Hospitals are inching away far away from the statistical and analytical approach to collection data models that have been in place for some time, and sprinting towards implementing Big Data. The model that is currently customary in many hospitals, when compared to the big data model, is almost too generic, as data harvested and stored is not syncing and meshing as seamlessly as it should, thus resulting in high readmission rates.
There is an urgency for big data implementation, as hospitals are seeing patient readmission rates rise. Readmission includes patients who return to the hospital within 30 days of their last visit for any ailment, including pneumonia, or those who have heart issues and have received care from a professional with ACLS recertification.
According to a study endorsed by Intel and Cloudera, a Apache Hadoop-based software company, Per capita healthcare costs in the U.S. are the highest in the world, and have trended upward for decades. $25 billion [is] spent annually in readmissions alone.
In an article published in Healthcare IT News, Pamela Peele, the chief analytics officer for University of Pittsburgh Medical Centers (UPMC) insurance division, said, Together with her team of 25, have done what many hospitals and payers are just beginning to do: They developed a conditional readmission model on the payer side that delivers a readmission risk prediction score before the patient even walks through the door, and then blends it with a provider-side model.
This pre-determined readmission-risk-prediction-score is huge for healthcares across the nation and even the world. In her interview with Healthcare IT News, Peele said, We’re pushing that information over to our provider side, so when somebody presents and they’re being admitted, the provider can see the a priori readmission risk that we’ve already calculated and can act upon that risk starting at the point of admission.”
Big Data is changing the analytical horizon for medical facilities across the board. In another article by Healthcare IT News, John Mattison, MD, chief medical information officer at Kaiser Permanente, said: Whereas the Big Bang started with a single point of origin and spread out from there, healthcare and other industries are moving toward big data in essentially the opposite way: An implosion triggered by combining previously disparate data sources tightly-bound to metadata into a database from which clinicians and researchers can extract whats interesting to them.
There are a few hospitals and other organizations that are using big data to not only reduce their readmission rates, but increase the quality of their patient care by digging a bit deeper into the depths of the big data pool.
In an article by Health IT Analytics, Ann Hendrich, Senior Vice President and Chief Quality and Nursing Officer for Ascension Health, the nations largest Catholic healthcare system, said this: All of our health care sites and hospitals have really been looking carefully at transitions of care and how we can best design a person-centered care model that anticipates the failures that really cause hospital readmissions. Those would include medication management, access to primary care physicians, and socioeconomic conditions that may contribute to that individuals health challenges.
The idea of collecting and interpreting data is not new, but big data has revolutionized the concept of the interpretation, as IT and CS employees are not the only ones dredging through the data. Doctors, specialists, hospital boards and administrators are all reviewing the information collected, as we learned from Peele. Connecting this data and introducing the human side to the endless stream of pure statistical analysis is what will, hopefully, aid in decreasing patient readmission.
