Information

Rating websites are all the rage on the internet. From RateMyTeachers.com to RateMyCop.com, you can rate practically anything nowadays.

A new website called Vitals.com allows you now to rate your doctor as well. In addition to being able to read reviews from other patients, there are also other physician statistics. For instance, Vitals.com informs you whether or not the doctor is board certified, where the physician graduated from medical school, and also the rating of the medical school where they graduated.

For me, the more information available for patients, the better.

The Scientific American magazine has an interesting article (”Science 2.0“) about the web, open-access, blogging and research. Should researchers post their results online? Should scientists blog about their methodology?

Pros

It seems like academic research is the perfect forum for social networking and blogging. The sharing of ideas is a key means towards scientific invention/innovation. Posting raw data is a great way for other researchers to verify results, or utilize the same data for different purposes. One cancer researcher noted:

• “To me, opening up my lab notebook means giving people a window into what I’m doing every day,” Hooker says. “That’s an immense leap forward in clarity. In a paper, I can see what you’ve done. But I don’t know how many things you tried that didn’t work. It’s those little details that become clear with an open [online] notebook but are obscured by every other communication mechanism we have. It makes science more efficient.”

The site OpenWetWare let’s laboratories share their daily experiences online. Further, researchers who are traveling can access their lab notebooks from anywhere in the world with OpenWetWare.

Further, social networking can allow easier collaboration between colleagues working in different parts of the country or different parts of the world.

It seems like researchers would be some of the first people to utilize Web 2.0, but…

Cons

• “It’s so antithetical to the way scientists are trained,” Duke University geneticist Huntington F. Willard said at the January 2007 North Carolina Science Blogging Conference, one of the first big gatherings devoted to this topic. The whole point of blogging is getting ideas out there quickly, even at the risk of being wrong or incomplete. “But to a scientist, that’s a tough jump to make,” Willard says. “When we publish things, by and large, we’ve gone through a very long process of drafting a paper and getting it peer-reviewed. Every word is carefully chosen, because it’s going to stay there for all time. No one wants to read, ‘Contrary to the result of Willard and his colleagues….’”

Beside the fact that writing about unfinished results is not the way scientists are usually trained, most individuals worry about having their ideas stolen. Having your idea “stolen” by another individual means you will not get the recognition you deserve for coming up with an idea, and your career path can be adversely affected. Doling out credit for work accomplished is an important component of the “old school” journal system.

Other worries include the fact that when junior faculty post critical comments of the work of senior faculty, they may fear some sort of reprisal. This has lead some individuals to use pseudonyms.

Summing up

There are some serious drawback to Science 2.0, but as Timo Hannay, head of Web publishing at the Nature Publishing Group, states, “Our real mission isn’t to publish journals but to facilitate scientific communication.”

KBPS reports that Google will begin digitizing tens of thousands of library books at my current school, UC San Diego (UCSD).

Why was UCSD chosen?  Google states that UCSD has an “exceptional collection of social sciences and east asian books.”  Within six months, all UCSD books will be available online.

Most economics simplify markets and assume that there is one market price. In reality, however, we observe significant price dispersion. Because of this, we see that that searching for the lowest price–while costly–can buyers to superior outcomes. George Stigler (1961) is a seminal paper on the Economics of Information which I will review here.

When should a person search? This occurs when the marginal benefit to search is equal to the marginal cost. Let us assume that the marginal cost of searching is proportional to the individuals cost of time. The marginal benefit of searching is:

• q*|∂P_min/∂n|

The number of searches is denoted as n. We see that people who buy a higher quantity, q, of goods will search longer. One implication of this is that business professional will search more than casual shoppers since they will likely buy higher quantities. Since tourist have little information and purchase low quantities, Stigler accurately predicts that tourist will pay higher prices than experienced buyers.

Also, “the expected savings from search will be greater, the greater the price dispersion of prices.” However, when goods are one-of-a-kind or unique, then the efficiency of searching is extremely low since it is difficult to identify potential sellers.

Brokers can be brought into the market to make it more efficient. These brokers “will eliminate the profitability of quoting very high selling and very low buying prices and will render impossible some of the extreme price bids.” Price dispersion will not disappear entirely when there are brokers since these brokers must make at least some profit to compensate them for their time. As price dispersion nears zero, brokers will leave the market.

What other conclusions does Stigler derive?

1. The larger the fraction of the buyer’s expenditures on the commodity, the greater the savings from search and hence the greater the amount of search.
2. The larger the fraction of repetitive (experienced) buyers in the market, the greater the effective amount of search (with positive correlation of successive prices).
3. The larger the fraction of repetitive sellers, the higher the correlation between successive prices, and…the larger the amount of accumulated search.
4. The cost of search will be larger, the larger the geographical size of the market.

• George J. Stigler (1961) “The Economics of Information” Journal of Political Economy, 69(3): 213-225.

According to Insurance and Technology, health insurer WellPoint has partnered with Zagat’s Survey to allow WellPoint’s members to rate their doctors.  Zagat’s will use its 30 point rating system to evaluate all doctors.  According to Spirit magazine, patients can grade doctors based on “including doctor availability, office environment, trustworthiness, and communication.”

Other websites, such as RateMDs.com, already offer patients the ability to rate doctors, but WellPoint is the first health insurer to offer this service to its members.

TechCrunch reports on a Facebook application that alerts potential donors to donate blood in times of shortage.  Take all Types (TAT) is the name of the innovative non-profit which invented this application.  The TAT website wisely states:

“There are always shortages of blood throughout the nation, even though there are plenty of potential donors out there. After All, we all have blood we can share. The main issue is communication!“

VentureBeat (”…Health 2.0…“) profiles six innovative Health 2.0 firms which were at the 2008 Health 2.0 Conference in San Diego.  Each firm on the list aims to reinvent the doctor patient relationship.

Included on the list is Carol.com which allows patients to do medical shopping.  PharmaSurveyor allows users to enter the medications they are on and see if there are any harmful drug interactions.

There are lots of innovative ideas coming down the Health 2.0 pipeline.

Electronic medical records (EMR) hold the promise of vastly improving the quality of medical care received in the U.S. today. One of the major issues with EMR is privacy however. Patients generally want their doctors to know as much about their health as possible in order to make the best possible medical diagnoses and treatment decisions.

Yet who should you trust with your EMR? Physician groups are generally too small to efficiently implement EMR. Further, if you switch doctors, most patients want their EMR to follow them. What if the health insurers are put in charge of the EMR? This may make the most sense, but some health insurers can use the EMR to learn more about the health of their enrollees. While this seems like a good thing, when a certain enrollee gets sicker, they may decide either to increase their premiums or to try to drop their coverage. A clear conflict of interest exists here.

What about a third party EMR vendor? Google and Microsoft both are offering EMR services. But do you really want one of these enormous corporations selling your most personal medical information to other companies?

One solution to this problem is Keyose.com. Created by Dr. Julio Bonis, Keyose is a completely anonymous EMR service. Here’s how it works:

1. Users sign up and enter their personal health information.
2. A username code is generated along with a public and private password. The public password is printed on an ID card that doctors can use to access medical information. The private password enables users to update their medical information. Further, Keyose allows patients to use their private password to enter confidential medical information that people with the public password (e.g.: physicians) will not be able to view. This allows patients to manage their own health care information.
3. You do not enter personal data (e.g.: not your name or an e-mail) when you sign up in Keyose. Thus, you will not receive any marketing materials. Even if a hacker breaks into the system, they will not be able to match your medical information to your name or email.
4. Finally, it is free to sign up.

As you know, there is nothing in life that is free. How does Keyose plan to fund this project? According to their “Help” section:

In the future we could include information about sponsors (including private health insurance companies, pharmaceutical or biomedical industries) mainly intended for doctors who access the personal health records. We could also charge for premium services (for instance translating the personal health record for international patients or providing contextual information about a patient’s diseases).

There are drawbacks to this patient-based EMR. Patients do not use the same jargon as physicians and, thus, much important information could be lost in translation between the physician and the patient. Also, the information is uploaded by the patient, and not physicians, nurses, or trained staff.

I tried out Keyose myself. It was pretty basic and could have used more pre-defined fields (currently there is only DOB, gender, blood type, allergies, and personal and family history). Specific fields detailing whether or not you have certain allergies, or whether you have received certain vaccines would be helpful. Also, I could view the confidential information section even when I logged in using the public password.

Nevertheless, Keyose does seem like an step in the right direction.

Google’s blog gives a preview of its Google Health application.  TechCrunch provides some analysis.

Yesterday, I spoke about the Swiss health care system. One of the main attributes of this system is that patients are allowed to choose from any health care plan and the health insurers can not refuse to cover them. Further, since the insurance benefit is mandated by law, there is very little quality difference between plans. Only 8% of health plans restrict provider choice in any way.

An NBER working paper by Frank and Lamiraud analyzes how the number of firms offering health insurance in Switzerland has affected price dispersion and expected price.

Economists generally believe that when more firms/products enter the market, this will increase the probability that a superior health insurer will be available to the public. However, when more products enter the market, search costs increase. Thus, it is theoretically possible for more health insurance offerings to decrease utility.

A paper by Janssen and Moraga-Gonzales (2004) show that an the welfare impact of an increase in the number of sellers depends on the consumers’ search intensity. When consumers search with low intensity, having more firms will reduce search, will not affect expected price and will to greater price dispersion. One can think that inexpensive and/or infrequently purchased items would fall in to this category. When consumers search with high intensity, an increase in the number of firms will increase searching and decrease prices. In the 401(k) market, when employees are offered more than 10 choices, there are reductions in consumer responses (i.e.: less investment switching occurs).

Frank and Lamiraud aim to analyze how the number of firms affects insurance choice in Switzerland. They use a panel survey from the Federal Office for Social Insurance (OFAS). It contains a sample of 2152 individuals and asks about their insurance coverage between 1997 and 2000. Yearly premiums are available at the Federal Office for Public Health (OFSP) website. While there was consolidation in the health insurance market among firms, the average number of health plans offered per canton increased from 39 in 1998 to 52 in 2003. The number of people switching plans was 4.8% in 1997, 5.4% in 1998, 2.7% in 1999 and 2.1% in 2000.

How do people choose their plan?

…40% of people choose a health plan following their parents’ and friends’ choices, and what they see as tradition. Furthermore, as many as 25% individuals declare that they do not strive to pick the health insurance plan with the lowest premium. A substantial number of people explicitly report staying with their health plan based on habit (13.5%) or because they are satisfied with their arrangement (79%)

What do the authors conclude from the data?

First, we show that consumers that switch health plan pay 15% to 16% less in health insurance premiums per month holding ceteris paribus. Second, we show that among consumers expressing dissatisfaction with their health plans those in markets with fewer choices are more likely to express intent to switch. Finally, consumers that used an agent to help them purchase insurance consistently paid significantly lower premiums. This set of results suggests that “mistakes may have been made”.

Despite what orthodox economic theory states, no market is perfect. Understanding these market imperfection imparts important knowledge for economists and health policy makers.

• Richard Frank, Karine Lamiraud, 2008. “Choice, Price Competition and Complexity in Markets for Health Insurance,” NBER Working Paper #13817.

Google is everywhere. CNN reports that Google is venturing into health records biz.

“Google Inc. will begin storing the medical records of a few thousand people as it tests a long-awaited health service that’s likely to raise more concerns about the volume of sensitive information entrusted to the Internet search leader.

The pilot project to be announced Thursday will involve 1,500 to 10,000 patients at the Cleveland Clinic who volunteered to an electronic transfer of their personal health records so they can be retrieved through Google’s new service, which won’t be open to the general public…

The third-party services are troublesome because they aren’t covered by the Health Insurance Portability and Accountability Act, or HIPAA, said Pam Dixon, executive director of the World Privacy Forum, which just issued a cautionary report on the topic.

Passed in 1996, HIPAA established strict standards that classify medical information as a privileged communication between a doctor and patient. Among other things, the law requires a doctor to notify a patient when subpoenaed for a medical record.

That means a patient who agrees to transfer medical records to an external health service run by Google or Microsoft could be unwittingly making it easier for the government or some other legal adversary to obtain the information, Dixon said.”

The New York Times blog BITS also has a post about Google Health as well.

Adverse selection is often seen as a major impediment to the efficient functioning of insurance markets. Rothschild and Stiglitz (1976) create a model where high risk people buy full insurance while low risk individuals buy partial insurance. Yet empirically, one finds that in some insurance markets, low risk individuals purchase more insurance than high risk individuals.

An NBER working paper by Cutler, Finkelstein and McGarry (2008) claims that preference heterogeneity may explain this phenomenon. If low-risk individuals also have a stronger risk aversion preferences, than they may buy more insurance than a high-risk individual who has risk loving preferences. This work is an extension of the Finkelstein and McGarry (AER 2006) article discussed in one of my earlier blog posts.

Using data from the Health and Retirement Study (HRS), the authors measure risk tolerance using the following variables: smoking, having 3+ alcoholic drinks per day, job-based mortality risk, receipt of preventive health services, and seat belt usage. The authors find a negative relationship between individuals who engage in risky behavior (i.e., smoking, drinking, and those working in a high-risk occupation) and the percent who purchase various types of insurance, and a positive relationship between those engage in risk reducing behavior (i.e., preventive medical care, seat belt usage) and the purchase of insurance.

In the above chart, ‘+’ represents a positive statistically significant correlation, ‘-’ represents a negative statistically significant correlation, and o indicates that the relationship is not statistically significant.

The authors can also measure risk preferences based on respondents answers to income gamble questions. There is a weak relationship between risk preferences and risk behavior however.

The authors confirm that risk behavior lead to an increased probability of adverse events which would be covered by insurance. For instance, smoking increases mortality which would lead to an earlier life insurance payout. Increased preventive health activities decrease the probability of a nursing home stay.

Thus the authors conclude the following:

Our analysis yields two main findings. First, in all five markets, we find that individuals who engage in what are commonly thought of as risky behaviors (smoking, drinking, or prior employment in jobs with higher mortality rates) or who do not take measures to thought to reduce risk (preventive health activities or wearing of a seat belt) are systematically less likely to hold each of these insurance products. Second, we find that these same individuals tend to have higher expected claims for life insurance and long term care insurance, but lower expected claims for annuities; for Medigap and acute health insurance, there is no systematic relationship between the behavior measures and expected claims.

These results can help to explain the puzzle of insurance we started with: why is adverse selection not more common? In annuity markets, there is clear evidence of adverse selection: people who live longer are more likely to buy insurance. The standard adverse selection model is one explanation for this, but so is variation in risk tolerance; people who have less risky behaviors live longer and are more likely to buy annuities. In life insurance, our results suggest that differential risk tolerance can help explain why people with lower mortality rates have more insurance. Similarly, in the case of long-term care insurance, people who use more preventive care or are more likely to wear seat belts buy insurance more readily but also stay out of nursing homes.

• David Cutler, Amy Finkelstein, Kathleen McGarry. 2008. “Preference Heterogeneity and Insurance Markets: Explaining a Puzzle of Insurance,” NBER WP #13746.
• Finkelstein, Amy and Kathleen McGarry. 2006. “Private information and its effect on market equilibrium: new evidence from long-term care insurance.” American Economic Review 96(4): 938 – 958

The Sacramento Business Journal has an interesting article (”Better communication…“) on Kaiser Permanente’s online system for patients called “KP HealthConnect.”  The system allows patients to schedule appointments, refill prescriptions, view lab results and email doctors.

Implementing electronic medical records (EMR) have been elevated to a top priority by healthcare policymakers. Using EMR, medical providers may be able to improve quality and better detect adverse events.

One way to improve quality with EMR is chart abstraction. After a physician-patient encounter, the doctor can review the medical chart to see if he or she prescribed the correct treatments or advice to the patient. This quality improvement methodology is certainly feasible without EMR, but is made much easier and inexpensive when patient records are held electronically. A paper by Luck et al. (2000) claims that chart abstraction may not capture all quality measures. In fact, chart abstraction typically underestimates the quality of care provided. This is likely due to the fact that physicians do not write down every minute detail of the visit and thus quality may be underestimated. Another issue is recording bias. “Busier practitioners may do more than they write down or good recorders may not be careful history takers (or physical examiners).” Plus, one must realize that a patients medical record serves multiple purposes. It is not only a medical document, but a legal record and a source of billing information. These facts may compromise the integrity of the EMR (e.g.: DRG creep).

A paper by Bates et al. (JAMIA 2003) claims that chart review or chart abstraction is overall a good technique, but may be too expensive for routine use and may fail to detect may adverse events. The Bates paper reviews a number of studies looking at electronic tools such as “event monitoring” and “natural language processing” which can help to detect problems before they occur.

EMR are only useful if physicians actually use them. A study by Miller and Sim (Health Affairs 2004) find that “the path to quality improvement and financial benefits lies in getting the greatest number of physicians to use the EMR (and not paper) for as many of their daily activities as possible. The key obstacle in this path to quality is the extra time it takes physicians to learn to use the EMR effectively for their daily tasks.” EMRs are only useful if all relevant data are included in the EMR. If most information is documented, but a physician records a patient’s allergies in the paper, but not electronic chart, there could be serious adverse medical affects. The Miller and Sim paper also proposes some solutions of how to best implement EMR.

Another unforeseen side-effect of using EMR that physicians will spend more time typing and gazing at a computer screen and less time interacting with the patient. A study by Margalit et al. (2006) finds that in Israel, “physicians spent close to one-quarter of visit time gazing at the computer screen.” The computer may enhance record keeping, but it also may diminish dialogue.

Finally, whenever EMR are implemented, they must be integrated in to the current systems in use. The new EMR must be integrated with the technology currently in use and well as the social system in place. One must also take into account the technical and physical infrastructure in place. For instance, Harrison, Koppel and Bar-Lev (JAMIA 2007) document that implementing computerized physician order entry at one children’s hospital “reduced beside nurse-physician interaction about critically ill infants. Nurses had fewer opportunities to provide feedback that sometimes led to beneficial medication changes.

• David W. Bates, MD, MSc, R. Scott Evans, MS, PhD, Harvey Murff, MD, Peter D. Stetson, MD, Lisa Pizziferri and George Hripcsak, MD. 2003. “Detecting Adverse Events Using Information Technology” JAMIA. 10:115-128.
• Harrison HI, Koppel R, Bar-Lev S. 2007. “Unintended Consequences of Information Technologies in Health Care–An Interactive Sociotechnical Analysis” J Am Medical Informatics Assoc. 14(5): 542-549.
• Jeff Luck, John W. Peabody, Timothy R. Dresselhaus, Martin Lee and Peter Glassman. 2000. “How well does chart abstraction measure quality? A prospective comparison of standardized patients with the medical record” The American Journal of Medicine. 108(8): 642-649.
• Margalit RS, Roter D, Dunevant MA, Larson S, Reis S. 2006. “Electronic medical record use and physician-patient communication: an observational study of Israeli primary care encounters.” Patient Educ Couns. 61(1):134-41.
• Miller RH, Sim I. 2004. “Physicians’ Use of Electronic Medical Records: Barriers and Solutions” Health Affairs. 23(2): 116-126.

A New Yorker article (”The Checklist“) recounts Peter Pronovost’s efforts to improve the delivery of medical care. One of his simplest ideas was to invent a 5 step checklist to reduce line infections:

Doctors are supposed to (1) wash their hands with soap, (2) clean the patient’s skin with chlorhexidine antiseptic, (3) put sterile drapes over the entire patient, (4) wear a sterile mask, hat, gown, and gloves, and (5) put a sterile dressing over the catheter site once the line is in.

All doctors know these 5 steps, but in the distraction-filled world of the I.C.U., it is very easy for the physician to forget any one of the steps. Dr. Pronovost’s checklist idea has extended to other treatment areas as well. Yet he believes that Americans are still not getting serious about treating medical care as a science.

“The fundamental problem with the quality of American medicine is that we’ve failed to view delivery of health care as a science. The tasks of medical science fall into three buckets. One is understanding disease biology. One is finding effective therapies. And one is insuring those therapies are delivered effectively. That third bucket has been almost totally ignored by research funders, government, and academia. It’s viewed as the art of medicine. That’s a mistake, a huge mistake. And from a taxpayer’s perspective it’s outrageous.�? We have a thirty-billion-dollar-a-year National Institutes of Health, he pointed out, which has been a remarkable powerhouse of discovery. But we have no billion-dollar National Institute of Health Care Delivery studying how best to incorporate those discoveries into daily practice.

Checklists are not the solution to every problem.  A large portion of medicine deals with complex condition with large uncertainties and many disease interactions.  Further, it may be more difficult for an insurance company to institute checklists than a hospital manager or someone further down the chain of command.  Nevertheless, standardization in medicine should help to dramatically improve quality.

I recently received an email from the Patient Privacy Rights organization. They are conducting a campaign to protect the privacy of individual’s prescription drug information. According to their website, a Newsday article reports the following:

Randee Lonergan filled her prescriptions at the same pharmacy for years. But a month ago she was shocked to find the pharmacy closed and all her family’s medical records sold to a nearby Target store in Levittown. Her information was sold legally because of a loophole in medical privacy law that allows pharmacies to “auction off” customer records - including prescriptions, information about medical conditions, Social Security numbers and insurance records - “to the highest bidder,” Sen. Charles Schumer said yesterday. The practice of selling off records, Schumer said, is a nationwide problem. Federal law requires doctors to let patients know when their medical history is being shared. But the law allows pharmacies to sell patient information to other pharmacies, Schumer said.

This sounds horrible. Patients should have a right to keep their medical information private. No one should be able to buy information that would tell them whether I take Zoloff for depression, Flomax for frequent urination, AZT to treat HIV or Viagra for erectile disfunction (see video).

I was about to sign the petition when I realized that sometimes I do want people to know what medications I am taking. One of the major benefits of electronic health records (EHR) is that emergency room doctors who I have never seem before can have instant access to my current medications and my allergies. EHR can help to provide a patient’s network of doctors with standardized information which can help in the treat of medical ailments.

How can we limit patient medical data to only the people and organizations that we want to have it? Would providing strict patient privacy protection make a standardized EHR impossible? Is it feasible to restrict access to medical records in a manner which protects patient privacy, but enhances medical care? These are complicated questions of which we need to find an answer.

GigaOM reports on some new Health 2.0 developments in Germany in its article “Health 2.0 Gaining Traction in Germany.”  Websites such as Helpster and Imedo are among a number of website which are now rating German physicians.  In order to take into account established medical institutions, Imedo is including the physician certification status as part of its rating.  This certification status is decided by the German Kassenärztliche Vereinigung, “a public organization in charge of distributing the lion’s share of physicians’ income.”

Will German physicians even engage in price competition for patients?  Two German websites believe so:

In the transactional domain, 2te-Zahnarztmeinung and Arzt-Preisvergleich have gained significant traction by running reverse auctions — primarily for dentists’ treatments — which allow for a high degree of comparability. Navigating closely on the edge of what German law allows, a potential patient does not have to automatically contract with the cheapest offering. Instead, she can compare all anonymously quoted prices and take into account the ratings given by patients who have undergone a similar treatment. The physician she chooses then awards 15 to 20 percent of his fee back to the platform.

Is this the wave of the future?  The Healthcare Economist says yes and no.

Yes because for medical procedures which are 1) fairly routine, 2) of relatively low risk and 3) where the patient has a high quality of information regarding the cost and benefits of the procedure, the internet can provide an important means of disseminating information and driving down price.  Dentist visits are predictable and quality is relatively easy to measure.  Thus using these websites will be a boon to consumers.

No because for more complicated procedures these website’s information will likely not be helpful.  Using outcome measures for complicated procedures must make risk adjustments to take into account patient case mix.  Process measures may be to simple to take into account factors such as disease interactions.  And structural measures (e.g.: nurse-patient ratio, the use of electronic medical records) may only be peripherally related to the quality of care.  I doubt that cardiothorasic surgeons will start bidding for patients any time in the near future.  Patients, whose cost for these expensive procedures will generally be covered by insurance, will choose a doctor likely based on their primary care doc’s referral or the opinions of their peers.

Thus, the Health 2.0 movement is likely to be a significant development for the low-cost, predictable sector of medical care.  Specialist and hospital care is much more difficult to measure and price and I believe will likely be hardly affected by these new developments.

Many people believe that better information technology (IT) can help improve the quality of medical care in the U.S. and around the world. For instance, if a doctor prescribes a drug which interacts harmfully with a drug the patient is already taking, a computer program could notify the doctor of this problem. If a patient has a certain disease, a computer program could help to narrow down the physician’s treatment options.

The problem with this top-down, one-size-fits-all doctoring is that every patient is unique. Of course, there are medical standards which should generally be followed, but often these standardized treatments have to be altered slightly or completely depending on patient wishes, the prevalence of multiple diseases, or economic constraints. Further, the physician typically has superior information regarding the patient’s condition than a standardized computer algorithm.

An NBER working paper by Javitt, Rebitzer and Reisman investigates a randomized trial of the implementation of a new IT system at a commercial HMO plan. The program was ‘turned on’ for some patients for each physician in the HMO and ‘turned off’ for other patients. The program would give three care consideration (CC) alerts: stop a drug, do a test, or add a drug. Each of these warnings had 3 severity levels.

The authors compare data on the CC alerts which occurred for the ’switched on’ patients with the hypothetical CC alerts which would have occurred if the software had been running. The authors found that the IT program lead to 6% lower charges in the turned on group compared to the control. The authors interpret this to be due to lower hospitalization rates and medication complications in the treatment group.

One key aspect of the program is that the CC alerts could be ignored by doctors.

“Physicians often have better information about their patients than does the error detecting software. Actions that look like a misstep to the computer may in fact be the result of informed physician choice, informed patient choice and/or patient non-compliance. For this reason, the HMO and the software company viewed CCs as recommendations that physicians were free to ignore if they disagreed.”

Physicians seemed to heed these CC alerts more than other studies had previously thought they would. Why?

Our results, however, suggest something more: the messages in the CCs seem to have had a bigger effect on physicians than the conventional medical channels used to promote the HOPE trial findings. It seems plausible that the CCs were influential because they linked a general recommendation (“take ACE inhibitors�?) to a specific patient [my italics] and a specific cite to the medical literature.”

To me, this seems to be the most appropriate way to implement medical IT.

• Javitt JC, Rebitzer JB, Reisman L, (2007) “Information Technology and Medical Missteps: Evidence from a Randomized Trial ” NBER WP #13493.

Yesterday, Microsoft announced the introduction of the online medical records system titled Health Vault. Online medical records would greatly increase productivity in the health care industry since:

1. Patients would be able to have all their health care information in one spot.
2. Moving from one state or country to another would not entail losing your medical records.
3. Physicians would have a standardized way of recording patient data
4. There would be no confusion with respect to messy physician hand writing.

The N.Y. Times states:

“The value of what we’re doing will go up rapidly as we get more partners,�? said Peter Neupert, the vice president in charge of Microsoft’s health group…

The company hopes that individuals will give doctors, clinics and hospitals permission to submit information like medicines prescribed and data on blood pressure and cholesterol levels.  Mr. Neupert said such data transfers would then be automatic, over the Internet, which is why the partnerships are so important.

Microsoft wisely decided to name to online medical system Health Vault. This is a shrewd strategic move because the major impediment to online medical records is patient privacy and information security.

This is where a paradox occurs. According to the Economist:

Sean Nolan of Microsoft explains that the business model depends on one thing: targeted search. Microsoft is betting that people will use its Health Vault Search to find out about their ailments. This service relies on an approach known as “vertical search�? which attempts to provide more relevant results than generalist search engines like Google and Yahoo! by specialising in a particular field. The firm’s recent acquisition of Medstory, a vertical-search engine focusing on health care, has given it a boost in this area.

Health Vault’s search engine would definitely work better than those of rival sites if it could examine users’ health records and past queries, and thus provide the responses that are most relevant to each individual’s situation. But in order to attract any users in the first place, Microsoft has promised to enforce strict privacy rules. These, says Mr Nolan, would preclude such data-mining.

More coverage is available here:

• The Economist
• N.Y. Times
• Read/Write Web
• NPR’s Marketplace

TechDirt has an interesting article (”The Doctr Is In“) about Dr. Jay Parkinson.  Dr. Parkinson started a boutique clinic in which patients pay for services out of pocket.  Unlike most boutique practices, patients can email or instant message the doctor about any medical questions from 8am to 5pm, or in the case of medical emergencies Parkinson is available 24/7.  Techdirt claims that “studies confirm that online chat with your doctor is nearly as effective as an in-person visit.”  Further, if the patient is sufficiently ill, Parkinson will make house visits (two are included in the $500 annual retainer fee).  Another interesting aspect of the Parkinson practice is the following:

“…since all of his clients are very price conscious (since they’re paying out of pocket), he actively shops around for the best value specialists to send his clients to. In the age of copayments and insurance, you very rarely see much price comparison shopping in health care .”

You can visit Dr. Parkinson’s website here.

A new website is available to help patients and physicians find information regarding various diseases.  The site is called CureHunter.  The site not only has a ton of information on different diseases, therapies, drugs, and bio-agents, but shows the relationship between each of these in a very appealing visual interface.  Since I am not a physician, I am not qualified to assess the quality of the information on the site, but the concept does seems appealing–especially for patients who require inter-specialty care.

You walk into your doctors office hoping for a diagnosis to your most recent ailment.  The doctor runs some tests and leaves the room telling you he will return.  Ten minutes later, the doctor tells you that you have diabetes.  What was the doctor doing during the ten minutes?  Most likely, he was reviewing the tests, thinking about your particular case, and checking the internet for more information.  What technical website did the doctor use for your diagnosis?  The most likely answer is Google. 

At least this is what an article on the Alt Search Engine website states.  The article continues:

We surveyed 6,000 physicians and learned that Google and other consumer search engines were frequently used by physicians to get clinical information. Among physicians 45 and under, more than 90 percent said they used the Internet and search engines frequently, and Google was the preferred search engine among all specialties surveyed. When asked why, the answer was simple. Forty-three percent offered the top response, “it seemed like a good place to start.�?

Google may be popular among physicians, but they also expressed a certain degree of dissatisfaction, with 65 percent of primary care physicians (PCPs) saying Google searches returned too many irrelevant results.

Despite the primacy of Google, new search engines are coming into the market specifically targeted to physicians, researchers and patients.  PubMed is a search tool most often used by researchers (like myself) to review scholarly journal articles.  There is also another tool called GoPubMed which also helps physicians and researchers stay abreast of the latest medical research.

SearchMedica is a site which I found to be more practical in that it links to government and physician authorities on different diseases.  For instance, searching for ‘diabetes,’ the first four results were the National Diabetes Information Clearinghouse (NDIC), the CDC’s Diabetes Public Health Resource, an article from the journal Diabetes Care, and the CDC’s Diabetes Frequently Asked Questions.

Other health search engines include: Healthline, GoPubMed, RevolutionHealth, Kosmix, CognitionSearch, GenieKnows (Health), MEDgle, Helia, ReleMed, MedWhat, MedicineNet, Diagnosaurus, WebMD, and HealthExcite.

The internet has an abundance of healthcare information available; the only problem left to solve is how to organize and access all of it.

The Nursing Home Compare website provides consumers with quality ratings of thousands of nursing homes (NHs) around the country. Are these ratings accurate? Could they be improved?

This is the question which researchers Arling, Lewis, Kane, Mueller and Flood analyze in their 2007 HSR paper. The authors find 2 major flaws with the rankings: 1) there is weak risk-adjustment and thus the ratings do not fully take into account the underlying characteristics of the population being served by the NH, and 2) there are no precision measures included in the rankings.

In order to improve the rankings, the authors use an empirical Bayesian (EB) shrinkage model with risk adjustment.

In the empirical Bayesian model, an empirical distribution serves as the prior. When new data are collected, these serve as the “Likelihood” or posterior distribution. Confidence intervals are constructed around the EB estimates from the posterior distribution. In this paper, the authors have data at both the resident and facility level. The prior distribution is estimated from using the total nursing home resident population. The posterior distribution is based on facility level data and the Likelihood function is the product of the two distributions. The authors explain in more detail:

“The influence of the facility’s observed QM [quality measure] rate on the posterior estimate will depend on the size of the facility and the amount of QM variation within and between facilities. The QM rates in larger facilities will be more certain (e.g., have lower standard errors) than in smaller facilities and, thus, will have greater weight or influence on the overall posterior (EB) estimate. Also, QMs with less variation between facilities have a more certain empirical prior (population average QM rate), which then has a greater influence on the posterior. As the prior tends to pull the posterior estimate toward the population mean, EB estimates are referred to as ’shrinkage’ estimates. “

Using the EB methodology, the standard deviations for most QMs “decreased considerably.” Smaller facilities experienced more shrinkage towards the mean due to their small number of residents. This is logical since one outlier patient would have a much higher impact on average QM rankings in a NH with 10 residents than another facility with 100 residents.

The risk adjustment is calculated in three ways: 1) simply excluding the sickest patients (i.e.: those with end-stage diseases or are in a coma), 2) group the sample in different risk strata, and 3) use a logistic regression to estimate a risk adjustment factor for each patient. Each of the risk adjustment methods was found to have a strong effect on the rankings.

One problem the authors acknowledge is that using EB and risk adjustment may let some facilities ‘off the hook.’ Small facilities with sicker than average patients may have low QM score because of an unlucky spate of ill patients or they may truly be poor facilities. Bayesian shrinkage moves their scores closer to the mean, so these facilities’ QM ratings are less responsive to quality improvements or backslides than larger facilities.

• Arling; Lewis; Kane; Mueller and Flood (2007) “Improving Quality Assessment through Multilevel Modeling: The Case of Nursing Home Compare” Health Services Review, 42:3, pp. 1177-1199.

Pay-for-performance (P4P) is supposed to improve the health care quality for all. One may not be surprised if it were the case that more affluent, more educated individuals benefit most from P4P and thus existing health care disparities may increase. Author Lawrence Casalino and Arthur Elster, however, posit that P4P may actually reduce the quality of care that the poorest and sickest individuals experience. Why would this be? Here are some examples from their paper “Will P4P and quality reporting affect health care disparities“:

• Reduction of physician income in poor, minority communities: The authors claim that doctors in poor communities will have less money for the IT, staffing and training needed to improve quality scores. When the doctors serving low-income households make less money, the supply of these doctors will inevitably decrease. Also, those doctors who decide to continue serving low-income communities may receive low P4P scores if patient compliance with physician recommendations is lower in poor communities. The low compliance rates could be due to language barriers, lack of education or lack of understanding of the doctor’s orders.
• “Teaching to the Test“: Doctors may preform services which evaluators deem important, but which in reality are less important to patient health in these under-served communities. “For example, with a relatively uneducated diabetic patient who speaks poor English, the physician might focus on making sure the patient has a hemoglobin A1c test (because this is measured) but not on the time-consuming task of explaining to the patient how to control his or her diabetes and blood pressure (because control of these things is not measured).”
• Avoiding ‘problem’ patients: If there is no risk adjustment measure in the P4P compensation schemes, physicians who have healthier patients will score higher on all measures. Thus, physicians may have an incentive to avoid the specific patients they are supposed to help the most: the sickest. The authors write: “It is often stated, without evidence being cited, that patients’ characteristics do not affect physicians’ scores on process measures and therefore that process measures, unlike outcome measures, need not be adjusted for patients’ health status or socioeconomic status. To practicing physicians, this might seem implausible. For example, it should be easier for physicians who practice in affluent areas such as Marin County, California, than for those who practice in poor areas of Oakland to achieve higher rates of screening mammography: Their patients are more likely to be wealthy, well-educated, well-insured women who are aware of the benefits of mammograms, are more likely to request them, and are more able to travel to obtain them. “

• Casalino and Elster (2006) “Will pay-for-performance and quality reporting affect health care disparities” Health Affairs, 26, no. 3 (2007): w405-w414

There is an interesting article (”Unintended Consequences“) from February 2000 edition of the New England Journal of Medicine in which author Dr. Lawrence Casalino gives his view of how measuring physician quality impacts care.  Dr. Casalino cites many unintended consequences of implementing an quality measuring system including 1) more paperwork, 2) the fact that certain quality measures will favor certain organizational frameworks, 3) the misallocation of physician time.

• Casalino (2000) “The Unintended Consequences of Measuring Quality on the Quality of Medical Care” NEJM, Volume 342, no. 7, pp. 519-520.

Why is it taking so long for the medical industry to adopt information and communication technology?  Is it a good thing that it is taking so long?  This is the question addressed by an interesting and non-technical NBER working paper by Michael Christensen and Dahlia Remler (”ICT in Chronic Disease Care“)

What is Information and Communication Technology (ICT)

Christensen and Remler divide ICT into 4 categories:

1. Technologies that support patient self care and education: These include  websites (such as Medco Health) to help people with chronic conditions.
2. Patient-provider and provider-provider communication: Email, targeted educational emails, etc.
3. Electronic data storage and sharing: EMR
4. Technologies that combine all of the above: Examples of this include clinical algorithm engines such as Aetna’s MedQuery.

Will ICT increase health costs?

The answer to this question is not clear.  The authors note “In general, while technological improvement can allow us to provide the same level of health at a lower cost, it can potentially also make it worthwhile to spend even more resources on health care to greatly improve health.”  Thus, ICT can result in significant efficiency gains to the health care sector without reducing the overall cost.

Why is it so difficult to adopt ICT?

There are many reasons why ICT adoption has been so slow:

1. Network Externalities:  Items such as EMR have this issue.  An investment in EMR increases in value as the number of users increases.  Since EMR usage is so fragmented at this point, the positive network externalities in this market are not yet accruing to those who have invested.
2. Switching Costs: If a provider (or health plan) chooses a EMR system, and they choose one that becomes a non-dominant system, they may be forced to switch to a new network.  Thus, all the fixed costs invested in the original EMR system will have been wasted.  A provider can instead decide to wait before purchasing an EMR system in order to see which system emerges as the market leader.  Thus, while not adopting EMR early on may cause short term losses, in the long run, it may be a wise decision in the presence of switching costs and thus slow adoption may be an optimal strategy. This issue is treated more thoroughly in the real option pricing theory.
3. Consumer Responses Blunted: In regular markets, consumers receive higher quality at lower prices by ‘voting with their feet’ and purchasing only high value items.  In the health care sector things are not so simple.  There are 4 interlinked markets: health care services, health care insurance, the labor market and political economy.  “economy). The result of all these complicated linkages is that market forces from consumers to the health care providers are far more indirect and blunter than are the market forces in most industries. The forces must be transmitted through each stage and each stage brings its own transaction costs.”  One example the authors use is trying to persuade doctors to use email.  Employers have motivation to demand that doctors supply this service to their employees to make their employees happier.  Employees, however, value their job much more than whether their doctor has email or not, so direct employee pressure, while it exists, is likely a weak agent of change in the health care market.

The authors conclude the paper making the following wise pronouncement: “A tradeoff therefore exists between seeking more standardization to allow for greater adoption and less standardization to allow for greater product differentiation.”

Pay-for-performance or health care quality report cards are the latest fad in medicine. Different types of report cards, however, measure different things. Eve Kerr and co-authors investigate (’Quality by any other name?’) how different quality measures compare against each other.

The authors look at 3 types of physician review:

1. Implicit Review: This involves using ’subjective’ expert opinions regarding whether or not a patient received appropriate care. Most everyone has undergone implicit review; for instance being reviewed by a superior in a subjective fashion is one example of implicit review. Supervisors are better able to measure non-quantifiable quality measures and physicians are less able to ‘game’ the system. On the other hand, the quality measurements are variable depending on who is doing the reviewing.
2. Focused Explicit Review: These report cards measure whether or not appropriate tasks were completed. Examples of focused review include the Health Plan Employer Data and Information Set (HEDIS), and the VA’s External Peer Review Program (EPRP). The benefit of these focused reviews are that they are concrete, easy to understand, and actionable. One problem is that quality evaluation is limited to certain easily quantifiable dimensions and the system is relatively easy to game.
3. Global Explicit Review. These systems “use a broader set of quality measures for a much larger number of conditions and the results are reported as summary scores.” The global review is tougher to game and more comprehensive, chart review becomes a more cumbersome process and the review is more expensive to implement. One example of this type of review is the RAND QA Tools system.

The authors conduct a stratified random sample of 26 VA sites of care in the West and Midwest; 621 patients were included in the sample. The authors found that inter-rater reliability was high and that there was high agreement across the 3 types of quality scores. Some important caveats are noted however:

“…it is possible that explicit systems’ process standards do not capture the bulk of important decision making, but providers who do well on aspects of care measured by the explicit tools also do well on aspects of care captured by implicit review but not by the explicit tools. On the other hand, implicit reviewers’ judgment as to what defines a quality problem may have been influenced (and narrowed) by widespread educational efforts focused around existing explicit measures. Finally, we must consider that the performance of explicit systems is dependent on the population. Some process measures may adequately assess quality of care among patients with mild but not severe disease, and agreement between different systems could therefore be dependent on the patient population [my emphasis] considered. “

• Kerr; Hofer; Hayward; Adams; Hogan; McGlynn, Asch; (2007) “Quality by Any Other Name?: A Comparison of Three Profiling Systems for Assessing Health Care Quality” Health Services Research, OnlineEarly Articles, 10.1111/j.1475-6773.2007.00730.x

Which hospitals are the innovators of 2007?  Joe Paduda points us to to the Fierce Healthcare website which lists the top 10 hopsital innovators of 2007.  Two of the top ten include St. Joseph’s Hospital in West Bend, WI and Cedars-Sinai Medical Center in Los Angeles.  Mr. Paduda is not overly impressed by the innovation levels recognized by the awards, but, he states, “…in the context of the multi-tiered, cumbersome, convoluted and pitfall-lined bureaucracy that is the typical NFP [not-for-profit] health care system, the creativity recognized by the Awards is remarkable [in that] it occurred at all.”

To follow up on Monday’s post, I take a few quotations from Rachel Werner and David Asch’s paper (”The Unintended Consequences“) in the March 9, 2005 edition of JAMA. First, I look at the unintended consequence of coronary artery bypass graft (CABG) report cards in a number of U.S. states:

In Pennsylvania, which also introduced CABG report cards, 63% of cardiac surgeons admit to being reluctant to operate on high-risk patients, and 59% of cardiologists report having increased difficulty in finding a surgeon for high-risk patients with coronary artery disease since the release of report cards. New York had a similar experience after the release of report cards, reporting that 67% of cardiac surgeons refused to treat at least 1 patient in the preceding year who was perceived to be high risk.

The authors worry that quality report cards may move the physicians emphasis away from providing guidance and quality medical care and towards achieving the target rates. The authors also cite a work by Walter et al. (JAMA 2004), which examines colorectal cancer screening compliance rates. Walter and co-author’s work finds that

“…among patients who did not undergo CRC screening, 47% had declined screening, 12% failed to complete or keep appointments for screening, and in 31% of patients, testing was not medically indicated or the patient had high levels of comorbid illnesses. While the hospital in question may have failed to meet the VA’s target rate of CRC screening, 90% of unscreened patients may have been appropriately unscreened.”

I repeat: while pay-for-performance or quality rating systems have the potential to increase quality throughout the medical system, these performance metrics, must be very carefully designed to take into account many of the issues addressed above.

Are quality based incentive programs the solution for improving physician quality without increasing cost? While I believe that measuring quality is an important avenue by which quality can be improved, it is not a magic bullet. A study by Gilmore, et al. (HSR 2007) looks at a pay-for-performance scheme developed in Hawaii. The authors look at administrative claims data for a large BC/BS PPO (Hawaii Medical Services Association). After the PPO implemented the Physician Quality and Service Recognition (PQSR) pay-for-performance program, recommended care metrics increased on the measured dimensions.

One issue is whether or not the quality increased in the medical care dimensions not measured by the PQSR. For instance, a physician may being to care less about medical care which is not captured by PQSR or may decide to shift resources from procedures not measured by PQSR to measures by which their bonuses are calculated. Also, the physician may provide inappropriate care to some patients just to reach the appropriate bonus. The authors note some other issues:

• Patient demand may drive some of the compliance rates. Low compliance rates were found for colorectal and cervical cancer screening, but this is likely due to the fact that the patients find that undergoing these procedures is unpleasant at best. Thus, the physician may not be at fault for the low compliance rates.
• Quality indicators that require specialist physician visits often have low compliance rates as well (e.g.: retinal exam for diabetics). This may by due to the added expense the patient must incur in order to receive the prescribed care.
• Finally, one may worry that performance-based metrics do not adequately take into account the physicians case mix.

While pay-for-performance or quality rating systems have the potential to increase quality throughout the medical system, these performance metrics, must be very carefully designed to take into account many of the issues addressed above.

The National Committee for Quality Assurance (NCQA) is a “not-for-profit organization dedicated to improving health care quality.” One of their major initiatives is the Health Plan Employer Data and Information Set (HEDIS) which aims to evaluate the quality of care offered by various health plans.

In a 2001 NBER working paper (”Learning…“), researchers Michael Chernew, Gautam Gowrisankaran and Dennis Scanlon look at employee data from General Motors and see whether or not the HEDIS health plan report cards affects plan choice. GM is a large national employer so authors are able to look at data for about 70,000 employees in many different markets over two years. Between years one and two, health plan report cards information was disseminated to non-union employees. GM employees use flex dollars to pay from health insurance plans categorized into 4 types: fee-for-service basic (FFSB), fee-for-service enhanced (FFSE), HMOs and PPOs.

Econometrics

The authors use a Bayesian learning model to evaluate the impact of the introduction of the health plan report cards to the employees. A person’s utility is assumed to depend on perceived health plan quality, price, other plan attributes and other unobserved factors. Using a nested logit model, the authors estimate the conditional distribution of quality at time 0 (i.e.: the prior) and the conditional distribution of quality at time 1 (i.e.: the posterior). The prior is a function of plan reputation and experience while the posterior is a function of the prior and the signal (i.e.: the HEDIS report card value). The authors also include plan-market fixed affects as well as plan type-time interactions.

The authors note: “Since we include a fixed effect for the prior quality of each plan in each market, endogeneity would occur only if particular ratings or changes in prices are correlated with changes in unobservable plan characteristics that might change market shares even in the absence of the changes in price or ratings.” The authors do not believe this to be the case, but it is very possible that health plans will shift resources from procedures which are included in the HEDIS quality measure and away from medical care whose quality level will not be captured in the HEDIS report card.

Results

The authors find that $100 increase in the price of a health plan will cause a 2.7% decrease in market share. Plans receiving a superior or average rating increased market share, but plans with below average or no data had reduced market share. The ‘no data’ category had the largest decrease in market share. The estimated value of the HEDIS information is about $20 for all people but $488 ex-post for the people who switched. The average value is small because only 12.4% of people switched plans in the sample after the HEDIS introduction and only about 3.9% switched plans due to the quality ratings. The authors conclude that “plan priors are more important than either ratings or prices,” which is likely due to 1) plan switching costs employees incur and 2) patient loyalty to their doctor.

Last month’s Wall Street Journal (”Faltering Family MDs get Technology Lifeline“) has an interesting article about how small-practice physicians are using technology as a weapon against the economies of scale which physicians working for large-scale practices enjoy. The article tells the story of Dr. Gordon Moore. When he worked at a large, hospital-owned medical practice, Dr. Moore soon became fed up with the time pressure to see over 30 patients per day and decided to start his own solo practice.

Patients at [Dr. Moore's] “micropractice” can call or email to get appointments the same day. Visits last 30 minutes. Dr. Moore can be reached day or night on his cellphone. To refill a prescription, he walks “zero feet,” he says, and taps a few keys on his laptop. “I was able to build a Norman Rockwell practice with a 21st-century information-technology backbone,” he says…

In the summer of 1998, Dr. Moore attended a talk on how technology could be used to improve medical “work flow,” and another by a Massachusetts Institute of Technology professor who talked about how a local bike shop got lean by cutting its area by 75% and still thrived. The professor suggested similar stripping of overhead would work in other fields.

In early 2001, after considerable anxiety about the decision, Dr. Moore opened a solo practice in an airless 150-square-foot room in a small office building. By keeping a tight lid on overhead costs, he hoped to see fewer patients — no more than about a dozen a day — and provide them better care, and still earn a decent wage for a doctor.”

The American Academy of Family Physicians has an interesting chart demonstrating that fewer U.S. medical School graduates are filling family-medicine residency programs.

Child mortality from unintentional injury or accident has been dropping over the last thirty years. Among children under 5, accident rates resulting in death have fallen from 44 deaths per 100,000 population in 1960 to 18.6 deaths per 100,000 in 1990. For children aged six to twelve years old, the analogous fall is from 19.6 to 9.8 deaths per 100,000. Why is this the case? A 1999 NBER working paper by Sherry Glied (subsequently published in Medical Care Output and Productivity) analyzes this phenomenon.

Glied gives four possible explanations for the decline: 1) a change in the children’s living conditions, 2) changes in the professional child injury knowledge base, 3) changes in regulation, and 4) changes in the information imparted to parents. We will look at each of these in turn.

1. Change in Living Circumstance. The U.S. Census Bureau shows that median family income has increased between 1959 and 1989 from $27,632 to $45,956 (in 1999 dollars). If safety is a normal good, this should help child safety. On the other hand, during this time period, a higher percentage of mothers were working. Glied assumes that this decrease in parental time spent with the child will increase the accident rate. Because of the offsetting income and time effects, no conclusion is reached regarding the impact of living circumstances on child safety.
2. Change in the professional injury knowledge base. Even if there were the same amount of accidents in 1960 as in 1990, if health care technology decrease the probability of death from a given accident, then the researcher could find decreased mortality with the same accident rate. Glied claims that there were “…substantial increases in the availability of childhood trauma care during the 1980s…[but] trauma care can contribute less to saving children’s lives than to saving adult’s lives.” Drawing on the epidemiology literature, the author claims that mortality rates likely did decline for each type of accident, but the magnitude of these declines seems to be small.
3. Changes in Regulation. There were significant changes in regulation during this time period as well. “The late 1960s and early 1970s were the heyday of the U.S. consumer protection movement.” Also, between 1977 and 1984 all states passed laws that children under five had to be in a child safety seat and some states passed mandatory seat beat laws for all individuals. Despite these efforts by state and federal government, Glied contends that the regulations were ineffective.
4. Changes in the Information imparted to parents. The author claims that this is the major driver of the changes in infant mortality. Changes in the information available to parents is difficult to measure. Glied cites an example of the increasing amount of information contained in Dr. Benjamin Spock’s parenting guidebook and the increase in the number of publications indexed in Medline regarding childhood injuries. A further example is taken from New York City. When the Department of Health found that 30-60 children under age 5 were dying each year from falling off buildings, the city led a campaign to inform parents that they should install guards on their windows.

The argument Glied makes is very interesting, but the empirical analysis she uses to back it up is less robust. The data used is the National Mortality Detail File from a variety of years between 1968 and 1990. A linear prediction is made using the early years of the data. The residual between the projected mortality data and the actual childhood mortality is assumed to be caused by the above cited informational issues. A number of covariates and major categories of deaths are included, but the data is time series in nature so it is difficult to distinguish which of the four suggested mechanisms are causing the change in childhood mortality.

• Glied (1999) “The Value of Reductions in Child injury mortality in the U.S.” NBER Working Paper, No. 7204.

• “Cancer cured with soothing balmy oils.” Appeal to Reason
• “According to this repeated nationwide survey, more doctors smoke Camels than any other cigarette.” - TV ad
• Regarding patent medicine sold at the end of the nineteenth century: “…the medicines were ineffectual. Some of the syrups contained as much as 80 per cent alcohol; many of the tonics used cocaine and morphine. Some of the medicines destroyed health, and make drunkards and dope addicts out of their users.” Weinberg and Weinberg (2006) The Muckrackers.

False advertising has been a problem since the beginning of commerce. Deliberately misleading messages lead consumers to purchase products which they do not need or ones which may even harm them. In the 2006 NBER Working Paper “Regulating Misinformation,” Edward Glaeser and Gergely Ujhelyi create a theoretical model to examine what are the optimal governmental strategies to combat misinformation.

Previous economic analyses have often espoused laissez faire polices. These studies would claim that misleading information is either ineffective because the wisdom of the masses will prevail or because the government is an poor arbiter of what exactly is defined as misinformation. Still, companies spend millions of dollars on misinformation advertising campaigns (see Zyprexa rep post) so they must be at least somewhat effective in changing popular opinion.

Model

Glaeser and Ujhelyi create a theoretical model using an Cournot-style oligarchical setting. Total quantity, Q(P), is equal to N(a-c-P)/a. There are N number of people who receive benefits from the product and there are J number of equally sized firms. The variable c is equal to the individuals expected health cost of using the product which can be erroneous. This perceived cost, c is equal to the true cost, c₀, minus an error term, ε (c=c₀-ε). Using the formulas for solving Nash equilibrium, we can see that each firm will produce q=N(a-c)/[a(J+1)] quantity of the good. On an industry-wide level, we find:

• Q(ε)=JN(a-c)/[a(J+1)] = J*q
• P(ε)=(a-c)/(J+1)
• Π(ε)=JN(a-c)²/[a(J+1)²]

We see that the cross derivative Q_εJ>0 because more firms increase their output in response to higher demand caused by the increase in misleading information transmitted to consumers.

Looking at the mathematics, the authors suggest that misinformation may actually be welfare enhancing if there are other market imperfections. For instance, in the case of monopoly, price is raised and thus the quantity consumed in a monopolistic environment is below the optimal level. With false advertising under monopoly, the authors argue, the equilibrium quantity could move closer to the socially optimal level.

The authors assume advertising is a public good on the industry level. Proposition 3 of the paper claims the following: 1) total advertising expenditure increases with market size, 2) total advertising expenditure decreases with the true health-cost, c₀; and 3) total advertising expenditure decreases with the number of firms. Conclusions (1) and (2) are relatively self-explanatory. Conclusion number (3) is sensible since it implies that as competition rises, misinformation falls. “This is the classic example of the free rider problem. All firms benefit by confusing consumers about the costs of the product, but if there are too many firms, they will fail to invest in this industry-level public good.”

Conclusions

The authors conclusions are as follows:

1. Misinformation may not be socially inefficient (as in the case of monopoly). Further “misinformation is more likely to be welfare reducing when prices are closer to marginal costs than in a more monopolistic setting.”
2. When advertising only misinforms, a tax on advertising or an equivalent quantity control (e.g.: Pure Food and Drug Act of 1906) is optimal.
3. Counter-advertising by the government is ineffectual because the government much incur costs to air the advertising and the government actions may lead to increased industry level advertising.

The health care industry is one where people pay large amounts of money for accurate information (e.g.: correct diagnoses, assessment of physician or hospital quality, etc.) and other individuals pay large amount of money to breed misinformation (e.g.: false advertising for pharmaceuticals). Understanding the role of information in the health care industry is very important. While the paper analyzed here is only theoretical - it does not give any empirical evidence to back up its mathematics - and the analysis only pertains to information problems with regards to advertising, the study is a step in the right direction towards helping society understand the role of information in the medical field.

A push towards more accurate evaluation of health care quality has become a major public policy goal in recent years. The Department of Health and Human Services now has a website ranking hospital quality and California has a Healthcare Quality Report Card. One problem with simple quality measures such as mortality or morbidity rates are that physicians who have a patient base with a lower initial health level are penalized—according to the scoring—for treating the patients who need care the most. For this reason, health care rankings have moved from outcome-based rankings to process-based rankings. For instance, the California Report Card ranks insurance plans by such factors as whether or not individuals with diabetes had an eye exam and the percentage of pregnant women who had a check-up visit 21-56 days after delivery.

One puzzling finding Kahn et al. (2007) encounters in their data is that ambulatory care centers with better process of care scores have patient with a worse health outcomes. Using a simple ordinary least squares (OLS) procedure would lead to the erroneous conclusion that more care leads to worse health. Of c