AHRQ

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Healthcare Cost and Utilization Project Databases

August 15, 2011 in Data | 1 comment

The Agency for Healthcare Research and Quality’s (AHRQ)  Healthcare Cost and Utilization Project (HCUP) is a family of databases and tools intended to improve the quality, safety, efficiency, and effectiveness of the U.S.   health care system.  HCUP results from Federal-State-Industry partnership to build a comprehensive all payer data system.  A summary of the databases available from HCUP can be found here.  A summary is also provided in the table below.

Abbreviation

 File Description States Participating

Available From:
SID State Inpatient Databases Data on 95 percent of community hospital discharges 44 1990
SASD State Ambulatory Surgery Databases Ambulatory surgical center data (hospital and free-standing) 29 1997
SEDD State Emergency Department Databases ED visits that do not result in hospitalization 29 1999
NIS Nationwide Inpatient Sample All-payer inpatient care database. A 20% stratified sample of U.S. community hospitals. varies by year 1988
NEDS National Emergency Department Sample All-payer inpatient ED database. A 20% stratified sample of U.S. community hospitals. 2006
KID Kids’ Inpatient Database All-payer inpatient database for children.  Contains 3 million discharges form 3,500 community hospitals. 1997

 

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Medicare Patient Safety Monitoring System (MPSMS)

August 4, 2011 in Public Health | No comments

The Medicare Patient Safety Monitoring System (MPSMS) is a national surveillance project aimed at identifying the rates of specific adverse events within the Medicare population.  It is administered by the Quality Improvement Group (QIG) in the Office of Clinical Standards and Quality (OCSQ).  The goal of the project is not to monitor physicians for best practices or determine physician errors.  Instead, the project is limited solely to detecting harm which comes to patients from the fault of the physician.

To identify patients who experience harm, the MPSMS system uses hospital records from over 40,000 hospital discharges.  Whereas AHRQ’s Patient Safety Indicators (PSI) relies exclusively on claims data to identify patient safety problems, the MPSMS sends medical records from these 40,000 discharges for review at one of two Clinical Data Abstraction Centers (CDAC). Using medical records has the advantage of being able to access more data than is available in the claim; using medical record data, however, is expensive, time-consuming, and relies on providers entering information into the patient’s chart in an honest fashion.

The safety measures cover these topics.  Any adverse medical event selected for monitoring must met the following criteria:

  • The adverse event can be found.
  • The adverse event is common
  • The adverse event is likely to be associated with exposure to a specific process of care
  • The adverse event is responsible for serious morbidity/mortality
  • The adverse event is preventable or repairable

Examples of the events monitored include adverse events associated with hip/knee replacement and post-operative pneumonia (the second most frequent postoperative complication after major surgery).  The most frequent postoperative complication after major surgery is wound infection, but it was not selected as a topic because the wound infection often appears during post-acute care, and thus many wound infections are missed in the data.

One drawback of the MPSMS program is that it is simply a monitoring program.  If MPSMS detects that a provider is harming a large number of patient, no action is taken since MPSMS’s mandate is simply to monitor trends in harm to patient.  Additionally, MPSMS is much more expensive to administer than a safety monitoring program relying on claims data.  Further, even if a harmful provider were identified, the delay caused by medical record review may mean that the provider has corrected his action or no longer practices.

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All-Payer Claims Data

August 3, 2011 in Data | 1 comment

One of the challenges of studying import issues in health economics is that the patients often come in and out of your data file.  For instance, beneficiaries often switch their private health insurance plan, or can become eligible for public insurance (e.g., Medicare or Medicaid) or the individual may lose their insurace coverage.  In each of these cases, it is difficult to track these patients over time.

One effort to solve this problem is AHRQ’s All-Payer Claims Databases (APCD).  AHRQ does not create the APCDs themselves but facilitates States efforts to create them.  According to a 2010 fact sheet:

Payers include insurance carriers, third party administrators (TPAs), pharmacy benefit managers (PBMs), dental benefit administrators,state Medicaid agencies, CMS (Medicare), Federal EmployeesHealth Benefit (FEHB) and TRICARE administrators.APCD systems collect data from existing claims transaction systemsused by health care providers (facility and practitioners) andpayers.

The information typically collected in an APCD includes patient demographics, provider demographics, clinical, financial,and utilization data. Because of the difficulties involved with thecollection of certain information, most states implementing APCD systems have typically excluded a number of data sources, such asdenied claims, workers compensation claims, and, because claimsdo not exist, services provided to the uninsured.

One concern with these data is that the APCD must maintain beneficiary confidentiality. On the one hand, one of the key benefits of APCD is that it allows researchers to examine patterns in the cost and quality of care for beneficiaries who change insurers.  The institution administering the APCD, however, must institute a strong institutional review board (IRB) or data use agreement (DUA) policy.

Also, the APCD must determine whether or not the claims files are updated.  Oftentimes, claims payment amounts can change or the claims can be later denied.  The APCD must determine a policy for updating the data files over time.

Although there are a number of challenges, some states are making progress.  ”Oregon and Tennessee willhave live systems in 2010. Hawaii and Colorado havecurrently submitted legislation for their 2010 session toauthorize development.”

Hopefully, the APCD will be a high-quality, useful data resource that can be used to answer a variety of research questions.

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The Impact of Comparative Effectiveness Research on Health Care Spending

April 14, 2010 in Health Insurance, Public Policy, Quality | 3 comments

Comparative Effectiveness Research (CER), as it name suggests, compares how well different medicines treat a given disease.  Politicians claim that using CER findings can help improve quality and decrease cost.  If one treatment produces better health outcomes on average than another and also costs less, we should always make people use that treatment, right?

Not according to a working paper by Basu and Philipson (2010).  Let us assume that health outcomes for Drug A are better than the health outcomes for Drug B.  If the results from this research were released, the public’s demand for Drug A would increase and the demand for Drug B would decrease.  However, this may not save money.  The demand for Drug B will decrease since fewer people want to buy it; this will reduce expenditures.  As the demand for Drug A increases, however, the price and quantity purchased will increase.  Thus, the net effect on spending is indeterminate.

In addition, if insurance companies or the government decided to subsidize or cover the entire cost of treatment using Drug A, the demand will increase even more.

Basu and Philipson, however, assume that the marginal cost to produce a drug increases as the quantity rises (i.e., the supply curve is upward sloping).  However, because there are economies of scale in the production of pharmaceuticals, the price of Drug A could actually decrease (increasing returns to scale) or stay the same (constant returns to scale) as demand increased.

The key assumption in the above analysis is that it assumes that all people with a given disease respond in a homogeneous way to Drug A.  If two-thirds of people have better health outcomes when treated with Drug A and one-third have better health outcomes when treated with Drug B, then it may be suboptimal to cover Drug A, but not Drug B.

To prove this, Basu and Philipson look at the Clinical Antipsychotic Trials of Intervention Effectiveness project (CATIE).  They find that “if Medicaid would have eliminated coverage for the least cost-effective treatments of the CATIE trial then under homogeneous effects, it would save about 90% of the $1.3B Medicaid class sales annually in non-elderly adult patient with schizophrenia. However, taking into account the observed heterogeneity in treatment effects, it would incur a loss of health valued annually at about 98% of class spending and thus a net loss of about 8% of annual class spending.”

However, one of their key assumptions is that not covering the less effective medicines means that no patients will take the uncovered drug.  This may be a fair assumption for the Medicaid population, but not for the population at large.  There is a big distinction that needs to be made between not covering a drug (but allowing for the purchase to purchase it out of of their own pocket) and prohibiting the drug entirely.  Basu and Philipson are looking at the most extreme case where not covering the drug means, de facto, that the drug will not be taken, but this need not be the case.

What is important to take from this research, however, is that the drug that is most effective on average may not be the best drug for everyone.  One must take into account heterogeneous treatment effects when designing any insurance benefit plan.

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Common Efficiency Measures

October 21, 2009 in Quality | 2 comments

Measuring efficiency is a difficult business. As AHRQ,  “In most cases, individuals and firms will define efficiency as a relationship between what it costs them and what service or outcome they receive, rather than as a trait inherent in the provider.”

Further, efficiency can be measured as either production efficiency or allocative efficiency.  “For example, a physician may produce CT scans efficiently in her office, but the physician may not appear efficient to a health plan if a less expensive diagnostic test could have been substituted in some cases.”

This table lists some of the more common efficiency measures.

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Ten questions to ask your doctor

October 1, 2009 in Public Health | 1 comment

After viewing a cute ad, I went to the AHRQ homepage.  The agency gives a nice list of 10 recommended questions you should ask your doctor. Additional questions directly pertaining to specific types of care are also available.

  1. What is the test for?
  2. How many times have you done this?
  3. When will I get the results?
  4. Why do I need this surgery?
  5. Are there any alternatives to surgery?
  6. What are the possible complications?
  7. Which hospital is best for my needs?
  8. How do you spell the name of that drug?
  9. Are there any side effects?
  10. Will this medicine interact with medicines that I’m already taking?

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$1.1 billion for Comparative Effectiveness Research

March 10, 2009 in Current Events, Public Policy | No comments

Regarding my post on Monday, Obama’s stimulus package–a.k.a. the American Recovery and Reinvestment Act (ARRA)–includes 1.1 billion dollars for clinical comparative effectiveness research.

According to the American Academy of Family Physicians (AAFP), ARRA “allocates $1.1 billion for comparative clinical effectiveness research, including $300 million for the Agency for Healthcare Research and Quality and $400 million each for HHS and NIH to conduct this research.”

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Should the U.S. get NICE?

March 9, 2009 in Medicaid/Medicare, Public Policy | 6 comments

All health services researches know that comparative-effectiveness research is a vital link towards improving quality and decreasing cost.  Comparative effectiveness examines different medical treatments and evaluates which are the most cost effective.  The UK’s NICE (National Institute for Health and Clinical Excellence) publishes clinical appraisals regarding which treatments the NHS should cover.

Should the U.S. create a NICE-style government agency to conduct comparative effectiveness research?  Few researchers doubt that comparative effectiveness research is needed.  The question is whether it should be provided by the government.

Pro

Comparative effectiveness research is a public good.  Information is a non-rivalrous good (when I learn something that does not stop you from learning it).  Once the best treatment for each disease is established, it is difficult (but possible) to exclude individuals.  Because comparative effectiveness research is a public good the government would seem to have a large role to play.  Further, the government may be a more unbiased researcher than would be the case if private insurance companies conducted comparative effectiveness research.

Even if the government decided to continue funding a comparative effectiveness agency such as AHRQ, this does not preclude the private sector or academia from conducting their own research.

Con

Michael Cannon makes a strong argument against a centralized NICE-style government body.  Most convincingly, he states that  ”If a government agency produces unwelcome research, those groups will spend vast sums on lobbying campaigns and political contributions to discredit or defund the agency.”  If AHCPR’s history (now AHRQ) is any indication, it will be difficult for a government-funded body to publish controversial findings.  Health Affairs reports that when AHCPR found limited health benefits to back surgery, back surgeons “found sympathetic ears among House Republicans.” AHCPR’s funding was cut by 21% due to lobbying by back surgeons and medical device manufacturer Sofamor Danek.

If the government does not do a good job, could the private sector?  The answer is likely yes.  Cannon suggests that prepaid group plans (PGPs) such as Kaiser Permanente would be in the best position to conduct the comparative effectiveness analysis.  ”PGPs therefore boost the production of a nonexcludable good (comparative effectiveness information) by bundling it with an excludable good (reputation).”

Although expanding AHRQ’s role does not preclude private sector health plans from conducting their own research, spending on AHRQ will likely crowd-out private health plan comparative-effectiveness research.

Conclusion

Should there be an agency similar to NICE in the U.S. Michael Cannon makes a compelling argument that the answer is no, but he does this in a fantasy world where he forms American institutions from scratch.  Private sector insurance companies would be more likely to conduct comparative effectiveness research if:

  1. Medicare was eliminated.  Seniors could instead receive vouchers to purchase their own private health care.  When people shop for their own insurance and pay for the marginal insurance premium dollar out of their own pocket, this will increase demand for cost effectiveness research.
  2. Medical licensing (but not certification) standards were eliminated.  This way, insurance companies could take advantage of using more cost-effective labor such as nurse practitioners and physicians assistants.  ”According to professor of health policy Jonathan Weiner, nonphysician  clinicians comprise 14 percent of primary  care providers nationally, but 17 percent at Kaiser Permanente and 25 percent at Group Health.”

If these two changes were instituted, then I agree that a government-run comparative effectiveness organization would be unnecessary.  However, this is not the world we live in.   Medicare’s budget for 2009 was $420 billion.  In this world, I believe that there should be a government cost-effectiveness agency in order to monitor Medicare’s the cost-effectiveness of Medicare spending.   Further, government funding for medical research is needed whether or not Medicare exists.

Thus, I see two feasible options: (1) Eliminate Medicare, subsidize health insurance through vouchers, and leave the cost-effectiveness research to private health plans; and (2) Keep Medicare and expand funding of a government-run comparative-effectiveness body (such as AHRQ).

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