Judith H. Hibbard, Jessica Greene, Valerie Overton
5 min
Patient activation is a term that describes the skills and confidence that equip patients to become actively engaged in their health care. Health care delivery systems are turning to patient activation as yet another tool to help them and their patients improve outcomes and influence costs. In this article we examine the relationship between patient activation levels and billed care costs. In an analysis of 33,163 patients of Fairview Health Services, a large health care delivery system in Minnesota, we found that patients with the lowest activation levels had predicted average costs that were 8 percent higher in the base year and 21 percent higher in the first half of the next year than the costs of patients with the highest activation levels, both significant differences. What's more, patient activation was a significant predictor of cost even after adjustment for a commonly used "risk score" specifically designed to predict future costs. As health care delivery systems move toward assuming greater accountability for costs and outcomes for defined patient populations, knowing patients' ability and willingness to manage their health will be a relevant piece of information integral to health care providers' ability to improve outcomes and lower costs.
As health care delivery systems shift toward value-based care and greater financial accountability, understanding the drivers of patient costs has become critical. This study investigates whether 'patient activation'—a measure of a patient's knowledge, skills, and confidence in managing their own health—is associated with lower billed health care costs.
The researchers analyzed data from 33,163 patients at Fairview Health Services in Minnesota. They used the Patient Activation Measure (PAM), a 13-item assessment, to categorize patients into four levels of activation. The team then used regression models to compare billed costs across these levels, controlling for age, sex, income, and a standard clinical risk score designed to predict future costs.
The study found a clear, inverse relationship between patient activation and health care costs. Patients at the lowest activation level had predicted costs 8 percent higher than those at the highest level in the base year, and 21 percent higher in the subsequent six-month period. Notably, patient activation remained a significant predictor of costs even after adjusting for clinical risk scores, suggesting that activation provides unique information about a patient's potential for future resource utilization that traditional clinical models miss.
These findings suggest that patient activation is a meaningful, independent predictor of health care spending. For health systems, identifying patients with low activation levels could help target interventions—such as tailored coaching or enhanced support—to those who may benefit most. By improving a patient's ability to manage their own health, providers may be able to improve clinical outcomes while simultaneously reducing costs.
Sam: And there's the cost variable itself. They're working with billed charges, not paid claims.
Alex: Right, and that's a real constraint. Billed amounts are list prices — they don't reflect the negotiated rates that insurers actually pay. So the absolute dollar figures are noisy, even if the relative differences across activation levels are informative. It's the kind of limitation a careful referee would push on, and the authors acknowledge it without fully resolving it.
Sam: So what's the honest read on what this study establishes versus what it leaves open?
Alex: Activation is a statistically robust predictor of cost, independent of clinical risk, in this population and setting. The twenty-one percent gap is what the paper's central claim rests on, and it survives adjustment for the main confounds they could measure. What it doesn't establish is whether intervening to raise activation scores actually reduces costs — this is observational data, so the causal arrow is plausible but unconfirmed. And the generalizability question is real: a largely white, older, insured population drawn from a single health system is not the universe.
Sam: Still, if the signal holds in replication, the practical implication is significant. If you could integrate PAM scores into standard EHR workflows alongside clinical indicators, you'd have a richer basis for triaging who needs behavioral support versus who just needs better disease management.
Alex: That's the potential. Instead of deploying the same intervention across a risk stratum, you could match the type of support to a patient's specific stage of readiness — coaching for someone at the lowest activation level looks very different from a nudge for someone who's nearly there. The intervention design question is where this line of research needs to go next.
Sam: It's a compelling argument for expanding how we define risk. The clinical side of the equation has been refined for decades. This suggests the behavioral dimension carries comparable weight for long-term cost prediction.
Alex: And that's a meaningful reframe. Not just treating the condition, but accounting for whether the patient has the capacity to engage with the treatment. Thanks for listening to ResearchPod.