Date Presented 4/1/2017
Evidence-based apps require multifaceted decisions during development. We describe processes used in the development of HESTIA to mitigate bias, including integrating technology and home assessment literature, data from research findings, and iterative design between software and content experts.
Primary Author and Speaker: Suzanne Burns
Additional Authors and Speakers: Noralyn Pickens, Roger O. Smith
PURPOSE: The purpose of this presentation is to discuss the evidence-based development and refinement methods of a home safety assessment app called HESTIA. In the current health care climate, mobile- and tablet-based technology drives service provision, including the assessment of populations with complex needs. Practitioners value integrating high-tech features and evidence-based practice in app-based assessment that supports decision making for novice to expert stakeholders (Burns & Pickens, 2016). Health care apps offer practitioners the opportunity to easily access health information and support a range of routine health care tasks.
While high-quality mobile apps are widely used by health care practitioners, a narrow yet sufficient scope of emerging evidence suggests that risks abound, specifically as the complexity of the app increases (Lewis & Wyatt, 2014). Potential risks with mobile medical app use may occur in a range of contexts: practitioners and clients using tools that are not accurate or reliable to support decision making, the possibility of app developers having limited health care expertise and not involving practitioners in the app development process, and the practicality of appraising apps due to the growing number of apps emerging exponentially (Lewis & Wyatt, 2014). Evidence-based practice requires rigor in assessment development, whether paper based or electronic.
DESIGN: Content development involved a qualitative descriptive approach, exploring interprofessional home evaluators’ perspectives on home assessment through interview and reviewing current home safety assessments and literature.
METHOD: We conducted semistructured interviews with home evaluators (N = 20) recruited through purposive and snowball sampling across professions. All regions of the United States were represented, and participants included expert occupational therapists (n = 5), novice occupational therapists (n = 5), an occupational therapy assistant (n = 1), physical therapists (n = 2), design professionals (n = 2), a contractor (n = 1), and social service professionals (n = 4). The interviews were analyzed with a qualitative descriptive approach (Lambert & Lambert, 2012) with NVivo Version 10 software (QSR International, Doncaster, Victoria, Australia) to support data management. In order to maintain integrity during app development, we developed and integrated a rigorous model to inform decisions and mitigate our own bias: use of objective and subjective data sources, constant iterative communication between content and design teams, seeking of input and probing for feedback on usability concerns from a range of end users, and consistent reflection on the technology acceptance model (Bagozzi, 2007).
RESULTS: Interview findings influenced design regarding processes, emphases, values, and professional cultures. We developed a best practice process map based on collective findings from the interview analysis. Our evidence-based app development process integrated the rigorous model to inform decision making during design, resulting in a beta version of the home safety app that remained consistent with our qualitative findings and the literature.
CONCLUSION: Our approach to evidence-based app development has the potential to influence future app design to support best practice. An evidence-based home assessment in app format is necessary to meet practitioner and societal needs in a context of evolving technology.
References
Bagozzi, R. P. (2007). The legacy of the technology acceptance model and a proposal for a paradigm shift. Journal of the Association for Information, 8, 244–254.
Burns, S. P., & Pickens, N. D. (2016). Embedding technology into inter-professional best practices in home safety evaluation. Disability and Rehabilitation: Assistive Technology. https://doi.org/10.1080/17483107.2016.1189000
Lambert, V. A., & Lambert, C. E. (2012). Qualitative descriptive research: An acceptable design. Pacific Rim International Journal of Nursing Research, 16, 255–256.
Lewis, T. L., & Wyatt, J. C. (2014). mHealth and mobile medical apps: A framework to assess risk and promote safer use. Journal of Medical Internet Research, 16(9), e210. https://doi.org/10.2196/jmir.3133
