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Value-based care requires providers to adopt a different, more proactive mindset toward treating patients. But it also requires – and can be enhanced by – emerging technologies that allow clinicians and medical researchers to leverage data.
Not only are providers collecting and storing more patient data than ever, powerful tools such as data analytics and artificial intelligence enable providers to learn more from patient data. This results in better outcomes and faster advances in medical science.
At a recent roundtable discussion sponsored by talent search firm Chasm Partners, Scott Weingarten, senior vice president and chief clinical transformation officer at Cedars-Sinai, made the connection between value-based care and new technologies.
"I believe that natural language processing, machine learning and artificial intelligence have the potential to significantly improve the interpretation, understanding and usefulness of information documented in the electronic health records and other information sources," Weingarten said. "I believe that these advances will enable provider organizations to unlock the promise of EHRs and other technologies and achieve a greater clinical and financial return on their technology investments."
Indeed, the entire point of value-based care is to achieve the Triple Aim of improving patient care and population health while reducing the costs of healthcare. Natural language processing, machine learning, artificial intelligence (AI), and data analytics will make it easier for providers to draw insights from individual patient data, analyze data sets to better understand population health issues, and streamline operations to reduce costs while improving efficiency.
Integrating these technologies with EHRs and provider financial data systems can provide insights into the costs of care in various units versus industry benchmarks, deliver ongoing clinical quality measurement metrics, and identify at-risk patients to improve outcomes and reduce costs.
By acting on the data created, collected, and analyzed by emerging technologies such as natural language processing, AI, and machine learning, providers will more easily be able to implement and derive the benefits from value-based care.
At least that’s the promise; sometimes there are bumps along the way. A session at HIMSS18 in early March will explore the mistakes made and lessons learned from three different clinical AI and machine learning case studies.