Overcoming Healthcare Big Data Challenges
Big Data, Little Knowledge
The healthcare industry has become extremely efficient at collecting data. The adoption of electronic claims systems, laboratory information systems, radiology information systems, electronic health records, and more have created massive stores of clinical and financial data that have the potential to drive significant
healthcare performance improvement. However, healthcare data challenges persist. According to a survey of the HealthLeaders Media Council in July 2014[
1], 89% of healthcare leaders agree that they need to be able to analyze this data across the care continuum in order to ensure their organizations' clinical and financial success.
While there is agreement on the importance of using these information resources, healthcare lags other industries in putting in place the healthcare data analytics and business intelligence solutions that will help them meet these healthcare big data challenges.
4 Major Healthcare Big Data Challenges
- The Deluge of Digital Data
- In 2012, the amount of U.S. healthcare data reached 150 exabytes, according to the Institute for Health Technology Transformation. It estimates that this amount will double in less than a decade. For perspective, an exabyte equals 1 billion gigabytes. Considering that the human brain can only process around seven variables at once, the sheer size of this data is a major challenge in healthcare.
- Not Enough Data Scientists
- The McKinsey Global Institute estimates that there will be a 100,000-plus-person analytic talent shortage at least through 2020, which could mean 50–60% of data scientist positions may go unfilled[
2]. Data scientists need more than the highly technical skillsets held by today's data analysts. They must have well-developed soft skills such as communication, collaboration, leadership, creativity, and more. Of the healthcare leaders surveyed in July 2014, 60% were unsure whether they had in-house expertise necessary[
- Electronic Health Records Are More Limited Than Expected
- EHRs have greatly simplified data acquisition, but don't have the ability to aggregate, transform, or create actionable analytics from it. Intelligence is limited to retrospective reporting, which is insufficient for forward-looking
healthcare data analytics that hold the key to performance improvement
- No One Likes Change
- Institutions are notoriously resistant to change. This is especially true as they grow larger and existing processes and procedures become "the way it's always been done." The shift to an analytics-based culture requires everyone in the organization to use a single source of truth to guide choices, stop making gut decisions, and avoid "data shopping" to find data that supports a conclusion that has already been made
By Its Nature, Big Data Itself Causes Many Healthcare Challenges
- Data—especially "big data"—isn't static. Its content and use are constantly evolving as it grows ever larger and more complex
- Data sources are highly variable, which causes inconsistencies, duplications, and inaccuracies that undermine people's trust in its validity
- Working with data can be messy due to a lack of agreed-upon industry standards and definitions. This is a major challenge for healthcare given the wide range of nomenclature, measurement units, and other site-specific standards used by hospitals and networks
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