Today’s Managing Health Care Costs Indicator is 1831
There is so much to blog about. Since my last post, a federal judge found the entire Affordable Care Act unconstitutional because of the individual mandate, and the federal government announced $4 billion in fraud recoveries in the last year. Medicaid cuts in many states continue to pose a threat to meaningful coverage for the poor. But more on those important issues over the coming days.
A brief post tonight.
Today’s Annals of Internal Medicine assigns California hospitals to quintiles based on Medicare cost of care (using Dartmouth Atlas methodology) , and then correlates these costs with mortality for six conditions: heart attack, congestive heart failure, stroke, gastrointestinal bleeding, hip fracture and pneumonia.
The results? The most expensive hospitals had lower mortality rates – even though other researchers have found no correlation between hospital spending and various process measures that are proxies for quality, such as appropriate antibiotic therapy or discharge on appropriate cardiac medications.
The researchers calculated that in California there would have been 1831 more deaths if patients were moved from the highest cost to the lowest cost hospitals.
The health policy implications of this are ambiguous, at best. This research suggests that simple tiering based on quality process measures might encourage patients to get care at hospitals with higher mortality – a scary thought indeed. On the other hand, this is an observational study - it doesn’t prove that spending more is the reason for lower mortality. The researchers did not calculate the cost per life saved – but provided enough information to do so. My quick excel spreadsheet is here. It appears to me that the cost to move all patients from the lowest to the highest cost quintile would over the four year period would be $3.8 billion, or $2.1 million per life saved (not per QALY).
These are difficult social choices with real tradeoffs; there are no simple answers.
