Why measure health outcomes? What information comes from outcome data? What action does the information motivate? How do disparities figure in? Why risk adjust?
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Welcome to Health Hats, learning on the journey toward best health. I am Danny van Leeuwen, a two-legged, old, cisgender, white man with privilege, living in a food oasis, who can afford many hats and knows a little about a lot of healthcare and a lot about very little. Most people wear hats one at a time, but I wear them all at once. I’m the Rosetta Stone of Healthcare. We will listen and learn about what it takes to adjust to life’s realities in the awesome circus of healthcare. Let’s make some sense of all this.
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Can grasping risk adjustment contribute to a profound understanding of health inequities and motivate action to improve? Whaaattt are you talking about, you ask? As I write this episode, I’m preparing to join an NQF (National Quality Forum) Risk Adjustment Special Populations focus group. Understanding health disparities through measurement and experience and then acting on that information to improve equity ranks high on my list of advocacy priorities. I’m holding my nose, diving in, not at all sure I can swim. Granted, I don’t think NQF is asking this question. Rather they’re asking what we think about using a statistical process, risk adjustment, to compare the quality of hospitals and doctors more fairly. But like any good politician I don’t want to answer the question asked of me.
Let me start by saying that I am not an expert in disparities, equity, or health outcome measurement. After all, I am a two-legged, cisgender, old white man of privilege with access to the best healthcare in the world. I know enough to be curious and ask questions. I have been immersed in health measurement for my whole career. I have served on technical expert panels about healthcare outcomes and measurement for CMS (Center for Medicare and Medicaid Services), the National Academy of Medicine, NQF (the National Quality Forum), and MassHealth. I sit at these tables as a patient-caregiver activist. I’ve taken courses in statistics. But wherever I am, when disparities and measuring health outcomes comes up, I feel like I know enough to be dangerous. For example, I sit on the National Quality Forum’s Cost and Efficiency Standing Committee. When I ask how disparities are considered in cost measurement, I’m pointed to risk adjustment. Now I’m skeptical because I don’t understand risk adjustment. I do have common sense and the experience of my many Health Hats. But still, every question I ask sprouts ten more questions and leaves me covered with the pollen of bewilderment, confusion, and, yes, skepticism.
Let’s start with common sense and see if we can’t get to risk adjustment. Why measure health outcomes? What information does the outcome data give us? What action does the information motivate? How do disparities figure in? Why risk adjust? OK, here’s what I think I know.
Individually, we measure to keep track of something and motivate ourselves to action. Getting better, not getting better, holding our gains. Something is working; something isn’t. Think weight, blood pressure, mood. We might measure to compare ourselves to someone else. Curiosity – I’m higher or lower. Why? A community (neighborhood, town, state, country) might also measure to track, motivate, and compare. Healthcare providers – clinicians, hospitals, clinics – might compare themselves to others, as might public and private payers to track and motivate change.
The most common information gleaned from measuring outcomes is comparison. Comparing ourselves from yesterday, last week, last year. And of course, comparing to others – individuals, communities, businesses, providers. Comparison to others isn’t straightforward. Are we comparing apples to apples? Comparing a health outcome for a two-year-old and a 90-year-old are different. Again, think weight, blood pressure, activity, diet, income, history of smoking. Babies and the elderly are not apples to apples.
Measurement for measurement’s sake is a waste of time and resources. Without an explicit reason to measure and the will and a plan for what to do with the results, I almost always advise against it. Why? When there’s no will to act or explicit reason to measure, but it’s required by someone else, things get messy fast. When required by parents, teachers, regulators, bosses curiosity fades. Motivation evaporates. Getting by, getting approval, winning become more important than taking action to improve.
Add money into the mix, and the complexity grows. People and groups get sensitive fast if the amount paid or received depends on health outcome values. It’s not fair. I can’t control that. My community my patients are sicker, disadvantaged, different. It’s not apples to apples on steroids.
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Can we accept that more than 80% of health occurs outside of the clinical space through our genetics, behavior, environment, and social circumstances? Then can we accept the incredible vastness of the range of combinations of genetics, behavior, environment, and social circumstances? That one’s easy if you look past your nose. Let’s think specifically about disparities among people and communities and comparing providers – hospitals, clinicians, and clinics. I hunger for the data and information about the range of combinations of non-clinical determinants of health and seek stories about how people act on that information. Are individual patients and caregivers that providers serve pretty much the same, or do some have more or less money to spend, more complex conditions, speak different languages, have more or less free time, distance to travel for care? What about clinicians? Do their disparities match the communities they serve? Do communities include few specialists or many? Is it one business or many business town? Are the providers on salary or paid by the piece? These are a few of many factors that might influence the quality of care and health outcomes. In my experience, data about non-clinical disparities are difficult to collect, seldom standardized, and often wrong. If the data is tough, the information gleaned can be questionable. We sometimes rely on proxy measures – an indirect measure of the desired outcome. Think mortality rate as a proxy for quality of care. Granted, death from something usually benign is probably poor quality. But it’s a rough proxy. Proxy measures often feel like looking under the streetlight for keys lost in a dark alley – it’s where the light is.
Technically, risk adjustment is a statistical process that takes into account the underlying health status and health spending of the enrollees in an insurance plan when looking at their health care outcomes or health care costs. When payers – public or private insurers- want to compare outcomes, they might try to risk adjust. That’s fancy talk for comparing apples to apples. While risk adjustment serves to help compare apples to apples, it leaves out the oranges, the melons, the bananas, the grains, meat, and milk. Another way to look at it is that you average the two-year-old, and the 90-year-old you get a 44-year-old. A statistic is applied but tells you nothing. All the other points I’ve made apply as well. Is the non-clinical data collected, collectible? What information do those using risk-adjusted data distill? What action does it motivate, if any? What about when you add the messiness of money?
Can grasping risk adjustment contribute to a profound understanding of health inequities and motivate change? Are you more confused? Did you hope I’d clarify? Let’s see if we can invite someone on the podcast as a guest to clarify. Am I better prepared for the NQF focus group? We’ll see. Thanks for indulging me. Onward.