A machine learning approach to improve implementation monitoring of family-based preventive interventions in primary care

Cady Berkel; Dillon C Knox; Nikolaos Flemotomos; Victor R Martinez; David C Atkins; Shrikanth S Narayanan; Lizeth Alonso Rodriguez; Carlos G Gallo; Justin D Smith

doi:10.1177/26334895231187906

A machine learning approach to improve implementation monitoring of family-based preventive interventions in primary care

Implement Res Pract. 2023 Jul 25:4:26334895231187906. doi: 10.1177/26334895231187906. eCollection 2023 Jan-Dec.

Authors

Cady Berkel^{1

2}, Dillon C Knox³, Nikolaos Flemotomos³, Victor R Martinez³, David C Atkins⁴, Shrikanth S Narayanan³, Lizeth Alonso Rodriguez², Carlos G Gallo⁵, Justin D Smith⁶

Affiliations

¹ College of Health Solutions, Arizona State University, Phoenix, AZ, USA.
² Ming Hsieh Department of Electrical Engineering, USC Viterbi School of Engineering, REACH Institute, Arizona State University, Tempe, AZ, USA.
³ Signal Analysis and Interpretation Laboratory, University of Southern California, Los Angeles, CA, USA.
⁴ Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, USA.
⁵ Department of Psychiatry and Behavioral Sciences, Northwestern University, Chicago, IL, USA.
⁶ Department of Population Health Sciences, Spencer Fox Eccles School of Medicine, University of Utah, Salt Lake City, UT, USA.

Abstract

Background: Evidence-based parenting programs effectively prevent the onset and escalation of child and adolescent behavioral health problems. When programs have been taken to scale, declines in the quality of implementation diminish intervention effects. Gold-standard methods of implementation monitoring are cost-prohibitive and impractical in resource-scarce delivery systems. Technological developments using computational linguistics and machine learning offer an opportunity to assess fidelity in a low burden, timely, and comprehensive manner.

Methods: In this study, we test two natural language processing (NLP) methods [i.e., Term Frequency-Inverse Document Frequency (TF-IDF) and Bidirectional Encoder Representations from Transformers (BERT)] to assess the delivery of the Family Check-Up 4 Health (FCU4Health) program in a type 2 hybrid effectiveness-implementation trial conducted in primary care settings that serve primarily Latino families. We trained and evaluated models using 116 English and 81 Spanish-language transcripts from the 113 families who initiated FCU4Health services. We evaluated the concurrent validity of the TF-IDF and BERT models using observer ratings of program sessions using the COACH measure of competent adherence. Following the Implementation Cascade model, we assessed predictive validity using multiple indicators of parent engagement, which have been demonstrated to predict improvements in parenting and child outcomes.

Results: Both TF-IDF and BERT ratings were significantly associated with observer ratings and engagement outcomes. Using mean squared error, results demonstrated improvement over baseline for observer ratings from a range of 0.83-1.02 to 0.62-0.76, resulting in an average improvement of 24%. Similarly, results demonstrated improvement over baseline for parent engagement indicators from a range of 0.81-27.3 to 0.62-19.50, resulting in an approximate average improvement of 18%.

Conclusions: These results demonstrate the potential for NLP methods to assess implementation in evidence-based parenting programs delivered at scale. Future directions are presented.

Trial registration: NCT03013309 ClinicalTrials.gov.

Keywords: health care; implementation; implementation outcomes; integrated care; preventive intervention; provider; treatment fidelity; validity.

Plain language summary

Research has shown that evidence-based parenting programs effectively prevent the onset and escalation of child and adolescent behavioral health problems. However, if they are not implemented with fidelity, there is a potential that they will not produce the same effects. Gold-standard methods of implementation monitoring include observations of program sessions. This is expensive and difficult to implement in delivery settings with limited resources. Using data from a trial of the Family Check-Up 4 Health program in primary care settings that served Latino families, we investigated the potential to make use of a form of machine learning called natural language processing (NLP) to monitor program delivery. NLP-based ratings were significantly associated with independent observer ratings of fidelity and participant engagement outcomes. These results demonstrate the potential for NLP methods to monitor implementation in evidence-based parenting programs delivered at scale.

Associated data

ClinicalTrials.gov/NCT03013309