Leveraging Providers’ Preferences to Customize Instructional Content in Information and Communications Technology–Based Training Interventions: Retrospective Analysis of a Mobile Phone–Based Intervention in India
Open Access
- 3 March 2020
- journal article
- research article
- Published by JMIR Publications Inc. in JMIR mHealth and uHealth
- Vol. 8 (3) , e15998
Abstract
Background: Many public health programs and interventions across the world increasingly rely on using information and communications technology (ICT) tools to train and sensitize health professionals. However, the effects of such programs on provider knowledge, practice, and patient health outcomes have been inconsistent. One of the reasons for the varied effectiveness of these programs is the low and varying levels of provider engagement, which, in turn, could be because of the form and mode of content used. Tailoring instructional content could improve engagement, but it is expensive and logistically demanding to do so with traditional trainingObjective: This study aimed to discover preferences among providers on the form (articles or videos), mode (featuring peers or experts), and length (short or long) of the instructional content; to quantify the extent to which differences in these preferences can explain variation in provider engagement with ICT-based training interventions; and to compare the power of content preferences to explain provider engagement against that of demographic variables.Methods: We used data from a mobile phone–based intervention focused on improving tuberculosis diagnostic practices among 24,949 private providers from 5 specialties and 1734 cities over 1 year. Engagement time was used as the primary outcome to assess provider engagement. K-means clustering was used to segment providers based on the proportion of engagement time spent on content formats, modes, and lengths to discover their content preferences. The identified clusters were used to predict engagement time using a linear regression model. Subsequently, we compared the accuracy of the cluster-based prediction model with one based on demographic variables of providers (eg, specialty and geographic location).Results: The average engagement time across all providers was 7.5 min (median 0, IQR 0-1.58). A total of 69.75% (17,401/24,949) of providers did not consume any content. The average engagement time for providers with nonzero engagement time was 24.8 min (median 4.9, IQR 2.2-10.1). We identified 4 clusters of providers with distinct preferences for form, mode, and length of content. These clusters explained a substantially higher proportion of the variation in engagement time compared with demographic variables (32.9% vs 1.0%) and yielded a more accurate prediction for the engagement time (root mean square error: 4.29 vs 5.21 and mean absolute error: 3.30 vs 4.26).Conclusions: Providers participating in a mobile phone–based digital campaign have inherent preferences for instructional content. Targeting providers based on individual content preferences could result in higher provider engagement as compared to targeting providers based on demographic variables.Keywords
This publication has 31 references indexed in Scilit:
- Medication intensification in diabetes in rural primary care: a cluster-randomised effectiveness trialBMJ Open, 2012
- Use of an Internet Portal to Improve Community-Based Pediatric ADHD Care: A Cluster Randomized TrialPediatrics, 2011
- A web-based diabetes intervention for physician: a cluster-randomized effectiveness trialInternational Journal for Quality in Health Care, 2011
- Role of Private Sector in Providing Tuberculosis Care: Evidence from a Population-based Survey in IndiaJournal of Global Infectious Diseases, 2011
- Is Physician Engagement With Web-Based CME Associated With Patientsʼ Baseline Hemoglobin A1c Levels? The Rural Diabetes Online Care StudyAcademic Medicine, 2010
- The Delivery of Public Health Interventions via the Internet: Actualizing Their PotentialAnnual Review of Public Health, 2009
- Medicine 2.0: Social Networking, Collaboration, Participation, Apomediation, and OpennessJournal of Medical Internet Research, 2008
- Evaluating the impact of health promotion programs: using the RE-AIM framework to form summary measures for decision making involving complex issuesHealth Education Research, 2006
- Silhouettes: A graphical aid to the interpretation and validation of cluster analysisJournal of Computational and Applied Mathematics, 1987
- Algorithm AS 136: A K-Means Clustering AlgorithmJournal of the Royal Statistical Society Series C: Applied Statistics, 1979