI medication prescription, 31.8% (n = 92) did not necessitate contact of a clinician, while 43.2% (n = 125) resulted in clinician contacted, and for 25% (n = 72) no appropriate clinician was able to be identified. Maintenance of up-to-date interpretations and recommendations for PGx results over the lifetime of a patient requires continuous effort. Reprocessing is a key strategy for maintenance and expansion of PGx content to be periodically considered and implemented.

A Tutorial for Pharmacogenomics Implementation Through End-to-End Clinical Decision Support Based on Ten Years of Experience from PREDICT.

Vanderbilt University Medical Center implemented pharmacogenomics (PGx) testing with the Pharmacogenomic Resource for Enhanced Decisions in Care and Treatment (PREDICT) initiative in 2010. This tutorial reviews the laboratory considerations, technical infrastructure, and programmatic support required to deliver panel-based PGx testing across a large health system with examples and experiences from the first decade of the PREDICT initiative. From the time of inception, automated clinical decision support (CDS) has been a critical capability for delivering PGx results to the point-of-care. Key features of the CDS include human-readable interpretations and clinical guidance that is anticipatory, actionable, and adaptable to changes in the scientific literature. Implementing CDS requires that structured results from the laboratory be encoded in standards-based messages that are securely ingested by electronic health records. Translating results to guidance also requires an informatics infrastructure with multiple components: (1) to manage the interpretation of raw genomic data to "star allele" results to expected phenotype, (2) to define the rules that associate a phenotype with recommended changes to clinical care, and (3) to manage and update the knowledge base. Knowledge base management is key to processing new results with the latest guidelines, and to ensure that historical genomic results can be reinterpreted with revised CDS. We recommend that these components be deployed with institutional authorization, programmatic support, and clinician education to govern the CDS content and policies around delivery.

Algorithmic Detection of Boolean Logic Errors in Clinical Decision Support Statements.

OBJECTIVE: Clinical decision support (CDS) can contribute to quality and safety. Prior work has shown that errors in CDS systems are common and can lead to unintended consequences. Many CDS systems use Boolean logic, which can be difficult for CDS analysts to specify accurately. We set out to determine the prevalence of certain types of Boolean logic errors in CDS statements. METHODS: Nine health care organizations extracted Boolean logic statements from their Epic electronic health record (EHR). We developed an open-source software tool, which implemented the Espresso logic minimization algorithm, to identify three classes of logic errors. RESULTS: Participating organizations submitted 260,698 logic statements, of which 44,890 were minimized by Espresso. We found errors in 209 of them. Every participating organization had at least two errors, and all organizations reported that they would act on the feedback. DISCUSSION: An automated algorithm can readily detect specific categories of Boolean CDS logic errors. These errors represent a minority of CDS errors, but very likely require correction to avoid patient safety issues. This process found only a few errors at each site, but the problem appears to be widespread, affecting all participating organizations. CONCLUSION: Both CDS implementers and EHR vendors should consider implementing similar algorithms as part of the CDS authoring process to reduce the number of errors in their CDS interventions.

Natural Language Processing and Machine Learning to Enable Clinical Decision Support for Treatment of Pediatric Pneumonia.

Pneumonia is the most frequent cause of infectious disease-related deaths in children worldwide. Clinical decision support (CDS) applications can guide appropriate treatment, but the system must first recognize the appropriate diagnosis. To enable CDS for pediatric pneumonia, we developed an algorithm integrating natural language processing (NLP) and random forest classifiers to identify potential pediatric pneumonia from radiology reports. We deployed the algorithm in the EHR of a large children's hospital using real-time NLP. We describe the development and deployment of the algorithm, and evaluate our approach using 9-months of data gathered while the system was in use. Our model, trained on individual radiology reports, had an AUC of 0.954. The intervention, evaluated on patient encounters that could include multiple radiology reports, achieved a sensitivity, specificity, and positive predictive value of0.899, 0.949, and 0.781, respectively.

Towards a Maturity Model for Clinical Decision Support Operations.

Clinical decision support (CDS) systems delivered through the electronic health record are an important element of quality and safety initiatives within a health care system. However, managing a large CDS knowledge base can be an overwhelming task for informatics teams. Additionally, it can be difficult for these informatics teams to communicate their goals with external operational stakeholders and define concrete steps for improvement. We aimed to develop a maturity model that describes a roadmap toward organizational functions and processes that help health care systems use CDS more effectively to drive better outcomes. We developed a maturity model for CDS operations through discussions with health care leaders at 80 organizations, iterative model development by four clinical informaticists, and subsequent review with 19 health care organizations. We ceased iterations when feedback from three organizations did not result in any changes to the model. The proposed CDS maturity model includes three main "pillars": "Content Creation," "Analytics and Reporting," and "Governance and Management." Each pillar contains five levels-advancing along each pillar provides CDS teams a deeper understanding of the processes CDS systems are intended to improve. A "roof" represents the CDS functions that become attainable after advancing along each of the pillars. Organizations are not required to advance in order and can develop in one pillar separately from another. However, we hypothesize that optimal deployment of preceding levels and advancing in tandem along the pillars increase the value of organizational investment in higher levels of CDS maturity. In addition to describing the maturity model and its development, we also provide three case studies of health care organizations using the model for self-assessment and determine next steps in CDS development.

Leveraging Knowledge Representation to Maintain Immunization Clinical Decision Support.

Immunizations are one of the most cost-effective interventions for preventing morbidity and mortality. As vaccines, related clinical knowledge and requirements change, clinical applications must be updated in a timely manner to avoid practicing outdated medicine. We use the Centers for Disease Control and Prevention (CDC) as a source for immunization knowledge for our Clinical Information Systems (CIS). After identifying knowledge management related gaps in the CDC's content and email notification service, we developed and adapted a knowledge management tool chain - called COMET - for facilitating automatic processing of the available immunization content to implement mature knowledge lifecycle management practices locally. The implemented features include error and change tracking, content discovery and analytics, and tracking of dependencies to dependent downstream CISs. We demonstrate the creation of a tool that enables content curators to visualize, track, and implement immunization changes.