OBJECTIVES: Patients often receive health care from multiple organizations. Privacy Preserving Record Linkage (PPRL) is a technology for linking patient records without releasing personally identifiable information. We compared a commercial PPRL tool that uses the XGBoost machine learning algorithm with Care Everywhere (CE), a widely used rule-based patient linkage module. MATERIALS AND METHODS: We matched the complete patient populations from Cedars-Sinai Health System and University of California, Los Angeles (UCLA) Health using the XGBoost PPRL tool at each of 3 score thresholds (98, 95, and 90), reflecting stricter vs more permissive matching. We compared PPRL matches with CE matches for the cohort of 849 157 patients who had been queried by CE from UCLA to Cedars-Sinai over 18 months. To classify proposed matches as false, uncertain or correct matches, 2 reviewers manually reviewed a random sample of 1200 patients representing each category of matches. RESULTS: Care Everywhere matched 18% of the cohort, whereas PPRL matched 9%, 27%, and 29% of the cohort using the 98, 95, and 90 thresholds, respectively. Projecting the false match rates from the manual review to the original populations, precision for CE was 99.6% (95% CI, 97.8%-100%). Precision for PPRL was 100% (95% CI, 99.2%-100%), 99.4% (95% CI, 97.4%-99.9%), and 98.7% (95% CI, 96.5%-99.4%) at the 3 thresholds, respectively. Using CE and PPRL matches together as a proxy gold standard, recall for CE was 61.5% (95% CI, 60.3%-61.9%) and for PPRL was 30.6% (95% CI, 30.3%-30.7%), 92.2% (95% CI, 90.2%-92.7%), and 96.8% (95% CI, 94.6%-97.5%) at each threshold, respectively. CONCLUSIONS: The precision and recall of PPRL matching differed substantially across the available match thresholds. Compared with the rule-based system, PPRL at the 95 threshold had 50% higher recall with similar precision. Privacy Preserving Record Linkage holds promise for improving research, but users must choose the precision vs recall needed for their application.

OBJECTIVE: To facilitate the development of standards-based clinical decision support (CDS) systems, we review the current set of CDS standards that are based on Health Level Seven International Fast Healthcare Interoperability Resources (FHIR). Widespread adoption of these standards may help reduce healthcare variability, improve healthcare quality, and improve patient safety. TARGET AUDIENCE: This tutorial is designed for the broad informatics community, some of whom may be unfamiliar with the current, FHIR-based CDS standards. SCOPE: This tutorial covers the following standards: Arden Syntax (using FHIR as the data model), Clinical Quality Language, FHIR Clinical Reasoning, SMART on FHIR, and CDS Hooks. Detailed descriptions and selected examples are provided.

Clinical coding and classification processes transform natural language descriptions in clinical text into data that can subsequently be used for clinical care, research, and other purposes. This systematic literature review examined studies that evaluated all types of automated coding and classification systems to determine the performance of such systems. Studies indexed in Medline or other relevant databases prior to March 2009 were considered. The 113 studies included in this review show that automated tools exist for a variety of coding and classification purposes, focus on various healthcare specialties, and handle a wide variety of clinical document types. Automated coding and classification systems themselves are not generalizable, nor are the results of the studies evaluating them. Published research shows these systems hold promise, but these data must be considered in context, with performance relative to the complexity of the task and the desired outcome.

Clinical decision support (CDS) in electronic prescribing (eRx) systems can improve the safety, quality, efficiency, and cost-effectiveness of care. However, at present, these potential benefits have not been fully realized. In this consensus white paper, we set forth recommendations and action plans in three critical domains: (1) advances in system capabilities, including basic and advanced sets of CDS interventions and knowledge, supporting database elements, operational features to improve usability and measure performance, and management and governance structures; (2) uniform standards, vocabularies, and centralized knowledge structures and services that could reduce rework by vendors and care providers, improve dissemination of well-constructed CDS interventions, promote generally applicable research in CDS methods, and accelerate the movement of new medical knowledge from research to practice; and (3) appropriate financial and legal incentives to promote adoption.

CONTEXT: Detection of outbreaks of infection in the hospital typically requires daily manual review of microbiology laboratory test results. This process is time-consuming, tedious, prone to error and may miss trends in infection. A standard formalism for procedural knowledge representation, the Arden Syntax, provides a vehicle for implementing algorithms for detecting such infections. OBJECTIVE: To design and implement a computer-based system for detection of concerning patterns of infection or antibiotic resistance. SETTING: Computer-based event monitor and central patient data repository at the Columbia-Presbyterian Medical Center (CPMC). RESULTS: We designed a two-phase system, including initial filtering of individual patient laboratory results by Arden Syntax Medical Logic Modules (MLMs) and subsequent aggregation and analysis across patients and locations using a statistical monitor. Preliminary data for the filtration phase demonstrate a 94.8% reduction in the volume of messages that must be considered in surveillance. CONCLUSIONS: Filtering raw laboratory results using a standard formalism eases the process of aggregating data across patients and sites as well as detecting trends in infection. There is a need for augmenting such formalisms in order to enable population-based decision support.