PRP139: Precision implementation: Developing and validating predictive models of information technology tool adoption
Nathalie Huguet, PhD; Miguel Marino, PhD; Katie Fankhauser, MPH; Jean O'Malley, MPH, PStat; Jennifer DeVoe, MD, DPhil; Heather Angier, PhD, MPH; Erin Jamieson
Objective: To develop and validate predictive models that estimate the likelihood of adoption of electronic health record (EHR)-related tool.
Study Design: Retrospective, observational study.
Setting or Dataset: EHR data from 351 CHCs from the OCHIN Network.
Population Studied: 351 practices eligible to receive insurance support information technology (IT) tool with an active EHR from 05/01/2017 to 06/30/2019.
Outcome Measures: Binary indicator of any tool uptake, rates of monthly tool use, and number of patients the tool was used on.
Results: We will use an ensemble of machine learning algorithms (i.e., Super Learner) to develop and validate a set of predictive models based on practice-level characteristics to predict our adoption outcome measures. We will split data into development and test data sets (80%/20% split) and use area under the receiver operating characteristics curve (AUC) and mean squared error to evaluate predictive performance. We will report a cross-validated estimate of performance of each algorithm in the ensemble as well as performance of the ensemble on external testing data. We hypothesize that EHR-based data will have moderate prediction performance (as evidence by an AUC>0.60) on tool adoption for CHC practices.
Conclusion: This work is the next step toward advancing the science of ‘precision implementation’ and how to efficiently tailor and deploy implementation support strategies for IT innovations.