VITAL - Assessment of a viral load result-driven automated Differentiated Service Delivery Model for participants taking antiretroviral therapy in Lesotho

To sustainably provide good quality care to over 16.4 million people living with HIV on antiretroviral therapy in sub-Saharan Africa, care delivery has to shift from a “one-size-fits-all” approach to differentiated service delivery models. Such models should reallocate resources from patients who are doing well to patient groups who may need more attention, such as those with treatment failure or medical and psycho-social problems. Ideally, such a reallocation allows health systems and patients to save resources while improving quality of care.

The concept of the proposed automated differentiated service delivery model is to use viral load (VL) results and other clinical characteristics, sub-population needs and participants’ and providers’ preference to automatically triage them into groups requiring different levels of attention and care. Innovatively, triaging will be done automatically capitalizing on a VL database platform built by our consortium. To ensure effective flow of information and participants’ empowerment, VL results, clinic visit reminders, preference-based tailored ART intake reminders and other relevant information is sent directly to participants’ phones whereas health care providers receive results directly on their tablet together with the recommended action as per treatment guidelines.

Reducing the intensity of monitoring in patients with suppressed VL will substantially reduce the workload at health care facilities and save time and transport cost for participants, thus potentially improve long-term engagement in care. Time and resources saved in patients with suppressed VL will allow focusing on those participants with an elevated VL, potentially improving their clinical outcome through intensified adherence support, clinical follow-up and timely switches of treatment regimen if indicated.

Contact

Niklaus Labhardt

Prof. Dr. Niklaus Labhardt, MD, DTM&H, MIH
Guest Scientist, Project Leader, Group Leader

n.labhardtswisstph.ch

Project Facts