Divining the Future of Healthcare with Predictive Analytics

Guest post by Sanjay Govil, founder and chairman, Infinite Computer Solutions

Sanjay Govil
Sanjay Govil

It’s impossible to see the future with certainty, but one branch of technology is playing a leading role in helping institutions and industries predict, on the basis of empirical research, the future behavior of participants and the outcomes of their decisions.

This relatively new branch of tech – predictive analytics (or PA) – has made inroads at a steady clip in the marketing, manufacturing and financial services industries. It is now gaining traction in healthcare as well.

Although debates around its ethical applicability to healthcare persist – the debate around data privacy, for one – the consensus emerging across the board is that with the right skills and in the right hands, PA has the power to effectively address challenges in the healthcare ecosystem in ways that human intelligence alone cannot.

Let us examine a few recent examples.

The power of PA

The Gold Coast Health Hospital in Southport, Queensland, Australia, dramatically improved patient outcomes and hospital staff productivity by applying a predictive model that was able to project with 93 percent accuracy emergency admissions before they happened. By analyzing admission records and details of sundry circumstances that led to patient admission to the ER, hospital staff were able to know how many patients would be coming in, on any day of the year, what they would be coming in for and methodically plan procedures that were now for all purposes elective rather than urgent.

Similarly, the El Camino hospital in California was able to drive a dramatic turn-around in its high rate of patient falls by collaborating with a tech company. The company, Qventus, linked patient EHR to bed alarm and nurse call light usage to derive an algorithm that was able to alert nurses in real time about the high-risk patients under their care and the exact times when they were most likely to be vulnerable. The result was a whopping 39 percent reduction in falls, improvement in patient health outcomes and a dramatically improved reputation for the hospital.

In fact, it isn’t only hospitals that are alive to the potential of analytics. Tech companies too are cognizant of how some of the newest technologies being developed under their roofs have immediate relevance to healthcare outcomes. In a paper published earlier this year, researchers associated with Google demonstrated how deep learning algorithms were able to correctly identify metastasized cancer tissue with nearly 90 percent accuracy as compared to just 73 percent when done by a human pathologist.

Leveraging collaborations between machine and human learning

Although any discussion of the entry of machine intelligence in an area as sensitive as healthcare continues to be prickly, I believe the way forward for the sector as a whole will entail these sorts of collaborative tie-ups with machine learning, deep learning algorithms and artificial intelligence.

This is because “mere” human capacity and know-how will not be able to keep up with the sheer volume of demand that various patient cohorts will be making on the healthcare ecosystem. From greater accuracy in diagnosis, more effective prevention to personalized care for the elderly – patients will insist on their need for care being met on their terms while the sector is projected to continue to deal with a paucity of physicians, rising health costs, an ageing population, and a continuously expanding base of demanding, self-aware patients who expect more for less.

Improvements in clinical outcomes

In fact, the machine-driven, algorithm-based intelligence that PA will help usher in to some processes of healthcare will deliver benefits across the healthcare system. It will help free up providers from time-consuming administrative tasks resulting in improved provider occupancy. It will enable more accurate prediction of patient outcomes based on more holistic and accurate profiling, lower and prevent hospital re-admissions and help correctly detect and prevent fraudulent and expensive claims to insurance.

What it will also do is enable more effective population health management. With precise mapping of cohorts currently considered healthy, PA will be able to forecast, on the basis of their current habits and family history, the exact conditions that they may become vulnerable to in the coming decades, if they don’t heed preventive care advisories. It is also likely that PA will actively enable the creation of highly personalized medication for every such screened patient further improving the potential to prevent their falling sick in the future.

However, as with all new technologies that are still evolving, PA will also pose a few challenges. Prime among these will be leadership buy-in by healthcare practitioners themselves. No campaign on behalf of PA, however persuasive, will work, unless those wielding the technology believe in its safety, efficacy and relevance. Next will be the current lack of proper infrastructure and skills to deliver PA’s correct application along with the expertise required to accurately interpret the technology’s models and prescriptions. Equally challenging will be addressing concerns regarding the seeming marginalization of human ability in accurately diagnosing, treating and curing patients without any reliance on technology.

But what is clear is that as a greater number of patients witness dramatic improvements in their health outcomes as a result of PA at play, it will make greater in-roads into the healthcare sector as a whole. This is bound to give impetus to skilling, investment in the right infrastructure and sector-wide support for PAs adoption.

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