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Learn how AI can help HCOs reduce fall-related injuries, ensure needs for functional support are met, and promote continuity of care. Discover how AI can help predict individuals at high risk of falling, anticipate the need for improved support and device usage, and identify risk factors (e.g., undetected frailty) that increase vulnerability to other adverse events.

Automatically ingest data from dozens of health data sources including...

Medical Claims

Data extracted from health insurance medical claims with details about dates and place of service, diagnosis codes, key procedures, use of medical equipment, and provider specialties.

e-Prescribing Data

Data from electronic prescriptions detailing key information about medications, dosage, patient instructions for frequency and timing, and available refills.

Remote Monitoring Data

Remote monitoring data capture key vital signs and health behaviors (e.g. blood pressure, heart rate, blood glucose, activity levels, etc.).

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ClosedLoop generates explainable predictions using

thousands of auto-generated, clinically relevant contributing factors

Nishiki Khatri
76-Year-Old Female
Risk of a serious fall-related injury in the next 12 months
Risk Score Percentile
Impact on Risk  |  Contributing Factor
+29% | Frailty Percentile
+21% | Decline in "Timed Up & Go" Test (seconds)
20 to 30
+16% | # of Medications on PIM List
1 to 2
+11% | # of Fall-related Injuries (12M)

Why It Matters

Serious fall-related injuries profoundly affect the lives of older adults, and falls are the leading cause of fatal and nonfatal injuries among adults age 65 and over.¹ Approximately one in four older adults falls each year and nearly 36 million falls were reported in 2018, resulting in more than 900,000 hospitalizations and 32,000 deaths.²

These numbers are expected to balloon as this older population continues to grow. Death rates from falls have already increased roughly 30% in the last decade, and the current $50 billion fall-related, annual costs are expected to climb accordingly.³𝄒⁴ Moreover, falling once doubles the chance of falling again; even individuals that fall but do not sustain serious injuries are at an elevated risk for subsequent falls that may require medical attention.⁵

Yet, falls are not reliably reported, despite their prevalence and the dangers they pose. 72% of patients who had received care for a fall-related injury did not report it when asked by their physician, leaving nearly three in four patients without the initiation of fall prevention activities.⁶

AI Presents an Opportunity

Fortunately, predictive analytics can enable practitioners to proactively identify high-risk patients in a timely manner and start conversations about the myriad of preventative measures available. From there, practitioners can leverage AI-based insights to create intervention strategies that may include individually-tailored combinations of preventative measures, such as strength and flexibility training, medication review, assistive devices, and home modifications. 

Did You Know…

  • 900,000 hospitalizations in 2018 were due to fall-related injuries²
  • 32,000 deaths were attributed to fall-related injuries in 2018²
  • $50 billion is the annual cost of fall-related medical spending⁴
  • 88 older adults die every day from fall-related injuries²
Citations & Footnotes

1. Bergen, Gwen, et al. “Falls and Fall Injuries Among Adults Aged ≥65 Years — United States, 2014.” MMWR Morbidity and Mortality Weekly Report, vol. 65, no. 37, 23 Sept. 2016, pp. 993–998. DOI: http://dx.doi.org/10.15585/mmwr.mm6537a2

2. Moreland Briana, et al. “Trends in Nonfatal Falls and Fall-Related Injuries Among Adults Aged ≥65 Years — United States, 2012–2018.” Morbidity and Mortality Weekly Report, vol. 69, no. 27, July 2020, pp. 875–881. DOI: http://dx.doi.org/10.15585/mmwr.mm6927a5

3. Burns, Elizabeth., and Kakara, Ramakrishna. “Deaths from falls among persons aged ≥65 years—United States, 2007-2016.” Morbidity and Mortality Weekly Report, vol. 67, no. 18, May 2018, pp. 509–514. DOI:10.15585/mmwr.mm6718a1

4. Florence, Curtis S, et al. “Medical Costs of Fatal and Nonfatal Falls in Older Adults.” Journal of the American Geriatrics Society, vol. 66, no. 4, March 2018, pp. 693–698. DOI:10.1111/jgs.15304

5. Stevens, Judy A., and Phelan, Elizabeth A. “Development of STEADI: a fall prevention resource for health care providers.” Health Promotion Practice, vol. 14, no. 5, Sept. 2013, pp. 706–714. DOI: 10.1177/1524839912463576

6. Hoffman, Geoffrey J., et al. “Underreporting of Fall Injuries of Older Adults: Implications for Wellness Visit Fall Risk Screening.” Journal of the American Geriatrics Society, vol. 66, no. 6, 17 Apr. 2018, pp. 1195–1200, DOI: 10.1111/jgs.15360

ClosedLoop is an exciting and important partner in our strategy to develop timely predictive insights through operationalized AI solutions that will drive member engagement and better health outcomes.

Pat Wang
President & CEO, Healthfirst

Our partnership with ClosedLoop adds the depth of AI to more accurately identify those patients who are most likely to benefit from our primary care interventions as well to evaluate these interventions and make more relevant and timely modifications to improve their effectiveness.

David Klebonis
Chief Operating Officer, PBACO

We are extremely impressed with the predictive modeling capabilities the ClosedLoop platform has delivered. The ClosedLoop team has exceeded all defined goals and benchmarks set to date and we anticipate a substantial return on our investment as these predictions are operationally deployed.

Cheryl Lulias
President & Executive Director, MHN

We’re able to store and operationalize analytics directly from ClosedLoop. That’s driving real value—it’s accelerated the implementation of key insights into clinical workflows and it allows us to more easily account for all of the different factors that influence intervention decisions.

Christer Johnson
Chief Analytics Officer, Healthfirst

At SWHR, our patient-centered network is committed to innovative care models that are value-based, high-quality, and data-driven. Because of our partnership with ClosedLoop, we’re better able to identify and act on the most impactful opportunities for our physician partners to improve patient results and reduce unnecessary costs.

Dr. Jason Fish
Chief Medical Officer & SVP, Southwestern Health Resources

We compared the ClosedLoop predicted spend to the spend predicted by a leading rules-based risk prediction engine and found that 75% of the time ClosedLoop was closer, and in most cases significantly closer, to the actual spend.

Phillip Bruns
Chief Technology Officer, CareATC

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