<p>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.</p>

<p><strong>BACKGROUND AND OBJECTIVE: </strong>Pediatric in-hospital cardiac arrest most commonly occurs in the pediatric intensive care unit (PICU) and is frequently preceded by early warning signs of clinical deterioration. In this study, we describe the implementation and evaluation of criteria to identify high-risk patients from a paper-based checklist into a clinical decision support (CDS) tool in the electronic health record (EHR).</p>

<p><strong>MATERIALS AND METHODS: </strong>The validated paper-based tool was first adapted by PICU clinicians and clinical informaticians and then integrated into clinical workflow following best practices for CDS design. A vendor-based rule engine was utilized. Littenberg's assessment framework helped guide the overall evaluation. Preliminary testing took place in EHR development environments with more rigorous evaluation, testing, and feedback completed in the live production environment. To verify data quality of the CDS rule engine, a retrospective Structured Query Language (SQL) data query was also created. As a process metric, preparedness was measured in pre- and postimplementation surveys.</p>

<p><strong>RESULTS: </strong>The system was deployed, evaluating approximately 340 unique patients monthly across 4 clinical teams. The verification against retrospective SQL of 15-minute intervals over a 30-day period revealed no missing triggered intervals and demonstrated 99.3% concordance of positive triggers. Preparedness showed improvements across multiple domains to our a priori goal of 90%.</p>

<p><strong>CONCLUSION: </strong>We describe the successful adaptation and implementation of a real-time CDS tool to identify PICU patients at risk of deterioration. Prospective multicenter evaluation of the tool's effectiveness on clinical outcomes is necessary before broader implementation can be recommended.</p>

<p><strong>OBJECTIVES: </strong>To evaluate the translation of a paper high-risk checklist for PICU patients at risk of clinical deterioration to an automated clinical decision support tool.</p>

<p><strong>DESIGN: </strong>Retrospective, observational cohort study of an automated clinical decision support tool, the PICU Warning Tool, adapted from a paper checklist to predict clinical deterioration events in PICU patients within 24 hours.</p>

<p><strong>SETTING: </strong>Two quaternary care medical-surgical PICUs-The Children's Hospital of Philadelphia and Cincinnati Children's Hospital Medical Center.</p>

<p><strong>PATIENTS: </strong>The study included all patients admitted from July 1, 2014, to June 30, 2015, the year prior to the initiation of any focused situational awareness work at either institution.</p>

<p><strong>INTERVENTIONS: </strong>We replicated the predictions of the real-time PICU Warning Tool by retrospectively querying the institutional data warehouse to identify all patients that would have flagged as high-risk by the PICU Warning Tool for their index deterioration.</p>

<p><strong>MEASUREMENTS AND MAIN RESULTS: </strong>The primary exposure of interest was determination of high-risk status during PICU admission via the PICU Warning Tool. The primary outcome of interest was clinical deterioration event within 24 hours of a positive screen. The date and time of the deterioration event was used as the index time point. We evaluated the sensitivity, specificity, positive predictive value, and negative predictive value of the performance of the PICU Warning Tool. There were 6,233 patients evaluated with 233 clinical deterioration events experienced by 154 individual patients. The positive predictive value of the PICU Warning Tool was 7.1% with a number needed to screen of 14 patients for each index clinical deterioration event. The most predictive of the individual criteria were elevated lactic acidosis, high mean airway pressure, and profound acidosis.</p>

<p><strong>CONCLUSIONS: </strong>Performance of a clinical decision support translation of a paper-based tool showed inferior test characteristics. Improved feasibility of identification of high-risk patients using automated tools must be balanced with performance.</p>