Category Archives: Bystander Speaks

Interesting crowdsourced applications developed by GIS Corps in response to the pandemic

During the time of pandemic, GISCorps —group of volunteers who contribute in spatial data creation for humanitarian cause —  developed interesting crowdsourced applications and web-mapping applications. These included allowing users to find vaccination sites based on where they live, identify grocery stores that practice social distancing and follow the safety protocol as well as report those that don’t adhere to the guidelines.

Below are some interesting applications developed by GIS Corps:

Here is an example of COVID-19 testing sites, type of site such as drive-through or not and the type of test that is offered.

Here is another example of a crowdsourced app on safety and  customer experience at grocery stores during the pandemic. This is how users can report their overall experience and alert other consumers about the safety protocols followed by the store, waiting time, and information that would help other consumers make a decision about visiting a particular store.


Additional examples of the apps related to COVID-19 can be found here.


Book Review: Steal Like An Artist


As an academic or as someone who enjoys writing to discover new trends and patterns, I view my quest to come up with research ideas as a creative endeavor. I also associate being creative requires an entrepreneurial spirit — with the difference being that a real entrepreneur  invests capital in some form to get returns, while a scientist or a writer has to invest his or her time to come up with ideas that might bear a return or not, depending on whether that research gets funded or whether that idea makes it into a fine editorial piece or not.

If you view that your work requires some degree of creativity, then Steal like An Artist is just a less than 100 pages book that would inspire you to develop some principles in your pursuit of creative ideas. Interestingly, the entire book consists of cartoons/graphics with short notes around different principles that the author recommends.

The three principles that resonated with me the most are:

  1. Start from the mundane to make something extraordinary: Often we are trying to think “outside the box”. But one has to be inside the box first and be able to see things from multiple perspectives before one can step outside the box and see a different paradigm. The book discusses examples of successful artists and painters and singers who first emulated their role models and in the process by doing so, they found their distinct identity.  Drawing a parallel to this, as a researcher or scientist, this could be following an existing method applied by several others in the field; then identifying a flaw in it or a marginal improvement that adds one’s distinct identity and viewpoint, to produce something new.
  1. Go unwired from technology:  I really love this because it is so easy to get swamped in the flood of information at a click of a mouse and multiple tabs open in your browser. As a researcher when I want to come up with new ideas, I like to stay away from screen and draw mind maps around a central theme that I would like to explore. By doing so I am often able to discover what I already know on the topic, or the different areas that I’d like to explore around the topic, and if nothing then at least it gives me a structure around the idea that I need to build on my writing or my search query in Google scholar. Similarly, when I already have a lot of information, I again step back from the screen, and do the same exercise – in this instance, to develop a coherent structure around all the information that I already have and then use my laptop to type it.
  1. Go on a creative date everyday and consistently: All creative work is a pile up of “boring”, “mundane” tasks performed consistently to make something extraordinary. I agree to this by far the most because consistency is the most important principle in writing or solving a problem. There are several days when we feel we are being “unproductive”, but being consistent in working on it is the only way to get a breakthrough. As Woody Allen said that success is about 80 percent just showing up.

Book Review: Geek Heresy: Rescuing Social Change from the Cult of Technology


Why is it that a job posted on LinkedIn is for anyone to apply, but the person who is most likely to get an interview call is someone who either knows the HR or knows someone inside the company who is on his LinkedIn network? Why is it that even though internet is widely available in China, it cannot break the censorship walls built by the Chinese government? Even when technology is available to everyone, why is it that highly motivated individuals use it very differently and for very different purpose then those who are less educated and have very less aspirations in life?

While technology can give equal access to everyone, it cannot replace social access that only few privileged people have, which  guarantees their entry for a job interview after controlling for skills and education. Similarly, without the right political and social institutions, freely available internet access to everyone still does not guarantee access to unbridled information. Or even when technology is available for anyone, the more educated, ambitious, and highly motivated individual will be very discerning in the use of internet for educational purpose as opposed to someone who would use it more for entertainment.  Sounds like common sense, isn’t it? As much as it sounds so obvious, Kentaro’s book is an eye-opener for anyone who believes that technology can be an end in itself in solving certain social problems. Kentaro Toyoma  makes a compelling case that technology cannot have any impact without  nurturing the right political and social institutions; it cannot transform an individual who lacks the right motivation, judgment to make the right choices, and self-discipline and control to follow through the right choices . Though written by a computer scientist, this book is more written from the perspective of ethics and philosophy.

I met Kentaro when I had just started my career as a journalist. Kentaro was heading then Microsoft India Research Foundation where he was involved in interesting initiatives that harnessed technology for social change. The book is based on Kentaro’s experiences in India as a part of Microsoft Research Foundation. It was during this journey that he encountered several non-profits and organizations that were intending to drive social change through the use of technology such as one laptop per child and similar initiatives, but these intentions were not matched with the right outcome.

While the book has numerous examples and case studies of organizations that were successful in harnessing technology along with building human capacity to use it, it falls short to a certain degree on rigorous scientific evidence. Several examples are from education and micro lending space and based on anecdotal evidence based on his personal experiences with these organizations. The book could have been enriched by including more evidence from public health where it is commonly used to target patient adherence to treatment, follow-up plans, using text-based services that are designed to change key behaviors towards reducing the risk of certain diseases.  Despite its shortcoming on rigorous scientific evidence from other fields like public health, the book is well-written, engaging, and drives home some of the fundamental principles that can predict success of a technology based program in transforming social change.

From macro-economists versus micro-economists to macro statisticians versus micro statistician: The gulf between big data and small data scientists

VW -think-small-adsIn the 1950’s and 60’s it was “Think small” ad for Volksgen’s Beatle car that marked a radical shift in an era that was dominated by large cars in the US. For anyone who studied advertising or works in the industry would recall this iconic add – considered as a classic in the field in several ways — but for the most part it created a new way of consumer thinking for the advantages of small cars versus big cars.

Today for someone in the field of data science, “big data” is the buzzword. More and more jobs are emerging in this area. Every day on my LinkedIn, I see several opportunities in this area. So I see the world of data science also facing a similar divide – big data versus small data. While we don’t hear much about small data as much as we hear about the term “big data” as it is more fancy, sexy, and requires highly sophisticated programming skills to model the problem, there are distinct problems and areas of application where each has its own place and utility.

Big data and some of its applications: Big data is associated with machine learning and applying algorithms to extract data from the web to search for pattern and trends based on millions of records. For instance, in text based analysis this would mean web crawling through millions of newspaper articles and editorials through which a computer can identify specific articles that a researcher or analyst is looking for. For a spatial statistician, this could mean employing computer algorithm to extract location information from millions of records about specific events of interest. The world of big data analytics is dominated by computer scientists, statisticians, political scientists interested in studying issues pertaining to conflicts, or public opinion. It is also dominated by companies and industries that are looking to capture consumer behavior and trends. So companies like Amazon and Google can model consumer pattern and forecast demand or decision-making.

Small data and its application: We don’t hear much of the term “small data”, but as a public health and policy professional, I see a lot of problems that need to be addressed in the field of public health, epidemiology, census that require one to deal with counts and small numbers that can be modeled correctly and be used to make valid inferences. This requires domain knowledge of distinct set of statistical models and tools. Organizations where knowledge of small area analysis and estimation would be helpful would be CDC, Census Bureau, and community level program planning and evaluation.

a) Survey methodology: For large scale health and population surveys implemented in developing countries, the sample is representative of the population at a larger regional scale, but often not for small geographic scale. In such a situation small area estimation techniques or interpolation is of interest to make inference about a geographic unit where sampling was not done on specific health outcome.

b) Sentinal Surveillance: This involves surveillance at a specific site or a location for detecting disease outbreaks or new cases of specific diseases. According to WHO sentinel surveillance is appropriate to gather high quality data when passive surveillance system ( generally based on data reported by health workers and health facilities) is not adequate to identify causal factors for certain diseases. However, because data is monitored at specific sites, hospitals, or locations, it may not be appropriate for detecting cases outside of the selected sites.

c) Community based program planning: In case of community and program planning, an application area would be improving a health intervention at a specific site and location. For instance, USAID allocates funds for HIV testing and treatment at specific sites and in several countries. Hence it might be interested in knowing which clinics are doing better in comparison to other clinics. According to the PEPFAR Annual report to the Congress, there exists a wide variation in disease burden and HIV risk at the sub-national level and sub-populations level. Hence, knowledge about distribution of cases around specific sites, uptake in the service utilization can help improve programs. Similarly, AidData, a collaboration between three universities, to track where aid money is going and in which programs by country and by year and based on the type of the project, works in the area of geospatial impact evaluation. Hence, it borrows traditional statistical methods such as difference-in difference and propensity score matching and other methods, but also takes into account site location of the project. It identifies sites where World Bank did not implement a project, thus acts as a control site. By accounting for location of the project implementation site, it considers heterogeneity in program outcomes while conducting impact assessments.

Economics as a discipline has always been demarcated between marco and micro economics. Is it time we divide statistics also as a discipline between macro and micro?

Where is the map, honey?

Last month I attended the International Geocomputation Conference hosted at University of Texas at Dallas. As a health policy researcher with a keen interest in the application of geospatial tools to solve problems related to services delivery and reduce disease burden, I was hoping to hear at least a few examples of what the next big trend would look like with the use of such tools for disease surveillance, preventing disease outbreaks, among others. However, the big trends discussed during the keynote session included applications mostly in the commercial space, while social and development sectors such as health sector trailing far behind. With increasing availability of smart phones, location based services are used commonly to obtain directions, find the nearest restaurant, or the drive time to the nearest clinic and so on. Companies like Uber, ride sharing app and community-traffic management and navigation app Waze have tapped on the location information of individuals.

But how about an app like Waze to know the waiting time and availability of doctors at crowded government or private hospitals in South Asia? Can such an app help a women who is in labor and about to deliver a child go to the right hospital instead of going to a crowded government facility, just to find that there is a long wait time after reaching there.
This is certainly utopian at this point, but it can be a possibility in the future. Health ministries in some countries have started using google map to display their health facility location information.

Here is the google application of BD hospital facilities data:
BD facilities

Brazil health facility search application
:Brazil facilities search

Argentina health ministry displays its health facilities infrastructure:


Several developing countries are using google maps for consumers to visualize health facilities location, type of health facilities, and the drive time to different health facilities However, in order for consumers to find utility with such visualization requires integration with other health indicators such as waiting time, availability of doctors, customer satisfaction rating, and other health infrastructure related indicators. This would help consumers make informed choices with regard to where to go for care. For policy makers, such health facilities data base needs to be integrated with information related to patient catchments i.e where are the patients coming from and for what type of services? Such information can help policy makers plug the gap in the system or identify specific locations or hot spots for certain diseases. There have been small scale initiatives ( in specific parts of some countries), mostly led by researchers and academicians in partnership with government and other institutions, but many of them have not been developed at the national level.
Most efforts to map health disparities, access, inequities come from health geographers and researchers instead of it being driven by the government and the policy planners. Hence the current scenario resembles more of a push rather than a pull strategy. To put it simply, it is more driven by supply from the researchers and academic community than it being a demand driven approach for decision making, where the health planners ask the question, “Where is the map, honey”? And only when health planners start asking such questions and demanding such information, we can expect location information to realize its full potential in the public health domain.

Reaping the technology dividend to capitalize on demographic dividend

Jan, 2014:

In health, the cliché is to get rich for countries before they get old. This is mostly in the context of reaping the demographic dividend that some of the developing countries through their young and growing population. But a young and a growing population can only be a ‘dividend’ for a country if it is healthy, well-nourished, and educated – a fact quite obvious to everyone.  Despite this, the challenge for  developing countries is to reduce the geographic variation in inequities in child health and development.

Take for instance, Bangladesh.  It’s population demographic can be very favorable to its economic growth considering that the population, which is less than  15 years of age in Bangladesh, is almost 35 percent, another 56.5 percent constitutes between 15-60 years of age and only 8.2  percent of population is above 60 years of age based on the latest estimates from demographic and health survey . But for a developing country, which has a large proportion of population that constitutes children,  the challenge of improving basic services delivery in terms of health and education also remains enormous.

One such attempt to improve child health was last month when the country launched one of the largest child immunization campaign for measles vaccine Rubella.  According to the WHO South East Asia Regional Office website about 52 million children are targeted to get vaccination between January 25 and Feb 13 and marks one of the biggest campaign in the country’s history since 1979.

While this is a welcome initiative for policy-makers, public health scholars among others, a concern that continues to daunt  involved in reaching out is to improve coverage in some of the geographic regions which are difficult to reach.   There is well known evidence (Mushtaque Chowdhury et al) that there continues to be deprivation in certain geographic regions which are located in the hilly regions or in the low-lying planes.  Furthermore, for those children that work in tea-estates and do not go to school might pose another challenge for policy-makers and planners in designing interventions that can help in reaching out to this unreached population.

For example, vaccination coverage is highest in Khulna over 94 percent and lowest in Sylhet at around 80 percent, and if one may peek within districts, then one may find more variation in equities.

One such attempt to map geographic inequities was by UNICEF to construct a child deprivation index at the lower administrative levels. The deprivation index that was created out of composite set of indicators was an attempt to map the geographic variation in social indicators at the zilla and upzilla level, and provide information to policy-makers and planners as to which are the worst districts and upazillas and which ones are faring better.  Similarly, UNICEF also released the Child Equity Report for Bangladesh which maps district-level variation as well as variation within the districts on several indicators.  In addition, organizations like ICDDRL-B also integrated the use of GIS in health and demographic surveillance system at its field site in Matlab since 1990’s to produce geo-referenced maps at the village level and Bari, which is cluster of group of households which share a comman yard, according to the Health and Demographic Surveillance System, Matlab 2012 report.  However, the extent to which these geo-referenced maps are actually used in government decision-making is not much known, in my opinion.

With the plethora of tools for information at the disposal of policy-makers across countries, it seems that state failure in improving the quality of public provision of services can no longer be an option but should be the norm.


The Baptist, the Bootlegger and the Indian Health Care System


August 8, 2013:

In 1963 when Kenneth Arrow wrote his seminal piece on ‘Uncertainty &Welfare Economics of Medical Care’, it not just stirred the scholarly debate on the economics of health care but it continues to form the basis of the theory taught even in today’s health economics class.

But is this now changing? Arrow’s theory was based on the premise that unlike other goods and commodities, when it comes to medical goods and services there exists information asymmetry between consumers of health care and the suppliers— in this case the physicians and health care providers. As a result, physicians will always have an advantage in pricing as consumer cannot completely know as much about their own health needs as would a doctor.

Today thanks to information technology that this asymmetry of information is narrowing in certain countries like the US.  In the US, the government has released data for the first time on variation in hospital prices for the top 100 diagnosis-related groups for Medicare patients.  Diagnosis-related Groups is a classification system to group patients based on certain diseases and procedures incurred to treat them, thereby making it possible to compare costs for the same disease. Since Medicare is a program funded by the US department of health and human services for its citizens above 65 and disabled, the government reimburses to the hospitals a part of the cost incurred by them for conducting certain procedures, and the remainder of the amount is billed to the patient or to the insurance companies. The way the reimbursement is calculated to the hospitals is based on a variety of factors such as its labor costs and other input costs based on a hospitals location in a certain metropolitan statistical area and its characteristics. Hospital characteristics that drive variation in prices for the same surgical procedure but in different hospital setting depend on whether the hospital is a teaching hospital or a non-teaching entity, the share of the population it treats with no insurance, the share of the population it treats which have a low income and thus participate in certain safety net programs, and on ownership factors such as whether it is a government, private, not-for-profit organization among others. The government then computes the reimbursement for all the hospitals based on some of these characteristics. Thus consumers, policy makers, and employers can compare not only what a hospital charges them for a specific disease  but also can compare it to benchmark average at a state and national level. And a prudent consumer can accordingly decide his or her options to avail care, given he has the knowledge about the standardized disease code or the procedure for which he or she will be operated.

Now is this relevant in the Indian context? Importantly, can it be possible for the government to regulate the hospitals and the insurance companies in publishing this kind of data on hospital costs associated with the same surgical procedure?  Researchers at the Public Health Foundation, Chatterjee, Sushmita and Laxminarayan, Ramanan recently conducted a similar study to detect variation in hospital costs with a sample of 5 hospitals with varying characteristics such as private, district level or tertiary care among others. And any such study that could cover more hospitals by a researcher will have limitations because only the government can have the wherewithal to furnish large scale national data of this kind.

While in a country where out of pocket expenses constitute a fairly high share of overall health spending in comparison to other countries and an increased share of tertiary care and multi-speciality hospitals are being concentrated in urban settings, this kind of information can certain help consumers within the same geographical area to make better informed decisions or those travelling from smaller cities as well.  At the same time increased transparency in hospital prices could also possibly benefit the medical tourism industry clients who currently can view the nearest hotels but not compare the prices of another hospital in its proximity.

But unlike in the US where the government provides subsidies even to the private hospitals depending on their teaching/non-teaching status, treatment of low-income population or without any form of health insurance, this is not the case in India.  This also makes hospitals less accountable to the government and in complying with the latter to provide such data.

When it comes to making public policy decisions by the government, there are always two parties that favor the same regulation for completely different reasons —the Baptists and the bootleggers. The Baptist will favor a regulation for ethical reasons; the bootlegger will because increased regulation would benefit his own industry. Extending the same analogy in the Indian health care setting, the Baptist would certainly be the consumer and non-profit organization advocates who will benefit and would favor such a regulation even on ethical grounds. Who will be the bootlegger? Will it be the medical tourism industry or the health insurance companies?


Health Reforms and Drug Prices

Oct, 2011 :

At a time when United States medical expenditure continues to be highest in the developed world and the federal deficit spiraling, the Patient Protection and Affordable Health Care Act, doesn’t seem to address one of the fundamental drivers of cost-high drug prices. By providing higher rebates on prescription drugs under both the Medicaid and Medicare program, it does not solve the problem of high medicine prices, an influential factor in pushing the United States per capita expenditure to almost twice the OECD average. i
The new law can foster competition among the drug companies by increasing the role of public-private partnerships in coming out with more affordable products. Competition induces innovation and innovation induces competition. One area where competition and innovation can play a vital role in lowering the cost is pharmaceuticals. The spurt in medicine prices also increases the overall out-of-pocket expenditure, especially for patients who may be suffering from chronic illness and are highly dependent on certain medicines. But ensuring lower prices without a concomitant drop in companies’ incentive to innovate remains a challenge. Already, the US Department of Health and Human Services invests massive amount in research by giving grants to universities, medical schools, hospitals, research organizations. Through its arm, the National Institute of Health, the budget for such research and development is $32 billion. More than 80% of the NIH’s funding goes to about 300,000 research personnel at over 3,000 universities and research institutions. In addition, about 6,000 scientists work in NIH’s own laboratories. iiDespite this substantial spending, the organization itself acknowledges lack of partnerships in product developmentiii, resulting in high private spending for advances in new products, thereby increased prices.
With a view to focus more on research and development that is geared more towards reaching the clinical trials stage, the organization will be setting up a National Center for Advancing Translational Sciences. So how can the new law create incentives for more public-private partnerships through which drug companies can lower their research and development cost?
One possible way could be creating incentives for private companies to leverage government funded research universities, hospitals and independent research organizations to be the hub for innovation and product development. The incentive could be well be ensuring a guaranteed access to markets for companies who partner with public institutions and are able to come up with products at a lower cost. Availing the existing public resources for research would lower the cost for companies to innovate, and ensuring access to markets and a certain volumes at a certain price would lower their marketing and other related costs. According to OECD estimates, United States retail drug prices exceed by the range of 127-134 percent of the average cost for its member countries.iv Therefore, pre-determined markets for products could help them lower the cost associated with marketing. This should lead to better pharmaceutical pricing. Considering the big role government now plays under the healthcare reforms, it surely has sufficient powers to grant such access to companies by deciding the preferred drug companies who could be a part of the insurance plans sold on the health exchanges.
Another way through which innovation could thrive is by linking a certain proportion of funding to research universities and medical schools based on the partnerships they build with pharmaceutical companies in product development. It is estimated that 90-95 percent of new compounds entering clinical testing do not succeed. Further, FDA approved drugs has declined to an average of 21 per year between 2000 and 2010 in contrast to an average of 37 per year between 1995 and 1999. The resulting discouragement is manifested in a 2007 survey which states that 10 out of 15 largest companies have either abandoned the process of product development for new drugs or shrunk their budgets in this field. And as recently as 2010, two companies had already decided to stop pursuing discovery for pain, schizophrenia, depression, anxiety among others. v The law could direct funding for universities such that there is a healthy mix between “basic” research and “applied” research when it comes to developing cost-effective solutions for new medicines.
i OECD Health Policy Studies, Achieving Better Value for Money in Health Care
ii National Institute of Health Website
iii National Institute of Health, FY 2012 Budget Request Documents
iv Pharmaceutical Pricing Policy in Global Markets, OECD, 2008
v National Institute of Health, Budget Documents, Fy 2012 Budget Request Documents
Additional reference
Vi Congressional Budget Office Study on Research and Development in the pharmaceutical industry
Vii Product Development Partnerships and the Health Impact Fund, 2010

Need for a standardized innovation policy index:

ADB Social Protection Index

July, 2013:

Last month the Asian Development Bank released the Social Protection Index, which ranks countries based on their welfare programs. The index is mostly based on two parameters- the depth & the breadth of the coverage. While the depth is measured based on the benefit package offered to the beneficiaries, the breadth is based on the number of beneficiaries it covers. It does so using a simple formula of dividing the total program expenditure on welfare schemes by the total number of intended beneficiaries in all the schemes in order to come up with a ratio.

Even though the index is a first step to develop a methodology towards making cross- country comparison on their welfare schemes based on certain standardized measures, the question still remains of the context. Can one country follow another country’s policy with a completely different context? And can they replicate the success of one county in an entirely different setting? This could be similar to an apple and an oranges comparison whereby one country may have different welfare scheme than the other or the time period could be different. So for example, if South Korea started in 1988 its policy to provide health care coverage to its entire population and achieved a certain depth & breadth in a specific time frame, would it be a fair to compare its ratio to another country’s welfare program ratio based on shorter time frame. Well, the ADB has tried to reduce this variation by coming up with index based on region as well as by income. Hence, countries with similar economic growth can compare how well their social protection index fares in comparison to another.

Thinking on similar lines in terms of policy innovation, there is always a question as to how is it some country has been able to deliver a policy successfully and scaled up while some others may not have been? Unlike technological or manufacturing innovation which can be measured in terms of a new product launch or path-breaking model, policy innovation can be in two instances:

1)  When a state truly launches a new program which has not been initiated before.  For example, the first cash transfer program in Latin American or a new way of financing health program or delivering a vaccine.

2)When a country is able to achieve scale that others with similar economic and political structures are finding it hard to achieve.

Considering that there are several research grants in this area and innovation being the buzzword, maybe there is lack of evidence due to which there is such a growing interest in the scholarly and development practitioner community. So if one was to come up with a policy innovation index, then, what would be the parameters to make a standardized comparison of policy or programs:

1)Time Period in which policy was initiated and launched, bringing consensus from all stakeholders

2) The scale at which it was delivered

3)The process which enabled it

One attempt to compile policy innovation in health care is done by the Center for Health Market & Innovation. They have built a huge portfolio of programs in several parts of the world and categorized their innovation in 5 areas: 1)Health financing 2)Organizing delivery 3)Regulating performance 4)Changing behavior 5)Enhancing process

But in each of the category all the programs are different in terms of their approaches. So then how do you make a comparison of which program was more successful, which were the processes followed that made it more successful while another program adopting a similar strategy would have failed.

In other words, how do we build evidence to find out what works and what doesn’t?  As Don Hicks, Professor of Research Design points out, “You really build evidence by taking the same dependent variable and the same main independent variable in different settings and then trying to assess what is leading to variation?”

This would make sense if we would like to know about a successful HIV Aids program or a Tuberculosis prevention and reduction program.  By successful I mean a program or a country that has demonstrated substantial reduction in disease prevalence or in improving overall outcomes.  If we were to take the same dependent variable on outcome measures and have the same intervention as the main independent variable across several different settings, while controlling for certain processes,  to see whether a program could be scaled or not, would be one possible way to understand which innovation in HIV aids really works. And if the same main independent variable fails to produce desired outcome in a different setting, then this would give a clue as to the institutional factors and problems in delivery of the program that made it successful or failure.

While knowing all the good and diverse recipes is a great thing, sometimes it is also useful to know a standardized recipe that works too.  If  “innovation lies in delivery”, and by delivery Dr. Hicks means scaling up, then certainly one needs a more standardized measure to compare similar programs, similar interventions, similar time period and different settings to truly know the missing gaps. Acknowledging that a randomized experiment may not be possible, this kind of comparison could possible answer to not just which innovation works but also the process associated with it.  Just as a standardized Z score makes it possible to compare variables with different units by converting it into a unitless statistic, metaphorically speaking, a standardized policy innovation index could well reduce the variation and serve a similar purpose.




Arrow & Information Assymetry


July 2013

A classic opportunity to test causality:

In 1963 when Kenneth Arrow wrote his seminal piece on ‘Uncertainty &Welfare Economics of Medical Care’, it not just stirred the scholarly debate on the economics of health care but it continues to form the basis of health economics theory even in today’s health economics class.

But is this now changing?  Arrow’s theory was based on the premise of asymmetric information between the consumers of health care and suppliers — in this case the physicians and health care providers — due to which the physicians will always have an advantage on pricing as consumer cannot completely know as much about their own health needs as would a doctor.

 However, with the increasing information on cost and quality being made available to consumers by the Center for Medicare & Medicaid seems to be bridging this asymmetry of information. In May 2013 the Centre released data for the first time on variation in hospital prices based on top 50 diagnosis-related groups. Thus consumers, policy makers, and employers can compare not only what a hospital charges them for a specific disease  but also can compare it to benchmark average at a state and national level.  Further, the Agency for Health Care Research and Quality along with other departments of Health & Human Services launched various initiatives to produce standardized measures on the quality front. The data on health quality outcomes and indicators of hospitals is becoming increasingly refined over the years as more standardized measures are being produced to compare hospitals, and increasingly more tools are being developed on this front. These are a mix of process and outcome indicators such as hospital associated infections acquired by patient, readmission rates, reduction in hospital readmission rates, various hospital care process measures, how well each provider compares at state and national level among several other metrics.

While health outcomes are a function of medical care inputs as well as socio-economic factors such as education, life style among others, a fact acknowledged not just by Arrow but also based on scientific evidence, the growing information brings a boon for consumers as well as researchers.

From a consumer perspective there is now more information to correlate cost and quality.  But it is even more interesting from a researcher’s perspective and those studying economics of health care.

The key question lies:

-How are hospitals responding to this increased pressure on publishing cost and quality data?

-How is this changing incentive structure changing the processes and quality measures?

-Is it changing the outcomes?

-How does this increased transparency in hospital data reduce the asymmetry of information?

-And what does it bring for the consumers?

This is a historic time for members in the research community – a classic opportunity for a pre &a post design or a comparative change design of two group equivalents.

The taste of pudding lies in eating, so lies the taste of any high quality research in building evidence. The time is ripe now.