Enhancing HIV Treatment Continuity: Innovations and Data Use in Kenya's Health Information Systems

Palladium Kenya (October 2023)

Countries: Kenya

Overview

The intervention focuses on improving HIV treatment continuity in Kenya. It aims to reduce the rate of Interrupted Treatment (IIT) to less than 1% by leveraging digital health solutions. The need arises from the challenges in managing patient appointments, ensuring adherence to treatment, and tracking patient movements across facilities.

The presentation covers many interventions within Palladium Kenya’s HIFADHI Project (HIV Treatment Continuity using Full Accountability of Data, Health Information Systems and Program Interventions), including technical interventions applied to preemptive interventions, tracking & tackling, and patient perspectives.

Presenters: Keziah Muiruri, Lilian Taligoola, Dr. Jacob Odhiambo (Palladium Kenya)

Link to presentation recording

Intervention Details

Scope	The intervention is national, covering 40 out of 47 counties in Kenya. There are considerations to scale the solutions to all counties and potentially adapt the framework for other health issues beyond HIV.
Tools/Technology	KenyaEMR: An electronic medical record system used in health facilities. Ushauri: An mHealth solution for sending SMS reminders and motivational messages to patients. Nishauri: A mobile application for patients to manage their health records, perform health journalling, and manage appointments. Machine Learning Models: Used for predicting IIT risk. ART e-Directory and Referral System: For managing patient transfers between facilities. PSurvey: A tool for conducting patient-centered surveys.
Data Processes	Business Process Rules: Regular updates of patient contact information, consent for SMS reminders, and linking patients to case managers. CDS rules are used to guide providers on handling patients with or at risk for IIT. Metrics/Calculations: IIT risk scores, appointment adherence rates, and patient movement tracking. Data Sharing: Data is shared with the National Data Warehouse and other reporting systems like DHIS2. Facilities use alerts and notifications to better manage transfers between facilities. Calculations/Predictions: Machine learning models predict the likelihood of patients becoming IIT. Aerts and flags placed on patient profile who are at risk for IIT.
People	Critical roles include business analysts, data use advisors, healthcare providers, and case managers. Training and regular updates for healthcare providers on using the digital tools are essential.
Implementation Considerations	Adaptations were made to ensure patient consent for SMS reminders and to regularly update patient contact information. The solutions also needed to be tailored to work in facilities with varying levels of internet connectivity.
Privacy & Security/Governance Considerations	Security concerns were addressed by obtaining patient consent for SMS reminders and regularly verifying contact information. Measures are in place to ensure messages are sent only to the patient's preferred number.
Cost Considerations	Costs include SMS and USSD charges, internet connectivity, and ongoing updates to the digital health solutions to meet evolving program needs.
Successes	Achieved paperless implementation in many facilities. Improved patient care management through clinical decision support features. Enhanced appointment management and patient retention through reminders, alerts, and reporting.
Challenges	Slow uptake of some solutions due to various reasons, including cost and internet connectivity issues. The need for constant updates to the health information systems to meet growing program needs. Delays in getting data from facilities can affect IIT reporting.
Impact	The intervention has significantly reduced the IIT rate, with the latest data showing a reduction to 2.9%. The use of digital health solutions has improved patient care management, appointment adherence, and overall treatment continuity.
Resources	Notes