A Simple Guide to Outsourcing Data Analytics in Healthcare

| 12 Feb 2018

A Simple Guide to Outsourcing Data Analytics in Healthcare

The global healthcare industry, more than any other, suffers from the “data rich but information poor” syndrome. While it finds itself on the brink of a major makeover with the advent of Big Data and analytics, it lags considerably compared to other industries.

Many researchers have raised this red flag. In a dissertation written by Rudolph Bedeley submitted to Graduate School at The University of North Carolina at Greensboro, USA, the author speaks of how, despite the “increasing interest in the implementation and utilization of Analytics & Business Intelligence (A&BI) techniques & technologies by various organizations to improve operational efficiencies, healthcare organizations or HCOs still trail other sectors in the adoption and use of A&BI capabilities. [1]

Motivated by the “data rich but information poor” condition of HCOs, Bedeley’s dissertation applied mixed-method research–case study (interpretivism) & survey (positivist) – to investigate just how healthcare organizations could leverage A&BI techniques and technologies to improve overall performance.

What Are the Advantages of Predictive Analytics in Healthcare Industry?

It’s no secret that healthcare analytics holds the potential to reduce costs of treatment & improve the quality of a patient’s life. Big Data is being seen as a “strategic asset” that HCOs and healthcare providers can use to polish decision-making in the patient-doctor relationship. With more and more connected devices coming into the market and collecting the necessary medical information, the next logical step would be of scrutinizing these mounds of data to offer physicians meaningful insights.

In the US, one of the biggest healthcare markets, the transformation journey started a few years ago with the introduction of the Affordable Care Act. One of the biggest trends seen in this eco-system since then is the use of digital data, largely driven by the deployment of data-spewing wearable technology, consumer devices, and mobile apps, to understand, anticipate even, a patient’s medical condition. Yet, somehow, the journey there, as in the rest of the world, so far can be best described as spasmodic.

Why so? That’s the trillion-dollar question

While there are plenty of reasons being cited for this lack of IT application, one major stumbling block in this journey has been the ‘build versus outsourcing” factor.

Health organizations and even individual medical practitioners are still very confused over whether to set up an in-house team of analysts or to outsource the job. They need to get on with it, as the Brits would say, as the data flow generated from the millions of devices keeps increasing with every passing day. A McKinsey report has estimated that as much as 30% of the globe’s stored data is generated in the health care industry. For now, a single patient is said to generate about 80 megabytes a year as imaging and electronic medical record (EMR) data. The value unleashed could bring down healthcare costs by about US $300 billion annually.

Another report released this week by global market intelligence, research, and advisory company BIS Research titled, “Clinical Decision Support Systems (CDSS) Market – Analysis and Forecast, 2017-2025”, has projected that this sector would reach a global market size of US$ 10.83 billion by 2025. The rate of medication errors and the harms they cause – both in terms of increasing healthcare costs and preventable fatalities – are expected to drive this growth. [2]

Most HCOs do agree that healthcare analytics can not only identify inefficient treatments, suggest better clinical alternatives, but also down on operational costs.

Yet, many have either made a half-hearted attempt or none at all to deploy analytics, even as the medical and research industry finds itself at the center of one of its largest-ever disruptions. Those who have found themselves either involved with the job of collecting data & refining it or simply sitting on the huge mounds of data.

… be continued Healthcare Analytics: Build-Versus-Buy Quandary A Hurdle
2) PharmaBiz

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