How AI Can Advance Health Equity

The following is a guest article by Sachin Patel, CEO at Apixio. The use of AI in healthcare is gaining fast traction, with the total market expected to grow over 46% CAGR, reaching $96 billion by 2028. While AI has the potential to improve health equity, intrinsic and extrinsic biases that exist from the lack of diverse data exacerbate the problem. Furthermore, the closer an activity is to the point of care, the greater the risk of unintended biases in clinical decision-making. For example, an AI-powered system to predict the likelihood of hospitalization to avoid in-patient admission can help target interventions, prevent adverse outcomes, and reduce healthcare costs. However, the challenge for using the hospitalization models is that training data from high utilization members does not include data from members affected by racial or socioeconomic disparities. While racial and ethnic biases are important areas of concern, geographic or location-based biases are also problematic and may be an equally good proxy for addressing factors for which clean data is more challenging to capture. Healthcare AI developers can limit biases by ensuring quality and diverse data for training AI models before implementing them on a large scale. Here are three examples of biases to look out for in AI solutions and how to curtail or prevent them altogether. Inadequate data. In some parts of the country, there are significant discrepancies between geographic and population coverage. For exampl...
Source: EMR and HIPAA - Category: Information Technology Authors: Tags: AI/Machine Learning Analytics/Big Data C-Suite Leadership Clinical Health IT Company Healthcare IT Hospital - Health System Clinical Decision Support Health Coverage Health Data Health Equity Healthcare AI Healthcare Data Healthc Source Type: blogs