Big data is growing and offering increasing promise for improved clinical and organizational decision-making. The analytics capability to access that data for decision-making, however, is not keeping pace. According to UC Berkeley health policy professor Stefano Bertozzi, “Healthcare data is constantly getting bigger, and that means hospitals have the opportunity to improve not only care delivery, but also, enterprise performance — but the analytic capability is the biggest bottleneck hospitals wrangle with today.”
When Aaron Levitt, PhD, wrote, “Unlocking the value of EHR through analytics,” he spoke of the promise of analytics:
“The application of analytics to EHR data holds great promise for helping organizations identify opportunities to improve the quality of care and preserve financial stability…. Many large health systems and academic institutions already have the manpower and analytics tools in place to perform this type of analysis, but often smaller organizations lack both the required technology and the in-house expertise.”
For example, the Carolinas HealthCare System has seen analytics provide the ability to identify, measure and improve care and quality outcomes. They have also found, among other outcomes, that data can now be used in predictive analytics to help predict disease outbreaks and population health measures.
Other ways in which analytics is offering value to healthcare systems appears in the HIMSS Health IT Value Collection. Here, we find story after story of ways in which analytics is being used to advance the quality of care, population health, and organizational performance. For example:
• Children’s Hospital of Pittsburgh of UPMC found that the EHR generates predictive and preventive analysis reports that prevent patient decline and improve patient outcomes.
• For the Cleveland Clinic, predictive analytics provides the ability to predict length of stay for patients anticipating surgery, which patients will be higher-acuity, and what the hospital occupancy and census will be up to 8 weeks in advance.
• At Kaiser Permanente of Northern California, predictive analytics uses a mother's clinical data and the condition of her baby immediately after birth to predict the risk of severe neonatal infection to determine which babies need antibiotics.
• For Lancaster General Health in Lancaster, Penn., the EHR's clinical data allows analysts to conduct risk stratification of patient populations.
• Analytic capability at Parkland Health and Hospital System in Dallas supports the timely identification of diabetes patients through population surveillance and helps predict readmission risk in patients with heart failure, thus enabling more-informed clinical decisions.
• With a focus on improved performance, Legacy Health (Portland, Ore.) finds that data analytics tools enable clinicians to view their outcomes via comparison to other clinicians from over 200 hospitals.
• The disease registry system stratified 1.1 million patients into various disease states to identify gaps in care at Meridian Health in Neptune, NJ.
• The Stanford Cancer Center found that with the use of analytics, they were able to reduce patient wait times at infusion centers by 30% at peak times, and the centers have been able to see 25% more patients with 15% lower costs.
These are only a few examples of how analytics, using data in the EHR, has been able to improve care, access, performance and costs at a few major health systems that have implemented and used analytics. As analytics capabilities become ubiquitous, and more professionals train in the use of those capabilities, organizational performance and the quality of care will continue to increase nationally and locally. The importance of analytics to the future of healthcare cannot be overstated.