Health technology innovators Vantage Health Technologies and the Institute of Human Virology Nigeria (IHVN) have partnered to develop an artificial intelligence (AI)-enabled Patient Retention Solution which 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.
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.”
The Patient Retention Solution was developed by Vantage Health Technologies, a company in the BroadReach Group , 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.
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.”
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
