Identifying at-risk patient populations is one way to use predictive analytics to generate rapid returns.
In non-healthcare sectors like retail and manufacturing, 'predictive analytics' was arguably the top buzz phrase of 2015. Respected industry analyst Gartner
even included predictive analytics in its 'Top 10 Strategic Technology Trends' roundup. Predictive analytics have become increasingly important in the healthcare industry, too, as the volume of electronic data grows.
But healthcare organizations have grappled with how to access, analyze and apply their data. Many lack the advanced automated capabilities needed to extract meaning from complex, unstructured data sets from multiple sources. However, it’s crucial to find a way, since the stakes are incredibly high: A McKinsey & Company study estimated that the industry could extract $300 billion in value annually from big data and drive overall healthcare expenditures down by 8 percent.
The key to extracting maximum value from healthcare data sets is to use predictive analytics and cloud-based technologies. By analyzing current and historical data and using the findings to predict future events and trends, healthcare enterprises such as accountable care organizations (ACOs) and others can address the cost-quality equation that is so essential to successful operations in an outcomes-based environment.
The pay-for-performance ecosystem ACOs and other healthcare organizations operate in today demands new strategies to handle bundled payments and population health management challenges, and predictive analytics are tailor-made to produce the insights they need. Using predictive analytics to assess current data sheds new light on the following key metrics:
- The relationships between cost, quality and patient outcomes;
- Clinical best practices that drive optimal patient outcomes; and
- Individual and population-level health risks.
By submitting current metrics to predictive analytics, healthcare organizations will gain incredibly valuable insights into how various factors intersect to affect outcomes and which issues they need to address first to drive improvements and value. As they respond to changes in payment models in 2016 and beyond, healthcare organizations will also use predictive analytics to refine their strategies by:
- Gaining insights into risk factors and how to optimize risk management;
- Identifying the practices, performers and results that affect organizational performance; and
- Assessing the impact of ACO reimbursement and bundled payment strategies.
Taken together, these are the six ways predictive analytics will move healthcare forward in 2016. By leveraging the power of predictive analytics, healthcare organizations will be able to clearly identify the factors that drive clinical quality and operational expenses. And by applying this information, they can predict and manage clinical and financial performance with greater accuracy. Moreover, they’ll have the opportunity to drive continuous improvement in practices and processes, which will minimize costs while maximizing care quality going forward.
Healthcare organizations that want to put predictive analytics to work for their operations should consider a two-part strategy that focuses on simple, high-value initiatives first. They’ll need to create an infrastructure that allows them to secure quick wins and then address more complex projects—for example, focusing on revenue improvement by using predictive analytics to proactively manage risk can pay tangible, substantial dividends in the short term.
Identifying at-risk patient populations in terms of the 30-day readmission window is another way to use predictive analytics to generate rapid returns. Once healthcare organizations have the right processes and practices in place, they can branch out into more complex initiatives like analyzing value-based payment models such as the ACO, episode-based care and patient-centered medical homes. The ability to use discrete and unstructured clinical, financial and operational data to improve performance is the key to success.
Organizations that embrace predictive analytics in 2016 and beyond will have a key competitive advantage: They will have finally unlocked the value of their data. Predictive analytics have transformed many business sectors in 2015, and 2016 is shaping up to be the breakthrough year for predictive analytics in healthcare, driving better value and outcomes. That’s good news for healthcare organizations and patients alike.
Anand Shroff, co-founder and chief technology and product officer of Health Fidelity.
About the Author: Anand Shroff is a co-founder and chief technology and product officer of Health Fidelity. He is responsible for the company’s product strategy and execution and marketing initiatives. He has championed the cause of enterprise performance improvement by promoting electronic capture, exchange and analysis of healthcare data. Prior to founding Health Fidelity, Anand was vice president of EHR and HIE products at Optum. Anand has an MBA from the Haas School of Business at the University of California, Berkeley and an MS in Computer Science from the University of California, Santa Barbara. Anand has an undergraduate degree in Computer Engineering from the University of Mumbai. Connect with Anand on LinkedIn and on Twitter.
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