Factors Determining the Success and Failure of eHealth Interventions: Systematic Review of the Literature

Abstract

Background

eHealth has an enormous potential to improve healthcare cost, effectiveness, and quality of care. However, there seems to be a gap between the foreseen benefits of research and clinical reality.

Objective

Our objective was to systematically review the factors influencing the outcome of eHealth interventions in terms of success and failure.

Methods

We searched the PubMed database for original peer-reviewed studies on implemented eHealth tools that reported on the factors for the success or failure, or both, of the intervention. We conducted the systematic review by following the patient, intervention, comparison, and outcome framework, with 2 of the authors independently reviewing the abstract and full text of the articles. We collected data using standardized forms that reflected the categorization model used in the qualitative analysis of the outcomes reported in the included articles.

Results

Among the 903 identified articles, a total of 221 studies complied with the inclusion criteria. The studies were heterogeneous by country, type of eHealth intervention, method of implementation, and reporting perspectives. The article frequency analysis did not show a significant discrepancy between the number of reports on failure (392/844, 46.5%) and on success (452/844, 53.6%). The qualitative analysis identified 27 categories that represented the factors for success or failure of eHealth interventions. A quantitative analysis of the results revealed the category quality of healthcare (n=55) as the most mentioned as contributing to the success of eHealth interventions, and the category costs (n=42) as the most mentioned as contributing to failure. For the category with the highest unique article frequency, workflow (n=51), we conducted a full-text review. The analysis of the 23 articles that met the inclusion criteria identified 6 barriers related to workflow: workload (n=12), role definition (n=7), undermining of face-to-face communication (n=6), workflow disruption (n=6), alignment with clinical processes (n=2), and staff turnover (n=1).

Conclusions

The reviewed literature suggested that, to increase the likelihood of success of eHealth interventions, future research must ensure a positive impact in the quality of care, with particular attention given to improved diagnosis, clinical management, and patient-centered care. There is a critical need to perform in-depth studies of the workflow(s) that the intervention will support and to perceive the clinical processes involved.