Big data means big potential for Tiwanese social enterprise


Remi Kanji | Social Enterprise Buzz | 10 January 2013



Robust public health data offers social innovators a myriad of potential opportunities, and Taiwan is sitting on a data goldmine. Taiwanese health records are digitized, kept on a Java Virtual Machine chip attached to each individual’s health card and updated in a central system.


The cards almost look like a credit or debit card, save for the photo and vital statistics. And carrying a Taiwanese health card is a bit like carrying a folder with your medical history: the chip stores individual patient data including information on allergies, medical history, and past test results. It even allows for efficient billing, so that hospitals are quickly reimbursed for their services.


While many in the medical field already consider these health cards cutting edge, the data generated by health cards should be used for more than simply informing government programs. If the data were made freely available (anonymized, of course), Taiwanese social innovators would find new ways of creatively tackling public health challenges.


Health innovation is becoming vital in Taiwan as its population rapidly ages, dramatically increasing costs to Taiwan’s National Health Insurance (NHI) program. In an effort to plan around cost increases, the government is increasingly looking at long term care. A key component of such care involves finding and funding innovative preventative and holistic medicine – pushing people to adopt healthier lifestyles and get early screening. No easy task.


One example of a program attempting to encourage healthier living is Zhishan Lohas, known as 5 Doctors 6 Patients in English. It provides its members with health checkups that focus on improvement, exercise programs, and consultation and access to nutritious food. The program is funded based on a capitation model – if patients collectively save the government an agreed upon amount of money, they get to reap the benefits in savings.


But how are these savings calculated? In the case of Zhishan Lohas, patients’ costs at the beginning of program participation are compared to those they incur during participation. This proxy is actually quite problematic, because individuals face more health problems as they get older – how to account for the complex impacts of aging? A better methodology would be to compare a program participant against a peer group with a similar age range, sharing similar risk factors.


Great data helps build realistic proxies, which form the basis of a lot of financial social innovations. Take social impact bonds (SIBs) – individuals or corporations buy bonds, which are then used to fund a social program. While a normal bond is a financial investment with a financial return, SIBs have a social return that in turn informs financial savings.


Case in point: Riker’s Island Jail in New York has recently implemented a social bonds program funding criminal rehabilitation. Program graduates’ recidivism rates are compared against those of the general prison population. If there is a drop, the state has saved the money it would cost to both try and jail people who might have otherwise become repeat offenders. These savings then determine the interest rate offered on SIBs.


Good data on overall recidivism – not just that of program participants – is required to ensure that programs have the impacts and savings the claim to engender, however. Similarly, health programs that are premised on decreasing costs need a robust point of comparison to showcase their impact.


Making data widely available requires public consent, and there may be concerns about security – after all no one wants information about their private illness to become public. A database on public health could perhaps refrain from providing individual case data, instead providing aggregate statistics searchable by disease, risk factors, and vital statistics like gender and age.


Geography could be kept as a broad category to ensure privacy. More specific and locally oriented data could require an organization to sign a non-disclosure agreement protecting patient confidentiality. Finally, if this database is introduced with adequate consultation of the Taiwanese people, it will likely help communicate and assuage concerns about privacy.


Privacy concerns aside, the fix to create such a program would be simple. It would require a searchable online database allowing programs like Zhishan Lohas to ‘construct’ a proxy for an individual taking their program, by aggregating information from non-participants sharing factors like age, gender, and risk.


An individual’s progress could then be compared to that of their peers, who are also aging and exposed to similar environmental factors, providing a clearer picture of program impact.


Zhishan Lohas is just one innovative program that could be improved using NHI data – the possibilities for a creative, data-minded social entrepreneur or programmer are limitless. Zhishan Lohas itself has recognized the potential of data: it is exploring collaboration with IBM to add their information to a cloud based health management platform.


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