A brief Patient-Reported Outcomes Quality of Life (PROQOL) instrument to improve patient care.

Jeff Sloan and colleagues describe the development of the Patient-Reported Outcomes Quality of Life (PROQOL) instrument, which captures and stores patient-recorded outcomes in the medical record for patients with diabetes. Please see later in the article for the Editors' Summary.

The Southeastern Minnesota Beacon Project for Community-driven Health Information Technology: Origins, Achievements, and Legacy.

INTRODUCTION: The Southeastern (SE) Minnesota Beacon organized all the health care providers, county public health organizations, and school districts in the deployment and integration of health information exchange (HIE) and targeted health communication around childhood asthma and diabetes. The community cooperated to establish a clinical data repository for all residents in the 11-county region. Through this community of practice approach that involved traditional and nontraditional providers, the SE Minnesota Beacon was able to realize unique applications of this technology. This manuscript overviews the associated organization and infrastructure of this community collaboration. BACKGROUND: The Office of the National Coordinator for Health Information Technology (ONC), as part of the American Recovery and Reinvestment Act of 2009 (ARRA) stimulus, established 17 projects throughout the United States targeting the introduction and meaningful use of health information technology (HIT). These 17 communities were intended to serve as an example of what could be accomplished. The SE Minnesota Beacon is one of these communities. METHODS: The community ultimately opted for peer-to-peer HIE, using Nationwide Health Information Network (NwHIN) Connect software. The clinical data repository was established using the infrastructure developed by the Regenstrief Institute, which operated as a trusted third party. As an extension to HIE, the consortium of county public health departments created a patient data portal for use by school nurses and parents. Childhood asthma was addressed by creating, exchanging, and maintaining an "asthma action plan" for each affected child, shared throughout the community, including through the patient portal. Diabetes management introduced patient treatment decision tools and patient quality of life measures, facilitating care. Influenza vaccination was enhanced by large-scale community reporting in partnership with the state vaccination registry. The methodology and principles for arriving at these solutions included community engagement, sustainability, scalability, standards, and best practices that fit a variety of organizations-from large, robust providers to small organizations. FINDINGS: The SE Minnesota Beacon demonstrated that all providers for a geographically defined population can cooperate in the development and shared governance of a low-cost, sustainable HIE, and the operation of a community-managed clinical data repository. Furthermore, these infrastructures can be leveraged to collaboratively improve the care of patients, as demonstrated for childhood asthma and adult diabetes mellitus. CONCLUSION: The shared governance of HIT by a community can palpably change the scope and success of collaborations targeted to improve patient and community health care.

Normalization and standardization of electronic health records for high-throughput phenotyping: the SHARPn consortium.

RESEARCH OBJECTIVE: To develop scalable informatics infrastructure for normalization of both structured and unstructured electronic health record (EHR) data into a unified, concept-based model for high-throughput phenotype extraction. MATERIALS AND METHODS: Software tools and applications were developed to extract information from EHRs. Representative and convenience samples of both structured and unstructured data from two EHR systems-Mayo Clinic and Intermountain Healthcare-were used for development and validation. Extracted information was standardized and normalized to meaningful use (MU) conformant terminology and value set standards using Clinical Element Models (CEMs). These resources were used to demonstrate semi-automatic execution of MU clinical-quality measures modeled using the Quality Data Model (QDM) and an open-source rules engine. RESULTS: Using CEMs and open-source natural language processing and terminology services engines-namely, Apache clinical Text Analysis and Knowledge Extraction System (cTAKES) and Common Terminology Services (CTS2)-we developed a data-normalization platform that ensures data security, end-to-end connectivity, and reliable data flow within and across institutions. We demonstrated the applicability of this platform by executing a QDM-based MU quality measure that determines the percentage of patients between 18 and 75 years with diabetes whose most recent low-density lipoprotein cholesterol test result during the measurement year was <100 mg/dL on a randomly selected cohort of 273 Mayo Clinic patients. The platform identified 21 and 18 patients for the denominator and numerator of the quality measure, respectively. Validation results indicate that all identified patients meet the QDM-based criteria. CONCLUSIONS: End-to-end automated systems for extracting clinical information from diverse EHR systems require extensive use of standardized vocabularies and terminologies, as well as robust information models for storing, discovering, and processing that information. This study demonstrates the application of modular and open-source resources for enabling secondary use of EHR data through normalization into standards-based, comparable, and consistent format for high-throughput phenotyping to identify patient cohorts.

The SHARPn project on secondary use of Electronic Medical Record data: progress, plans, and possibilities.

SHARPn is a collaboration among 16 academic and industry partners committed to the production and distribution of high-quality software artifacts that support the secondary use of EMR data. Areas of emphasis are data normalization, natural language processing, high-throughput phenotyping, and data quality metrics. Our work avails the industrial scalability afforded by the Unstructured Information Management Architecture (UIMA) from IBM Watson Research labs, the same framework which underpins the Watson Jeopardy demonstration. This descriptive paper outlines our present work and achievements, and presages our trajectory for the remainder of the funding period. The project is one of the four Strategic Health IT Advanced Research Projects (SHARP) projects funded by the Office of the National Coordinator in 2010.