The perils of meta-regression to identify clinical decision support system success factors
| Autor: | Mingyuan Zhang, Casey Rommel, Christopher L. Fillmore, Kensaku Kawamoto, Brandon M. Welch |
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| Rok vydání: | 2015 |
| Předmět: |
Computer science
Decision Making Psychological intervention Context (language use) Meta-regression Health Informatics Outcome (game theory) Clinical decision support system Health informatics Article Automation User-Computer Interface Meta-Analysis as Topic Randomized Controlled Trials as Topic business.industry Management science Regression analysis Clinical decision support Decision Support Systems Clinical Computer Science Applications Risk analysis (engineering) Regression Analysis business Algorithms Medical Informatics Software |
| Zdroj: | Journal of Biomedical Informatics. 56:65-68 |
| ISSN: | 1532-0464 |
| DOI: | 10.1016/j.jbi.2015.05.007 |
| Popis: | Display Omitted Meta-regression can be used to identify important features of CDS interventions.However, this approach can lead to misleading conclusions.This paper provides a case study on this issue in the area of CDS success factors.Recommendations for addressing this issue are provided. Clinical decision support interventions are typically heterogeneous in nature, making it difficult to identify why some interventions succeed while others do not. One approach to identify factors important to the success of health information systems is the use of meta-regression techniques, in which potential explanatory factors are correlated with the outcome of interest. This approach, however, can result in misleading conclusions due to several issues. In this manuscript, we present a cautionary case study in the context of clinical decision support systems to illustrate the limitations of this type of analysis. We then discuss implications and recommendations for future work aimed at identifying success factors of medical informatics interventions. In particular, we identify the need for head-to-head trials in which the importance of system features is directly evaluated in a prospective manner. |
| Databáze: | OpenAIRE |
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