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Applying OpenText™ Analytics technology to the health care industry allows these organizations to pull large amounts of information from multiple sources for comprehensive and predictive analysis, while ensuring the security and availability of data for better decision-making and optimal patient care.

Health Care Challenges

Health Care Metrics
Health care providers are facing the onslaught of Big Data head-on due to the following:
  • Federal push for electronic health records
  • New reimbursement models and accountable care organizations that require more understanding of what occurs with patients
  • Widespread adoption of new technology: devices, implants, mobile applications on smartphones and tablets
  • Pressure to become evidence-based and predictive with health care services by leveraging historical data to create predictive models
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Challenges around HIPAA privacy, security, data ownership, regulation, and integration are particularly acute in health care, but with Big Data comes big goals:

  • Increasing provider and payer efficiencies, reducing errors, and costs
  • Enabling comparative effectiveness research for current treatments
  • Moving toward patient-centered, outcome-oriented medicine
  • Empowering consumers – “Health 2.0,” participatory health care
  • Making personalized medicine possible for everyone

Health Care Analytics

Applying Big Data Analytics in the Health Care Industry

Now that they are required to collect this data, many health care organizations have initiatives in place to put data analytics to work. As a result, they hope to provide:

  • Better point-of-care decisions at the physician/bedside level.
  • Reduced readmissions – hospitals can determine risk levels for readmittance within a timely manner based on predictive analytics, and will send alerts to primary care practices to have them schedule follow-up appointments that substantially reduce readmittance rates (improving care quality and saving money). To be accurate, these predictions require huge data sets from very large patient groups.
  • Population health management – to identify risks in patient populations from patterns that were previously unidentifiable. This results in endless permutations of coordinated and specialized care initiatives.
  • Research advancement – Big Data speeds traditional research in a trend called “bench to bedside.”
  • Operational improvements – publishing and creating transparency in staff performance metrics to enhance everyone’s game. Using data to improve decision-making from the back office to inpatient care.


Learn More About OpenText™ Actuate Big Data Analytics

Health Care Industry Stats

  • $2.9 trillion – U.S. health care spending in 2009
  • 17.6% - health care percent of GDP
  • 25% - projection of GDP in 2025
  • $1.875 trillion – annual cost in 2009 spent on chronic diseases
  • 96/100,000 – patients who die from preventable conditions annually in the U.S.
  • >1/5 – patients readmitted after 30 days
  • $300 billion – potential annual savings to U.S. health care spending from using Big Data (McKinsey estimate)

Where Actuate Helps Health Care Providers

For those hospitals willing to attain the Meaningful Use defined by the Centers for Medicare & Medicaid Services (CMS) Incentive Programs, in order to be eligible to earn incentive payments, data quality is essential.

Data Integration

Hospitals have many systems, often siloed and isolated. This hinders efforts towards the “360 degree” view of patients. In addition, most providers are implementing EHR (electronic health records) that include diagnostics, procedures, tests, and personal data. iHub integrates data from these disparate sources to deliver personalized patient analytics and insights.


Hospitals run under reduced margins and often struggle to balance the best quality of patient care with cost pressures. In addition, due to informational gaps and system inefficiencies, many health treatments never get paid. This raises price pressure on those treatments that are paid. It can be a costly system. OpenText™ Analytics technology helps uncover inefficiencies and errors to improve performance and ensure adequate compensation to the provider.

Care Quality

Self-service, highly adaptable information provisioning for physicians can inform diagnosis and treatment. Analyzing patient populations across very large data sets can improve epidemiology practices. Ultimately, predictive analytics and visual data mining can optimize care delivery, improving post-surgery care to reduce readmissions and predicting problems with treatment compliance and aftercare down the road.

“The Actuate reporting tools allow us to display performance data quickly and efficiently, providing both high-level views of performance along with a powerful drill-down capability that we can use to identify best practices and root causes for improvement.”

St. Louis Children’s Hospital Leverages Actuate’s BIRT OnPerformance to Enhance Patient Care and Drive Performance

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Big Data Analytics for Health Care
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