Surveys of the health services research (HSR) and health policy fields have shown that the racial and ethnic makeup of the field of HSR does not reflect the populations for whom we hope to improve health and health care.

As the professional society for the field, AcademyHealth believes that the inclusion of diverse individuals, with equally diverse perspectives and training, leads to a richer and more nuanced understanding of health and health care issues. Diversity among our members and the field at large, in terms of race, ethnicity, disability, sexual orientation, gender identity, and other historically underrepresented backgrounds, is therefore a priority for AcademyHealth.

It is because of this commitment that AcademyHealth, in partnership with the Aetna Foundation, has established the AcademyHealth/Aetna Scholars in Residence Fellowship Program to promote diversity and inclusion in science. Housed within the AcademyHealth Center for Diversity, Inclusion and Minority Engagement in HSR Research, this fellowship is designed to retain underrepresented racial/ethnic minorities* in health services research by providing professional, training, and networking opportunities for junior to mid-level researchers and clinical practitioners, who are conducting disparities research with a focus on population health, to present their research.

Read more about this fellowship and the application process on the AcademyHealth website.

*AcademyHealth defines underrepresented racial/ethnic minorities in the field of HSR as the following groups: Black/African American, Hispanic/Latino, American Indian/Alaska Native, Native Hawaiians and Other Pacific Islanders. This definition is supported by findings of a recent study by Johnson and Holve



I have long argued against the argument that tort reform is a viable means to cost control. One of my main reasons for doing so is that there is very little evidence that passing tort reform results in significant changes in physician practice.

One of the reasons for this is that there are many reasons for ordering tests, procedures, and hospitalizations outside of a fear of litigation. An obvious one, sometimes overlooked, is that there is a financial benefit to many physicians, offices, and hospitals for ordering that stuff as well. It’s sometimes easier to say that you’re doing so for “defensive” reasons because it gets you off the hook for overusing services and care.

It’s for this reason that many blunt assessments of the cost of defensive medicine may be overblown. Lots of docs may say that they’re doing something defensively when they are actually doing it for many reasons, even if malpractice litigation fears are legitimately part of the cause.

That’s why I was so pleased to see a new study published in the journal JAMA Internal Medicine, “The Cost of Defensive Medicine on 3 Hospital Medicine Services“. The authors rounded up 36 physicians who rated more than 4200 orders for 769 patients. Orders included tests, procedures, and hospitalizations. They were all ranked on a 5 point scale from 0 (not at all defensive) to 4 (completely defensive). Costs were analyzed using both cutoffs of 1 (defensive at all) and 4 (completely and only defensive).

The number of orders places was similar between physicians with fewer and more defensive order. The costs per patient were also similar between these two types of doctors ($1679 versus $1700). The mean cost per patient overall was $1695.

Of this, 13%, or $226, was found to be at least partially defensive in nature. But completely defensive orders, or those that had no justification outside of a fear of litigation, comprised only 2.9% of costs. Most of this was due to additional hospital days.

This is an important point. As the researchers note, past studies have found that “27% of computed tomographic scans, 16% of laboratory tests, and 14% of hospital admissions were ordered owing to concerns about liability.” But such studies would include any level of defensiveness in the orders at all. We can realistically expect, however, that only completely defensive orders would be eliminated by tort reform. After all, if there are other reasons to order tests above our fears of being sued if we don’t, those reasons will still exist even after comprehensive malpractice reform became law.

If we assume that overall health care spending is about $2.7 trillion, then 2.9% of that would be about $78 billion. That’s not chump change, mind you, but it’s still a very small component of overall health care spending. Given that there’s little evidence that tort reform would lead to a significant reduction in this already small percentage of spending, there seems little reason to pursue it as a means to dramatically reduce health care spending in the United States.

None of this should be taken as a message that the malpractice system is not dysfunctional in this country, and could not benefit from significant reform. But evidence seems to indicate that in doing so, a reduction in the numbers of nuisance cases and an increase in the number of legitimate claims might be a more achievable outcome than a significant reduction in spending.




On 11 September, 2014 Louise Sheiner presented a paper at Brookings that challenges some of the the interpretations of Dartmouth research on geographic variation in health care. Her work suggests that patient, not provider, factors explain most of geographic (in her case, state) variation in spending. Coincidentally, Austin Frakt had already prepared a post reviewing work that comes to the opposite conclusion. That post appears below, and is not intended as a rebuttal to Sheiner’s work.  

One of the most important and vexing strands of research on the American health care system began with a 1973 study of tonsillectomy in Vermont. That year, John Wennberg and Alan Gittlesohn published “Small Area Variations in Health Care Delivery,” in which they showed that in one Vermont town, 66% of children had their tonsils removed, but in neighboring towns 16-22% did.

As Aaron wrote at The New York Times,

There were no differences in the children [...]. Tonsillitis was not more common in one town, and the children were no more or less healthy before or after the procedure. The doctors just seemed to have very different attitudes toward how to take care of tonsils.

Since the Wennberg/Gittlesohn study, many investigators have examined geographic variation in health care utilization and spending and their relationships to health outcomes. A great deal of this literature is summarized in a Handbook of Health Economics chapter by Amitabh Chandra, David Cutler, and Zirui Song, addressing they key question, “What drives who gets what care?” These scholars categorize the possible factors as either demand side (e.g., income and preferences for care), supply side (e.g., quality), or situational (e.g., idiosyncratic contextual or behavioral influences).

There is no debate that large regional differences in health care provision, costs, and outcomes exist. For example, as Atul Gawande famously documented, in 2006 Medicare spent $15,000 per beneficiary in McAllen County, Texas and half that in the very similar county of El Paso, Texas. But there is considerable debate as to what drives such differences, in general, as Chandra et al. explain.

Some researchers argue that variation is accounted for by population disease burden (Zuckerman et al., 2010), but other authors argue that prevalence of diagnoses itself is endogenous across areas (Song et al., 2010Welch et al., 2011). Most of the literature agrees that patient characteristics and preferences do not explain much of the differences across areas, and that substantial variations in treatment practices remain after controlling for patient characteristics (Anthony et al., 2009O’Hare et al., 2010Baicker et al., 2004).

The authors are also brutally honest, writing, “None of the theories for which there is a lot of evidence can be shown to explain a major part of cross-individual or cross-area variation in treatments.” However, their tentative conclusion is that supply side factors are more important than demand side factors in driving treatment choice.

An August 2013 paper by David Cutler, Jonathan Skinner, Ariel Dora Stern, David Wennberg is consistent with this conclusion. The authors examined the extent to which patient and physician preferences (collected by surveys) explained regional variation in health care spending.

Ultimately, the largest degree of regional variation appears to be due to differences in physician beliefs about the efficacy of particular therapies. Physicians in our data have starkly different views about how to treat the same patients, and these views are not highly correlated with demographics, background, and practice characteristics, and are often not consistent with professional guidelines for appropriate care. As much as 36 percent of end-of-life Medicare expenditures, and 17 percent of overall Medicare expenditures, are explained by physician beliefs that cannot be justified either by patient preferences or by clinical effectiveness.

In other words, many physicians believe in the value of certain types of care even when the evidence does not support that belief, and this explains a substantial fraction of spending.

Theirs is not the first work to highlight the importance of supply side (physician) factors. In Gawande’s examination of  McAllen and El Paso, he found that McAllen physicians seemed to be more entrepreneurial than their counterparts in El Paso, for instance owning “imaging centers, surgery centers, or another part of the hospital they directed patients to.” Francis Lucas and colleagues found significant peer effects: “27% of respondents reported ordering a cardiac catheterization if a colleague would in the same situation frequently or sometimes.”

Clinicians’ beliefs may play an important role in geographic variation in health care and the inefficiencies such variation suggests. (This is not to the exclusion of patient factors, of course.) As such, an interesting line of research would focus on the extent to which those beliefs are malleable. Overpowering them with payment incentives could work, if those incentives are large enough, but I wonder if beliefs can be addressed head on.

Austin B. Frakt, PhD, is a health economist with the Department of Veterans Affairs and an associate professor at Boston University’s School of Medicine and School of Public Health. He blogs about health economics and policy at The Incidental Economist and tweets at @afrakt. The views expressed in this post are that of the author and do not necessarily reflect the position of the Department of Veterans Affairs or Boston University.



One of the most popular provisions of the Affordable Care Act is the one that allows young adults up to age 26 to stay on their families’ plans. Since this age group is pretty healthy in general, the cost of allowing them to remain on others’ plans is relatively low. It’s also resulted in what appear to be significant drops in the uninsurance rate for those in this age group.

A recent study in JAMA Pediatrics explores this phenomenon further. “Limited Impact on Health and Access to Care for 19- to 25-Year-Olds Following the Patient Protection and Affordable Care Act“:

IMPORTANCE The Patient Protection and Affordable Care Act (PPACA) allowed young adults to remain on their parents’ insurance until 26 years of age. Reports indicate that this has expanded health coverage.

OBJECTIVE To evaluate coverage, access to care, and health care use among 19- to 25-year-olds compared with 26- to 34-year-olds following PPACA implementation.

DESIGN, SETTING, AND PARTICIPANTS Data from the Behavior Risk Factor Surveillance System and the National Health Interview Survey, which provide nationally representative measures of coverage, access to care, and health care use, were used to conduct the study among participants aged 19 to 25 years (young adults) and 26 to 34 years (adults) in 2009 and 2012.

EXPOSURE Self-reported health insurance coverage.

MAIN OUTCOMES AND MEASURES Health status, presence of a usual source of care, and ability to afford medications, dental care, or physician visits.

Researchers in this study used data from the BRFSS and the NHIS to look at both access and utilization of health care for young adults age 19 to 25 years. They also looked at adults 26 to 34 years as a comparator. They did this in both 2009 (before the new provision) and in 2012 (after it). The main exposure of interest was, of course, whether they were insured. The main outcome measures were their health, whether they had a usual source of care, and how they rated their ability to afford care.

Confirming previous reports, the percentage of young adults who were insured increased from 2009 to 2012, from 68% to 72%. The percentage of adults who were insured, however, dropped dramatically over this time, from 78% to 70%. In this respect, it appears that the new provision was associated with significant improvements in insurance compared to adults 26+. Both groups reported a decrease in having a usual source of care, but the decrease was less for young adults, likely because adults saw a much higher drop in their chance of being insured.

However, there was no improvement in the chance that young adults went for a routine checkup. Young adults saw no greater improvement in their health status compared to adults over this time. They were not more likely to be able to afford prescription drugs, dental care, or visits to the doctor. They weren’t even more likely to get a flu shot.

As with any study, this one has limitations. All of these data are self-reported, so it’s possible that improvements occurred but aren’t being seen. It may take a longer time for the effects of coverage to have an impact on utilization or health. It may be that the accompanying downturn in the economy had a large effect that washed out that of insurance.

But the bottom line is that this confirms something we need to acknowledge. Access is necessary, but not sufficient, for good health. Many other things matter, too. In terms of improving the health care system, improving access and reducing uninsurance is almost the easiest step. Improving quality is much harder. But if we want better outcomes, we need to do it.




In recent years, there has been a steady movement away from small, private practices of physicians into larger group practices. The Affordable Care Act likely is hastening this transition with its encouragement of Accountable Care Organizations. It’s impossible for small or individual practices to meet the requirements of being an ACO, and as more and more reimbursement is tied to metrics associated with them, it’s becoming harder to avoid joining them.

Many are comfortable with this development because they believe in the idea of a “medical home”. Some research has shown that when a personal physician who knows a patient well and can coordinate all aspects of their care, both primary and specialty, then patients do better. It’s much easier to run medical homes out of larger practices that have more resources and practitioners of all types.

A recent study in HSR showed that we might save money through medical homes as well. It examined Medicare Part A and B claims over three years, and looked at how practices designated as NCQA-recognized patient centered medical homes compared to practices which weren’t. They found:

Relative to the comparison group, total Medicare payments, acute care payments, and the number of emergency room visits declined after practices received NCQA PCMH recognition. The decline was larger for practices with sicker than average patients, primary care practices, and solo practices.

According to this study, medical home patients were seeing improved quality. Medical home practices had reduced numbers of emergency department visits, with the largest improvements seen where patients were sicker. Not only that, but they saw reductions in total Medicare payments. Medical homes were associated with improved quality and reduced spending.

A more recent study in Health Affairs, however, countered this claim:

Nearly two-thirds of US office-based physicians work in practices of fewer than seven physicians. It is often assumed that larger practices provide better care, although there is little evidence for or against this assumption. What is the relationship between practice size—and other practice characteristics, such as ownership or use of medical home processes—and the quality of care? We conducted a national survey of 1,045 primary care–based practices with nineteen or fewer physicians to determine practice characteristics. We used Medicare data to calculate practices’ rate of potentially preventable hospital admissions (ambulatory care–sensitive admissions).

This study actively surveyed practices to determine their practice characteristics. Then they used Medicare data to see what their preventable hospital admission rates were. They found that compared to practices with 10-19 physicians, practices with 3-9 physicians had 27% fewer preventable admissions. But practices with only 1-2 physicians had ever fewer, one third fewer preventable admissions. Practices owned by doctors had fewer preventable admissions than those owned by hospitals.

This, of course, contradicts the other study. It would seem to show that moving into larger group practices, and away from single or small practices, might reduce patient quality.

But there are some nuances to these studies worth considering. The first study did not look at practice size, per se. It looked at medical home designation. It’s totally possible for solo practices to become medical homes, and those that were performed better. In fact, if you delve into the tables, it seems that the largest improvements for hospitalizations were seen in solo practices with medical home designation. It also seems that the largest improvements in medical specialist visits were in 2-person practices.

In fact, there seems to be some general agreement that there’s a lot of good outcomes associated with small and solo-practices. Medical homes may lead to better outcomes, but that doesn’t necessarily mean that large ACOs will do just as well.




The White House Blog has a post up on “How Low-cost Randomized Controlled Trials Can Drive Effective Social Spending“:

The Office of Science and Technology Policy and the Coalition for Evidence-Based Policy convened leaders from the White House, Federal agencies, Congress, philanthropic foundations, and academia this week to explore an important development in the effort to build credible evidence about “what works” in social spending: low-cost randomized controlled trials (RCTs). The goal of the conference was to help advance a broader Administration effort to promote evidence-based policy, described in the evaluation chapter of the 2014 Economic Report of the President, and the Performance and Management section of the President’s budget.

Large and rigorous RCTs are widely regarded as the most valid method of evaluating program effectiveness, but they are often perceived as too costly and burdensome for practical use in most contexts. The conference showcased a new paradigm: by measuring key outcomes using large administrative data sets already collected for other purposes – whether it be student test scores, hospitalization records, or employment and earnings data – sizeable RCTs can be conducted at low cost and low burden.

The conference showcased a number of RCTs that were conducted for between $50,000 and $350,000 (a fraction of the usual multimillion dollar cost of such studies), yet produced valid evidence that informed important policy decisions.

First of all, I’m thrilled at the idea, in general, of using data, especially those derived from randomized controlled trials, to make policy decisions. Using evidence to drive policy has always been one of the goals of this blog, as well as others I write for.

But this post focused on how it’s possible to do randomized controlled trials at an amazingly low cost. It describes, for instance, a study of Recovery Coach services to substance-abusing parents who had temporarily lost custody of their children. The cost of the nine-year trial was about $100,000, which is stunning.

But these types of opportunities are the exception, not the rule. I think it highlights a misunderstanding of where grant money goes much of the time.

If you looked at the budgets of any R01 funded randomized controlled trial I’ve had funded, the number one cost, by far, is the salaries of those who are conducting the study. Even if it’s just 10-20% of someone’s time, and it’s just a few people, the salaries add up. When you add in benefits and overhead, you can get into the hundreds of thousands of dollars pretty quickly. Also, research comes with indirect costs. The rent for people’s offices, the electricity, their phone lines, support staff, taxes, etc. There are a lot of costs.

You often need to pay for a number of people because you’re setting up some new intervention! That takes time, it takes effort, and it takes money to pay for it.

The opportunities like those described in the White House blog are relatively rare. All of the interventions discussed were already being run by personnel who were likely employed through other public lines of support. That’s great, but that doesn’t happen often. If the intervention is already paid for, if those running it are already salaried, then most of the costs of a grant are wiped clean. If the data are already collected as part of administrative data, that’s awesome, but that wasn’t free. It was paid for by someone else. Yes, that makes the “grant” cost less, but not the research itself.

It appears that all that had to be paid for in the studies presented was the cost of the analysis. That may easily be $100,000. But that’s only the last part of an RCT, usually.

It’s totally worth it to try and do research on the cheap when you can. It’s incredibly efficient. But we shouldn’t be under the illusion that research is cheap. Lots of great things can’t be done this way, and it’s important to do those things, too.



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In spring 2014, AcademyHealth convened expert researchers and thought leaders from the disciplines of public health, health care, and health information technology to discuss a critical issue –what opportunities do new and existing sources of data bring to the research community as it continues to inform health system transformation?

This meeting was part of AcademyHealth’s Public Health Services and Systems Research (PHSSR) program, supported by the Robert Wood Johnson Foundation. To focus the discussion, AcademyHealth commissioned three papers on the thematic topics outlined in PHSSR’s national research agenda: health information technology, organization and structure, and financing. Meeting participants provided constructive feedback on the papers, and then engaged in further dialogue around these issue areas. Specifically, attendees identified priority research questions for the field of PHSSR; suggested what types of data and what elements of data are needed to answer those priority questions; proposed data resources that are so far untapped; provided strategies for cultivating new PHSSR data; and finally, debated how analytic approaches and methods could be refined, enhanced or adapted to take full advantage of all data resources for the field.

Better Leveraging Data Resources

The resulting list of research questions and topics for further exploration is below. We know that we have just begun to scratch the surface of these important questions. To continue the momentum, and keep pushing the field forward, we want your expert input. What do you think should be PHSSR’s priorities? And what data (both existing and untapped resources) are needed to answer these questions?

We are looking to learn from your experiences and expertise as we continue to define the ways in which we can move PHSSR forward! Please add your thoughts and/or suggestions in the comments section below this post.

PHSSR Research Questions:


  • What is the business case for consolidating local health departments or sharing services across health departments?


  • What is the capacity of local health departments to carry out basic financial management/ accounting? How does capacity affect agency performance?

HIT and Informatics Systems

  • What system approaches optimize public health community readiness and opportunism to effectively harness increased information flows to improve population health?
  • What technology components (e.g., sharable platforms, flexible standards or identity management tools) leverage the current information explosion, and how, to transform the current healthcare system?
  • How can the PHSSR informatics workforce be trained and maintained?
    • What deliverables are informatics staffs producing? How are the deliverables accounted for? What is the business case for moving dollars into informatics?
  • What is the minimum set of necessary data that comes out of electronic health records (EHRs)? Who defines this set? Who cleans the set? What should be done with it?
  • What are examples of successes and failures in obtaining public health information from EHRs?
  • What is the regulatory environment needed to facilitate data exchange?

Visit our website to learn more about PHSSR at AcademyHealth, and complete this form to join our Interest Group.



I highly recommend the recent paper in The Milbank Quarterly by Harvard health economists Joseph Newhouse and Tomas McGuire on the cost, beneficiary selection (as in favorable vs. adverse), market power, and quality of Medicare Advantage (MA). Just as it sounds, it summarizes just about everything a wonk might want to know about the program. In this post, I will make just three points articulated in the paper.

Historically, the case against MA was easy: it cost too much and quality was uncertain and, possibly, suspected to be no better or worse than traditional Medicare (TM). (See the work of Miller and Luft.) More recent data compels at least a partial reassessment of the program. Newhouse and McGuire make, perhaps, one of the stronger, evidence-based cases in its favor I’ve read in a long time.

First, yes, MA plans are still overpaid relative to the cost of providing the Medicare benefit via TM. This is nicely illustrated in the chart below, from Bloomberg View.


But things are at least slightly less bad than they appear. As found by Kate Baicker, Mike Chernew, and Jacob Robbins and explained by Newhouse and McGuire, there is a spillover effect whereby MA causes offsetting savings for TM, as well as in the commercial market. (An earlier version of their publication exists as an NBER working paper as well, which I summarized.)

We find that when more seniors enroll in Medicare managed care [an MA plan], hospital costs decline for all seniors and for commercially insured younger populations. Greater managed care penetration is not associated with fewer hospitalizations, but is associated with lower costs and shorter stays per hospitalization. These spillovers are substantial – offsetting more than 10% of increased payments to Medicare Advantage plans. [...]

So, that’s not a total offset to the extra MA cost, but it’s a little off the top.

Next, Newhouse and McGuire share results from a paper by John Ayanian and colleagues that demonstrates that MA offers higher quality than TM, at least in the dimensions measured. The chart just below shows the proportion of beneficiaries in a Medicare Advantage HMO and TM receiving each of several preventive services. Women enrolled in an HMO are more likely to receive mammography screening,* those with diabetes are more likely to receive HbA1c testing, retinal exams, and those with diabetes or cardiovascular disease are more likely to receive cholesterol testing.

newhouse-mcguire 1

The next chart shows that HMO enrollees are more likely to receive flu and pneumonia vaccinations and about as likely to highly rate their personal doctor and specialists.

newhouse-mcguire 2

Finally, Newhouse and McGuire point to both published and unpublished work by Bruce Landon and colleagues that finds reduced resource use (which they call “social cost”) by beneficiaries in MA plans.

Rates of ambulatory surgery and emergency department use were 20% to 30% lower in the MA plans [relative to TM]. The difference was concentrated in elective procedures regarded as more “discretionary,” such as knee or hip replacements. Repair of a fracture of the femur, a less discretionary procedure, was actually greater in MA.

Minimizing social cost also involves using more durable procedures if equally effective. Landon and colleagues found that coronary problems were more frequently treated with coronary bypass surgery in MA rather than the less durable percutaneous coronary intervention, suggesting not just lower long-run costs on average but a more appropriate use of services, since the patient was potentially spared a repeat procedure.

They go on to describe other work illustrating reduced social cost in MA.

In conclusion, Newhouse and McGuire wrote,

We found several reasons to maintain the level of payment to MA plans at or above the level of TM. First, the quality and appropriateness of care appear to be at least as high in MA as in TM. Second, the social cost of care in MA appears to be lower than in TM. Third, we found evidence for positive “spillovers,” meaning that higher MA enrollment in a county reduces hospital costs in TM in that county. Medicare does not immediately capture the savings, since it pays per admission (unless an admission without a procedure replaces one with a procedure). Rather, the savings would have to be captured later by a smaller update factor. Reducing the percentage of the benchmark paid to MA plans, as was done in the ACA, generates program savings for Medicare, but from the standpoint of the Medicare program’s social efficiency, cuts in MA plan payments may be shortsighted.

This is somewhat hedged, but I’d hedge further because a full welfare analysis has not been done to justify a specific MA payment level, relative to TM. It’s possible that MA is still overpaid from a social perspective, and we don’t know by how much.

There’s a lot more in the paper. I recommend you read the whole thing.

* According to the original paper, this measure applies to all women, not just those receiving diabetes care as suggested in the chart.

Austin B. Frakt, PhD, is a health economist with the Department of Veterans Affairs and an associate professor at Boston University’s School of Medicine and School of Public Health. He blogs about health economics and policy at The Incidental Economist and tweets at @afrakt. The views expressed in this post are that of the author and do not necessarily reflect the position of the Department of Veterans Affairs or Boston University.



Hospital admissions and readmissions, being both costly and prevalent, have become a key policy issue. Furthermore, although patients may be admitted to the hospital for a variety of reasons, research shows that many admissions and readmissions are also preventable. Thus, reducing hospital readmissions is a way to both improve care and reduce avoidable costs. However, while hospital readmissions have been investigated in a variety of populations, including Medicare, there have been fewer studies in the Medicaid population.

A recent study, led by state Medicaid Medical Directors (MMD) and conducted by AcademyHealth, with funding support from the Agency for Healthcare Research and Quality (AHRQ), sought to contribute to better understanding of Medicaid readmissions and state-level efforts in addressing this issue.   Having previously investigated antipsychotic medication use in Medicaid children and adolescents, a working group of states and investigators sought to characterize acute care hospital admissions and 30-day readmissions in the Medicaid population through a retrospective analysis. The team developed detailed definitions and specifications, as well as empty template tables for data entry. The form was emailed to states and those who chose to participate filled it out and sent aggregate data back to AcademyHealth for review and analysis. Nineteen states submitted data.

The study found that 30-day Medicaid readmissions rates for the 19 participating states varied from 5.5 percent to 11.9 percent, with an average of 9.4 percent. State Medicaid payments for readmissions were substantial: they averaged $77 million per state for study states, and they represented 12.5 percent of the payments for all Medicaid hospitalizations. Five diagnostic groups appeared to drive Medicaid readmissions, accounting for 57 percent of readmissions and 49 percent of hospital payments for readmissions. These five diagnostic groups in order of their prevalence are: mental and behavioral disorders; pregnancy, childbirth and their complications; diseases of the respiratory system, diseases of the digestive system, and diseases of the circulatory system. The two most prevalent diagnostic categories — mental and behavioral disorders and diagnoses related to pregnancy, childbirth, and their complications together accounted for 31.2 percent of readmissions.

Because Medicaid has the largest number of beneficiaries among all U.S. payers and is growing even larger with the implementation of the Affordable Care Act, understanding the potential for improving hospital use in this population has particular importance. This study offers valuable new information about Medicaid hospitalizations: it allows MMDs to better understand the nature and prevalence of hospital use in the Medicaid population and provides a baseline for measuring improvement. Providing states with information about admissions and readmissions by diagnostic groupings enables a discussion of the interventions aimed specifically at these conditions. These interventions can range from better access to primary, behavioral or mental, or obstetrical care to more effective management of care transitions.

The study was published in the August issue of Health Affairs. Study investigators included MMDs, Judy Zerzan, M.D. (Colorado), and David Kelley, M.D. (Pennsylvania), as well as Tara Trudnak, Ph.D., Gerry Fairbrother, Ph.D., and Katherine Griffith, M.H.S., from AcademyHealth and Joanna Jiang, Ph.D. from AHRQ.



Educating congressional staffers on the importance of health services research—or even research more generally—can sometimes prove challenging:

Staffer: “Now tell me, why should I fund this research? How is it going to better my community/state/country?”

Researcher/Research Advocate: “Well, our hypothesis is that XYZ will happen, but we’re not sure yet.”

Although scientists have undoubtedly been among the core building blocks of modern society, in an era of competing political interests and limited funding, policymakers sometimes brush off studies whose practical applications are not easily recognized for those that have a more “immediate” use or effect.

Earlier this month, economists Robert Wilson, Paul Milgrom, and R. Preston McAfee received the 2014 Golden Goose Award, which “recognizes scientists and engineers whose federally funded research has had a significant human and economic benefits.” More specifically—and more intriguingly—the award highlights examples of those “seemingly obscure studies” that have made tremendous breakthroughs and led to some sort of major societal impact.

The highly theoretical research of this year’s winners on auctions and game theory—described as using mathematical models to study how people and organizations make decisions—eventually enabled the Federal Communications Commission (FCC) to allocate the nation’s telecommunications spectrum through auctions. During a spectrum auction, a government sells the rights (licenses) to send signals over specific bands of the electromagnetic spectrum and assigns its scarce resources. As Rep. Charlie Daniel (R-PA) stated in the award press release, “The theoretical work done by Professors Wilson, Milgrom, and McAfee has revolutionized federal auctions and returned the federal government’s investment many times over.”

 Including that first FCC auction in 1994, the agency has conducted 87 auctions, raising over $60 billion for the U.S. Treasury and enabling the proliferation of wireless technologies that make life convenient, safe and connected. Additionally, the basic auction process they developed has been used the world over not only for other nations’ spectrum auctions but also for items as diverse as gas stations, airport slots, telephone numbers, fishing quotas, emissions permits, and electricity and natural gas contracts.

As AcademyHealth has previously mentioned, today the “soft sciences,” such as health services research are losing ground to the “hard sciences,” such as biomedical research in the minds of some policymakers. Health services research has a definite role to play in the social and economic advancement of the country, but its genuine potential—and its true impact—may not yet be recognized.

As we advocate for the field of health services research in Washington, we can’t help but wonder, “Where are our Golden Geese?” What are the research studies that may have at first seemed overly theoretical, but have produced a similarly great impact?

AcademyHealth President and CEO Lisa Simpson posted a variation of this question on the organization’s members-only social networking site We welcome your thoughts.