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As a mounting body of non-scientific healthcare-related papers deal ad-hoc with the hypes of Information Technology (IT) today, throwing an anchor into recent history and the foundations of healthcare system management sounds like an imperative duty to both the scientific community and the general public.
In the beginning, there was a spark…
I started my healthcare-IT journey as a Medical Doctor and Software Engineer in the late 1990’s, in Austria. Back then, I was assigned to a state project to design and implement an Electronic Medical Records (EMR) solution into 23 public hospitals. It was exciting! The EMR concept was new to many and held a myriad of promises for everybody: hospital managers would have modern tools to do a better job, doctors would have instant access to patient’s digital data across the continuum of care, patients would receive better care and… vendors will make a lot of money. Everybody wins. What an amazing perspective!
Many projects later and fast-forward to 2010, we started implementing the National Electronic Health Records (NEHR) in Singapore and subsequently moved to the Australian my-PCeHR a year later. Not without excitement, but curiously, the more healthcare IT projects I did, the closer that experience pulled me back to the roots of hardcore clinical care.
And then a question mark.
Have these multi-million, high-intensity, high-visibility EMR projects delivered on expectations? An honest, objective reply to this question would be restrained, but a straightforward one would be: incrementally, maybe and by far, not commensurate to the investments! In other words, at this point in time and following Gartner’s hype cycle, EMR solutions are way past the peak of inflated expectations and nearing the end of the plateau of productivity. Notwithstanding, EMRs have produced a huge volume of consolidated quality data and that shed some light on interesting pathways for advancing medical sciences.
A new hype replacing the former
Because nature hates a vacuum and the expectations from EMRs were plateauing, the emergence of new tech buzzwords like ‘Big Data’, ‘Internet of Things’ (both meaning absolutely nothing in medical practice), immediately flanked by the use-case dubbed ‘Artificial Intelligence’ (AI), and the context of a fizzing startup scene came to define what we now know today as ‘healthcare 2.0, 3.0’ and so on. But hold on! Before we get too excited, and because healthcare is a matter of life and death, let’s take a moment to anchor technology back into some vital healthcare foundations.
The 4 anchors…
There are 4 dimensions any technology applied to healthcare should submit to:
lower disease burdens in populations through prevention,
enable better access to wellness and healthcare services,
achieve better health outcomes, and
through honed experiences, better data analysis and higher volumes, support the process of making the costliest healthcare services cheaper. The combination of 3 and 4 is known as value-based care and we’ll talk about it later.
What we learned from this global EMR experiment is, like any medical prescription, the implementation of technologies in healthcare should have clear indications backed by objective data and objective ways to measure their impact on health indicators (not opinions and vendor claims). In 2009 and in this light, I launched a global think-tank we called the Alliance for Clinical Excellence (ACE) with the purpose to independently assess the effectiveness of IT solutions applied to healthcare. Due to lack of funding, the initiative was short-lived but as you can tell, its spirit lives on.
And some really scary statistics.
Healthcare still is very much about treating the sick. That’s why we build hospitals, send people to school to teach them the profession and buy medical equipment and other tools to help them do their jobs. Although life expectancy has been extended by around 20% on average in the last 50 years, the prevalence of deaths due to non-communicable diseases (NCD) is rising rapidly and will reach 85% in some middle and high-income countries by 2030. NCD are the direct (but not sole) consequence of poor behavioural choices like smoking, poor diet, obesity, lack of physical activity and alcohol abuse. The resulting NCD from these behavioral patterns are diabetes, cancers, chronic pulmonary disease, cardiovascular disease and some mental illnesses. The prevalence of obesity is increasing at alarming rates. Globally in 2015, over 200 million school-age children were overweight, making this generation the first predicted to have a shorter lifespan than their parents, according to the International Obesity Task Force. Adding insult to injury, the economic impact of NCD on middle-income countries is devastating: in recent studies we conducted at national levels, the cumulated economic loss through 2030 due to NCD burdens ranges from 12 to 26% of the Gross Domestic Product (GDP) of the studied geographies.
A fact of today’s healthcare is that we’re living longer not because we’re healthier, but because we’re finding more ways to extend the lives of the sick. Henceforth, because of the dramatically increasing burdens of NCD, investing in technologies supporting curative care ought to be balanced by accelerated efforts in interventions targeting [NCD] prevention. While an increasing number of countries have already embarked on this preventive journey, the vast majority of technology vendors and startups alike, are still heavily focused on the wrong end of the problem: curative care. Indeed, reading 9/10 papers and posts positioning AI on the diagnostics side of healthcare, claiming machines can make better decisions than trained doctors is highly entertaining, but in the face of the aforementioned burdens, simply not practical.
Digital Transformation? Maybe, but don’t expect any disruption, yet!
Paper was first introduced by the Chinese around 300 A.D., used by Central Asian scientists during the age of enlightenment and reached Europe only about a millenium later. In the light of paper’s adoption path, if the digital era consists of the wave that is replacing it, one can safely state that recent digital advances in medicine can only represent the first microscopic filaments of a massive future cyber-tissue.
Moreover, because of the intertwined complexities of clinical sciences and the compelling need to scientifically validate new technologies and their impact, progress in medicine has always been incremental. While these efforts are accelerating and their pace should actually be sustained, redistributing the focus on NCD prevention through encouraging behavioural changes at community and individual levels represent a vital strategic path to follow. Fortunately, an increasing number of tech startups are taking that path and time will tell the real impacts from hyperbolic hypes.
People. Process. Tools.
After decades of global practice as a healthcare professional and technologist, I came to appreciate the challenges posed by both worlds as well as the immense potential that lays before us. In this high-stakes environment, where the main driver of the tech industry is revenue growth, one needs to place the fate of the patient back into the Hippocratic Oath: the first interest to be considered is the best interest of the patient. These 2 forces are not mutually exclusive, but sanity and ethics carry the duty to reconcile them.
One of the reasons leading to the massive underperformance of the EMR experiment is that most of the projects (if not all of them) were led by technology which most clinicians didn’t yet have a clue about. Sounds like a double-blind study? Just kidding! For reasons irrelevant to this essay, when implementing a tool becomes the final objective of the intervention, then it also becomes its sole focal point. Flanking EMR projects with armies of change management officers to backlog process change in the wake of a new technology adoption has always been a slap in the face of logical thinking — and to a certain extent one of the main contributors to user frustration and at times abysmal adoption rates.
The very promising field of value-based care — curiously called ‘population health management’ by some folks, outlines a very pragmatic pathway in which people, process and tools interplay in the right chronological order:
Start defining strategic objectives related to people. Hone their knowledge and skills,
Support them with thought-through standards and processes,
Equip them with the right amount of technology which will support their quest in achieving those objectives.
Repeat!
Unfortunately, in Asia and in many other maturing geographies, an overwhelming majority of key decision-makers are still getting caught in a thick technology smoke, perpetuating the bottomless cycle of double-blind digital experiments. This results in a regrettable scramble of the aforementioned order of doing things, resulting in turn in lack of visibility, confusion, strategic face-plants and most of all, lower-value healthcare.
Today, the naive claim of being able to transform a healthcare organisation simply by acquiring a set of sophisticatedly sugar-coated digital tools, but without developing its workforce and its processes prior to that, is the beginning of the déjà-vu situation we’ve had with EMRs 20 years ago.
However, one might argue, this is a new beginning and a new spark! — Granted! But in the light of history, as the healthcare tech community decided to outsource healthcare learning to their machines and before we start raving again, one vital question remains unanswered: when will humans learn?
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