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Information Technology Trends Disrupting Healthcare


Wellness and healthcare are paramount to the lives of everyone on earth. It’s a necessity that transcends all barriers of ethnicity, culture, history or social classes: to quote the great Leonard Nimoy, we all want to “live long and prosper”, Vulcan salute.

Looking at “ The Legatum Institute ”11th annual global Prosperity Index ranking that appeared in November 2017, it’s clear that the big components of the countries ranking is the overall health of their populations. Health is measured by three key components: a country's basic mental and physical health, health infrastructure, and the availability of preventative care.

Unsurprisingly, the world's most prosperous are simply the world’s healthiest. These are generally big, developed economies with large amounts of resources, and great technological frameworks.

In fact, the use of information technology is already contributing in many ways to enhancing healthcare delivery and to booting the quality of life. Trends in IT, backed by academic research can lead to major changes in the delivery of a quality and personalized healthcare. It can also establish the foundations for new directions in the clinical sciences via tools and analyses - ones that potentially leverage the fusing of clinical, behavioral, environmental, genetic, and epigenetic data.

Such research is particularly timely now, in light of the worldwide priority to improve human health and the growing interest in healthcare legislations. This article attempts to take a closer look to some IT trends that have the potential to transform healthcare, as we know it today. Let’s dig deeper.

The “Paperless” Era

In the past, healthcare data have been created and stored on paper based folders and charts. The process is being transformed with the appearance of PCs and tablets with a great storage capability. The new “paperless” system is centralized, safely shared and available on a hunch to yield better knowledge. Although the migration is still ongoing, it’s fair to speculate that the future of healthcare information systems looks towards a near “paperless” era.

Source: Pixabay

Better Stories Told By Bigger Data

For many years, patient data has been paramount to the general healthcare quality. It stretches from patient care management all the way to clinic research and education.

With the recent advances in information systems technology, the ability to gather and process huge amounts of data has greatly improved. It now encompasses numeric data, alphanumeric data, imaging and even molecular data (Maojo & Martin-Sanchez, 2004).

The advances in methods for capturing and sharing large amounts of data synergize with advances in core analytical, learning, and inferential tools that consume such data. In fact, new approaches in applied mathematics and statistics can transform raw data into knowledge. Subsequently such measurable knowledge turns into Key Performance Indicators or KPIs. Such metrics provide a better guidance on pinpointing ideal actions by consumers, healthcare providers, and payers alike.

In a discipline that involves numerous stakeholders, leveraging big data starts by collecting it from all parties involved, be it Pharmacies, Clinics, Patients or Insurance Companies.

Big data provenance in Healthcare. Source: Healthworks Collective

Furthermore, targeted investment in Big Data research will clear the path towards a better understanding of diseases, and enables truly personalized healthcare delivery − not based on biological and genomic information alone, but also incorporating environmental factors, local clinical effectiveness assessments and information on the course of treatment.

Machine Learning To Enhance Diagnosis

Machine Learning is by far one of the most disruptive technologies out there. Its use cases are clearly materializing in real world by the day, and already redefining entire disciplines like finances and social sciences.

When it comes to healthcare, great strides have been made and we are clearly at the beginning of the era of understanding how to infer actual causality versus probabilistic association from data and to design efficient studies that identify causal influences.

Source: Health Catalyst

A significant application of ML in healthcare come from the genetics field. It aims to decipher a mystery that buffles scientis to this day: how does DNA impacts life. This is where ML algorithms come in and the advent of systems such as Google’s Deep Mind and IBM’s Watson. Now, more than ever, it has become possible to digest immense amounts of data (e.g. patient records, clinical notes, diagnostic images, treatment plans) and perform pattern recognition in a short period of time — which otherwise would have taken a lifetime to complete.

Another exciting application of AI/ML in healthcare is the reduction of both cost and time in drug discovery. New drugs typically take 12 to 14 years to make it to market. A long process during which chemical compounds are tested against every possible combination of different cell type, genetic mutation and other conditions relating to a particular ailment.

ML algorithms can greatly reduce the time and cost involved in new drugs commercialization. It allows computers to “learn” how to make predictions based on the data they have previously processed or choose what experiments need to be done. Similar types of algorithms also can be used to predict the side effects of specific chemical compounds on humans, speeding up approvals.

When it comes to disease diagnosis, medicine has always been governed by symptomatic detection. But often the arrival of detectable symptoms is too late, especially when dealing with diseases such as cancer and Alzheimer’s. With ML, the hope is that faint signatures of diseases can be discovered well in advance of detectable symptoms, increasing the probability of survival and treatment options.

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