July 2009

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Lessons from high school: How to steal drugs from a hospital

July 13, 2009 in Hospitals | No comments

The Denver Post has an interesting article about employee theft of drugs from hospitals. Stealing from a hospital is not easy:

“Hospitals guard drugs carefully, requiring staffers to use individual codes or even fingerprints to unlock cabinets where drugs are stored. At Rose [Medical Center], an electronic system tracks narcotics from the time they arrive at the hospital until they are thrown away. The hospital also conducts a daily reconciliation of every drug used.”

So how do hospital employees get away with this theft? Easy, they used lessons from their teenage years:

  • To Steal liquor from your parents liquor cabinet, simply drink the liquor and refill the bottles with water: “A surgical nurse at Boulder Community Hospital, has pleaded guilty to stealing large amounts of fetanyl and refilling vials with water.”
  • To get out of class, write yourself a fake permission slip: “A Milliken hospice nurse was accused of writing forged prescriptions for more than 4,000 pain pills.“

Who said no one ever learned anything in high school.

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Universal Coverage in Thailand

July 13, 2009 in Health Care Around the World | 3 comments

Puenpatom and Rosenman (2008) examine universal health coverage in Thailand:

“In 2001 Thailand became the first developing country to introduce universal health insurance coverage (UC). Six of 76 provinces adopted UC in April 2001, while the remaining provinces implemented UC in October of that year. One of the key elements of the program is capitated-based reimbursement of public hospitals based on enrolled populations in the hospitals’ service areas. Before 2001, the only capitation-based public health insurance program was the Social Security Scheme (SSS), which covered only 9% of the population in 2000. With UC fully implemented almost 90% of the population is covered by capitation. UC capitation is geographically structured so hospitals have fixed revenues based on the local population and financial viability depends on an ability to control costs. One consequence is that hospitals face increased demand from the 75% of the population previously not covered by any formal insurance system.”

Hospitals

This table shows the distribution of hospital beds. About 75% of all beds are in government hospitals and about 21% are in private hospitals. Most public hospitals are run by the Ministry of Public Health (MOPH). Larger regional hospitals provide tertiary and primary care services. Bangkok has a unique structure within Thailand. Most large teaching hospitals are in Bangkok and forty percent of private hospitals are located in Bangkok. Under the new UC system, patients are assigned to hospitals by MOPH.

Physicians

“Physicians in Thai public hospitals are employees of the hospital and as such are paid by MOPH according to budgetary structures through the hospitals.”

Old System vs. New System

This table gives a comparison of health insurance coverage under the old an new system. In 1991, two thirds of Thais had not health insurance. By 2000, only 20.3% of Thai individuals were uninsured. After the enactment of Universal Health Coverage (UC), this number fell to less than 4%.

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How to pay for health reform: Taxes

July 10, 2009 in Taxes | 1 comment

The New York Times is reporting that “to pay for a sweeping overhaul of the health care system, House Democrats will propose a surtax on individuals earning $280,000 and up and couples earning more than $350,000.”  Now, taxes in and of themselves need not be distortionary.  Let us assume that your employer takes $10,000 from your paycheck currently to pay for your health insurance.  If the government would no provide you with health insurance, you would receive $10,000 in extra wages from your employer and you pay a tax of $10,000 to the government for health insurance.  In this cases, taxes are not distortionary.

This scenario of course assumes that government and private health insurance are of equal value.  If private health insurance is of higher quality, this could result in a decrease in efficiency; if government health insurance is of higher quality, this could be an efficiency improvement.

However, taxes are charged according to health insurance demand or even in a lump sum fashion.  Instead, the rich will pay more in taxes than they will receive from government health insurance benefits and the poor will pay less in taxes than they will receive in health insurance benefits.  Further, since the proposed tax is only on those individuals earning $280,000 or more, the tax will almost certainly be distortionary in some manner.  Although this progressive tax will create some distortions, it could cure others.  For instance, government health insurance could help to reduce labor market inefficiencies caused by Job Lock and Job Stretch.

I wonder how many doctors will support health reform now?   A public plan will increase their revenues as more of their patients receive insurance, but a higher percentage of their profits will be taken by the government due to the proposed surtax.  As any economist knows, life is full of tradeoffs.

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As if yoga classes aren’t expensive enough…

July 10, 2009 in Uncategorized | No comments

State Governments want to mandate that yoga instructors get licensed.

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Job Stretch

July 10, 2009 in Health Insurance, Labor Economics | 3 comments

In this blog, I have frequently discussed the concept of Job Lock.  Job Lock occurs when you don’t leave a job that you wish to leave (either because it is low paying or you do not like the work) simply because you do not want to lose your health insurance.  Leaving your current job for one that is more lucrative may not be worthwhile if you lose health insurance.  Even if a new job’s potential salary is more than your salary+benefits at your old job, you may still not leave since the price of group health insurance is much less than the price of non-group health insurance, especially for those with pre-existing conditions.

Yesterday, Reuters reported on another phenonmenon: Job Stretch.  Take a look at the following excerpt from the article:

Real estate agent Lisa DeWaal serves coffee at a Starbucks outlet for four hours every morning before she goes to the office to start her “day job.”  The reason has little to do with the state of the housing market and everything to do with the one big perk that 20 hours a week at the coffee counter provides: affordable health insurance for her and her three children.

Job Stretch is a term (that I just invented) where individuals take a second (or third) job in order to get health insurance.  Taking a second job in and of itself is not evidence of Job Stretch; many people prefer additional income over additional leisure time. However, when high-skilled workers take low-skill jobs just for health insurance benefits, this is an example of a labor market inefficiency.  The inefficiency is caused by a system of health insurance provided at the employer level.

Now we have two labor market inefficiencies to worry about: Job Lock and Job Stretch.



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Modelling Shocks from extreme events in finance

July 9, 2009 in Econometrics | No comments

Many people model trend in the stock market using either Autoregressive (AR), Moving Average (MA) or Autoregressive Moving Average (ARMA). In these models, shocks to the stock price in prior periods help to determine the price in the current period. Shocks in the more distant past are generally assumed to have less influence on current stock price than that of shocks in the more recent past. However in simple models, the standard deviation of the shocks in each period are assumed to be the same (e.g., σ2ε,t=σ2ε ∀ t).

GARCH models estimate volatility of these shocks in each period as a function of both the shocks and their standard deviations in prior periods. Generally, more volatility in the recent past will result in more volatility in the present.

A paper by Bauwens and Storti (2009) examines an interesting phenomenon: “the persistence of the volatility process tend to decrease after extreme events such as those observed in October 1987 and September 2001.” Standard GARCH models equally weight all shocks regardless of whether or not they occur during extreme events. In order to incorporate the observation that volatility decreases after extreme events, the authors propose a Weighted GARCH (WGARCH) model. In their model, shocks are modelled as follows:

  • ut = zt[st-dh1,t + (1-st-d)h2,t]1/2
  • hk,t=a0k + Σi=1 to p aiku2t-i + Σj=1 to q bjkhk,t-j for k=1,2

The term s is in essence a weight which determines the volatility. If h1,t represents the high volatility period and h2,t represents the tranquil period, the term s balances the regime in which we are currently situated. For instance, when s approaches 0 we are in the tranquil regime and when s approaches 1, we are in the volatile regime. Model parameters can be estimated using MLE or Bayesian approaches.

This WGARCH model seems to be useful when the persistence of volatility varies into two different types of regimes. It will be interesting to see if the model produces a good fit after analyzing the stock returns after the most recent financial meltdown.

Health Wonk Review: Crunch Time For Health Reform

July 9, 2009 in Carnivals | No comments

Ken Terry of BNET Healthcare Blog has posted a “Crunch Time for Health Reform” edition of this week’s Health Wonk Review.

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Seattle doctors try flat-rate no-limit primary care

July 8, 2009 in Supply of Medical Services | 5 comments

Primary care doctors in Seattle are looking to eliminate insurers from the medical care process. 

“Qliance customers pay $99 to join, then a flat monthly rate of $39 to $119, depending on age and level of service. Patients can quit without notice and no one is rejected for pre-existing conditions…Co-founder Norm Wu said per-patient revenue is triple that of insurance-based clinics. He said many costs are fixed so the firm, now losing money, will turn to profit as business grows.  More than 50 noninsurance clinics operate in 18 U.S. states, based on different business models, Wu noted.”

In essence, primary care doctors are providing taking on the risk of excessive patient illnesses.  However, since Qliance only treats patients in the primary care setting, its risk is minimized.  If a patient gets too sick or needs to be hospitalized, Qliance is not liable for these types of medical treatment.  Patient who participate in the Qliance plan need to buy catastrophic health insurance in order to cover hospitalization and speicalist care visits.  This health care model seems feasible for Qliance’s end since primary care visits much more predictable than hospitalizations. 

Will the primary care docs at Qliance simply refer all patients to specialists to save money?  They will certainly have this incentive is the clinic becomes busy, but that will be tempered by competitive pressure to provide quality service.  If quality drops, patients may return to a traditional insurance plan. 

The key assumption here is that patients are able to judge quality in the primary care setting (e.g., physician friendliness, wait times to see a doctor, time spent with a doctor), whereas they may not be able to judge hospital quality or specialist quality when they are severely il and have complicated diseases.

I doubt the membership model will revolutionize healthcare, but I am willing to bet that it will carve out a significant market share from patients who are willing to pay for better primary care.

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Marginal Revolution on Medicare Administrative Costs

July 7, 2009 in Medicaid/Medicare | No comments

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Health Care Efficiency: Academic vs. Vendor Measures

July 7, 2009 in Hospitals, Quality, Supply of Medical Services | 1 comment

Measuring efficiency in health care is extremely difficult.  If there was an accurate scientific measure of patient health (e.g., a 1-100 scale) before and after treatment.  That way, one could measure changes in health before and after treatment per every dollar spent.  However, measuring health outcomes is very difficult. 

In the academic literature, hospital efficiency most commonly measured as: “risk-adjusted average length of stay (Weingarten et al. 2002); cost per risk-adjusted discharge (Conrad et al. 1996); and the cost of producing both risk-adjusted hospital discharges and hospital outpatient visits (Rosko 2004).”  Measures of physician efficiency often use RVU measures.

On the other hand, private vendors often uses “groupers.”  Groupers are algorithms that group different treatments into a single episode of care for a specific illness.  For acute illnesses, hospital and physicians treatments are grouped together into one episode of care.  For chronic illnesses, vendors look at costs over a specific period of time.  ”The market leaders among episode-based measures are Episode Treatment Groups (ETGs) and Medical Episode Groups (MEGs), which use algorithms primarily based on diagnosis codes and dates of services to group-related insurance claims into episodes.”

If the grouper algorithms are correct (a big if), I believe private vendor methodology provides a better measure of efficiency since they examine the entire episode of care.  Although they may be a superior measure of efficiency, they may not help improve efficiency.  If I see certain episodes of care are efficiency or not in certain areas, it may be difficult to pinpoint which providers are being inefficient if the patient visits multiple physicians or hospitals.  On the other hand, the academic literature is more likely to use the hospital or physician as the unit of measurement.  This allows each physician or hospital to improve on their efficiency measures, but may not reflect the true quality of care if patients see many physicians or are hospitalized at a number of hospitals.  For instance, a hospital may have a high efficiency score (low cost per procedure), but if they do a bad job and the patient is re-hospitalized at a different hospital, this will not show up in the academic efficiency measures, but will be captured by the vendor measures.   

  • Hussey et al. (2009) “A Systematic Review of Health Care Efficiency Measures,” Health Services Research, v44(3):784-805.

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