Using technology to accurately assess and manage risk

The shift from fee-for-service to value-based healthcare is accelerating. This year, the Centers for Medicare & Medicaid Services announced plans to link half of all Medicare reimbursements to quality via value-based payment models by 2018, more than doubling the current rate, which is about 20%, paid to programs like Accountable Care Organizations (ACOs).

The accelerating shift has healthcare organizations looking at new ways to use technology to improve risk management. Effective risk management is critically important in successfully adopting a value-based healthcare model since organizations that manage population health take on higher levels of risk and generate revenue by managing it carefully. The ability to accurately assess risk is critical to ensure the financial health of insurers, including those that offer Medicare Advantage plans, Medicaid coverage and commercial plans under the ACA. From a provider perspective, effective risk management is also vitally important for ACOs.

Data produced by electronic health records can provide valuable insights that healthcare organizations can use to manage risk. But since up to 80% of the available data is unstructured, risk managers haven't been able to easily access it electronically or conduct automated analysis. They've used costly, error-prone and labor-intensive manual analysis instead.

After assessing available technologies to apply unstructured data to streamline operations, improve compliance and achieve greater risk management efficiency, one large integrated system's health plan with a reputation for innovation took a different path. The health plan created a technology solution by working with a partner that had developed cutting-edge natural language processing (NLP) and analytics technology to manage risk. The organization developed a comprehensive, scalable solution to continuously improve the risk adjustment cycle.

By incorporating NLP technology to allow extraction of previously unknown risk factors, the new solution was designed to facilitate more accurate risk assessment and management. It was also designed to integrate with existing processes and streamline workflows to reduce the complexity of risk adjustment operations. The health system, along with its health plan business unit, deployed the new solution, testing it at scale to prove that it could reveal more risk factors while measurably increasing operational efficiency and significantly enhancing compliance.

As a result of this test, the integrated system's health plan found that the new solution not only enables discovery of new risk factors, it makes retrospective and prospective analyses more efficient with an intuitive workflow, consolidating multiple tasks to streamline processes and make collaboration between business units easier. In the value-based healthcare environment, organizations that have the ability to continuously improve the risk adjustment cycle gain a significant advantage.

The shift in the healthcare business model has been ongoing for several years now, but as reimbursement incentives drive acceleration of the trend, healthcare organizations that want to stay ahead of the curve must embrace more effective risk management strategies. As innovative health systems are learning, technology can be a critical success factor. Organizations that find a way to get a more accurate view of risk and manage it effectively and efficiently can gain a major competitive edge.

Anand Shroff is the Co-Founder, Chief Technology and Product Officer of Health Fidelity, a healthcare tech start-up that is focused on helping payer and provider organizations address risk and quality in value-based payment models. At Health Fidelity, Anand helped found the company, guided the development of its technology and fostered key relationships with UPMC and Columbia University. Anand has an MBA from the Haas School of Business at the University of California, Berkeley in addition to an MS in Computer Science from the University of California, Santa Barbara.

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