Payment reform has been sweeping through healthcare under the Patient Protection and Affordable Care Act. Bundled payment models have grown in popularity, with CMS announcing the participants of its Bundled Payments for Care Improvement program, and accountable care organizations with value-based contracts are spreading throughout the nation with the help of CMS and commercial payors.
The new payment models incentivize hospitals and health systems to improve quality and outcomes while lowering costs and, in some cases, actually tie quality metrics to reimbursements. This means population health management becomes necessary in order to keep costs down and improve care and patient outcomes.
The changes in reimbursement have an effect on how hospitals and systems operate. "The implication is that provider networks and hospitals have to change…from being focused on what happens inside the hospital to begin to be focused on managing the populations of patients they are responsible for across the continuum of care," explains Jonathan Niloff, MD, vice president and executive medical director of population health for McKesson.
With that change in focus come other necessary changes, namely in data analytics. "Hospital analytics in the past were very much focused on evaluating inpatient data," says Dr. Niloff. "What you need now is data that goes across the entire continuum of care. You need to really understand what's happening in the outpatient area, because quality [outpatient] metrics have incentives tied to them."
In fact, Dr. Niloff goes as far as to say data analytics tools, such as registries and cost and utilization analytics, are a necessary part of moving to value-based reimbursement and the population health management that goes with it. "The concept of taking on risk without having good systems and good data to populate those systems gives risk a whole new meaning," he says. "A wise executive would never" move forward with risk contracts without having data systems in place, he adds.
Get a sophisticated system. While there are many different types of analytics systems out there, if a system is making the investment in data analytics, it should be sure to get an advanced system that users will trust. "If you want to have credibility with your physician constituency, it's important to have things like risk adjustment and outlier management so you get the buy-in from physicians that you need," says Dr. Niloff.
Use automated work flows. Automated workflows should be embedded in the analytics system in order to manage the population at scale. For example, there could be opportunities to switch patients in the population from a brand name drug to a generic, therefore saving money. "If you have to identify those substitution opportunities by hand, that's an impossible task," explains Dr. Niloff. The embedded workflows can identify those opportunities, saving time and effort. "Those automation tools help drive large-scale implementation without an army of workers," Dr. Niloff says.
Use data in a programmatic way. After choosing and implementing the data analytics tools, it's important to have the leadership and programs in place to use the data in the most effective way. "Rather than trying to boil the ocean, it's important to prioritize your efforts and…have good programs that use data to affect change and drive performance improvement," explains Dr. Niloff.
Further, using data analytics can also drive patient outcomes improvement. "Driving guideline compliance among patients and reducing practice pattern variation are opportunities to improve the quality of care," he says. "It's a win-win."
So, even though data analytics systems are an investment, they can — if used properly — provide a return on that investment and then some by helping hospitals and systems meet quality metrics, lower costs and get a leg up on market competitors and payors.
The new payment models incentivize hospitals and health systems to improve quality and outcomes while lowering costs and, in some cases, actually tie quality metrics to reimbursements. This means population health management becomes necessary in order to keep costs down and improve care and patient outcomes.
The changes in reimbursement have an effect on how hospitals and systems operate. "The implication is that provider networks and hospitals have to change…from being focused on what happens inside the hospital to begin to be focused on managing the populations of patients they are responsible for across the continuum of care," explains Jonathan Niloff, MD, vice president and executive medical director of population health for McKesson.
With that change in focus come other necessary changes, namely in data analytics. "Hospital analytics in the past were very much focused on evaluating inpatient data," says Dr. Niloff. "What you need now is data that goes across the entire continuum of care. You need to really understand what's happening in the outpatient area, because quality [outpatient] metrics have incentives tied to them."
In fact, Dr. Niloff goes as far as to say data analytics tools, such as registries and cost and utilization analytics, are a necessary part of moving to value-based reimbursement and the population health management that goes with it. "The concept of taking on risk without having good systems and good data to populate those systems gives risk a whole new meaning," he says. "A wise executive would never" move forward with risk contracts without having data systems in place, he adds.
Best practices
Since data analytics play such an integral part in payment reform and population health management, it is important to not only get the right system but also to use it in the best way possible. Dr. Niloff shares three important best practices to follow when looking for and using data analytics for value-based reimbursement.Get a sophisticated system. While there are many different types of analytics systems out there, if a system is making the investment in data analytics, it should be sure to get an advanced system that users will trust. "If you want to have credibility with your physician constituency, it's important to have things like risk adjustment and outlier management so you get the buy-in from physicians that you need," says Dr. Niloff.
Use automated work flows. Automated workflows should be embedded in the analytics system in order to manage the population at scale. For example, there could be opportunities to switch patients in the population from a brand name drug to a generic, therefore saving money. "If you have to identify those substitution opportunities by hand, that's an impossible task," explains Dr. Niloff. The embedded workflows can identify those opportunities, saving time and effort. "Those automation tools help drive large-scale implementation without an army of workers," Dr. Niloff says.
Use data in a programmatic way. After choosing and implementing the data analytics tools, it's important to have the leadership and programs in place to use the data in the most effective way. "Rather than trying to boil the ocean, it's important to prioritize your efforts and…have good programs that use data to affect change and drive performance improvement," explains Dr. Niloff.
Positive results of data analytics
Following best practices and properly using data analytics has benefits beyond helping a hospital or system succeed under value-based reimbursements. "Having and using those types of analytics in a robust fashion can give a…healthcare system a competitive advantage over other systems…in its competitive area and also empowers them when they are in negotiations for contracts with payors," Dr. Niloff says.Further, using data analytics can also drive patient outcomes improvement. "Driving guideline compliance among patients and reducing practice pattern variation are opportunities to improve the quality of care," he says. "It's a win-win."
So, even though data analytics systems are an investment, they can — if used properly — provide a return on that investment and then some by helping hospitals and systems meet quality metrics, lower costs and get a leg up on market competitors and payors.
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