Public health programmes are complex and dependent on committed and high quality human resources — who are in short supply and fairly difficult to keep motivated. These constraints limit the impact of large scale health programmes, often leaving out families that actually need these programmes. The progress made in the field of Artificial Intelligence (AI) and Machine Learning (ML) in the last decade promises to bridge this important gap.
The concept of Artificial Intelligence is not recent and the term was coined way back in 1957. However, it’s only in the last decade that we have had an explosion of data, and data is the key fuel for AI and ML algorithms. As patient data and data collected through research is digitized, these algorithms can use it to detect patterns or learn what to look out for — and then assist health workers with early detection of warning signs as well as clinical decision-making.
So how can it be used?
AI is entering the field of health in quite a few ways, across a variety of care settings. From precision medicine, medical record storage and retrieval, medical report diagnosis, and robotics in clinical settings, to virtual consultations and personal fitness trackers that can be used at home, AI is increasingly making its presence felt.
Some of the hot areas of AI/ML in public health are:
- Diagnostics and screening: Identifying or predicting diseases based on expressed symptoms
- Health worker performance and productivity: Tracking the data captured by health workers, and using it to direct their efforts where they are most needed
- Improving client adherence: Identifying patterns and gaps in people’s health-seeking behaviour and suggesting who might drop out of a health programme or course of treatment
This landscape of work is being pursued by a variety of players, including numerous startups, research studies by leading academic institutions, and international organisations like the International Telecommunications Union (ITU) and the World Health Organization (WHO). The landmark Astana Declaration on Primary Healthcare also identified technology as a key driver to improve accessibility, affordability and transparency on the road to achieving #HealthForAll.
Co-authored by Rhea John, Knowledge Distiller at the Learning4impact Knowledge Collaborative.