Press release

Research Shows Machine Intelligence Can Dramatically Reduce Emergency Department Visits for Millions of Medicare Members

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Health at Scale, the leader in machine intelligence for care optimization, today released a report, Precision Interception: Machine Intelligence for Actionable Prediction and Prevention of Emergency Department Visits, detailing the findings of a study conducted by its research team. The study examined profiles of more than two million Medicare plan beneficiaries to understand how Health at Scale’s technology could leverage machine intelligence for precision interception to reduce emergency department visits in the future. The Health at Scale platform accurately identified members who were at an increased risk of emergency department visits over the next six months, with 81% going on to seek emergency care. This predictive information allows for early preventive action to reduce costs and eliminate the present strain on emergency departments.

The study is significant as the number and costs of emergency department visits are growing at an alarming rate, particularly in comparison to other services. In 2016 alone, there were 145.6 million emergency department visits, or 45.8 visits per 100 people, in the United States. Yet it is estimated that as many as 56% of these visits could be prevented through a combination of proactive and preventative care and more cost-effective outpatient alternatives.

Precision Interception Powered by Machine Intelligence Delivers Unprecedented Opportunities to Reduce Costs and Emergency Department Visits

To determine if machine intelligence and precision interception techniques could successfully identify individuals at high risk for emergency department visits, Health at Scale’s research team studied future emergency department visits in the top 1% of members ranked by risk level by its precision interception technology in a national population of two million Medicare plan beneficiaries. The identified members and their predicted causes of emergency department visits were compared to the actual outcomes for these members over the next six months.

Study Findings:

  • Of the sub-cohort Health at Scale estimated to be high risk, 81% presented themselves at emergency departments within six months.
  • The average number of emergency department visits for these identified members was 4.3 visits apiece over just six months.
  • The average cost of emergency department visits and subsequent admissions of the sub-cohort’s high risk members was $16,000 per member.
  • The total cost of emergency department visits and subsequent admissions in the sub-cohort estimated by Health at Scale as high risk totaled $266.5 million over six months.
  • Health at Scale’s technology produced a 10x improvement over background probability in the accuracy of predicting one out of 85 possible health issues as the reason for future emergency department visits.

Results indicate that Health at Scale’s technology accurately predicted the small fraction of population members at disproportionately high risk of emergency department visits (4.5x more likely than the average patient with 12x increase in emergency department visits and 9.5x increase in emergency department costs). In addition, Health at Scale’s precision interception also successfully predicted which of 85 specific health issues may lead to these emergency department visits (10x more accurately than these issues might otherwise be expected to be predicted based on background probability).

This ability to accurately predict members at risk of seeking emergency care and the health issues underlying the need for care enables health plans, ACOs and self-insured employers to meaningfully reduce the growing burden of emergency department visits through preemptive and personalized care delivery. Machine intelligence and precision interception provide the opportunity to drive significant improvements in outcomes and costs, reducing the need for emergent care, improving member health and lowering the total cost of care.

“Emergency department visits are spiraling out of control with many of these visits potentially preventable through appropriate early care,” said Zeeshan Syed, CEO of Health at Scale. “If these trends of increasing emergency department utilization continue, more people will be showing up at hospitals than at their primary care physicians, placing a tremendous, unsustainable burden on the entire healthcare system and blocking patients experiencing true emergencies from getting the care they need in a timely manner. As our study shows, technology that can predict which patients are likely to experience emergency department visits and the causes underlying these visits holds the key to heading off this crisis.”

To learn more and review the full results of the report please request a copy at https://healthatscale.com/research-precision-interception/.

About Health at Scale Technologies:

Health at Scale is on a mission to transform healthcare outcomes and economics by matching every patient to the right treatment by the right provider at the right time. Its machine intelligence software platform and applications for precision healthcare delivery are among the largest deployments to date of artificial intelligence and machine learning technologies for care management. The company is deeply committed to the goal of achieving longer, healthier and happier lives, and continuously pushes the envelope on healthcare-specialized machine learning and artificial intelligence as a means for sustainable and affordable progress for patients, payers and providers. Its proprietary and patented machine intelligence technologies are designed by an award-winning team of faculty, engineers, and clinicians with strong ties to MIT, Harvard, Stanford and the University of Michigan. The company’s solutions uniquely target multiple opportunities along the care continuum to improve the management of complex populations across complex care networks. The company is based in San Jose, California. Please visit www.healthatscale.com to learn more.