Artificial Intelligence [AI] project vastly increases HIV patients on sustained treatment in sub-Saharan Africa

Keeping the world’s estimated 38 million HIV positive people on effective and sustained treatment is one of the most critical parts of controlling the global HIV epidemic. Now a collaboration between health technology innovators Vantage Health Technologies (https://VantageHealth.Tech) and the Institute of Human Virology Nigeria (IHVN) (http://IHVNigeria.org) has led to a breakthrough and new hope for patient retention in the region.

It is estimated that 25 million – or 67% – of all people living with HIV, live in sub-Saharan Africa, and 8.1 million of these people are virally unsuppressed. Through Vantage’s artificial intelligence (AI)-enabled Patient Retention Solution, the IHVN team – funded through a grant from the United States’ Centres for Disease Control and Prevention – has been able to predict and positively influence the behaviour of high-risk HIV/AIDS patients.

The Patient Retention Solution was developed by Vantage Health Technologies, a company in the BroadReach Group (https://BroadReachCorporation.com), a social enterprise focussed on innovation and health technology that empowers human action. The collaboration with IHVN includes a pilot implementation in three sites – where the implementing teams were able to retain 91% of the high-risk patients they engaged with on HIV medications.

Annika Lindorsson Krugel, Solutions Manager of Vantage Health Technologies, says collaboration between public health partners, combined with the use of state-of-the-art AI technology, is proving to be a highly effective strategy for improving retention for HIV/AIDS out-patients. “The burden of disease and treatment challenges in sub-Saharan Africa has made it especially crucial to use technology and partnerships to improve the care process.”

Krugel explains how the programme works: “The Patient Retention Solution is an AI-driven model that uses data from patient history to predict if patients will miss their next clinic appointment with the assumption that missing the appointment means the patient will drop off treatment as they are not present to collect their medication. The solution uses a machine learning model to identify patients at high-risk of missing their next appointment and produces patient lists that are given to clinical staff to conduct various interventions to prevent patients from missing their next appointment. SMS messages, calls and home visits for those without phone numbers are then arranged to provide personal attention to each patient ahead of their scheduled clinic appointments.”

Mercy Omozuafoh, Programme Manager for Care and Support with the IHVN, and her teams have been using the Patient Retention Solution as a tool to prioritise existing interventions for high-risk HIV/AIDS patients in Nigeria. The predictive model was rolled out to about 30 000 patients at the General Hospital Kudwa at Bwari in the Federal Capital Territory, the Dalhatu Araf Specialist Hospital in Lafia in the Nasarawa State, and General Hospital Ahoada in the Rivers State. “The project has demonstrated the effectiveness of proactive tracking of Patients Living with HIV (PLHIV) and has made us understand the importance of interventions we are implementing. It has broadened our minds and we are able to scale up the solution to include more facilities,” says Omozuafoh.

The Patient Retention Solution algorithm was trained on 330 000 IHVN patients, and this pilot project was focused on the approximately 5 000 identified at-risk patients. Of these, 91% of patients on the predictive list who received an intervention (SMS, phone call or home visit) were up to date in the month of intervention, meaning that they were retained in care. This compares to 55% retention in a comparison group who did not receive the intervention.

The Patient Retention machine learning model was independently validated by Dartmouth Institute for Health Policy and Clinical Practice. A case study by the Dartmouth Institute, which looked at eight months of data from the three Nigerian locations, found that the main barriers to treatment adherence included stigma, side-effects, logistical challenges, economic barriers and forgetfulness. The study found that caregiver support, peer support and understanding one’s status helped patients overcome these barriers. The institute also found that cultural sensitivity, ongoing patient connections with trusting relationships at its core, and the facilitation of large-scale improvement efforts by local teams, contributed to the success of the programme.

The Vantage Patient Retention Solution has been implemented in HIV treatment and care programmes across Nigeria and South Africa and is yielding similar successes. “The solution is an innovative example of what can be achieved when artificial intelligence truly powers human action,” says Krugel.

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