Data Science and Qualitative Research for Decision Support in the HIV Care Cascade (CASCADE)

Data Science for Decision Support in the HIV Care Cascade

The goal of this study is to determine whether clinical prediction algorithms derived using statistical machine learning methods can be used to improve patient outcomes in large HIV care programs in sub-Saharan Africa and elsewhere.

There are two main questions to be answered. First, can the prediction algorithms accurately identify those who are at risk for (a) missing scheduled clinic visits and/or (b) treatment failure, evidenced by elevated HIV viral load? And second, can the risk predictions be used in a structured way to (a) improve retention in care and/or (b) reduce the number of patients having elevated viral load? Researchers will develop machine learning prediction algorithms, incorporate the risk prediction information into the electronic health record, provide guidance to clinical health workers on use of the point-of-care interface tools that display risk prediction information, and incorporate feedback from clinic staff to modify and co-develop the protocol for using risk predictions for improving patient outcomes.

They will then compare the proportion of patients having missed visits and longer-term loss to follow up, and the proportion with elevated viral load, between clinics that use the information from the risk prediction algorithms and those that do not.

Study Overview

• Status: Enrolling by invitation
• Conditions: Patient Engagement; Treatment Adherence; Human Immunodeficiency Virus; HIV Viremia; Treatment Compliance; Patient Dropouts; Patient No Show
• Intervention / Treatment: Behavioral: Activation of the CDSS system

Detailed Description

Clinical decision support systems (CDSS) tailored to the requirements of low- and middle-income countries (LMIC) have been shown to improve compliance with guidelines and quality of care by a range of healthcare staff. To be most effective CDSS should be developed and tested with large clinical data sets from the local region. Use of machine learning algorithms allows the development of prediction models for clinical complications and outcomes, which can guide health care staff in early identification of problems and appropriate interventions. This requires well established electronic health record (EHR) systems acting as both data sources and as platforms for delivering feedback through CDSS (Learning Health System approach). The EHR at the Academic Model Providing Access to Healthcare (AMPATH), a large HIV care program in western Kenya funded in large part by President's Emergency Plan for AIDS Relief (PEPFAR), has used a version of the OpenMRS EHR for nearly two decades and provides a unique environment for this research.

The objective of this proposal is to develop and implement data-driven tools to aid health-related decision-making at patient, clinic and county levels, and evaluate the efficacy of using these methods. The hypothesis is that health facilities utilizing these data driven CDSS will show improvements in the processes and outcomes of care compared to health facilities not utilizing data driven CDSS within their EHRs.

The two primary endpoints for the study are retention in care and viral load suppression.

The motivation is driven by 95-95-95 HIV cascade of care benchmarks established by UNAIDS for eradicating HIV worldwide. In brief, the framework calls for diagnosis of 95% of individuals who have HIV, initiating antiretroviral (ART) treatment for 95% of those who have been diagnosed, and achieving suppression of viral load (VL) for 95% of those who are on treatment. Our project addresses the second and third phases.

Regarding the second 95, retention is a necessary condition for maintaining persons living with HIV (PLWH) on antiretroviral therapy (ART) because global care guidelines now specify that all PLWH initiate ART once engaged in care. Regarding the third 95, in Kenya and many other LMIC, viral load testing for most adult clients is done six months after treatment initiation and annually thereafter. Even after a measured VL indicating suppression, viral failure due to nonadherence or drug resistance can occur well before the next follow up one year later. Hence our models will generate predicted viral load values in the interim and use them to flag individuals who should have a VL measurement prior to the scheduled follow up.

The trial is part of a larger NIH-funded study. The aims related to the trial are as follows:

Aim 1: Develop and validate statistical machine learning models and algorithms for clinical and programmatic decision support.

1a: Develop and validate statistical machine learning algorithms to identify those at high risk for disengagement from care and viral failure, and to generate predicted values of current viral load.

1. b: Develop representations of statistical uncertainty about the predictions to enable optimal decision making.

   Aim 2. Develop, implement and field test decision support and data visualization tools to enhance data driven decision making by physicians and program managers.

2. a: Create the server architecture to support the prediction models in the OpenMRS user interface (UI).

2b: Develop and refine the specific protocol for using the risk predictions to reduce missed visits and reduce incidence of viral load failure.

Aim 3: Conduct evaluation of the impact and efficacy of the clinical decision support tools in the AMPATH Care Program

3a: Implement the CDSS at the point of care in all clinics using the AMPATH Medical Records System (AMRS) in Uzima and Dumisha catchment areas. These clinics have varying size and geographic location.

3b: Following a pilot phase, conduct a stepped wedge randomized longitudinal comparison of retention rates and viral load suppression rates in 30 clinics at AMPATH

The successful completion of the work will provide effective CDSS tools to improve HIV care in Kenya and other LMICs, as well as a set of tools for the development, updating and evaluation of CDSS for other clinical problems. Previous work by the investigators and colleagues in development and wide deployment OpenMRS in more than 44 countries provides a platform for broad dissemination of this work.

• Study Type: Interventional
• Enrollment (Estimated): 80000
• Phase: Not Applicable

Contacts and Locations

Study Locations

• Kenya
  • Eldoret, Kenya
    • AMPATH

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

Description

Inclusion Criteria:

• The study will include adult patients (age 18 and over) receiving HIV care through the AMPATH program in Eldoret, Kenya. There is not a patient-level enrollment process. The primary endpoints will be summarized at the clinic level (e.g., proportion of patients who keep an appointment within 7 days of the scheduled appointment).

Exclusion Criteria:

• None

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Health Services Research
• Allocation: Randomized
• Interventional Model: Parallel Assignment
• Masking: None (Open Label)

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
No Intervention: Usual Care; Usual Care at AMPATH involves telephoning clients or care supporters the day prior to their appointment (at some clinics) and/or telephoning or making a home visits after appointments are missed. This will be in place at usual care clinics until the date at which the clinic is randomized to receive the CDSS support.
Experimental: Clinical decision support, CDSS; When a clinic is assigned to receive the CDSS support intervention two components will be enacted to enable proactive outreach that prevents a missed visit. These patients are considered to be in the active, experimental arm. Please seem the section above on Detailed Description for background and details on how this intervention is implemented.	Behavioral: Activation of the CDSS system; Activation of the CDSS system, whereby outreach workers and clinicians have access to and ability to act upon lists of patients who are at highest risk of missing their upcoming clinical appointment.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
The proportion of scheduled patient visits kept by the patient (1-day)	The proportion of scheduled patient visits where the patient returns on or before the scheduled visit date. Measured weekly at the clinic level.	The study has 6 waves (or wedges in the stepped-wedge design). The proportion will be measured weekly for the 4 weeks preceding the first wave of CDSS implementation, and then weekly until 8 weeks after the date of the final wave of CDSS implementation.
The proportion of scheduled patient visits kept by the patient (7-day)	The proportion of scheduled patient visits where the patient returns on or before the 7th day following the scheduled visit date. Measured weekly at the clinic level.	The study has 6 waves (or wedges in the stepped-wedge design). The proportion will be measured weekly for the 4 weeks preceding the first wave of CDSS implementation, and then weekly until 8 weeks after the date of the final wave of CDSS implementation.
The proportion of patients with suppressed VL among those with measured VL	The proportion of patients with suppressed VL among those with measured VL. This outcome reflects the fraction suppressed only among those who show up for their VL measurement and is the metric designed for tracking progress toward 95-95-95 goals. This endpoint will be measured monthly at the clinic level. The denominator will be number of patient-level VL measures, and the numerator will be number of occasions where the measured VL is undetectable.	The study has 6 waves (or wedges in the stepped-wedge design). The proportion will be measured for the month preceding the first wave of CDSS implementation, and then monthly until 2 months after the date of the final wave of CDSS implementation.
The proportion of patients with suppressed VL among those with scheduled VL measurement, whether or not that measure was taken.	The proportion of patients with suppressed VL among those with scheduled VL measurement, whether or not that measure was taken. This outcome is designed to reflect the fraction of the overall patient population having suppressed VL and is potentially more relevant as a population-level parameter. This endpoint will be measured monthly at the clinic level. The denominator will be number of VL measurements scheduled, and the numerator will be number of occasions where the VL is measured and is undetectable.	The study has 6 waves (or wedges in the stepped-wedge design). The proportion will be measured for the month preceding the first wave of CDSS implementation, and then monthly until 2 months after the date of the final wave of CDSS implementation.

Collaborators and Investigators

• Sponsor: Brown University
• Collaborators: National Institute of Allergy and Infectious Diseases (NIAID); Moi Teaching and Referral Hospital; Moi University College of Health Sciences
• Investigators: Principal Investigator: Joseph W Hogan, ScD, Brown University

Study record dates

Study Major Dates

• Study Start (Actual): May 20, 2024
• Primary Completion (Estimated): January 31, 2026
• Study Completion (Estimated): October 31, 2026

Study Registration Dates

• First Submitted: September 9, 2024
• First Submitted That Met QC Criteria: September 17, 2024
• First Posted (Actual): September 19, 2024

Study Record Updates

• Last Update Posted (Estimated): January 12, 2026
• Last Update Submitted That Met QC Criteria: January 9, 2026
• Last Verified: September 1, 2025

More Information

Terms related to this study

• Keywords: Artificial Intelligence; Machine Learning; Electronic Health Records; Electronic Medical Records; Clinical Decision Support System; Clinical prediction models; HIV Treatment Interruption; HIV Treatment Failure; Bayesian statistics
• Additional Relevant MeSH Terms: Blood-Borne Infections; Urogenital Diseases; Genital Diseases; Immune System Diseases; Infections; RNA Virus Infections; Virus Diseases; Communicable Diseases; Sexually Transmitted Diseases, Viral; Sexually Transmitted Diseases; Lentivirus Infections; Retroviridae Infections; Immunologic Deficiency Syndromes; Slow Virus Diseases; HIV Infections; Behavior; Health Behavior; Patient Acceptance of Health Care; Acquired Immunodeficiency Syndrome; Patient Participation; Treatment Adherence and Compliance; Patient Compliance; No-Show Patients; Patient Dropouts
• Other Study ID Numbers: 2022003414; R01AI167694 (U.S. NIH Grant/Contract)

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No
• product manufactured in and exported from the U.S.: No