Accepted for/Published in: JMIR Medical Informatics
Date Submitted: Aug 14, 2020
Date Accepted: Oct 30, 2020
Cystic Fibrosis Point of Personalized Detection (CFPOPD): Co-producing an interactive web application to monitor and predict lung function decline
ABSTRACT
Background:
Despite steady gains in life expectancy, individuals with cystic fibrosis (CF) lung disease still experience rapid pulmonary decline throughout their clinical course, which can ultimately end in respiratory failure. Point-of-care tools for accurate and timely information regarding risk of rapid decline is essential for clinical decision support.
Objective:
To translate a novel algorithm for earlier, more accurate prediction of rapid lung-function decline in CF patients into an interactive web-based application that can be integrated within electronic health record systems, via collaborative development with clinicians.
Methods:
Longitudinal clinical history, lung function measurements, and time-invariant characteristics were obtained on 30,879 cystic fibrosis CF patients followed in the US CF Patient Registry (2003-2015). We iteratively developed the application, using the R Shiny framework and conducting a qualitative study with care provider focus groups (N=17 participants).
Results:
A clinical conceptual model and four themes were identified through coded feedback from application users: (1) ambiguity in rapid decline, (2) clinical utility, (3) clinical significance, and (4) specific suggested revisions. These themes were used to revise our application to the currently released version, available online for exploration. This study has advanced the application’s potential prognostic utility for monitoring individuals with CF lung disease. Further application development will incorporate additional clinical characteristics requested by the users and also a more modular layout that can be useful for care provider and family interactions.
Conclusions:
Our framework for creating an interactive and visual analytics platform enables generalized development of applications to synthesize, model, and translate electronic health data, thereby enhancing clinical decision support and improving care and health outcomes for chronic diseases and disorders. A prospective implementation study is necessary to evaluate this tool’s effectiveness regarding increased communication, enhanced shared decision making, and improved clinical outcomes for CF patients. Clinical Trial: NA
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