I have posted previous notes about predictive analytics, particularly with regard to their use in the clinical labs (see, for example: Successfully Screening for Lung Cancer Based on Predictive Analytics; Diagnostic and Predictive Analytics and Their Possible Link to the Future of the LIS). However, of at least of equal importance, will be the use of predictive analytics in hospitals to improve clinical care and reduce costs. Such projects are even more important in an era of value based case with emphasis on efficiency and effectiveness. I therefore thought that it would be useful to provide some example of projects in this area (see: 3 Examples of How Hospitals are Using Predictive Analytics). Below is an excerpt from it:
Predictive analytics is increasingly key to powering hospital initiatives that maximize efficiency, realize cost savings, and help deliver superior care....The goal is often to improve operational efficiency or to proactively provide services that prevent greater problems and spending. Many hospitals have started with applications aimed at reducing readmissions and predicting which patients are at risk of developing sepsis. Other common use cases focus on optimizing staffing and resources. Here are three other examples of hospitals successfully putting predictive analytics into action.
Operating room bottlenecks: The University of Chicago Medical Center (UCMC) used predictive analytics to tackle the problem of operating room delays. Such delays are aggravating for clinicians, patients, and families, and they are wasteful since ORs are expensive to run. But delays are hard to prevent, with so many individuals and teams working on each surgical case. When one procedure ends, there is a sequence of certain tasks that must be completed before the next surgery can start. UCMC combined real-time data with a complex-event processing algorithm to improve workflows, create notifications, and streamline the handoffs from one team to the next for each step of the OR process. The effort decreased turnover time 15% to 20% ..., which was expected to save the hospital up to $600,000 annually. The new system also increased visibility into what was causing each delay and how to intervene in real time to get things back on track.
Newborn antibiotics: Kaiser Permanente led the development of a risk calculator that has reduced the use of antibiotics in newborns. Antibiotics are necessary for a small percentage of newborns who are at risk for early onset neonatal sepsis, an infection that can lead to meningitis or death. Researchers developed a risk prediction model after drawing data from the EHRs of about 600,000 babies and their mothers....The effort safely reduced antibiotic use by nearly 50% in newborns delivered at Kaiser’s Northern California birthing centers in 2015....Kaiser makes the risk calculator available online (see: Neonatal Early-Onset Sepsis Calculator)
Care transitions after knee and hip replacement: Cleveland Clinic, feeling the pressures of fixed reimbursements and bundled payments, wanted to find ways to decrease the length of stay for patients receiving total hip and knee replacements. It focused on discharge delays. Often, these were caused by patients’ unexpected need for post-acute rehabilitation in a skilled nursing facility. Researchers used analytics to predict which patients would recover successfully at home and which ones required inpatient rehab. The goal was not to prevent a rehab stay, but rather to better prepare for it. Cleveland Clinic pulled on existing research and a validated prediction model, which drew on variables captured in the physician’s office prior to surgery. The propensity score was put into the clinical workflow so all providers could use it in their preoperative discussions with patients. The program was successful at taking into account patients’ needs, decreasing lengths of stay, driving down costs, and improving the system’s patient experience scores in the HCAPHS Care Transition measures.
I have nothing to add beyond these presenting these examples because they are highly illustrative of the ways in which predictive analytics can be used to improve care.
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