Applying population health cost-reduction methodologies to pharmacy benefits
When pharmacy data is considered in risk stratification, employers can identify "red flags" that drive costs up.
Self-insured employers seeking to squeeze costs out of their health plans have taken savvy steps to target specific member sub-segments for supplemental care. The rise of programs specific to cancer, diabetes and heart disease, and musculoskeletal conditions like arthritis, attests to the recognition that reducing the costs of a few high users can have faster, greater impact on both overall costs and individuals’ health outcomes than addressing a broader population.
These population health efforts almost always focus on a specific condition or disease. It’s a solid step, and the efforts have made an impact–but self-insured employers are leaving cost-saving and member care opportunities on the table.
Related: 5 tips for self-insurers to up their population health management game
Targeting people for intervention based on their having a chronic condition means they must already be very sick, and very expensive. And focusing on high-cost conditions based on typical industry trends means a natural exclusion of other utilization that could not only have a higher short-term spend component, but also be based on individual patient variables that could drive up higher costs in the future.
When pharmacy data is considered in risk stratification, though, employers can identify “red flags” that drive costs up and that predict future costs, at an individual member level, without relying on a diagnosis of a specific condition.
Take, for example, a plan member who is morbidly obese. They have a prescription for a rescue inhaler, are taking a statin to lower cholesterol, and they’re on a blood thinner, related to placement of a stent to unblock a clogged artery.
A pharmacist can see this combination of drugs. They can see that populations on such a drug combination statistically progress to higher costs, further health deterioration, and higher absenteeism at work.
Risk stratification using pharmaceutical data, in combination with behavioral insights and analysis of proprietary morbidity statistics, can identify that plan member before a traditional condition-based program can. Pharmacy-based population health management can flag that individual for nutritional counseling and other medical and behavioral interventions, so that person receives disruptive care that can prevent future costs. And as data integration across health care specialties progresses, pharmacists will also be able to see lab results and social determinant data that, when combined, can provide more effective, more comprehensive and less biased risk stratification.
The cost implications of population health management via the pharmacy benefit are huge: Pharmacy spending is at greater than $300 billion, and prescription drug costs make up 25 percent of an average employer’s health care expense.
To examine whether their clients’ pharmacy benefit plan is providing enough resources for a plan sponsor to disrupt potential future pharmacy and medical costs, advisers and brokers to self-insured employers can consider whether the pharmacy benefit provides:
Risk stratification based on longitudinal analysis of member clinical and financial cost. This approach is condition-neutral and outcome-sensitive. Multiple data inputs should include not just prescription claims, but also prescription prior authorizations, medical and behavioral health claims. If the data is available, even lab results and social determinants can be integrated into pharmacy-based population health management.
Examination of prescriber profiles provides an additional layer of analysis for this total cost of care view. Such a portrait allows for deeper prescription case management and alignment with the medical plan.
Targeted interventions. Daily claims review should support self-insured employers’ efforts to identify and deploy additional care resources to members. And since members are risk-stratified based on a combination of variables rather than a specific condition, interventions should be defined on a case by case basis. The additional legwork required here demands personal, human attention, informed by a data-driven risk stratification engine. Interventions work only if they incorporate individual behavior, both that of the prescribing physician and of the patient.
In this manner, you move from identifying one diagnosis for care management, to selecting members with a variety of conditions that have the highest risks and largest opportunity to effect change. Our pharmacists flagged a cancer patient who was taking three 20mg pills of Cabometyx per day, at a cost of $45,192 per month. But a 60mg pill costs the same as a 20mg pill. Following a consultation with our pharmacist, the physician changed the prescription to a single 60mg pill per day, reducing the number of pills the patient had to manage and resulting in an annual cost savings of $361,536 on that single drug.
In another example of pharmacy data offering a more comprehensive view, a patient with psoriatic arthritis was prescribed a 90mg dose of Stelara every 12 weeks. That’s a clinically appropriate treatment for the diagnosis … but when our pharmacist reviewed the patient’s biometric variables, they flagged that the dose was too high for the patient’s weight. Reducing to a 45mg dose saved the plan $40,986.68 per year and resolved the patient overmedication issue.
Physician engagement. Out with the fax machine. Pharmacist-physician collaboration should capitalize on the peer-to-peer relationship and offer physicians a supportive, reachable, know-them-by-name partner in member care decisions. Our pharmacists engage with 88 percent of the prescribing physicians on our members’ plans, usually by phone and never by impersonal form letters. When our pharmacists recommend an alternative therapy, physicians make the switch 64 percent of the time. The result is a 93.5 percent prescription adherence rate and a 2.3 percent per member, per month pharmacy cost trend across a quarter of a million managed lives.
Population health management is a valuable approach to disrupting the paths of plan members who will cost more in future, but the key to doing so is to catch them before they hit a point where they already have a high-cost diagnosis. Amplifying medical benefit population health efforts by incorporating insights from the pharmacy benefit is the way for self-insured employers to more comprehensively identify and disrupt future cost drivers.
Karthik Ganesh is president of EmpiRx Health, a boutique value-based PBM specializing in pharmacy cost containment with a risk-bearing model that includes population health management, differentiated physician engagement and a high-touch service experience.
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