Multisite Evaluation of a Data Quality Tool for Patient-Level Clinical Data Sets.

INTRODUCTION: Data quality and fitness for analysis are crucial if outputs of analyses of electronic health record data or administrative claims data should be trusted by the public and the research community. METHODS: We describe a data quality analysis tool (called Achilles Heel) developed by the Observational Health Data Sciences and Informatics Collaborative (OHDSI) and compare outputs from this tool as it was applied to 24 large healthcare datasets across seven different organizations. RESULTS: We highlight 12 data quality rules that identified issues in at least 10 of the 24 datasets and provide a full set of 71 rules identified in at least one dataset. Achilles Heel is a freely available software that provides a useful starter set of data quality rules with the ability to add additional rules. We also present results of a structured email-based interview of all participating sites that collected qualitative comments about the value of Achilles Heel for data quality evaluation. DISCUSSION: Our analysis represents the first comparison of outputs from a data quality tool that implements a fixed (but extensible) set of data quality rules. Thanks to a common data model, we were able to compare quickly multiple datasets originating from several countries in America, Europe and Asia.

Research Data Explorer: Lessons Learned in Design and Development of Context-based Cohort Definition and Selection.

Research Data eXplorer (RedX) was designed to support self-service research data queries and cohort identification from clinical research databases. The primary innovation of RedX was the electronic health record view of patient data, to provide better contextual understanding for non-technical users in building complex data queries. The design of RedX around this need identified multiple functions that would use individual patient views to better understand population-based data, and vice-versa. During development, the more necessary and valuable components of RedX were refined, leading to a functional self-service query and cohort identification tool. However, with the improved capabilities and extensibility of other applications for data querying and navigation, our long-term implementation and dissemination plans have moved towards consolidation and alignment of RedX functions as enhancements in these other initiatives.

Style Guide: An Interdisciplinary Communication Tool to Support the Process of Generating Tailored Infographics From Electronic Health Data Using EnTICE3.

PURPOSE: In this case study we describe key features of the structured communication tool-a style guide-used to support interdisciplinary collaboration, and we propose the use of such a tool for research teams engaged in similar projects. We employ tailored infographics to present patient reported outcome data from a community health survey back, in a comprehensible and actionable manner, to the individuals who provided it. The style guide was developed to bridge the semantic gap between the domain and programming experts engaged in this effort. INNOVATION: The style guide supports the communication of complex design specifications in a highly structured format that is nevertheless flexible enough to accommodate project growth. Unlike the typical corporate style guide that has a more narrative format, our style guide is innovative in its use of consistent fields across multiple, standalone entries. CREDIBILITY: The process of populating the style guide prompted the designer toward greater design efficiency and led to consistent and specific instructions that met the framework architect's stated information needs. DISCUSSION AND CONCLUSION: The guiding values in the creation of the style guide were consistency, clarity, and flexibility. It serves as a durable reference to the desired look and functionality of the final infographic product without dictating an implementation strategy. The style guide format can be adapted to meet the communication needs of other interdisciplinary teams facing a semantic gap.

Towards symbiosis in knowledge representation and natural language processing for structuring clinical practice guidelines.

The successful adoption by clinicians of evidence-based clinical practice guidelines (CPGs) contained in clinical information systems requires efficient translation of free-text guidelines into computable formats. Natural language processing (NLP) has the potential to improve the efficiency of such translation. However, it is laborious to develop NLP to structure free-text CPGs using existing formal knowledge representations (KR). In response to this challenge, this vision paper discusses the value and feasibility of supporting symbiosis in text-based knowledge acquisition (KA) and KR. We compare two ontologies: (1) an ontology manually created by domain experts for CPG eligibility criteria and (2) an upper-level ontology derived from a semantic pattern-based approach for automatic KA from CPG eligibility criteria text. Then we discuss the strengths and limitations of interweaving KA and NLP for KR purposes and important considerations for achieving the symbiosis of KR and NLP for structuring CPGs to achieve evidence-based clinical practice.

Method for the development of data visualizations for community members with varying levels of health literacy.

Many Americans are challenged by the tasks of understanding and acting upon their own health data. Low levels of health literacy contribute to poor comprehension and undermine the confidence necessary for health self-management. Visualizations are useful for minimizing comprehension gaps when communicating complex quantitative information. The process of developing visualizations that accommodate the needs of individuals with varying levels of health literacy remains undefined. In this paper we provide detailed descriptions of a) an iterative methodological approach to the development of visualizations, b) the resulting types of visualizations and examples thereof, and c) the types of data the visualizations will be used to convey. We briefly describe subsequent phases in which the visualizations will be tested and refined. Web deployment of the final visualizations will support the ethical obligation to return the data to the research participants and community that contributed it.