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

Abstract

BackgroundeHealth 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.ObjectiveOur objective was to systematically review the factors influencing the outcome of eHealth interventions in terms of success and failure.MethodsWe 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.ResultsAmong 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).ConclusionsThe 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.

Factors for Supporting Primary Care Physician Engagement With Patient Apps for Type 2 Diabetes Self-Management That Link to Primary Care: Interview Study (Preprint)

“…This attitude is consistent with reviews of online health technology implementation [18,[20][21][22]. Lastly, PCPs were also reluctant to endorse an app that would reduce the frequency of face-to-face care.…”

“…For example, increased workload is a common factor for poor uptake of new health technologies [14,15,[18][19][20][21][22]. In this study, PCPs anticipated an increase in workload, particularly in the initial stages of implementation, and argued that their acceptance of this increase was contingent on the perceived benefit of the app for the patient.…”

“…Although this inherently increases the complexity (and workload) of using the app, PCPs perceived that this feature was essential and its absence would compromise PCP uptake and endorsement. A balance between these aspects is needed as workload is a key barrier to provider uptake [18].…”

“…Research suggests that their effectiveness is often limited by poor uptake and sustained use by health care providers [18,19]. Recent systematic reviews have suggested that key barriers for health care providers are increased workload, and disruption to existing clinical processes and staff roles as well as concerns about remuneration, data security and liability [18,[20][21][22].…”

User-Centered Adaptation of an Existing Heart Failure Telemonitoring Program to Ensure Sustainability and Scalability: Qualitative Study (Preprint)

“…However, when one considers that HF directly impacts 1 million Canadians [5] and that in 2013 only 5000 patients across all disease types were enrolled in a TM program [6], it is clear the diffusion of TM is lagging. This can partly be explained by higher than anticipated costs of implementing TM programs and the lack of user input in the conception of TM interventions [7]. In addition, while meta-analyses generally conclude positive outcomes, inconsistencies at the individual study level, particularly with respect to the economic impact, are difficult for stakeholders to ignore [8].…”