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Comparative Study
. 2024 Jul 31:26:e50527.
doi: 10.2196/50527.

A Patient-Driven Mobile Health Innovation in Cystic Fibrosis Care: Comparative Cross-Case Study

Pamela Mazzocato  1   2 , Jamie Linnea Luckhaus  1   3 , Moa Malmqvist Castillo  1 , Johan Burnett  4 , Andreas Hager  4 , Gabriela Oates  5 , Carolina Wannheden  1 , Carl Savage  1
Affiliations
Comparative Study

A Patient-Driven Mobile Health Innovation in Cystic Fibrosis Care: Comparative Cross-Case Study

Pamela Mazzocato et al. J Med Internet Res. .

Abstract

Background: Patient-driven innovation in health care is an emerging phenomenon with benefits for patients with chronic conditions, such as cystic fibrosis (CF). However, previous research has not examined what may facilitate or hinder the implementation of such innovations from the provider perspective.

Objective: The aim of this study was to explain variations in the adoption of a patient-driven innovation among CF clinics.

Methods: A comparative multiple-case study was conducted on the adoption of a patient-controlled app to support self-management and collaboration with health care professionals (HCPs). Data collection and analysis were guided by the nonadoption, abandonment, spread, scale-up, and sustainability and complexity assessment tool (NASSS-CAT) framework. Data included user activity levels of patients and qualitative interviews with staff at 9 clinics (n=8, 88.9%, in Sweden; n=1, 11.1%, in the United States). We calculated the maximum and mean percentage of active users at each clinic and performed statistical process control (SPC) analysis to explore how the user activity level changed over time. Qualitative data were subjected to content analysis and complexity analysis and used to generate process maps. All data were then triangulated in a cross-case analysis.

Results: We found no evidence of nonadoption or clear abandonment of the app. Distinct patterns of innovation adoption were discernable based on the maximum end-user activity for each clinic, which we labeled as low (16%-23%), middle (25%-47%), or high (58%-95%) adoption. SPC charts illustrated that the introduction of new app features and research-related activity had a positive influence on user activity levels. Variation in adoption was associated with providers' perceptions of care process complexity. A higher perceived complexity of the value proposition, adopter system, and organization was associated with lower adoption. In clinics that adopted the innovation early or those that relied on champions, user activity tended to plateau or decline, suggesting a negative impact on sustainability.

Conclusions: For patient-driven innovations to be adopted and sustained in health care, understanding patient-provider interdependency and providers' perspectives on what generates value is essential.

Keywords: adaptability; adoption; chronic illness; health care provider; implementation; innovation; interdependency; mHealth; mobile health; motivation; patient-driven innovation; spread.

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Conflict of interest statement

Conflicts of Interest: AH and JB are employees of Upstream Dream, the company that developed the innovation. Upstream Dream was not involved in the study design but did facilitate data collection, and JB helped interpret the data based on knowledge of the participating CF clinics. To mitigate bias, interviews and preliminary analysis were conducted without participation of the supplier. AH and JB were interviewed, and their transcripts were subjected to the same data analysis methods, kept separate from the analyses of the other participants, and then used for clarification purposes for the statistical process control timeline analysis and complexity analysis.

Figures

Figure 1
Figure 1
SPC analysis of end-user activity at the different clinics organized as high (top row), middle (middle row), and low (lower row) end-user adoption. The UCL and LCL were defined as +/– 3 SDs from the centerline [37]. The y-axis uses different scales to be able to better discern the trends over time. The blue circles represent common cause variation, whereas the red squares represent special cause variation, with numbers indicated according to SPC rules. LCL: lower control limit; SPC: statistical process control; UCL: upper control limit.
Figure 2
Figure 2
Complexity assessment for NASSS domains grouped by adoption level based on maximum end-user activity. Green circles represent simple, blue labyrinths complicated, and red network symbols complex. *Minimum activity levels were not reported because data collection started when there were 0 active users for all clinics. **For clinic A, n was calculated based on active users over the pilot study. NASSS: nonadoption, abandonment, spread, scale-up, and sustainability.
See this image and copyright information in PMC

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