Promoting Learning Health System Cycles by Optimizing EHR Data Clinical Concept Encoding Processes.

Electronic health records (EHRs) and other real-world data (RWD) are critical to accelerating and scaling care improvement and transformation. To efficiently leverage it for secondary uses, EHR/RWD should be optimally managed and mapped to industry standard concepts (ISCs). Inherent challenges in concept encoding usually result in inefficient and costly workflows and resultant metadata representation structures outside the EHR. Using three related projects to map data to ISCs, we describe the development of standard, repeatable processes for precisely and unambiguously representing EHR data using appropriate ISCs within the EHR platform lifecycle and mappings specific to SNOMED-CT for Demographics, Specialty and Services. Mappings in these 3 areas resulted in ISC mappings of 779 data elements requiring 90 new concept requests to SNOMED-CT and 738 new ISCs mapped into the workflow within an accessible, enterprise-wide EHR resource with supporting processes.

Expanding Interprofessional EHR Data in i2b2.

Emerging issues of team-based care, precision medicine, and big data science underscore the need for health information technology (HIT) tools for integrating complex data in consistent ways to achieve the triple aims of improving patient outcomes, patient experience, and cost reductions. The purpose of this study was to demonstrate the feasibility of creating a hierarchical flowsheet ontology in i2b2 using data-derived information models and determine the underlying informatics and technical issues. This study is the first of its kind to use information models that aggregate team-based care across time, disciplines, and settings into 14 information models that were integrated into i2b2 in a hierarchical model. In the process of successfully creating a hierarchical ontology for flowsheet data in i2b2, we uncovered a variety of informatics and technical issues described in this paper.

Behavioral Health Information Technology: From Chaos To Clarity.

The use of health information technology (IT) in general health care has been shown to have significant potential to facilitate the delivery of safe, high-quality, and cost-effective care. However, its application to behavioral health care has been slow, limiting the extent to which consumers seeking care for mental health or substance use disorders can derive its benefits. The goal of this article is to provide an overview of the use of health IT in behavioral health and to describe some unique challenges experienced in that domain. We also highlight current obstacles to, and recommendations for, the use of health IT in improving the quality of behavioral health care. We conclude with recommendations for prioritizing the work that we believe will move the US health care system toward more effective, efficient, and patient-centric care in behavioral health.

Psychological assessment instruments: a coverage analysis using SNOMED CT, LOINC and QS terminology.

Psychometric instruments, inventories, surveys, and questionnaires are widely accepted tools in the field of behavioral health. They are used extensively in primary and clinical research, patient care, quality measurement, and payor oversight. To accurately capture and communicate instrument-related activities and results in electronic systems, existing healthcare standards must be capable of representing the full range of psychometric instruments used in research and clinical care. Several terminologies and controlled vocabularies contain representations of psychological instruments. While a handful of studies have assessed the representational adequacy of terminologies in this domain, no study to date has assessed content coverage. The current study was designed to fill this gap. Using a sample of 63 commonly used instruments, we found no concept in any of the three terminologies evaluated for more than half of all instruments. Of the three terminologies studied, SNOMED CT (Standard Nomenclature of Medicine - Clinical Terms) had the greatest breadth, but least granular coverage of all systems. While SNOMED CT contained concepts for over one third (36%) of the instrument classes in this sample, only 11% of the actual instruments were represented in SNOMED CT. LOINC (Logical Observation Identifiers, Names, and Codes), on the other hand, was able to represent instruments with the greatest level of granularity of the three terminologies. However, LOINC had the poorest coverage, covering fewer than 8% of the instruments in our sample. Given that instruments selected for this study were selected on the basis of their status as gold standard measures for conditions most likely to present in clinical settings, we believe these results overestimate the actual coverage provided by these terminologies. The results of this study demonstrate significant gaps in existing healthcare terminologies vis-à-vis psychological instruments and instrument-related procedures. Based on these findings, we recommend that systematic efforts be made to enhance standard healthcare terminologies to provide better coverage of this domain.

A framework and standardized methodology for developing minimum clinical datasets.

The concept of the minimum dataset (MDS) is taking on an increasingly important role in healthcare. In the current environment of health information exchange and universal implementation of electronic health records, work related to the development of one specific type of MDS, the minimum clinical dataset (MCDS), is beginning to permeate the literature. While there is currently no unified definition of either an MDS or an MCDS, an MDS is generally agreed to be a coherent set of explicitly defined data elements. Despite the growing body of literature on MCDSs, very little empirical evidence exists in the literature related to best methods for developing them. The primary objective of the current study is to fill this gap. By presenting a streamlined approach to the development of MCDSs the current study attempts to provide individuals and organizations with a coherent methodology and framework for developing a high quality MCDS.