Palindrome Data, a data science implementer that specialises in alternative data and machine learning tools for community development, has created what it believes to be South Africa's first suite of digital and paper-based HIV tools backed by machine learning, designed to help frontline healthcare workers triage at-risk patients. The solution, leveraging machine learning and multiple data sources, is designed to be used in both digital and paper-based environments so that healthcare workers can identify and manage high-risk patients and relevant interventions to increase HIV treatment retention and mitigate the risk of loss to follow-up (LTFU). The solution can correctly predict a patient's viral load (suppressed versus unsuppressed) for three out of four patients; and can anticipate two out of three times when a patient will drop out of care. "The biggest obstacle facing HIV patients is dealing with an overburdened healthcare system that can't afford to take the time to deal with their unique challenges," says Lucien De Voux, director of market strategy at Palindrome Data. "There is a need to retain and engage patients in a relevant way.