Smart Cities, Communities and Improving Healthcare Efficiency

Over recent months there has been a lot of interest in healthcare for obvious reasons. Many of the ideas that emerged in my PhD thesis are becoming increasingly apparent. Two ideas that seem particularly relevant are: firstly the NHS is not good at dealing with data management and secondly more private sector involvement is not likely to improve the situation. Both of these seem relevant to the recent test and trace mix up outlined here. The use of Excel as a database just goes to show how far the current UK healthcare systems need to travel if it is going to take advantage of the potential of cutting edge digital technologies.

In this peer reviewed academic journal article I outline and define and discuss the concept of smart community in the context of healthcare efficiency. I argue that this digital concept contains insights to improving health care and healthcare efficiency.

Concept of healthcare app on a smartphone. Vector of professional medical team connected online to a patient giving a medical consultation.

In this video I outline some of my ideas from my thesis. I argue that improving efficiency through digital technology is not just about getting the technology right, it is also about how technology is implimented into the system. I argue that a top down system is not likely to be effectitive. The top down way that the Covid19 crisis has been handled in the UK has been ineffective as frequently argued, including in this Guardian article, This Telegraph article and this BMJ article that I co-authored

Another criticism that I made about the current system is that it is too responsive and doesn’t focus enough on prevention. I would place the same criticism on the handling of Covid19 crisis in the UK. As I argue in this previous blog post I believe that the lives vs economy trade off is a false dichotomy. Bringing down the rate of infection would save lives and enable the economy to reopen.

The biggest issue with poor data handling in the current system in the current system is that it prevents it from harnessing the positive changes that have taken place in many other industries. Whilst digital health literature such as this portray a utopian vision for what health care could look like, in most areas of healthcare reality does not live up to the hype. After starting my study and really drilling down into how healthcare actually works I became quite shocked at how primitive the current systems is. Limitations include: disconnected datasets, lack of time, outdated knowledge, lack of focus on prevention, the influence of psychosocial factors such as mental health, social context and lifestyle are typically ignored. Let’s look at these in relation to interactions with GP’s, Most patients in the UK start by going to see a GP.

  • Outdated knowledge and understanding. Typically a GP will guess at what might be wrong based mostly on current trends and understanding based on their medical training. In most cases the medical training is out of date and digital technology and potential healthcare treatments move forward rapidly, healthcare professionals, including GP’s are not able to keep up to date with the potential treatments that become available.
  • Lack of time. A typical GP appointment is ten minutes. During that time there is not enough time to collect enough data to do any kind of sophisticated analysis linked to the patients specific medical history, DNA and social context
  • Disconnected datasets. Even if it was possible to collect more data during a GP consultation, there would be little value in doing so as there is no single comprehensive dataset based on all the medical interactions each patient has had during their lifetime
  • Psychosocial factors. Each person’s chances of getting ill, in particular their chances of developing a chronic long term health condition is not random, nor is it evenly divided through different geographical and social sectors of society. The differences are stark. In Sheffield, for example, average life expectancy between the most affluent and poorest areas is 12 years and the differences between average healthy life expectancy is 25 years. The socio-economic and lifestyle factors that influence health are rarely considered when treating illness.
  • Lack of focus on prevention. Related to the consideration of the social, psychological and economic influences of health is the idea of intervening earlier. Many illnesses such as cancer are less disruptive and costly to treat if they are spotted and treated earlier. Better still would be to address the risk factors such as unhealthy lifestyles that make people more susceptible to disease in the first place.

An issue that cuts across several of the points outlined above is a tendency for our health care system to prescribe drugs to treat the symptoms without really drilling down into what the cause was or what the risk factors are. In a ten minute consultation it is impossible to do much more than prescribe drugs that address symptoms without really engaging with the underlying causes. Here I can refer to my own life experience. 20 years ago I was diagnosed with cancer. Six visits to a GP failed to spot the condition, it was only after going to A&E that I was sent off for tests. In my case eventually persistence worked, although by the time it was spotted the tumor was 7 inches across, borderline stage 2-3. If I had been less pro-active I might not be around to write this now.

Digital technology has created the potential for a vastly different and much more patient centered health care system. I am not however going to understate the scale of the change that would be required to harness that technologies such as, the internet of things, genetic mapping and artificial intelligence (AI) offer. Harnessing the potential would require a total system change, including a culture change throughout.

The healthcare system I would like to see would include continuous monitoring linked to a healthcare file for each person that includes their entire medical history and DNA. I’d like to see comprehensive monitoring and evaluation of the impact of each medical intervention given, to track the effectiveness of each drug or other treatment prescribed. I would like this data to be harnessed in aggregate by AI so that each time a person develops symptoms diagnostics could be conducted to indicate the most effective treatment for that specific person, with their medical history, with their DNA, their lifestyle, their social context and so on. Better still would be analysis linked to continuous monitoring, such as monitoring of exercise performance so that illness can be addressed before any symptoms materialise.

I would like this future system to be collaborative. I would like it to inform both patients and healthcare professionals of relevant health research, enabling both to engage in informed discussion to agree on any treatments required. Treatment options would ideally include lifestyle interventions such as social prescribing as well as drug and hospital interventions . Ideally both patients and doctors would engage in discussion in online health forums to help develop their knowledge and understanding, especially when a patient develops a long term health condition.

Whilst these ideas may seem like something from some kind of utopian science fiction film, there are pockets of reality that are not so far removed from what I outlined. As I outline is this talk some of these have materialized during the Covid19 epidemic. For example Blue Dot AI predicted Covid19 in January 2020. In other areas: microscopic robots have been developed to attack cancer cells and smart toilets that monitor signs of disease Such developments remain far from ubiquitous, however, as William Gibson once said, the future is here, it’s just not very evenly distributed.

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