The recent COVID-19 pandemic has emphasised the importance of healthcare, whereby a healthcare crisis transformed into an economic and social crisis. Considering the same and in striving to achieve the SDG target of Universal Healthcare Coverage, India must take steps to improve healthcare accessibility and affordability in the country. Yet, healthcare policy must not become beholden to “saliency bias”, where policy over-weights a recent phenomenon that may represent a six-sigma event. This is especially pertinent given the fact that countries with much higher healthcare investments and concomitant health infrastructure have struggled to contain the pandemic. The next health crisis may not possibly involve a communicable disease.
Therefore, India’s healthcare policy must continue focusing on its long-term healthcare priorities. Simultaneously, to enable India to respond to pandemics, the health infrastructure must be agile.
For instance, every hospital may be equipped so that at least one ward in the hospital can be quickly modified to respond to a national health emergency while caring for the normal diseases in usual times. Research in building such health infrastructure can guide how to build such flexible wards.
The ongoing COVID-19 pandemic has helped showcase the role of technology-enabled platforms as an alternate distribution channel for remote delivery of healthcare services. These technology-enabled platforms offer a promising new avenue to address India’s last-mile healthcare access and delivery challenges. These technology platforms coupled with digitization and the promise of artificial intelligence at-scale, have led to a drastic uptake in the utilization of telemedicine for primary care and mental health.
Given India’s unique last mile challenges, such technology-enabled solutions need to be harnessed to the fullest. As we show, telemedicine depends crucially on internet connectivity and health infrastructure. Therefore, both Central and the State governments need to invest in telemedicine on a mission mode to complement the government’s digital health mission and thereby enable greater access to the masses.
The National Health mission has played a critical role in mitigating inequity in healthcare access. The percentage of the poorest utilising prenatal care through public facilities has increased from 19.9 per cent to 24.7 per cent from 2004 to 2018. Similarly, the percentage of the poorest accessing institutional delivery increased from 18.6 per cent to 23.1 per cent and from 24.7 per cent to 25.4 per cent for post-natal care. The poorest utilising inpatient care and outpatient care has increased from 12.7 per cent to 18.5 per cent and from 15.6 per cent to 18.3 per cent.
Therefore in conjunction with Ayushman Bharat, the emphasis on NHM should continue.
From a financial perspective, India has one of the highest levels of OOPE in the world, contributing directly to the high incidence of catastrophic expenditures and poverty. A negative correlation exists between the level of public spend and OOPE both across countries and states.
In fact, at small levels of public spend (less than 3 per cent of GDP), even marginal increases in public spend generate substantial “bang for the buck” in reducing the OOPE. An increase in public spend from 1 per cent to 2.5-3 per cent of GDP – envisaged in the National Health Policy 2017 – can decrease the OOPE from 65 per cent to 30 per cent of overall healthcare spend.
PMJAY has been a marquee evolution in this direction, providing financial affordability to a large percentage of the Indian population.
As a bulk of the healthcare in India is provided by the private sector, it is critical for policymakers to mitigate information asymmetry in healthcare, which creates market failures and thereby renders unregulated private healthcare sub-optimal. Therefore, information utilities that help mitigate the information asymmetry can be very useful in enhancing overall welfare.
The Quality and Outcomes Framework (QOF) introduced by the National Health Service (NHS) in the United Kingdom 2004 as well as other quality assessment practices introduced by NHS provide a good example in this context. These should be evaluated carefully and considered for implementation. Similarly, data from the National Digital health mission can be utilised even within the framework of data privacy with the aid of artificial intelligence and machine learning algorithms to mitigate information asymmetry with respect to the patients. A standardized system for quality reporting on healthcare for hospitals, physicians and insurance companies can start with basic input indicators to be reported mandatorily by every healthcare stakeholder.
Over time, this can evolve to cover output and outcome indicators such as infection rates and re-admission rates. A start has been made in this direction by the Niti Aayog through the Health Index at the state level. Finally, a sectoral regulator to undertake regulation and supervision of the healthcare sector must be seriously considered. This is especially pertinent as regulation has grown in importance as a key lever for governments to affect the quantity, quality, safety and distribution of services in health systems.
With limited visibility into patients’ medical records and no standardised treatment protocols, insurance companies have a risk of adverse selection at the time of policy issuance and a risk of moral hazard at the time of claims. To safeguard against this risks, insurance companies resort to high premiums and restriction of services covered in the insurance policy.
Addressing this information asymmetry can help lower premiums, enable the offering of better products and help increase the insurance penetration in the country.