Methodology

Using IT to monitor the deployment, use, evaluation and support of HIS.

Consolidate Existing Evidence Base of HIS Models, Methods and Metrics

Thus far, the strength of evidence still varies in the adoption/use and impact of HIS. Also lacking is an unifying framework/theory to provide a coherent view of HIS beyond that of a technology artifact to account for its effects on professional practice, and on a broader level, the often unintended, socio-organizational consequences. Equally important is the need for research methods and metrics that are both robust and pragmatic for the entire HIS implementation lifecycle including early and late adopters.

In 2006, we published a meta-synthesis paper on 28 systematic reviews that examined 1000+ HIS studies in the last 15 years.¹ While there is evidence that HIS with reminders/alerts in preventive care and medication orders have led to significant improvements in clinician performance, it is less clear if HIS has improved patient outcomes.^2,3

As for theories/frameworks, there are several well known models in the business IT literature, including the IS Success Model by DeLone,⁴ the IT Interaction Model by Silver,⁵ and the Unified Theory of Acceptance and Use of Technology by Venkatesh.⁶ However, these models are not health specific, thus to date they have received little attention from the eHealth community.

In 2006, we worked with Canada Health Infoway to adapt the IS Success Model for eHealth by refining the quality, use and net benefit dimensions of this model that included an initial set of 30+ performance/outcome metrics, drawn partly from our earlier meta-synthesis paper.^7,8 While this model excluded contextual factors and the implementation process, it was well received by the eHealth community for its simple yet coherent view, and has been adopted as a pan-Canadian HIS Benefits Evaluation (BE) Framework.

To advance HIS knowledge, this eHealth Observatory will consolidate the existing evidence base of HIS knowledge with relevant conceptual HIS models, research methods and published measurement metrics.

References

1. Lau F. Increasing the rigor of HIS studies with systematic review? Proceedings in the 11th International Symposium on Health Information Management Research, Halifax NS, July 2006.
2. Chaudhry B, Wang J, Wu S, Maglione M, Mojica W, Roth E, Morton SC, Shekelle PG. Systematic review: impact of health information technology on quality, efficiency, and costs of medical care. Annals of Internal Medicine 2006;144:E-12-22.
3. Garg AX, Adhikari JKJ, Rosas-Arellano MP, Devereaux PH, Beyene J, Sam J, Haynes RB. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes – A systematic review. Journal of American Medical Association 2005;293(10):1223-38.
4. DeLone WH and McLean ER. The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems 2003;19(4):9-30.
5. Silver MS, Markus ML and Beath CM. Information Technology Interaction Model: A Foundation for the MBA Core Course. MIS Quarterly 1995; Sep 361-387.
6. Venkatesh V, Morris MG, Davis GB, Davis FD. User acceptance of information technology: toward a unified view. MIS Quarterly 2004;27(3):425-78.
7. Lau F, Hagens S, Muttitt S. A proposed benefits evaluation framework for health information systems in Canada. Healthcare Quarterly 2007; 10(1):112-6,118.

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