Randomized trial of automated, electronic monitoring to facilitate early detection of sepsis in the intensive care unit*.

OBJECTIVE: To determine whether automated identification with physician notification of the systemic inflammatory response syndrome in medical intensive care unit patients expedites early administration of new antibiotics or improvement of other patient outcomes in patients with sepsis. DESIGN: : A prospective randomized, controlled, single center study. SETTING: Medical intensive care unit of an academic, tertiary care medical center. PATIENTS: Four hundred forty-two consecutive patients admitted over a 4-month period who met modified systemic inflammatory response syndrome criteria in a medical intensive care unit. INTERVENTION: Patients were randomized to monitoring by an electronic "Listening Application" to detect modified (systemic inflammatory response syndrome) criteria vs. usual care. The listening application notified physicians in real time when modified systemic inflammatory response syndrome criteria were detected, but did not provide management recommendations. MEASUREMENTS AND MAIN RESULTS: The median time to new antibiotics was similar between the intervention and usual care groups when comparing among all patients (6.0 hr vs. 6.1 hr, p = .95), patients with sepsis (5.3 hr vs. 5.1 hr; p = .90), patients on antibiotics at enrollment (5.2 hr vs. 7.0 hr, p = .27), or patients not on antibiotics at enrollment (5.2 hr vs. 5.1 hr, p = .85). The amount of fluid administered following detection of modified systemic inflammatory response syndrome criteria was similar between groups whether comparing all patients or only patients who were hypotensive at enrollment. Other clinical outcomes including intensive care unit length of stay, hospital length of stay, and mortality were not shown to be different between patients in the intervention and control groups. CONCLUSIONS: Realtime alerts of modified systemic inflammatory response syndrome criteria to physicians in one tertiary care medical intensive care unit were feasible and safe but did not influence measured therapeutic interventions for sepsis or significantly alter clinical outcomes.

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.

Creating an immunization content database for knowledge management across clinical systems.

PURPOSE: A initiative at an academic medical center to create a single database of immunization-related content to inform the build and configuration of immunization-related knowledge assets across multiple clinical systems is described. METHODS: Semistructured expert interviews were conducted to ascertain the immunization information needs of the institution's clinical systems. Based on those needs, an immunization domain model constructed with data available from the Centers for Disease Control and Prevention (CDC) website was developed and used to analyze and compare current immunization-related content from CDC data sources with the content of the institution's clinical systems. RESULTS: Five identified clinical systems that used immunization-related content collectively required 22 unique information concepts, 11 of which were obtainable from CDC vaccine code sets. The proportion of vaccines designated by CDC as active products (i.e., currently available administrable vaccines) that were included in the 5 clinical systems ranged from 59% to 95%; in addition, some non-active-status vaccines were listed as active-status products in the various clinical systems. Upon further review, updates to immunization-related content in the 5 clinical systems were implemented. CONCLUSION: Creating a single database for immunization-related content based on CDC data facilitated an explicit and tractable knowledge management process and helped ensure that clinical systems had correct and current content. The immunization domain model created has the potential to assist in the automated detection of updates and relaying those updates to the applicable clinical systems.

Rebuilding the Standing Prescription Renewal Orders.

BACKGROUND: Managing prescription renewal requests is a labor-intensive challenge in ambulatory care. In 2009, Vanderbilt University Medical Center developed clinic-specific standing prescription renewal orders that allowed nurses, under specific conditions, to authorize renewal requests. Formulary and authorization changes made maintaining these documents very challenging. OBJECTIVE: This article aims to review, standardize, and restructure legacy standing prescription renewal orders into a modular, scalable, and easier to manage format for conversion and use in a new electronic health record (EHR). METHODS: We created an enterprise-wide renewal domain model using modular subgroups within the main institutional standing renewal order policy by extracting metadata, medication group names, medication ingredient names, and renewal criteria from approved legacy standing renewal orders. Instance-based matching compared medication groups in a pairwise manner to calculate a similarity score between medication groups. We grouped and standardized medication groups with high similarity by mapping them to medication classes from a medication terminology vendor and filtering them by intended route (e.g., oral, subcutaneous, inhalation). After standardizing the renewal criteria to a short list of reusable criteria, the Pharmacy and Therapeutics (P&T) committee reviewed and approved candidate medication groups and corresponding renewal criteria. RESULTS: Seventy-eight legacy standing prescription renewal orders covered 135 clinics (some applied to multiple clinics). Several standing orders were perfectly congruent, listing identical medications for renewal. We consolidated 870 distinct medication classes to 164 subgroups and assigned renewal criteria. We consolidated 379 distinct legacy renewal criteria to 21 criteria. After approval by the P&T committee, we built subgroups in a structured and consistent format in the new EHR, where they facilitated chart review and standing order adherence by nurses. Additionally, clinicians could search an autogenerated document of the standing order content from the EHR data warehouse. CONCLUSION: We describe a methodology for standardizing and scaling standing prescription renewal orders at an enterprise level while transitioning to a new EHR.

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.