Why do Healthcare Organizations Still Struggle with Data Integration?

Abhinav Shashank
Wed 13 Sep 2017
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Data integration is a daunting task for organizations in every sector. We are generating and collecting more data than ever before. However, due to system and integration challenges, we are only halfway through understanding that data and transforming it into knowledge.

This goes especially bad for the healthcare sector. The amount of data generated tops exabytes. However, due to system and integration challenges, almost 44% of healthcare organizations are unable to use all the available data. This may seem to be a little number, but it costs healthcare dearly- $342 billion if we were to put a number.

Why do we need to transform data integration?

Simply put, healthcare data integration is about collecting, auditing, and monitoring healthcare data. It goes beyond to leverage it for simple reporting, analysis and optimizing organizational performance. Data integration can find multiple areas of applications like maintaining universal patient records, risk stratification, analytics, reporting for quality measures, care coordination, and population health management.

However, the healthcare industry stands at crossroads when it comes to enabling vast and varied systems to talk to one another and generate value- so where are we lacking? Where can we improve?

Reworking approach

To begin with, we lack when it comes to data integration in healthcare. Existing legacy systems and the labyrinth of multiple data formats are only the tip of the iceberg. At the same time, it’s important for healthcare organizations to understand that true and meaningful data integration is going to need a fundamental change. Only then can we give way to minimal metadata translation services and semantic interoperability.

According to a survey, 47% healthcare IT executives believed that improving data integration would reduce their expenses and improve care delivery exponentially. All this is only possible if healthcare leverages better integration and analytics. It’s all about accessing far more raw data than ever, gaining tremendous insights into patient health and not compromising accuracy and performance. But before moving towards integrated, data-driven strategies, there are some stark challenges that demand attention.

The process-oriented challenges

Before integrating data, we need to first integrate teams and people who handle data. Process-related in challenges in healthcare data integration may not be many, but we can’t move forward if we don’t connect the dots behind us.

Firstly, often there is an integration gap between the teams. The teams that handle data collection process are too disconnected from the teams that use data. On the other hand, those responsible for data use don’t have a clear understanding of the data collection process. Not only teams have different mindsets but this gap can also make the process of data handover and exchange sloppy and important details can be missed.

For data integration to improve, it’s important that these teams are made of skilled professionals who not only have the understanding of data requirements but also have the skills for retrieval, organization, and interpretation of data. That would help mitigate inefficiencies and result in quicker, effective advancements.

The fundamentals where technology falls behind

In the past few years, several healthcare organizations have undertaken new healthcare data management, mostly centered around the implementations of electronic medical records. EMRs have allowed for greater data collection but they have their own set of challenges.

For starters, each data system like an EMR will have their own data schema- a different definition for ‘patient,’ ‘eligible member’ and ‘disease registries.’ Either way, there is a high possibility that data points may not map accurately across systems. Data unification across systems is possible, but often requires skilled staff, external key lookups, and constant updating.

Second, we happen to be dealing with a considerable number of legacy systems. These systems often prove ineffective when dealing with the explosive amount of healthcare data and can rarely collaborate effectively with new systems. On top of these legacy systems, there is a widespread use of general-purpose data integration tools which are later customized to fit the needs of organizations. This requires a huge investment to make up for the lack of healthcare data expertise, making the path from raw data to insights much longer.

The barriers to governance

Even if the challenges related to data integration are countered, the healthcare vertical needs to address governance-related issues in healthcare. There has to be an understanding of laws around data privacy and confidentiality. Several healthcare organizations are under the misconception that data is already protected and its integration will sabotage the security. Both of these misconceptions are false.

There has to be a culture of data sharing, especially among healthcare providers who have traditionally protected data and kept it isolated. We need good layers of security and proper authorization regarding exchange and the end result should be a consistent view of patient information with the problem to solve.

The road ahead

Data integration isn’t an issue that we can solve in a jiffy, but if we are able to mobilize people, optimize processes, and transform technology, we can immediately begin to witness effective and impactful outcomes. More effort has to be put in, starting with the planning. The world of healthcare is disconnected and this is costing us dearly, but we have to do something about it. Healthcare is all about curing people- let’s not have it fail people.


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Tags: Accountable Care, Big Data, Healthcare, Integration
Abhinav Shashank
Why do Healthcare Organizations Still Struggle with Data Integration?

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