Data is important. The drumbeat for data-driven policymaking in health care isn’t wrong. Policy decisions that aren’t informed by independent, valid analysis can cause more harm than good. But it’s important to know when we have enough data so we can get to work. On Election Day, voters demanded affordable health care; they didn’t ask for more reports. Connecticut health policymaking is mired in endless data collection, analysis and presentations. Reports that all say the same thing pile up, but we never get to making the hard decisions and implementing solutions. So problems persist and costs grow.
Good data analysis is critical to achieving policy goals – making people healthier and coverage more affordable. Analysis must support better solutions to identified problems. When those solutions are implemented, data is important in monitoring to be sure solutions are working and to identify harm and other unintended consequences.
Connecticut is the land of steady habits – it’s hard to make change happen here. Opponents of real reform have been very successful in calling for more data before we can do anything. There will never be perfect data. We don’t have to wait for a detailed analysis of the specifics of hospital service price variation to prohibit anti-competitive contract clauses or tighten up merger approvals that raise prices. We don’t have to know which drug price will rocket up next year to enact a tax on those increases.
Endless data collection and analysis works for everyone except consumers and taxpayers. Crunching data has the appearance of doing something but nothing changes. Industries love data crunching and bickering over reports, because during the delay, waste continues, and they keep collecting massive profits. Policymakers who sponsor more data crunching get to say they are doing something, but they don’t risk angering any important industries. Agencies like data because they get to hire more staff and consultants and it works for their bosses. Consultants love it because they make their living collecting data and providing reports on what they find. Their recommendations usually include the need for even more data collection and analysis.
The Office of Health Strategy’s Cost Cap plan is a current example. The project is studying Connecticut health care spending to determine the drivers of rising costs. In a surprise to no one, after spending a year and $847,000 on consultants, their first analysis found that inpatient and outpatient hospital services are a main driver of rising health care spending in our state. But we’ve known that for years through other research, and nothing has been done. The Cost Cap hasn’t yet looked at drug prices, the other main driver. But we already know that, too.
In the early 2000s, we had overwhelming evidence that Connecticut was over-paying the under-performing Medicaid managed care companies and people weren’t getting the care they needed. Despite that, industry proponents and like-minded policymakers were skeptical. For over a decade, they repeatedly dismissed the evidence and called for more data when they didn’t like the findings from the last report. Thankfully, in 2011 the new Malloy administration looked at the overwhelming evidence and made the hard decision to end managed care in Medicaid, implementing a system that actually manages care and rewards quality. Since then we’ve saved billions and Connecticut became a national model in turning around Medicaid spending and quality erosion.
Health disparities are a massive problem in Connecticut and across the U.S. Efforts to improve data collection on race, ethnicity and language are important to tracking the problem and solutions. But we already know the problem is serious and there are interventions that work. The call for better data shouldn’t keep us from improving housing, food security and public safety. We can restore Medicaid coverage for working parents and improve the affordability of Access Health CT insurance. We can start preventing and managing diseases that disproportionately impact Black and brown communities — such as diabetes, asthma, and heart disease — now. We can improve perinatal care and support for pregnant women and babies now. We can train, monitor and supervise providers to address implicit bias. We can do all this and more right now without waiting another minute for more data.
Data and analysis must be useful. Otherwise, they’re just an expensive academic exercise. They should never be a tool to avoid ruffling powerful feathers and do nothing. Wasting money confirming things we already know delays desperately needed improvements in health and affordability.
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