The Health Pulse

Data scientist Hiwot Tesfaye joins Greg for a conversation about the use of algorithms in health care and how models can introduce bias. Algorithms are used broadly in health care to influence access to care, improving health outcomes, addressing costs, etc. A lot of the use cases Hiwot sees are related to risk mitigation. In these cases, algorithms and artificial intelligence (AI) are used to learn historical patterns and make predictions around the risk of things like a patient not showing up for an appointment or not adhering to treatment plans. There is great promise in these types of use cases, but also a lot of pitfalls that we need to be aware of. The accuracy of health care algorithms is really important, and we need to design them so that they are accurate for everyone. Hiwot and Greg discuss examples where health care algorithms missed the mark, especially around race and the impact on care in communities of color. Hiwot explained that awareness of the potential for bias, particularly around issues tied to race, is the first and most critical step to improving the accuracy of health care algorithms. Age, gender and socioeconomic biases are also important to examine. 

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