Cost-effective healthcare analytics

Updated on: Feb 06, 2011




It means disease can be prevented or treated in its earliest stages.

Mrs Brown is a 68-year-old patient with congestive heart failure. Through claims and pharmacy data, analytics software is able to monitor prescription refills. If Mrs Brown fails to pick up her medication on schedule, she receives a reminder, while her provider is alerted to the gap in care as well.

Describing the above scenario, Siva Namasivayam, founder and CEO of the US-based SCIOinspire (, adds that rules and algorithms can similarly be configured to send patients within a specified population, say, an automatic notice when they turn 50 that they should schedule a screening colonoscopy.

“The effect of this utilisation of healthcare analytics is clear: Disease can be prevented or treated in its earliest stages. Monitoring compliance with care plans helps in preventing unnecessary hospitalisations, visits to the emergency department, and escalation of the severity of the condition. The result: lower costs of care and improved health,” notes Siva, during the course of a recent interaction with Business Line , on the sidelines of an REC-Trichy reunion ( Our conversation continues over email…

Excerpts from the interview.

First, would you explain the importance of analytics in healthcare?

Compared with other information-driven industries — such as financial services, technology, travel, and retail — healthcare has taken the passenger's seat when it comes to the effective use and analysis of the vast amount of data that is available.

To a certain degree, healthcare's disinclination to embrace data analytics is understandable.

Software, to date, has been highly complex, rendering it inflexible, non-scalable and less than user-friendly, and also expensive to deploy.

On top of it, the skills required to perform the analytics are unique and require cross-functional skills that are difficult to find. These disincentives are compounded by grave concerns about the security of data and the privacy of individual patients.

This attitude, however, is undergoing a radical change in Western nations such as the US, driven equally by market forces and government regulation. The average age of the population is rising rapidly, spurring increased utilisation of healthcare services and, consequently, increased costs.

This phenomenon has, in turn, unleashed a wide range of business challenges for payers and providers, as follows:

The escalating need to transition away from fee-for-service arrangements that encourage the fragmentation of care delivery, towards a holistic approach to the treatment of chronic conditions;

Pressures to scrutinise claims payments more closely to detect errors, abuse and waste; and

Improved design and implementation of care and wellness programmes to ensure that the efforts target the right individuals.

Recognising that the current model is unsustainable, the government, health insurance companies, and healthcare providers alike are seeking new opportunities for reducing cost. Healthcare analytics can be a strong enabler. In fact, the field of healthcare analytics has received a boost in the US due to healthcare reform activities and economic stimulus plans. Incentives are now in place to encourage new payment mechanisms designed to improve quality; promote cooperation among providers; decrease administrative costs; detect instances of abuse and waste; and hasten the adoption of electronic health records.

What have been the traditional approaches to information needs?

There is a vast amount of invaluable data available for healthcare companies – in claims submitted by physicians and hospitals for every visit and procedure performed, as well as through medical devices, clinical systems, laboratory information and pharmacy claims. However, there are significant issues impeding the use of this data for bottom-line business purposes:

Lack of collaboration among entities;

Cleanliness and quality of the data;

Ineffective use of standardised data such as claims;

Deployment of flexible and robust software for data mining and associated business processes;

Data mining techniques; and

The analyst skill set — which must encompass a wide range of areas such as clinical, pharmacy, claims, coding, data warehousing, database and healthcare informatics — and the resultant cost vs benefit associated with the analysis.

The traditional approach in healthcare analytics has been to develop complex software applications; but, due to the associated expense, this software could be used for focusing only on critical areas and processes.

Even within this limited scope, the effectiveness of analysis could be constrained by problems, such as the quality of data, the expensive and rigid analytics software platforms, and the cost vs benefit of using high-priced, sophisticated analyst resources.

Can you tell us about your approach to analytics?

The ROI (return on investment) from the use of analytics is at its best when the findings from analytics can be deployed quickly and economically to drive business results. The deployment of analytics in business processes requires two key delivery mechanisms: Software applications that are tailored for each business process by integrating data mining and workflow for specific business processes; and economical, yet high-value analyst resources that have a combination of deep domain expertise and production skills spanning multiple areas.

A more accessible approach, spearheaded by organisations such as SCIOinspire, focuses on propelling the business benefits of using healthcare analytics to new business areas and also to greater depth within existing processes. This is being accomplished by using relatively inexpensive, but highly effective, software applications that are characterised by rapid software development, identification of new analytical rules quickly, and rapid deployment of rules to the claims data to identify patterns, and the use of a global delivery model consisting of teams of clinical, pharmacy, claims, coding, informatics and technology analysts for data quality, data mining and analysis, with the domain expertise provided by experts in the US.

In addition, because of the flexible and scalable nature of these programs, analytics can be deployed into areas that were unreachable in the past due to the economics.


Published on February 06, 2011

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