Mike Barlow - AI and Medicine: Data-Driven Strategies for Improving Healthcare and Saving Lives

by Mike Barlow

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For centuries, physicians and healers focused primarily on treating acute problems such as broken bones, wounds, and infections. If you had an infectious disease, you went to the doctor, the doctor treated you, and then you went home, says Balaji Krishnapuram, director and distinguished engineer at IBM Watson Health.

Today, the majority of healthcare revolves around treating chronic conditions such as heart disease, diabetes, and asthma. Treating chronic ailments often requires multiple visits to healthcare providers, over extended periods of time. In modern societies, the old ways of delivering care will not work, says Krishnapuram. We need to enable patients to take care of themselves to a far greater degree than before, and we need to move more treatment from the doctors office or hospital to an outpatient setting or to the patients home.

Unlike traditional healthcare, which tends to be labor-intensive, emerging models of healthcare are knowledge-driven and data-intensive. Many of the newer healthcare delivery models will depend on a new generation of user-friendly, real-time big data analytics and artificial intelligence/machine learning (AI/ML) tools.

Krishnapuram sees five related areas in which the application of AI/ML tools and techniques will spur a beneficial revolution in healthcare:

Identifying risks, determining who is at risk, and identifying interventions that will reduce risk.

Designing care plans for individual patients and closing gaps in care.

Supporting and enabling customized self-care treatment plans for individual patients, monitoring patient health in real time, adjusting doses of medication, and providing incentives for behavioral changes leading to improved health.

Optimizing healthcare processes (everything from medical treatment itself to the various ways insurers reimburse providers) through rigorous data analysis to improve outcomes and quality of care while reducing costs.

Helping doctors and patients choose proper dosage levels of medication based on most recent tests or monitoring, assisting radiologists in identifying tumors and other diseases, analyzing medical literature, and showing which surgical options are likely to yield the best outcomes.

Applying AI/ML strategies in each of those five areas will be essential for creating large-scale practical systems for providing personalized and patient-centric healthcare at reasonable costs. In this report, I explore these areas and more through interviews conducted with leading experts in the field of AI and medicine.