17,18 Currently Medicare publishes information for the public to assess hospital performance, 19 and a recent court ruling in the United States will require the U.S. 13 Indications from the IOM and United States–based insurers are that clinicians will soon see more of their compensation similarly tied to such quality measures. In the United Kingdom, a recently deployed physician reimbursement system will tie 30% of physicians' salaries to their performance as defined by 130 quality indicators. 16 In some cases, hospital administrators are proactively gathering clinical data to demonstrate to insurers the appropriateness of the care delivered.
15 Major third-party health care reimbursement organizations are requiring outcomes information to ensure that their standards of quality and cost-effectiveness are met. Patients seeking to make informed decisions and accustomed to the instant information delivery of the World Wide Web expect provider-specific clinical outcomes data. Nearly all healthcare stakeholders have expressed an increased need for measures to assess the quality of delivered care. This article presents the COAT architecture, the details of its implementation, and an overview of two ongoing clinical outcomes assessment efforts supported by COAT. The use of a consistent set of clinical data structures enables integration of assembled pipelines with a user interface, allowing nontechnical end users to control, display, and audit the results of instantiated pipelines. Created as part of an ongoing collaboration between the University of California, Los Angeles (UCLA) and the Center for Surgery and Public Health at Brigham and Women's Hospital (BWH), COAT provides developers a collection of clinical data structures and reusable functionalities that can be rapidly assembled to create information pipelines for importing, extracting, standardizing, and analyzing clinical data. The Clinical Outcomes Assessment Toolkit (COAT) was designed to explore the challenges related to the use of automation to facilitate medical records–based clinical outcomes assessment and performance measurement research. Beyond abstracting information from the record, clinical records-based research poses logistical challenges, including the import of data from multiple sources into a single repository, the standardization of that data for statistical analysis, and the auditing of extracted results to guarantee data integrity. Both the cost and the technical complexity of these tasks can increase considerably in attempts to extract data from the records of multiple institutions. This can be a costly and resource-intensive endeavor. First, some degree of customization is often required to account for the sublanguage and documentation practices of specific medical subdomains. 14Īlthough several automated approaches have proven capable of extracting individual values from clinical records, formidable challenges remain in realizing reliable and quick access to current clinical outcomes and performance measurement data.
Pdf toolkit clinical manual#
12,13 As a result, so too will the need for automated systems capable of supplementing, if not replacing, traditional methods of manual chart review and abstraction. 5–7 Despite unanswered questions regarding the quality of medical record data and the consequent effect on efforts to measure the quality of care, 8–11 the use of the medical record as a source of quality measures is expected to increase.
2,4 Because of availability, the most common source of quality measures is data abstracted from existing medical records.
Pdf toolkit clinical series#
The release of the Institute of Medicine's (IOM) series of reports on disparities in the quality of health care and their effects 1,2 has led to a greater emphasis on the analysis of key quality measures in both national 3 and individual studies.
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