Establishing Trust in Pharmaceutical Data with an Independent Verification and Validation Methodology.

Our aim is to demonstrate a general-purpose data and knowledge validation approach that enables reproducible metrics for data and knowledge quality and safety. We researched widely accepted statistical process control methods from high-quality, high-safety industries and applied them to pharmacy prescription data being migrated between EHRs. Natural language medication instructions from prescriptions were independently categorized by two terminologists as a first step toward encoding those medication instructions using standardized terminology. Overall, the weighted average of medication instructions that were matched by reviewers was 43%, with strong agreement between reviewers for short instructions (K=0.82) and long instructions (K=0.85), and moderate agreement for medium instructions (K=0.61). Category definitions will be refined in future work to mitigate discrepancies. We recommend incorporating appropriate statistical tests, such as evaluating inter-rater and intra-rater reliability and bivariate comparison of reviewer agreement over an adequate statistical sample, when developing benchmarks for health data and knowledge quality and safety.

Standardization of patient registries for improved data collection and outcome measurement.

Patient registries provide key data for clinical trials, patient safety and disease progression research. Current standards are insufficient to guide consistent authoring and reuse of registry questions. An outcome measure and its accompanying measurable indicators provide the means for the collection of data over a continuum of care. These data can be translated into comparison research, supporting the development of evidence-based knowledge. Lack of standardized approach to question/answer authoring and identification of outcome measure indicators have been an obstacle to interoperability of registry data with electronic medical and personal health records.

Enhancement of interoperability of disaster-related data collection using Disaster Nursing Minimum Data Set.

Data collected by nurses have the potential to aid in the disaster response if the necessary information can be extracted. The Disaster Nursing Minimum Data Set is proposed to strengthen the interoperability of information and data sharing between agencies to improve relief services to individuals and communities affected by a disaster. This study will examine disaster-related data elements need to be collected by all relief agencies to facilitate interoperability and secondary use of data for research and disaster management.