One of the most interesting new areas in medical and pathology informatics is predictive analytics. It uses statistics, modeling, machine learning, and artificial intelligence to analyze clinical data to make predictions about the health of individual patients and patient populations. A recent article discussed how this technology can be used to select the best patients for remote monitoring (see: UMMC pinpoints ideal patients for remote monitoring with predictive analytics). Below is an excerpt from it:
The $1.6 billion University of Mississippi Medical Center emphasizes a value-based approach to healthcare. As part of that approach, the organization’s Center for Telehealth uses remote patient monitoring to better manage chronic diseases, including diabetes, hypertension and heart failure – diseases where education and timely interventions can improve individual healthcare and ultimately overall population health. An important part of remote patient monitoring is identifying patients who are the best candidates for the technology, patients who will respond well to the technology. But how does a healthcare organization best identify these optimal candidates for remote patient monitoring? That’s tricky, said Richard Finley, MD, professor of medicine for infectious disease, professor of emergency medicine, and director of medical analytics for the Center for Telehealth at the University of Mississippi Medical Center.....“But given our population, our health problems, and such a big rural and indigent population, we have a lot of difficulties in terms of trying to reduce hospital readmissions and doing so efficiently,” he said. “What I am working on right now is using different analytics tools to conduct pattern recognition to partition patients based on risk factors.....But Finley would like the medical center’s remote patient monitoring program and the process of identifying patients to be more aggressive than that, zeroing in on patients before hospitalization.“We have to recognize factors that we can clearly identify to make reasonable predictions and thus allow us to efficiently do interventions, whether they are simply education or in more extreme cases to use remote monitoring at home, especially for heart failure and diabetes, to try to prevent them from coming into the hospital to begin with,” Finley explained.
Remote monitoring of patients with chronic diseases by hospital personnel is beginning to receive the attention that the technology richly deserves (see: Telemedicine Transforms Intensive Care Units in Smaller Hospitals with Remote Monitoring; Keeping Tabs on Patients Post-Discharge Via Telemonitoring; Telemedicine Transforms Intensive Care Units in Smaller Hospitals with Remote Monitoring; Cost Savings Associated with Home-Based Physiologic Monitoring). Part of the reason why hospitals are paying more attention to their patient readmission rate is that they are being financially penalized by CMS when it rises too high (see: Talking to Patients Helps Reduce Hospital Readmissions; The Hospital Readmissions Reduction (HRR) Program). As noted in the excerpt above, home patient monitoring can now become an even more useful tool, particularly when coupled with predictive analytics. The latter is used to predict which of the patients being monitored at home have the highest probability for readmission to the hospital. This will cause a redoubling of efforts to intervene early and often for those patients.
Regarding the use of predictive analytics in this way, here is the bottom line lesson to be learned from a recent article on the topic (see: 4 Essential Lessons for Adopting Predictive Analytics in Healthcare):
In order to be successful, clinical event prediction and subsequent intervention should be both content driven and clinician driven.....Notably, prediction should be used in the context of when and where needed – with clinical leaders who have the willingness to act on appropriate intervention measures. The more specific term is prescriptive analytics, which includes evidence, recommendations and actions for each predicted category or outcome. Specifically, prediction should link carefully to clinical priorities and measurable events such as cost effectiveness, clinical protocols or patient outcomes.
So the use of predictive analytics for the subject at hand, reducing hospital readmissions in remotely monitored patients with chronic diseases, the prescriptive analytics seem clear. For patients deemed at risk, determine which clinical protocols will be the most effective in stabilizing or improving their health status in an ambulatory care or home environment and put them into effect.