Changes are emerging in healthcare, where patients are now being viewed more like people and customers and less like “cases to be managed.” Amongst the myriad of problems being addressed are missed patient appointments, which cost providers roughly $150 billion each year. While many specific reasons exist for these “no shows” – from lack of trust in doctors, to issues in finding transportation – the specific cause of one patient missing one appointment is tied to the decision-making of that singular individual.
Healthcare systems are encouraging patients to get more involved in their clinical care (Wall Street Journal). Involving and motivating the patient can take many forms including designing treatment walk-throughs, which are interactive videos about how and why different treatment options work, as well as their benefits and drawbacks. The goal of these “decision aids” is to provide patients with more comprehensive understandings of the options available to them that is easy to access and understand.
Data analytics feeds this system by providing physicians with tools and insights about the best courses of action to present to patients, helping patients make more informed decisions. Health analytics serve a dual purpose: 1) they help predict and prescribe potential courses of action for patients; and 2) they use patient responses as feedback to deliver insights concerning patient indifference to their healthcare.
Understanding patient behavior and decision-making, and their effects on revenue, has fueled the big data analytics market. Generic interventions such as overbooking and slapping on missed appointment fees work as deterrents from the doctor’s side of things, but they don’t address patient issues in a more personalized manner. More healthcare systems are turning to prescriptive analysis to examine ways to drive behavior in a more positive direction. The revenue gain from personalized interventions is significant; one study showed a revenue increase of between 3.8% and 10.5% when using such methods to reduce no-show rates.
As highlighted recently by this WSJ article, patients are generally reluctant to take a more involved role in their care due to feeling intimidated by their doctors or being overwhelmed by many potential decisions. As physicians and healthcare providers place more attention on strengthening patient-provider relationships, the power provided by prescriptive analytics will help doctors better understand their patients and address their needs in a personalized and effective manner.
It is, therefore, imperative to view consumer behavior as influenced by factors beyond general demographics, and seek to explore the attitudinal roots of decision-making that are unique to each individual. The development of this novel approach to analyze patient behavior is an exciting new front for data analytics.