By LINDA T. HAND
Every day, we make thousands of choices. Some of them – even those that seem trivial at the time – will change the course of our lives. This concept was memorably illustrated in the 1998 film Sliding Doors, which imagined two very different paths for Gywneth Paltrow’s character, Helen, based entirely on whether or not she makes or misses the London Tube on her commute home—the film’s eponymous sliding doors.
Helen doesn’t have the luxury of weighing her possible futures and altering her choices accordingly, perhaps quickening her pace or stopping for a latte along the way. Fortunately, for today’s healthcare decision-makers now facing their own Sliding Doors moment, the diverging paths of reactive versus proactive healthcare are much easier to contrast.
Staying the course with reactive healthcare
To date, most health systems and insurers have had little choice but to stick with the familiar path of reactive healthcare. The status quo since medicine’s earliest days, reactive healthcare passively waits for people to get sick before “reacting” with all available measures to return them to health. As a result, patients wait longer to enter the system and arrive sicker, and end up receiving avoidable or more expensive care than if they had come to our attention earlier. And rising costs often serve as an additional deterrent to patients seeking care.
Take Maggie*, for example. She’s a 53-year-old home care worker who struggles with obesity and depression, both exacerbated by ongoing, chronic pain, despite previous orthopedic surgeries. In the world of reactive healthcare, a patient like Maggie would remain under the radar for clinical outreach until her health issues spiral out of control, requiring multiple high-cost interventions, such as an ER visit due to heart attack or inpatient treatment due to opioid addiction and depression.
Reactive healthcare waits until she’s already in the top ten percent of high-cost patients before she receives the very kind of attention that could have prevented those outcomes in the first place. Unfortunately, that’s typical of a system that wastes $ 205 billion per year on inefficient or uncoordinated care, with an additional $ 200 billion for unnecessary medications and $ 32 billion on avoidable ER visits. According to the Department of Health and Human Services, reactively managing patients with chronic conditions alone accounts for >85% of total spending.
Rather than cover for these inefficiencies by pouring in additional dollars –– already 18% of GDP and rising –– innovative insurers and provider organizations across the country are eager to utilize new technologies to find a better path forward.
Making proactive healthcare possible
In stark contrast to reactive care, proactive healthcare doesn’t wait; rather, it actively seeks to preserve and improve health. It’s a radical shift in mindset that’s only possible thanks to recent technological solutions that empower provider organizations and insurers to not only predict future healthcare episodes, but identify the underlying clinical drivers and guide engagement strategies for individual patients like Maggie.
As healthcare organizations are learning, the large investments in value-based care initiatives of recent years – such as care managers, education, adherence programs, and chronic disease support – fail to succeed unless we can engage with the right patients at the right time.
First-generation population health tools represented a major leap forward, allowing organizations to tap their wealth of EHR data and begin to think systematically about managing groups of high-risk patients. By relying on broad historical indicators such as financial spend, chronic conditions, and ER utilization, those tools can make generalized predictions about population-level risk. Now, however, organizations are looking to go deeper, uncovering insights that are both precise and actionable at the patient level, mitigating risk through effective proactive outreach and engagement in programs that will direct members to the next best action.
Applying machine learning to member-specific data
Insurers and care organizations seeking to implement a truly proactive healthcare model are turning to next-generation predictive analytics, which provide both a clearer picture of each patient’s health trajectory, as well as his or her receptiveness to earlier care and care outreach.
Where traditional rules-based modeling incorporates dozens of static features to make predictions, next-generation predictive analytics employs recent advances in machine learning to utilize dynamic features in the hundreds of thousands or millions, as algorithms continue to train on member-specific claims data. It’s an exponential increase in predictive power that will allow organizations to expand their focus beyond members who are already high cost, extending the benefits of proactive outreach to their entire member population.
A new reality for individual patients
Let’s turn back to Maggie. Fortunately for her, her health plan has already stepped through the sliding doors of proactive healthcare. Powered by predictive insight, Maggie was identified early as rising risk for poor outcomes and higher costs, and received proactive outreach from a care manager at her health plan. Working with this new intelligence, the care manager was able to connect Maggie with a smoking cessation program, as well as behavioral health counseling. This earlier intervention meant that Maggie also got connected with alternative methods for pain management, ensuring she was not another victim of the opioid crisis.
For Maggie, proactive healthcare meant being able to quit smoking sooner and making strides in her weight loss program so she could start to feel better every day. Proactive healthcare helped her reduce her use of pain medication by 40%, and reduce outpatient visits by 25%. Without intervention, her projected spend was $ 88,000. With proactive healthcare, her actual spend was $ 8,400, allowing her to keep more hard earned dollars in her pocket, and helping her plan to spend more efficiently.
Maggie’s success shows the power of proactive healthcare to couple prediction with personalized engagement most likely to improve a patient’s health. Next-generation health analytics, like those developed by our company, are making this possible by leveraging machine learning to scale those successes across entire member populations. With proactive healthcare, one customer reported a 20 point increase in NPS, and another reported a 20% improvement in overall member engagement.
Organizations who have deployed similar programs have also seen immediate and longer term improved patient outcomes and lower total spending through the elimination of avoidable or inefficient care.
Clear advantages for proactive organizations
Given its numerous benefits, there’s no doubt that proactive healthcare will positively impact the health trajectories of millions of patients in the near future. For organizations that are ahead of the curve in terms of adoption, the advantages will be transformative in terms of costs, outcomes, and member satisfaction.
The key to realizing that transformation is to remember that accurately identifying patient risk is only the first step. Ultimately delivering on a differentiated patient experience will come down to how effective we can be in connecting those patients with the proactive measures most likely to help them.
Armed with the ability to see the possibilities, the future of proactive healthcare is wide open. Forward-looking healthcare organizations are not going to hesitate to walk through those doors.
*Name has been changed to protect the patient’s identity.
Linda T. Hand is CEO of Prealize Health, which uses machine learning to transform healthcare from reactive to proactive so more people can live healthier lives.
