anya.vane

The ‘Rules’ of Preventative Healthcare

Blog Post created by anya.vane on Jan 30, 2017

Preventative healthcare profits from keeping people healthy and well. For a concept so simple, it’s remarkably complex to execute. Designing programs that prevent illness and complications requires complex models to determine the care best suited to each patient’s medical history, lifestyle, environment, and unique personality. Designing such models is worth the effort: According to one company known for its preventative health care programs, people who report high well-being cost 20% less to treat and are up to $20,000 more productive. Not to mention they’re, well, better.

 

Brilliant as the human mind may be, hard-coding preventative health care decision logic for one individual is tremendously costly, in terms of both finances and human error – and the United States has some 326 million people in it. Millions of these people are hospitalized for chronic conditions. When they leave, they do so uneducated, confused, and unprepared to manage their condition. In 2004, some 20% of the 12 million Medicare hospital discharges resulted in readmission within 30 days. This number has improved only 1-2% since, and as a result the American economy loses billions of dollars each year.

 

It would be overly simplistic and unfair to blame health care providers alone for these high readmission rates. Health care is immensely complex. Health information is transitory and high-stakes. To manage it all across millions of unique patients something a bit more artificially intelligent is necessary. Fortunately, emerging technologies are offering solutions that cut costs even as they improve the quality of preventative patient care.

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Chief among these upcoming technologies are well-designed analytic models that can adapt to an individual patient more efficiently and accurately than humans. Using massive amounts of patient data to design the right set of questions, a machine can learn about the patient, storing and correlating data as needed to answer those questions, analyze the outcome of various care decisions, and suggest actions better suited to improve that patient’s health conditions.

 

FICO® Blaze Advisor provides a platform to build such sets of rules and analytical models. A carefully designed solution could analyze an individual’s health profile and environmental conditions to make health recommendations or alert potential concerns, and advise the health practitioner on the best way to engage the patient to pass along that knowledge or treatment plan. It could remind practitioners to follow up with patients flagged as at high-risk of prematurely quitting their treatment regimen or otherwise impairing their own recovery.

 

Technology that enables healthcare providers to identify each patient’s unique best post-treatment care can go a long way towards empowering change. One example of rules implementation scenarios that FICO® Blaze Advisor helps create is already in place and making an impact for a health initiatives provider covering nearly 70 million lives. The initiative uses predictive analytics to identify patients at high risk of readmission post-treatment, then invoke a collaborative care model to inform discharge planning and follow up. In its initial phase, 22% fewer patients were readmitted than prior to the system’s implementation as a result of the more tailored care. Applied to the aforementioned 2004 scenario, that’s 2,640,000 fewer people returning to the hospital, and that many more people receiving better care.

 

The system described is essentially a brilliant repurposing of those familiar business rules that Blaze is designed to formulate, execute, and test. Tuned to health concerns, the same technologies that drive targeted marketing campaigns, shopping recommendations can enable health care providers to approach every individual and their condition effectively. Within an arena so variable and complex as healthcare, an efficient and simple rules management platform can empower doctors and nurses to apply patient care rules within their everyday practice. These practioners’ broad knowledge of medicine and acute view of their patients can be used to build out a system that first deeply understands the patient profile and then applies health care models (rules) to generate data-driven healthcare decisions.

 

With respect to Grey’s Anatomy, each patient’s conditions are far more than the sum of their symptoms, and require a more adaptive guide. Patients’ lives deeply influence both what illness they might contract and how likely they are to respond to a certain intervention or treatment. Designing a system to help keep people healthy, then, means computing a person’s best treatment based on a ‘360° view of the patient.’ This includes factors beyond the scope of their health records; it is also relevant where they live and who with, their financial resources, their personality, and more.

 

A rules system can evaluate virtually unlimited external factors to determine a patient’s health risk, and enable health professionals to adopt or adapt an effective treatment model for that unique patient. Using a data-informed system grounded in designed logic, treatment can be more consistent and intervention can be thoughtfully executed when necessary, improving the patient's chances for sustained health. Such understanding and agility is unparalleled by anything a single health care provider can provide unaided.

 

That’s not to suggest that healthcare decisions should be exported to technology. Non-tech savvy clinical employees – that is, those closest to the patient – are still the best people to utilize models to improve patient care. After all, these are the creative and nimble ones holding the practical knowledge to implement the best possible methods of care. So it stands to reason that these doctors, nurses, and clinicians should have an analytical execution environment in which they can organize data-informed rules and rule flows to run predictive models and maintain rules themselves – without IT intervention.

 

With such a platform, these healthcare providers can locally deploy campaigns to intuit new information about their patients. They could build macro-level analyses of patients to, for instance, allow practitioners to assess post-recovery challenges common within the hospital and thus more likely to affect new patients. Or, they could use an individual patient’s record and profile to deliver more specific care for one injury, based on how their body has responded to treatments in the past.

 

With a deep understanding of the patient, a system can determine the best course of action to keep that patient well. It can similarly determine, deploy, and monitor the allocation of resources necessary to provide that care. It’s a complex set of rules and variables that no mind should have to attempt to calculate alone. For the sake of our population and economy, not to mention the health and sanity of our healthcare providers, it’s a problem worth conquering.

 

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