I'll admit it ... I had never even heard of "predictive analytics" while practicing medicine or even in the decade after leaving clinical practice.
Having spent years in both academic and community hospital operating rooms, intensive care units, patient wards, and outpatient clinics, I was among the majority of practicing physicians and non-physician inpatient bedside providers who had never seen, let alone utilized, patient-specific "predictions.
Nor did this bother me. After all, isn't prediction the overall job description for every physician? Haven't we spent years in training, learning the signs and symptoms and myriad of other clinical factors that predict "what's going on" with each of our patients, that generate a differential diagnosis list, that guide our diagnostic work-up and, ultimately, that drive us to the correct diagnosis? The answer is, yes. And no.
Yes, as physicians, we are trained and gain increasing experience in incorporating patient signs and symptoms and lab results and imaging studies into our own "predictive analytics" system: our brains. And yes, this often works well in predicting and confirming a patient diagnosis or treatment outcome.
However, we are far less successful in accurately predicting individual risks for specific inpatient health and care events, such as predicting each of our inpatients' risk of sepsis or risk of mortality or risk of transfer to an intensive care unit (ICU) during hospitalization, the risk of an extended length of stay, and similar inpatient risks that have dramatic impact on the patient, the provider, and the payer.
Thus, while we can diagnose sepsis (that is, predict based on initial clinical information that a patient is indeed septic), we are significantly less successful in predicting who is at risk of becoming septic during their hospitalization and, more specifically, at what level of sepsis risk any individual patient truly is and how that sepsis risk is changing at any given time for any individual patient.
As a result, sepsis remains a major driver of morbidity and mortality for thousands of inpatients annually, as physicians correctly predicting that a patient has sepsis are already "too late" to intervene and prevent the patient's journey to sepsis. The result for septic patients is increased hospital stays, additional organ insult and injury, and, for many, in-hospital death. For providers, the inability to routinely stratify sepsis risk and appreciate risk trending represents a major challenge in the delivery of quality healthcare. And for payers, simply waiting for members to become septic and then paying the high cost of care associated exemplifies poor cost efficiency.
"Risk prediction" is where predictive analytics enters the clinical equation. Complex computer algorithms are capable of incorporating and weighing multitudes of potentially meaningful clinical factors, resulting in individual patient risk stratification and trending information.