Given India’s dismal track record in enabling access to high quality healthcare (we rank 145 out of 190 countries which puts us below both Bangladesh and Bhutan), the government’s recent push for universal coverage is in many ways too little too late. What’s more, despite the rhetoric around expanding access and the Modi government’s promises of raising expenditure from the current ~1.2% to 2.5% of GDP, precious little airtime is given to measuring or advocating for better efficiency and effectiveness of the money spent. Simply put we need to figure out how to make our money work harder. One way to improve the quality of public health spending is to ‘nudge’ doctors and patients towards better decisions.
Nudge theory, as behavioral economics is popularly known, is a young but fast-growing field of economics that leverages insights from psychology and neuroscience to study ways in which to influence the behavior and decision-making of individuals. Its acceptance as a legitimate branch of economics has not come easy but was given a shot in the arm in 2017 when the Nobel Prize Committee awarded Richard Thaler the Prize for contributions that ‘built a bridge between the economic and psychological analyses of individual decision-making’.
Perhaps the most famous example of nudge theory in action concerns urinals. An airport manager frustrated with dirty toilets in Amsterdam’s Schiphol Airport once etched small fly into the centre of men’s urinals. Famously, this small intervention led to an 80% reduction in spillage as it turns out that when men have an object to aim their urine at, they can’t help but try and hit it. This particular anecdote elegantly illustrates the basic insight at the core of applied behavioural science – small and inexpensive interventions based on insights into human psychology can help influence human behavior in predictable ways. Nudge theory when brought to bear on policymaking applies such insights to policy as well as program design to lower costs and/or improve outcomes without a concomitant increase in spend.
In healthcare, the potential for nudging both doctors and patients towards decisions that lead to better (and cheaper) care is all but unlimited. The best nudges unobtrusively steer doctors and patients in directions we know are usually desirable – while still giving them freedom to deviate where appropriate. Intuitive examples include making organ donation in case of a fatal accident an auto-enrollment or opt-out rather than an opt-in policy, which leads to an immediate spike in the rates of organ donation. Another such example is nudging people to become repeat blood donors by sending simple thank you messages to donors when their blood is administered to a needy patient.
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Like with many significant discoveries, some of the most effective nudges have been the products of lucky accidents. One such accident took place when a rogue software engineer in the US accidentally configured hospital software to automatically prescribe the cheapest version of the drug, irrespective of which brand the doctor had typed, unless the doctor checked a box marked “dispense as written”. This tiny change reduced the prescription of relatively expensive branded drugs from 25% to 2%, which in turn resulted in estimated savings of 32 million USD over two years. Given the Indian government’s emphasis on universal coverage, not to mention recent attempts to push doctors to prescribe generic rather than branded medications, such interventions are both timely and sorely needed.
Other areas in which nudges can have an immediate impact include reducing the rates of inappropriate antibiotic prescription, such as when antibiotics are prescribed for viral infections. Interventions such as ‘suggested alternatives’ popup messages within electronic prescription software, ‘peer comparisons’ that give clinicians an insight into ordering patterns relative to model peers, ‘accountable justification’ requirements that randomly require clinicians to justify prescription of strong antibiotics, have all been proven to help reduce inappropriate prescriptions. What’s more, all these interventions respect clinical judgment and are not punitive in nature. Other nudges that have been shown to work include simple interventions that help reduce alcohol consumption, facilitate smoking cessation, help individuals with chronic diseases such as diabetes make important lifestyle changes, reduce hospital-acquired infection rates by prompting caregivers to wash their hands more often, etc. It’s also worth noting that it’s not just the public sector that could benefit from these insights –private players like health insurance could also leverage such tools in a variety of ways ranging from nudges that make individuals more likely to renew their policies to those that increase screening rates for diseases such as cervical and prostate cancer where early detection leads to lower treatment costs and better outcomes.
Despite evidence of impressive outcomes, caution is recommended. Many nudges that appear sensible at face value have limited or no impact in practice. In rare cases, a nudge attempt gone wrong might result in the opposite of the desired impact. For instance, one experiment found that telling people how much heat their houses were losing due to poor insulation made them less likely to get energy-saving insulations installed, not more. Perhaps they reasoned that the amount of heat being lost wasn’t significant enough to justify paying for better insulation, or that they could afford to put it off. At any rate, this example illustrates the importance of robust performance tracking, at least until the effectiveness of the intervention is confirmed.
Setting up a behavioral insights unit and embedding it within the policymaking apparatus is inexpensive but far from trivial. It’s important is to treat any such group like a scientific research unit whose funding and survival are linked to outcomes. Not only does that create a systemic incentive for policymakers to measure the impact of their interventions, it helps build a culture of sharp focus on outcomes rather than inputs. For instance, the unit set up by British Prime Minister David Cameron in 2010 was set up with a sunset clause which stipulated that it would be shut down if it wasn’t delivering results within two years.
The Modi government has made tentative steps towards setting up a behavioral insights group. In 2016 NITI Aayog moved a cabinet note to set up a ‘nudge unit’ in partnership with the Bill and Melinda Gates Foundation but this never materialized. The 2018-19 Economic Survey recommended setting up a nudge unit within NITI Aayog and, ambitiously, that every program should go through a ‘behavioral economics audit’ prior to implementation. Public circulars indicate that as of July this year NITI Aayog was actively looking for specialists in the field. Given the demonstrated potential for such tools to positively impact healthcare outcomes, one can only hope that this is not another stillborn attempt.
Source: ET HealthWorld