Commentary: Electronic Health Records for Comparative Effectiveness Research
Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD 20852, USA.Medical care (Impact Factor: 3.23). 07/2012; 50 Suppl(7):S19-20. DOI: 10.1097/MLR.0b013e3182588ee4
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ABSTRACT: Quasi-experiments are likely to be the workhorse study design used to generate evidence about the comparative effectiveness of alternative treatments, because of their feasibility, timeliness, affordability and external validity compared with randomized trials. In this review, we outline potential sources of discordance in results between quasi-experiments and experiments, review study design choices that can improve the internal validity of quasi-experiments, and outline innovative data linkage strategies that may be particularly useful in quasi-experimental comparative effectiveness research. There is an urgent need to resolve the debate about the evidentiary value of quasi-experiments since equal consideration of rigorous quasi-experiments will broaden the base of evidence that can be brought to bear in clinical decision-making and governmental policy-making.
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ABSTRACT: The field of clinical research informatics includes creation of clinical data repositories (CDRs) used to conduct quality improvement (QI) activities and comparative effectiveness research (CER). Ideally, CDR data are accurately and directly abstracted from disparate electronic health records (EHRs), across diverse health-systems. Investigators from Washington State's Surgical Care Outcomes and Assessment Program (SCOAP) Comparative Effectiveness Research Translation Network (CERTAIN) are creating such a CDR. This manuscript describes the automation and validation methods used to create this digital infrastructure. SCOAP is a QI benchmarking initiative. Data are manually abstracted from EHRs and entered into a data management system. CERTAIN investigators are now deploying Caradigm's Amalga™ tool to facilitate automated abstraction of data from multiple, disparate EHRs. Concordance is calculated to compare data automatically to manually abstracted. Performance measures are calculated between Amalga and each parent EHR. Validation takes place in repeated loops, with improvements made over time. When automated abstraction reaches the current benchmark for abstraction accuracy - 95% - itwill 'go-live' at each site. A technical analysis was completed at 14 sites. Five sites are contributing; the remaining sites prioritized meeting Meaningful Use criteria. Participating sites are contributing 15-18 unique data feeds, totaling 13 surgical registry use cases. Common feeds are registration, laboratory, transcription/dictation, radiology, and medications. Approximately 50% of 1,320 designated data elements are being automatically abstracted-25% from structured data; 25% from text mining. In semi-automating data abstraction and conducting a rigorous validation, CERTAIN investigators will semi-automate data collection to conduct QI and CER, while advancing the Learning Healthcare System.
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