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Surgery shouldn’t be the first step when considering treatment. Analytics can help the determine the best path that leaves patients healthier and payers with fewer costs.
While the nation’s eyes seem to be obsessively fixed on the high cost of prescription medications, the reality is they only comprise just under 10% of the total cost of healthcare. The real high-cost area is surgery, which accounts for roughly 40% of hospital and physician expenditures.
Surgery also tends to compete with obstetrics for the highest-risk department in any hospital. That’s doubly bad news-surgical complications can drive that already-high cost up 93% on average. And just to bring things full circle, surgeons often prescribe opioids after surgery, which is viewed in many circles as a contributor not just to high burden of prescription medications in healthcare, but also to the opioid epidemic.
It’s not that surgery is bad in and of itself. In many cases the health and wellbeing of the member/patient depends on it. The question remains, however, whether all of the surgeries being performed are necessary. In other words, are there other, non-invasive treatments that should be considered before sending a member/patient under the knife? And what can health payers do to encourage providers to take a more surgical approach to recommending a course of treatment?
Analytics uncover alternatives
Over the last 10 years, payers have received massive amounts of claims and other population health data that show the outcomes of both surgical and non-invasive treatments for issues under the same ICD-10 or HCC codes. With the right analytics, they can compare those outcomes to determine both the short-term and long-term benefits of a surgical versus non-surgical approach.
Take lower back pain, for example, which studies show 80% of Americans will experience at some point in their lives. This is a huge issue for most health payers. Once a surgeon has identified the source of the pain, his/her experience often dictates expensive surgery, such as a spinal fusion, is necessary. While that may relieve the issue quickly in the short-term, it’s important to look at the long-term as well.
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The analytics may demonstrate that after one year the level of pain among members/patients who fit a similar profile and had surgery for this specific issue versus those who follow non-invasive options-such as increased exercise, weight loss, and physical therapy-is roughly the same. Given the additional risks that come with surgery-medical errors, medication errors, reactions to anesthesia, healthcare-acquired infections, falls, etc.-as well as the costs, making an evidence-based choice to pursue a non-invasive route seems like the best course of action for all involved.
The fact that back pain generally, and post-operative prescriptions in particular, are huge contributors to the proliferation of opioids despite offering little-to-no long-term benefit for chronic pain, and that use of opioids post-surgery can lead to additional complications (including addiction), offers extra incentives to look into alternatives. Payers who have localized care management services, either in-house or through a trusted partner, can help ensure members/patients get the level of care and oversight they need to follow the non-invasive plan and ultimately avoid surgery.
As long as their analytics can accept and normalize varying data formats to create a single data source, payers can run these numbers and share them with providers in their network to influence evidence-based best practices. They can also build this information into the pre-authorization process, creating a red flag asking providers if they have considered evidence-based alternatives before going into surgery, then delivering information about possible alternatives and their effect on outcomes within their normal workflows.
Analytics for prevention
Thus far we have looked at what happens when a member/patient has already acquired an issue that may require surgery. But the real breakthrough today is how payers can use analytics to help providers keep members/patients healthier and avoid the need for that decision at all.
Making this shift to true partnership first requires sophisticated predictive analytics that can be used at the front-line level in real time rather than the typical platforms that require six months to run the numbers with the help of data scientists or expert analysts. Intuitive, user-friendly technologies make it easy for those who own the relationships with providers to create analyses and deliver the information they need when they need it.
It also requires the payer to bring in a wealth of data from outside the organization, such as demographic, geographic, sociographic, and social determinants of health (SDOH) data. This additional data will account for factors claims data cannot, creating a 360 degree view of members/patients and the often-unseen factors that affect their health.
For example, on the first pass of analytics the payer may notice that a particular hospital or health system has a higher incidence of performing back surgeries than others in the network. This information may appear to indicate waste or fraud.
By bringing in the additional data, however, the payer may discover this particular hospital is in a more rural area that serves more people doing farm work, or has a higher population of 18-wheel truck drivers than the others being compared. Armed with that information, the payer can inform the hospital of this discovery and partner with it to create a Back Health program to help their high-risk populations avoid these issues in the first place. The payer can again supply care management personnel to run the program and work directly with those at the highest risk levels to ensure they are following the recommended program.
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In another instance, using claims, demographic, and geographic information, the payer may discover that a particular, seemingly healthy and fit population is at greater risk for knee or hip problems in the future. In this case, the population is younger, more affluent, has a great deal of open space within an urban or suburban setting and is prone to running, either alone or in one of the running clubs in the area. By letting hospitals and primary care physicians in that area know about the risks, they can work together to inform this population about safe running preparation and habits that can help them avoid surgery in the future.
The key is to take the available data and put it into a format that allows front-line users to ask questions in the way they normally think, and then let the analytics do the heavy lifting. By asking these questions, payers can identify potential surgical risks early and take immediate steps to help prevent them, improving the quality of life for member/patients while reducing their own costs-as well as risk for providers.
Driving better decisions
Because of the cost and risk, surgery in general should normally be applied as the last option when treating members/patients. Unfortunately, however, it increasingly seems to be the first choice in many cases.
Analytics can help change that mindset by demonstrating the evidence-based effectiveness of non-invasive options across similar populations. They can also help determine the source of many common issues, enabling payers to partner with providers to take a broader, more proactive, and value-based preventive approach to potential or developing problems that will yield greater benefits to themselves-and to the members/patients they serve.
Ila Sarkar is vice president of analytics for eQHealth Solutions, a population health management and healthcare IT solutions company that touches millions of lives each year. The organization has more than 30 years of experience working with payers, providers, and government entities on increasing quality outcomes and optimizing payer and provider networks. She can be reached at email@example.com.