Objective To summarize the Canadian health information technology (HIT) policy experience and impart lessons learned to the US as it determines its policy in this area.
Design Qualitative analysis of interviews with identified key stakeholders followed by an electronic survey.
Measurements We conducted semi-structured interviews with 29 key Canadian HIT policy and opinion leaders and used a grounded theory approach to analyze the results. The informant sample was chosen to provide views from different stakeholder groups including national representatives and regional representatives from three Canadian provinces.
Results Canadian informants believed that much of the current US direction is positive, especially regarding incentives and meaningful use, but that there are key opportunities for the US to emphasize direct engagement with providers, define a clear business case for them, sponsor large scale evaluations to assess HIT impact in a broad array of settings, determine standards but also enable access to resources needed for mid-course corrections of standards when issues are identified, and, finally, leverage implementation of digital imaging systems.
Limitations Not all stakeholder groups were included, such as providers or patients. In addition, as in all qualitative research, a selection bias could be present due to the relatively small sample size.
Conclusions Based on Canadian experience with HIT policy, stakeholders identified as lessons for the US the need to increase direct engagement with providers and the importance of defining the business case for HIT, which can be achieved through large scale evaluations, and of recognizing and leveraging successes as they emerge.
Keywords: Health information technology; policy; electronic medical records; electronic health records; Canada; health care quality; patient safety; patient-centered care; patient satisfaction; patient safety; decision support; data exchange
Journal Article. 6587 words.
Subjects: Medical Statistics and Methodology ; Bioinformatics and Computational Biology ; Biomathematics and Statistics
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