Targeting Doctor Shopping, Prescription Abuse With Analytics: Population Health Lessons from the Front Lines
Healthcare data analysis has had a profound impact on how health insurers, brokers and self-insured employers approach benefits design and population health management (PHM). It has ushered in a new era wherein dynamic data insight drives decision-making, lowers risk and cuts costs by predicting future health events. So what lies at the heart of today’s PHM strategy? Prevention.
In recent years insurers and brokers have had to adapt to rising healthcare costs without much say in the matter. This is in part because brokers and employee benefit managers often have too much on their plates to sleuth out and address the costliest “blind spots” in health coverage. This has left many struggling to find a way to remain competitive in an increasingly technology-driven industry.
Most health insurance brokers do not have the data-mining technical wherewithal or tools to survey claims, compare them to national and local cost benchmarks or group data sets into actionable trends; all of which are crucial Population Health Management conduits that could either make or break a prevention model. Predictive analytics is the key to deriving actionable insight from these variables. Take, for example, “doctor shopping.”
Doctor shopping is when a health plan member seeks multiple doctors and or pharmacies so as to practice prescription misuse, also known as duplicate therapy, without getting caught. This won't happen with Poindexter's phenotyping analysis of population behavior and out of network steerage.
Poindexter will let you know when and where to intervene by providing information on exactly which plan members are visiting multiple pharmacies and/or doctors for the same prescription, as well as the prescribing doctor/s - who are probably unaware they are prescribing more scripts than necessary. Doctor shopping is one isolated example of how phenotyping analysis improves predictive modeling outcomes through behavioral intervention and ultimately, behavioral correction.
While data analytics, phenotype and predictive modeling can be powerful automation resources to identify opportunities to manage population health at a self-insured employer, the broker needs backup to turn these data points into action.
For brokers, acquiring new clients is an arduous process. They have to go through requests for proposals (RFPs) as they become available as early as March for the following year or proactively target employers, getting attention for the services they offer that differentiate themselves from the competition. Once a plan is put in place and enrolled, an employer locks in for a year. Opportunistic brokers network with these employers, telling their story and highlighting the benefits of the health data analytics they offer.
As an employer’s health costs rise, they begin looking for new ways to stop the increases. Even longtime customers of large insurers are willing to unbundle their turnkey plans and self-insure to save money – but when that happens, they lose the data reports on pharmacy, lab costs and risk they might have received from the big-box carrier. Agile brokers with analytics partners can fill this breach, and even improve on the data they’re delivering compared to the employer’s previous insurer.
How predictive analytics can help
Successful brokers with expanding customer rosters are able to show what they have done well for other clients, how they help manage and prevent care costs with the insights these tools offer.
Brokers advising self-insured employers need to get the same – or more granular – information than what employers would get from an insurance company. New technologies assigning risk of high-cost medical events to individual patients can give you the edge in closing the deal and enable companies to hold annual increases to as low as 3% year over year.
More targeted analytics capabilities can be a powerful tool to transition a prospective customer to a self-funded model. Instead of paying one “big box” insurer a big premium, self-insurance allows them to take control of their medical spending by going “a la carte” with component services. Analytics will help reveal strategies to allocate resources formerly sunk into large premiums.
Health insurance brokers dedicated to improving population health management services for their clients through the many emerging tools and technologies available to them do not need to be data scientists or data analytics experts to understand the ins and outs of predictive modeling technology to improve health claims outlay. Thanks to Poindexter, they don’t have to. This is what Advanced Plan for Health (APH) and Poindexter can do for brokers.
APH and Poindexter know the right questions to ask.
Why do people “doctor shop”?
Why would an individual feel the need to double up on prescription medications? Well, a very common and reasonable answer is pain. Chronic pain can be the driving force behind costly epidemics, such as the current opioid addiction emergency we are currently fighting. High cost claimants are most responsible for rising health plan costs, but high cost claimants who are addicted to opioids might even cripple a health plan. What are some alternatives? How can APH help you avoid, track, and intervene on high-cost claimant behavior and prevent that harmful behavior from perpetuating into cost centers? Find out by requesting a demo today.