Growing up in Kolkata, India—we had a family pediatrician, a family GP, and a few specialists that we knew and trusted. Moreover, doctors were always found through recommendations—the whole health care system seemed to be a large social network. While on the one hand, it had little or no computerization to speak of, there was nonetheless a lot of retention of data because of the longevity of the relationships and the semisocial nature of the arrangements. For instance, when you went to your doctor for say a problem, he would remember instantly that you were diabetic, had a kidney stone removed a year ago, and were allergic to penicillin. This was fine, until you moved cities and had to start all over again. This is still how a lot of health care works in many parts of the world. The poor laborer on a building site has no social connections to good doctors and is reliant on a ramshackle public infrastructure, and there is no universal insurance or social security. This is being tackled at a number of levels, but at the core of these is the ability to handle data. When there is no data retention via relationships or systems, only the fortunate few can get the appropriate quality of health care. The situation for countries like India is exacerbated due to the size of population and the economic constraints.
Conversely, the NHS in the UK was underpinned by a vision and execution that outstripped many other countries, for decades. Of late, the structural, political and operational complexity of the NHS and its funding process has become a barrier to innovation. One of the most obvious areas where you could see this work was in terms of data—every visit to a doctor often required you to start all over again. This is changing as we speak, as electronic patient records are being adopted at scale.
Public health care in the UK covers 89 percent of the population, and even for the remaining 11 percent, it provides accident and emergency, mental health, and other services.1 Public health care spending in the UK grew steadily till 2009 but has been stagnant at about £130bn. The estimated funding shortfall was anticipated to increase to between £16bn and £30bn by 2020/2021,2 even before COVID-19 and related expenses hit the budget. Thanks to improved health care, people continue to live longer in each generation, ironically shackling the health care system into funding shortfall and debt. It is quite likely that the retirement age will also creep up, but the ratio of working to nonworking people will still rise. This poses a ticking time bomb of a problem—not just financially, but operationally as well. Future governments will not have the budgets to simply solve by spending more, and nor will they have access to the skills and people needed. There is a pressing need for innovation in how the same level or better health care can be delivered with ever lower budgets. Technology will almost certainly be required to bridge this gap in the UK, as with many European nations with public health care funding. The COVID-19 pandemic, which disproportionately impacted older people, was a harsh reminder of how exposed the health care systems are to shocks.
The United States spends more per capita on health care than any other country but still has a lower life expectancy than most European countries. There have been many papers and articles published on this, but it is also true that health care is unevenly distributed in the United States. And even basic health care is a high cost service that many people can’t afford. Health care, coupled with aging populations, is therefore a universal problem, and digital tools and optimization are not an option any more.
Connected Health
Technology intervention in health care takes many forms. It includes smartphones and apps for patients, wearables for fitness, use of AI for anticipating secondary infections, fundamental changes in medicine driven through bio-electrical methods, phantomization of health care, shift of focus to population health and prevention over cure, use of video to connect patients to experts, overcoming disabilities through assistive tech, 3D printing of prosthetics, and many others. In fact, many of these are already in place. I believe that the 2020s will be the decade of health care innovation.
As with most things digital, the initial focus of digital health care will involve a surge in connections. This process has already begun, and we see examples of this all around us already. Here are some examples you may have seen.
Fitness tracking wearables: in 2020, just under 450 million wearable devices were shipped across the world. If you have witnessed friends and relatives being anxious to complete their 10,000 steps for the day, or perhaps are yourself, you’ve seen the influence of wearable fitness devices. This already extends to other consumer health devices—including blood pressure and blood sugar monitors, and weighing scales, which are increasingly smart and connected, with reminder services that encourage you to be regular with your daily check-in.3 Waire, a Scotland-based startup offers a single device to be worn with a strap on the arm, which will offer cuffless blood pressure estimation, respiration rate, temperature, oxygen saturation, three-axis position, motion, ECG, heart rate, and more.4 Virtual and video checkups are now regularly used in the United States,5 and the pandemic has also made this a worldwide phenomenon.
All of these improve the efficiency of the system as well as patient experience, for example, getting patients to be monitored more effectively and regularly, or getting patients to speak with specialists faster. Digital solutions are also usually lower cost and more self-service-oriented, which creates more ownership of individual health and more compliance and awareness, generally lowering the rate of admissions and readmissions. But all of this is only the first step, and even that is not complete. We are not yet in a world where we’ve connected the patient to the care ecosystem. As more and more connections are created in the health care space, the more data we’ll generate and how we deal with the data will drive the second level of improvements.
Quantifying and Optimizing Health Care
Nurses working in hospitals in Ireland might complete up to 72 tasks in an hour. But they constantly work on an interruptive rather than scheduled basis. This means that they are constantly interrupted by patient needs. This can lead to nurses missing signals, or tracking changes in vital signs across multiple patients. Syncrophi,6 an award-winning software, connects all the existing medical systems to create a single dashboard with a supervised queue system. The nurses work on a risk score and decisions are based on this, but in the high-pressure environment, there can be up to a 50 percent chance of error—ranging from adding mistakes to data entry errors on multiple systems. The KEWS system from Syncrophi connects all the individual systems and creates a single queue of tasks, which are being reordered through the day as the patients’ conditions and needs change. The continuous quantification of risks ensures that nurses can focus on their tasks and also retain a view across an entire ward or a whole hospital.
Acute kidney infection (AKI) is hard to predict and can escalate quickly. It’s a key risk for people already undergoing a major operation or treatment. DeepMind, an Alphabet subsidiary, analyzes multiple streams of patient data to calculate the risk of AKI. In the Royal Free Hospital in London, clinicians use DeepMind as a warning system7 in order to prevent or plan for the onset of AKI in a patent. Data sharing concerns notwithstanding, this is a very good example of the levels of optimization that the health care industry will be reaching for in future.
Healthy aging is another critical area, given the demographic profile of many European nations. Japan has already gone down this path, with the United States likely to follow Europe. Today, a number of providers are setting up homes with sensors so that activities of daily living can be tracked. The idea is that the data this generates creates a complete picture of the habits and lifestyles of the resident. Also, once this has been running, it can catch any deviations from regular patterns, such as not waking up at the normal time, or an increase in the use of the toilet, or disturbed sleep, and so on. These can be early pointers for the onset of specific conditions, or alerts can be set up for carers or family members to check in when there is unexpected data or movement. Ultimately, this changes the way that care is planned and delivered, with remote monitoring of data playing a key role in the mix. TCS has piloted this kind of solution in Singapore and Ireland.
Similarly, the use of electronic patient records at large scale allows the focus on population health. This includes measures to keep people out of hospital, control epidemics, track the growth of specific diseases, and more. The optimization of health care involves less, rather than more health care. But this can only happen when patients are connected and their data is quantified. The CEO of Waire recently pointed out to me that we track our automobiles more diligently than we track our own body and health. I believe we’re on the cusp of not only changing how we track and monitor, but how we quantify and optimize health care.
Tip: Make a mental list of what data you actually capture and track about your own health unless you have a specific diagnosed problem.