Technological change in healthcare is not simply about adoption

10 December 2018
Healthcare systems around the world are facing new challenges. It's time to rethink how we deliver health and care. How can digital health technologies help?  Health systems across the OECD are facing a wide range of pressures. The share of the population aged over 65 is set to double by 2050, placing new demands on health systems. Changes in lifestyles also affect the patterns of morbidity, while there are growing demands for more and better care from individuals. Spending on health accounts for a significant share (8.9%) of the economy across the OECD, and it is forecast to grow further. Given that three quarters is funded from government budgets, ensuring that health systems deliver good outcomes for people at an affordable cost is more relevant than ever. However, health systems are also quite rigid, still too focussed on episodic and hospital care, with rigid workforce models and many physicians and nurses saying they do not have the soft skills needed to perform at their best. Leveraging data and digital technologies can help health systems become more agile. Big data offers tremendous opportunities for better research, clinical optimisation, disease surveillance and system management. Critically, they can help make health systems more people centred. Some estimates suggest that 20% of health spending is at best ineffective and, at worst, wasteful. How can we make healthcare effective and resilient?  Waste is pervasive across health systems. OECD analysis has shown that as much as one fifth of health spending is at best ineffective and at worst harmful. For example, one in ten patients in OECD countries are unnecessarily harmed at the point of care, and over 10% of hospital expenditure is spent on correcting such harm. A sizeable share of emergency hospital admissions is for care that could have been addressed outside the hospitals. Up to 50% of antimicrobial prescriptions are unnecessary, with up to 90% of antibiotics inappropriately used in general practice. The potential for generic medicines remains underexploited in many countries as well. A number of administrative processes add no value, with losses to fraud and error running at more than 6% of health expenditure. Health care systems can do much more to address these issues. For a start, they need to acknowledge the problem, and from there they can better inform patients and clinicians to stimulate behavioural change. They can seek to change the habits of professionals via clinical guidelines, or by raising the health literacy levels of patients to encourage more rational behaviours. And they can reward the right services in the right place, by leveraging payment systems and other financial incentives.
Improvement anywhere requires better knowledge – and healthcare is not an exception
Healthcare has a lot of data, mostly closed in data silos. If we were to link these big datasets, how could healthcare profit? Clinical and patient data have huge potential for improving health system performance, research and patient care. Better linkage across different datasets has the potential to create new learning, knowledge and intelligence. The promise of precision medicine, for example, will never be realised unless a range of datasets are routinely linked for analysis. Breakthroughs in such difficult areas as dementia can be encouraged by better linkages across clinical, epidemiological and other datasets. There is potential to bring in ‘outside’ data as well, such as that collected from individual digital applications and activity trackers. The challenge, of course, is to make this secure (ensure privacy) and frictionless (interoperability and common standards across digital platforms) – but this is both possible and within reach. However, there is still a way to go, and a previous OECD survey suggested that only 2 out of 23 countries regularly linked across all relevant health data sets, for example. How can public health harness big data to make progress in such areas as new drug development, quality of care, or access to healthcare services? Improvement anywhere requires better knowledge – and healthcare is not an exception. Harnessing Big Data helps to ensure that new knowledge is accumulated every day and then used to constantly refine and improve the things you mention - drugs, interventions, care quality, access, and more. For example, leveraging big data can facilitate the development of care models that are more people-centric; improve health literacy, which is shown to be linked to better patient outcomes; be a tool for better clinical judgement by health professionals; deliver more personalised prevention strategies; while ‘real-world’ data as well as routine data can also be harnessed to better monitor the safety and effectiveness of technologies. But to achieve this, the ‘learning’ part of health care (research) and the ‘doing’ part (everyday activities and the data it produces) must be better integrated. Currently they are kept separate for various institutional and policy reasons, and due to the lack of appropriate data governance. There are huge advantages to making this change. For example, a study using ‘real world’ data collected as part of routine health care reproduced the findings of a randomised controlled trial that took 7 years and several million dollars. The ‘real world’ study took 12 weeks at a hundredth of the cost. One of the simplest uses of big data involves the reports prepared by OECD, such as "Health at a glance" or "OECD Health Statistics". How does this knowledge help to improve healthcare systems?  OECD has for several years published key statistics on health and health systems, providing international comparisons of the performance of health systems. While these cannot be defined as big data, such international comparisons are extremely powerful. Two key mechanisms are at play. The first is that direct comparison can lead to improvement. By comparing themselves with their peers, countries have strong incentives to fill in any gap, and to improve where there are clear weaknesses. This stimulates further processes, such as open discussions where countries can learn from each other’s successes and failures. The second mechanism is that the provision of comparable statistics to OECD continually raises the standards of data collection and its quality within a country – it is an incentive to do so, and OECD helps countries on this path. The data can then be deployed internally to improve processes and outcomes, and help to achieve policy objectives. It’s a case of the rising tide lifting all the boats. You said that to ensure the sustainability of future health systems, we have to make the most of new technologies. What do you mean by that?  Health technologies have huge potential to deliver better care and outcomes – take for example advances in medical technologies that offer less invasive treatments than those available in the past and which can be applied to a larger population of beneficiaries. However, technology has been the key driver of health spending increases across OECD countries, through both higher prices and volumes of care. Meanwhile, productivity gains are made difficult due to the labour-intensive nature of health care services. What this implies is that the challenge with technological change in health is not simply about adoption; it is about how technologies are managed to ensure higher outcomes for patients and benefits for health systems at an acceptable cost. How do you see the role of big data and artificial intelligence in the healthcare of the future?  There is no doubt that it will play an increasing role because the technology is advancing so rapidly. The number of “AI” patents in medical technologies has risen 6-fold since 2000, while the cost of sequencing a genome has dropped from $1M to $1K over the past 10 years. The question is how can this be harnessed to meet societal and health objectives? While these are exciting times, the outcomes are by no means assured. Will any benefits accrue to the many or the few? Will we be able to modernise the assessment tools for health technologies, to adapt different types of innovation? Will people’s privacy be protected adequately without impeding progress in research, care, and system management? How will the transition process for transforming health workers skills be managed? Only the right policy frameworks can ensure that the objectives are met, benefits maximised and harms minimised. OECD has developed a Council Recommendation on Health Data Governance to guide countries and policy makers in these very endeavours and is working with counties to understand how to translate those principles into policies to modernise health service delivery and research.   The views expressed in this article are those of the authors and do not necessarily reflect those of OECD or of its member countries. The author would like to thank Slawomirski, Policy Analyst, OECD Health Division, for his helpful input.