4 Barriers to Big Data Analytics in Healthcare Organizations

84% of CIOs and other C-Suite health care executives believe that the application of big data analytics in healthcare organizations is a significant challenge, according to a survey from the eHealth Initiative and the College of Health Information Management Executives.

Key stakeholders from over 102 healthcare organizations participated in the survey conducted over a four week period from May 30 to June 28, 2013 examined the attitudes toward data use, trends in business use cases for data and analytics, the technological solutions employed by organizations, and associated challenges and barriers.

To adapt the growing volume of electronic data, healthcare organizations are increasing their focus on building a scalable plan to leverage data and predictive analytics that meets their organization’s strategic plans.

Despite the growing focus on big data and analytics, the survey identified four major barriers:

  1. Lack of appropriate trained staff (64%)
  2. Data ownership and/or governance issues (53%)
  3. Data integration (40%)
  4. Lack of funding (39%)

Other survey findings include:

  • A large majority (82%) indicated that bi-directional sharing of clinical and/or patient data with local healthcare organizations is important or very important to their organization.
  • Nearly 90 percent of respondents use analytics for revenue cycle management. The most common use case was managing accounts receivable metrics (82%), including denial rates, take back rates, claim/payment volumes and outstanding receivables.
  • Two-thirds of respondents use analytics to prevent fraud and abuse, and only 26% of respondents viewed the use of analytics for fraud and abuse as a key business area in the coming years. The most common use cases were cost trending/forecasting (38%) and care utilization analysis (35%).
  • 82% of respondents identified population health management as a key analytics business area in the coming years.
  • Quality improvement was the most commonly reported use case (90%) for analytics. Inpatient care utilization and outcomes analysis (80%) and adverse event reporting (75%), were among the most widely reported functionalities.
  • The two most common data sources were administrative data (77%) and claims based data (75%). Unstructured textual data (47%) and remote monitoring device/sensor data (31%) will likely rise in prominence in coming years as technology advances and devices become more ubiquitous.

Click here for the full survey findings

Image credit: Kevin Krejci via cc

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  • DataH

    Jasmine, insightful article! We are seeing an increase in businesses seeking specialized skills to help address challenges that arose with the era of big data. The HPCC Systems platform from LexisNexis helps to fill this gap by allowing data analysts themselves to own the complete data lifecycle. Designed by data scientists, ECL is a declarative programming language used to express data algorithms across the entire HPCC platform. Their built-in analytics libraries for Machine Learning and BI integration provide a complete integrated solution from data ingestion and data processing to data delivery. More at http://hpccsystems.com

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