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Learn how AI can help to reduce readmissions, promote effective care transitions, and facilitate improved pre-discharge evaluation of care needs. Discover how AI can help to identify the individual patients most likely to be readmitted, determine which patients may be clinically unstable at transfer, and predict patients likely to be admitted to a SNF following an adverse event.

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.

Care Quality

Data with details from CMS Hospital Compare and other quality measures related to timely and effective care, complications, and readmissions and deaths.

Operations & Services

Data from health plans, clinics and service providers that capture details about service interactions, billing and payment, technical issues, and complaints.

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ClosedLoop generates explainable predictions using

thousands of auto-generated, clinically relevant contributing factors

Gwen Baker
77-Year-Old Female
Risk of admission to SNF in the next 12 months
Risk Score Percentile
Impact on Risk  |  Contributing Factor
+28% | Frailty Percentile
+18% | # of Days Since Last PCP Visit
+12% | # of Units of Durable Medical Equipment (3M)
+8% | Pct with Severe Housing Cost Burden

What Are SNF Admissions?

Following a hospitalization, many patients require skilled post-acute care to support recovery, improve functional status, or manage chronic illness, and skilled nursing facilities (SNFs) are the most common setting for this critical care.¹ More than five million patients are transferred from hospitals to SNFs annually,² and in 2018, 20% of all hospitalized fee-for-service (FFS) Medicare beneficiaries were discharged to a SNF representing 2.2 million SNF stays.³ But despite the prevalence of SNF admissions, they frequently presage avoidable rehospitalizations and adverse events.  

Why It Matters

The rate of 30-day readmissions to hospitals from SNFs is high. Close to one in five patients are rehospitalized within 30 days of transfer to a SNF, and a substantial percentage of patients are rehospitalized within just two days of initial SNF admission.⁴ Hospital readmissions following discharge to SNFs are also extremely costly. Total Medicare FFS spending on SNF services was $28.5 billion in 2018,³ and hospital readmissions from SNFs have been directly attributed to more than four billion dollars.⁵ 

However, studies have shown that hospital readmissions from SNFs disproportionately occur for preventable conditions. Readmitted patients are often clinically unstable at the time of transfer, and as many as two-thirds of these readmissions are estimated to be potentially avoidable.¹ 

AI Presents an Opportunity

AI can help healthcare organizations to accurately identify the patients most likely to be discharged to a SNF and ensure the transition goes smoothly—reducing hospital readmissions and improving health outcomes. With predictive analytics, organizations are able to proactively target high-risk patients with individually-tailored interventions before they are transferred to SNFs. These interventions can include intensive monitoring during the first 48 hours of SNF admission, specialist consultation follow-ups, and better pre-discharge evaluation of care needs.⁴

Did You Know…

  • 20% of all Medicare beneficiaries were discharged to SNFs in 2018³
  • 1 in 5 patients discharged to SNFs are readmitted within 30 days⁴
  • $563 million was the total amount CMS penalized hospitals in 2019 for excess readmissions⁶
Citations & Footnotes

1. Neuman, Mark D et al. “Association between skilled nursing facility quality indicators and hospital readmissions.” JAMA vol. 312, no. 15, Oct. 2014, pp: 1542-1551. DOI: 10.1001/jama.2014.13513. 

2. King, Barbara J et al. “The consequences of poor communication during transitions from hospital to skilled nursing facility: a qualitative study.” Journal of the American Geriatrics Society, vol. 61, no. 7, Jul. 2013, pp: 1095-1102. DOI: 10.1111/jgs.12328.

3. “Report to the Congress: Medicare Payment Policy.” Medicare Payment Advisory Commission, Mar. 2020. Accessed on 12/8/2020. 

4. Ouslander, Joseph G., et al. “Hospital Transfers of Skilled Nursing Facility (SNF) Patients Within 48 Hours and 30 Days After SNF Admission.” Journal of the American Medical Directors Association, vol. 17, no. 9, Sept. 2016, pp. 839–845, DOI:https://doi.org/10.1016/j.jamda.2016.05.021. 

5. Mor, Vincent et al. “The revolving door of rehospitalization from skilled nursing facilities.” Health affairs (Project Hope), vol. 29, no. 1, Jan. 2010, pp: 57-64. DOI: 10.1377/hlthaff.2009.0629. 

6. Rau, Jordan. “New Round of Medicare Readmission Penalties Hits 2,583 Hospitals.” Kaiser Health Network, Oct. 2019. https://khn.org/news/hospital-readmission-penalties-medicare-2583-hospitals/. Accessed 14 Dec. 2020. 

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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