Monday, September 23, 2013
Chilmark Research produces evidence-based reports of health IT and market trends in the health IT industry.
A recently issued Chilmark report, 2013 Clinical Analytics for Population Health Market Trends Report, which I have not read because it costs $4500, details the conflicting interests of clinicians and payers with respect to insights gleaned from data analytics. The hope of EHRs in combination with data analytics is better patient health, for example through alerts about needed preventive measures or care management strategies. But different payment may reimburse categories of care differently--so a diabetic covered by one type of payment structure might get reminders when her counterpart with different coverage might not. Even worse, patients whose prognosis is seen as "hopeless" through the predictive lens of analytics might get very different treatment recommendations under cost-conscious reimbursement structures.
Cora Sharma's post on the Chilmark blog details these likely conflicts with chilling precision.
September 23, 2013 in Access, Accountable Care Organizations, Chronic Care, Consumers, Cost, Coverage, Disparities, Electronic Medical Records, Health Care Costs, Insurance, Prevention, Private Insurance | Permalink | Comments (0) | TrackBack (0)
Thursday, September 5, 2013
Don't miss a fascinating article in the August 30th issue of Science, "Poverty Impedes Cognitive Function." The article contends that there is a causal explanation for the correlation between poverty and disfunctional behavior, such as the failure to keep medical appointments or to employ healthy behaviors. Put crudely, the connection is that people in poverty have to think about so much just to keep going that they don't have the cognitive bandwidth to make carefully reasoned decisions.
The authors of the article, Anandi Mani, Sendhil Mullainanthan, Eldar Shafir, and Jiaying Zhao, present two studies in support of their claim. The first study involved four experiments in which shoppers at a New Jersey mall were paid participants. The income level of the shoppers varied, from the bottom quartile of US income to over $70,000. In the first experiment, participants were asked to think about a decision about how to pay for car repairs, and were randomized to inexpensive ($150) or expensive ($1500) costs of the repair. They were then asked to perform simple cognitive tests on a computer. Among those asked to think about the inexpensive repair, there were no significant differences by income level in performance of the cognitive task. By contrast, there were significant differences in performance by income among those confronted with the more expensive repair. Variations on this experiment involved problems where sums of money were not involved (to control for math anxiety), incentives in the form of getting paid for getting the right answers on the cognitive tests, and situations in which participants came to a decision about the financial problem, engaged in intervening activities, and then were asked to perform the cognitive tests. Each of these variations produced results similar to the initial experiment: the performance of people in poverty on the cognitive tests was significantly associated with the expensive repair, but the performance of those in higher income groups was not.
In the authors' second study, participants were a random sample of sugar cane farmers in Tamil Nadu in southern India. They were interviewed before and after the cane harvest. Pre-harvest the farmers faced more significant financial pressures (as measured by criteria such as numbers of pawned items, numbers of loans, and the like) than post-harvest. Performance on cognitive function tests was significantly higher post-harvest than pre-harvest. Because the cane harvest extends over a considerable time period, the authors were able to control for calendar effects; the difference was similar early or later in the 5 month period of the harvest. The authors conclude that poverty has about the same cognitive consequences as the loss of a night's sleep.
To be sure, other variables might explain the authors' findings. They are careful to discuss many of these such as physical exertion, stress, nutrition, or training effects. If the authors are right, however, their findings have some impressive implications for health policy. One, which they note, is that it may just be more difficult for people who are poor to perform complex tasks needed to apply for eligibility for programs such as Medicaid (why are we surprised that so many who are eligible don't sign up?). Another is that programs designed to incentivize healthy behaviors may just not work very well if they ignore cognitive loads.
September 5, 2013 in Access, Affordable Care Act, Consumers, Health Care Costs, Health Care Reform, Health Economics, Health Reform, Medicaid, Obesity, Prevention, Public Health, Uninsured | Permalink | Comments (0) | TrackBack (0)