The healthcare industry, including hospitals, doctors, insurance companies, pharmaceutical companies, government regulatory agencies, et al., currently and in the future will be more impacted by AI, machine learning, natural-language processing (NLP) and other technology than any other industry. As context, healthcare expenditures in the U.S. amount to almost 20% of GDP. Still, based on numerous surveys, patient dissatisfaction with healthcare and its providers is rampant. 

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AI-based health-care delivery aims for a combination of data-driven personalization of healthcare and a coordinated team approach to patient care involving both physicians and non-physicians across multiple interconnected provider facilities, all using the same systems to measure and analyze results in real time. Primary, specialty, and acute care will be integrated rather than siloed. Today’s physicians, overburdened with administrative chores, will be armed with AI, machine learning, natural-language processing (NLP) and EMR to direct and manage clinical-care teams.

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There also is a national push for informed patients, central to the drive for better healthcare. Under HIPAA, and regulations from the Centers for Medicare and Medicaid (CMS), patients’ electronic access to their health information is a federal right. Universal patient data access in itself is “revolutionary,” including being able to review lab results in real-time (ask Mary Jo what that meant for her while in blood-cancer treatment). 

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The goals and benefits of all dimensions of value-based healthcare are patients spending less money to achieve better health, including management of and recovery from chronic conditions, illnesses, injuries, etc., as well as prevention, making fewer doctors visits, spending less on prescriptions, etc. With an aging population, national expenditures on prescription drugs will continue to rise substantially without tying the use and price of drugs to actual (data-based) value for patients, and the personalization of healthcare therapies. Using AI, analytics, NLP, and other technology, providers can achieve efficiencies while at the same time improving doctor-patient relationships. Costly hospitalizations can be reduced. 

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At the heart of the “revolution” in healthcare, therefore, is the combination of AI, machine learning and NLP data generation, sharing and analytics, and networked care-coordination teams in which each member shares information, expertise, risks, rewards, and incentives to improve relationships with and quality of care for patients and patient outcomes. This model contrasts with fee-for-service healthcare, in which providers are incentivized to order more test and procedures and maximize patient counts in order to earn more money while spending less.

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For technology giants like Microsoft, Google and Amazon, there is no bigger potential business opportunity than supporting the “revolution” in U.S. and global healthcare delivery as they compete with each other for cloud and hybrid cloud share. In the next 10 years, transformation of healthcare towards value-based healthcare and data-driven diagnoses and treatment will impact and potentially benefit as many as 95% of the U.S. population.
  
In terms of societal benefits, the tech giants will be able to claim some credit for reducing or eliminating adverse or harmful healthcare events, improving health outcomes for large socio-economic sectors of the U.S. and global population, increasing transparency for healthcare consumers, and reducing the percentage of GDP accounted for by healthcare expenditures.