How Safe are Outpatient Electronic Health Records? An Evaluation of Medication-Related Decision Support using the Ambulatory Electronic Health Record Evaluation Tool.

BACKGROUND: The purpose of the Ambulatory Electronic Health Record (EHR) Evaluation Tool is to provide outpatient clinics with an assessment that they can use to measure the ability of the EHR system to detect and prevent common prescriber errors. The tool consists of a medication safety test and a medication reconciliation module. OBJECTIVES: The goal of this study was to perform a broad evaluation of outpatient medication-related decision support using the Ambulatory EHR Evaluation Tool. METHODS: We performed a cross-sectional study with 10 outpatient clinics using the Ambulatory EHR Evaluation Tool. For the medication safety test, clinics were provided test patients and associated medication test orders to enter in their EHR, where they recorded any advice or information they received. Once finished, clinics received an overall percentage score of unsafe orders detected and individual order category scores. For the medication reconciliation module, clinics were asked to electronically reconcile two medication lists, where modifications were made by adding and removing medications and changing the dosage of select medications. RESULTS: For the medication safety test, the mean overall score was 57%, with the highest score being 70%, and the lowest score being 40%. Clinics performed well in the drug allergy (100%), drug dose daily (85%), and inappropriate medication combinations (74%) order categories. Order categories with the lowest performance were drug laboratory (10%) and drug monitoring (3%). Most clinics (90%) scored a 0% in at least one order category. For the medication reconciliation module, only one clinic (10%) could reconcile medication lists electronically; however, there was no clinical decision support available that checked for drug interactions. CONCLUSION: We evaluated a sample of ambulatory practices around their medication-related decision support and found that advanced capabilities within these systems have yet to be widely implemented. The tool was practical to use and identified substantial opportunities for improvement in outpatient medication safety.

Towards a content agnostic computable knowledge repository for data quality assessment.

BACKGROUND AND OBJECTIVE: In recent years, several data quality conceptual frameworks have been proposed across the Data Quality and Information Quality domains towards assessment of quality of data. These frameworks are diverse, varying from simple lists of concepts to complex ontological and taxonomical representations of data quality concepts. The goal of this study is to design, develop and implement a platform agnostic computable data quality knowledge repository for data quality assessments. METHODS: We identified computable data quality concepts by performing a comprehensive literature review of articles indexed in three major bibliographic data sources. From this corpus, we extracted data quality concepts, their definitions, applicable measures, their computability and identified conceptual relationships. We used these relationships to design and develop a data quality meta-model and implemented it in a quality knowledge repository. RESULTS: We identified three primitives for programmatically performing data quality assessments: data quality concept, its definition, its measure or rule for data quality assessment, and their associations. We modeled a computable data quality meta-data repository and extended this framework to adapt, store, retrieve and automate assessment of other existing data quality assessment models. CONCLUSION: We identified research gaps in data quality literature towards automating data quality assessments methods. In this process, we designed, developed and implemented a computable data quality knowledge repository for assessing quality and characterizing data in health data repositories. We leverage this knowledge repository in a service-oriented architecture to perform scalable and reproducible framework for data quality assessments in disparate biomedical data sources.

Biobanking informatics infrastructure to support clinical and translational research.

The University of Utah Health Sciences (UUHSC) and Intermountain Healthcare (IH) support high value clinical and translational research programs. The Utah Biohealth Initiative will facilitate next generation research by leveraging the combined resources of both institutions through an infrastructure which links biospecimens and electronic health records (EHR). During phase I of the Utah BioHealth Initiative (UBI) the participating institutions developed a legal, regulatory and information technology infrastructure that supports clinical and translational research, and advances our understanding of health and disease, improves healthcare value and health for current and future generations of Utahns. We used the Federated Utah Research and Translational Health electronic Repository (FURTHeR) 1 to combine EHR and biospecimen data from an actual study populated by both institutions to demonstrate the robustness of the infrastructure.

Assessing clinical researchers' information needs to create responsive portals and tools: my Research Assistant (MyRA) at the University of Utah: a case study.

QUESTION: How can health sciences librarians and biomedical informaticians offer relevant support to Clinical and Translational Science Award (CTSA) personnel? SETTING: The Spencer S. Eccles Health Sciences Library and the associate vice president for information technology for the health sciences office at the University of Utah conducted a needs assessment. METHODS: Faculty and staff from these two units, with the services of a consultant and other CTSA partners, employed a survey, focus groups, interviews, and committee discussions. An information portal was created to meet identified needs. RESULTS: A directive white paper was created. The process employed to plan a virtual and physical collaborative, collegial space for clinical researchers at the university and its three inter-institutional CTSA partners is described. CONCLUSION: The university's model can assist other librarians and informaticians with how to become part of a CTSA-focused infrastructure for clinical and translational research and serve researchers in general.