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The Triangle Model for evaluating the effect of health information technology on healthcare quality and safety
Authors:Jessica S Ancker  Lisa M Kern  Erika Abramson  Rainu Kaushal
Affiliation:1.Department of Pediatrics, Weill Cornell Medical College, New York, New York, USA;2.Department of Public Health, Weill Cornell Medical College, New York, New York, USA;3.Health Information Technology Evaluation Collaborative (HITEC), New York, New York, USA;4.Department of Medicine, Weill Cornell Medical College, New York, New York, USA;5.New York-Presbyterian Hospital, New York, New York, USA
Abstract:
With the proliferation of relatively mature health information technology (IT) systems with large numbers of users, it becomes increasingly important to evaluate the effect of these systems on the quality and safety of healthcare. Previous research on the effectiveness of health IT has had mixed results, which may be in part attributable to the evaluation frameworks used. The authors propose a model for evaluation, the Triangle Model, developed for designing studies of quality and safety outcomes of health IT. This model identifies structure-level predictors, including characteristics of: (1) the technology itself; (2) the provider using the technology; (3) the organizational setting; and (4) the patient population. In addition, the model outlines process predictors, including (1) usage of the technology, (2) organizational support for and customization of the technology, and (3) organizational policies and procedures about quality and safety. The Triangle Model specifies the variables to be measured, but is flexible enough to accommodate both qualitative and quantitative approaches to capturing them. The authors illustrate this model, which integrates perspectives from both health services research and biomedical informatics, with examples from evaluations of electronic prescribing, but it is also applicable to a variety of types of health IT systems.
Keywords:Evaluation studies   research design   quality of healthcare   medical errors   medical informatics applications   cognitive study (including experiments emphasizing verbal protocol analysis and usability)   classical experimental and quasi-experimental study methods (lab and field)   uncertain reasoning and decision theory   delivering health information and knowledge to the public   human-computer interaction and human-centered computing   quality of care   measuring/improving patient safety and reducing medical errors
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