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In an effort to provide better healthcare at lower costs, an increasing number of payers are focusing on providing a “continuum of care.” This model strives to identify and deliver the most appropriate care for an individual based on factors such as age, lifestyle decisions, genetic predispositions, prior care received, and more. The ultimate goal is to have a personalized relationship where providers guide a patient’s experience across their full care journey which may consist of preventative care, treatment or surgery, rehabilitation, and any follow-up health maintenance required.
While simple to describe, a continuum of care is an almost utopian vision that can be incredibly challenging to deliver in the real world. Failure to do so, however, results in significant payer costs: an estimated $200 billion annually for unnecessary medical tests, $150 billion for appointment no-shows and up to $2 trillion a year treating preventable long-term illnesses.
But healthcare decision makers have one tool in their tool box to help control these costs: longitudinal data. In this blog, we examine the complex challenges involved in gathering the longitudinal patient data required to provide an effective and efficient continuum of care.