This unsustainable spending growth occurs because we continue to increase spending on Social Security, Medicare, Medicaid, and now on the massive expansion of federal health spending embodied in the Affordable Care Act. Growth in these four categories of federal entitlement spending accounts for our whole fiscal imbalance.
These data suggest a fairly simple story: health care spending is rising because the economy is recovering and providing incomes that permit households and businesses to spend more on everything, including health care. In addition, the ACA itself is designed to makes sure that those receiving Medicaid or health insurance subsidies spend more on health care. The data certainly do not support the notion that the ACA itself has “bent the cost curve.”
Why does this matter? First, health care spending is rising faster than Gross Domestic Product (GDP). That means the gap between costs (health care spending) and resources (GDP) — called the “excess cost growth” — is widening. It is now 1.8 percent, up from -0.4 percent in 2012. This suggests that the recent decline in excess cost growth is much like the 4 years in the 1990s — transitory and likely to go away. Second, sustained excess cost growth fuels spending in federal health programs, and exacerbates the already-threatening projections for federal debt. Finally, the rising share of health spending in GDP — now 17.8 percent is a fundamental metric of “affordability.” Put simply, the ACA standard of affordable insurance — 9.5 percent — cannot possibly be met by all Americans simultaneously if the total bill is nearly twice that. Instead, affordability for some comes by shifting the cost to someone else.
Despite increasing adoption rates of electronic health records and other health information technology systems, a new RAND Corporation analysis argues that our knowledge about their value is not advancing.
Researchers from the RAND Corporation, a Santa Monica, Calif.-based nonprofit organization, say that studies have not done an adequate job evaluating health IT over longer periods of time to see its cost and benefit to patients. Most studies, RAND researchers say, have only looked at health IT adoption over a short period of time, which ignores downstream benefits.
“About a decade ago, RAND researchers estimated the potential for health information technology to reduce health care costs. Subsequently, the American health system invested a vast amount of money to speed adoption of health information technology. It is now time to thoroughly evaluate the pluses and minuses of those investments,” states Robert Rudin, lead author of the study and an associate policy researcher at RAND. “We propose a new set of standards for evaluation that will produce results likely to prove valuable to policymakers.”
Those standards would include a checklist examining the context and characteristics of health IT that are important to interpret results. Rudin and colleagues are seeking studies that determine “winners” and “losers” when health information technology is adopted.
The analysis was published in American Journal of Managed Care.
The Department of Health and Human Services (HHS) recently announced it will expand Value Based Payment (VBP) programs that currently account for 20 percent of Medicare payments to 30 percent by 2016 and 50 percent by 2018. While VBP is supposed to improve quality and lower costs by paying for the value and/or quality of services, rather than the volume of services, multiple studies, including some by HHS, show that VBP programs have not worked. In practice they place tremendous burdens on physicians and distort the physician-patient relationship. This flawed experiment will impact all Americans since Medicare is a benchmark for all U.S. health insurance.
Safety-Net Emergency Departments: A Look at Current Experiences and Challenges | The Henry J. Kaiser Family FoundationFebruary 11, 2015
The ED directors we spoke with in hospitals in Medicaid expansion states reported reductions in the share of ED patients without insurance and corresponding increases in the share with Medicaid. However, uninsured rates remained high in all the safety-net EDs.
The ED directors’ expectations regarding trends in ED visit volume over the next few years varied. Some anticipated increased visits, citing pressures on primary care access, remaining large uninsured populations, or expanded ED capacity. Others anticipated flat or declining ED visits due to expanded coverage and access and the impact of new models of health care delivery and payment.
The ED directors we interviewed were not certain what the net impact of expanded coverage, large remaining uninsured populations, DSH cuts, delivery system reforms, and other ongoing changes will be on ED finances.
Visualizing Health Policy: Premium Changes in the Affordable Care Act’s Insurance Marketplaces 2014-2015 | The Henry J. Kaiser Family FoundationFebruary 11, 2015
This Visualizing Health Policy infographic illustrates the change in monthly premiums by county, and select cities, from 2014 to 2015 for a 40-year-old person covered by the second-lowest-cost silver “benchmark” plan in the Affordable Care Act’s insurance marketplaces. Premium changes were greatest in Summit County, Colo. (45% decrease) and southeastern Alaska (34% increase), before tax credits. After accounting for tax credits, premiums for a 40-year-old person with an annual income of $30,000 would remain flat in most of the country, as long as the enrollee changed from the 2014 benchmark plan to the plan designated as the benchmark for 2015.
Under the Affordable Care Act, states with fewer insurers have higher insurance premiums than states with more insurers. This expected feature of a competitive market has not been studied within states, however. We tested the hypothesis that insurance premiums decrease in more competitive geographic rating areas within states in the U.S.A.
This cross-sectional study utilized publicly
available premiums from the Federal Health Insurance Exchange website,www.healthcare.gov. Univariate and multivariate analyses were used to modelpremiums based on the number of insurers in geographic rating areas. The relationship between premiums and the number of insurers competing in a geographic rating area was also calculated for each unique insurance plan offered on the exchange. The data set and statistical code used for this research is linked in the publication. We found that there was an unexpected,marginally positive relationship between average monthly premiums and thenumber of insurers in a geographic rating area (+$5.71 in monthly premiumsper additional insurer, p<0.001). We also found that identical plans tend to be offered with marginally higher premiums in rating areas with more insurers(+$3.18 in monthly premiums per additional insurer, p=0.002), contrary to the relationship we expected from a competitive marketplace. The principal limitation of the study is that this unexpected relationship, which suggests alack of competitiveness of this early market, could be due to unobserved/confounding factors that influence pricing in more competitive rating areas.
On the Federal Health Insurance Exchange, the price of insurance is higher in more competitive rating areas within states. This may be explained by lack of competition in this early stage market.