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Learn how AI can help to promote care concordant with patient wishes, enable earlier end-of-life conversations and advance care planning, and inform difficult decisions regarding hospice and palliative care. Discover how AI can help to predict individuals at the greatest risk of mortality, identify and potentially address preventable mortality risk, and surface a constellation of disparate risk factors to support difficult decisions.

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

Electronic Health Records

EHR data with comprehensive patient histories of vital signs and symptoms, problem lists and chief complaints, tests results, diagnoses and procedures, and prescriptions.

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.

Social Needs Assessments

Self-reported data that identify an individual's specific needs and the acute social and economic challenges they are experiencing.

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ClosedLoop generates explainable predictions using

thousands of auto-generated, clinically relevant contributing factors

Eric Muller
75-Year-Old Male
Risk of all-cause mortality in the next 12 months
Risk Score Percentile
Impact on Risk  |  Contributing Factor
+29% | Increase in Charlson Comorbidity Index
8 to 12
+19% | Diagnosis of Chronic Skin Ulcers (12M)
+14% | Admission for Serious Infection (3M)
+10% | Levels of Caregiver Support

Why It Matters

Patients in the last days of life can experience unrelieved physical suffering as well as significant emotional, spiritual, and social distress. Unfortunately, defining when this phase begins is not always straightforward. Patients at the end of life are frequently not recognized as dying. As a result, suffering may not be properly appreciated or managed, and the patient’s overall condition may even be exacerbated by the continuation of standard medical care. Despite the fact that more than 80% of Medicare beneficiaries aged 65 and over would want to die at home, in 2013, one-third of deaths among older adults occurred in the hospital.¹ Even among terminally ill patients, fewer than 50% have an advance directive in their medical record, and between 65% and 76% of practitioners whose patients had an advance directive were not aware that it existed.²

This disparity in care preference and actual treatment can lead to unnecessary suffering and dramatic economic costs. More than one in three patients that prefer palliative care do not receive it, and these patients incur 1.4 times the costs of patients who receive end-of-life care consistent with their wishes.³ In 2014, inpatient hospital spending among decedents was seven times higher than among survivors on average, and in 2015, decedents accounted for over 20% of all Medicare spending.⁴𝄒⁵

AI Presents an Opportunity

Organizations must ensure patients the quality of life they desire in their final days, and AI-based models are ideal for identifying patients at high risk of mortality in the coming year. Predictive analytics can empower practitioners with the insights they need to start difficult conversations about beginning palliative care and can ultimately help to identify and support the best possible outcome for patients and their families. 

Did You Know…

  • More than one in three end-of-life stage patients that prefer palliative care do not receive it³  
  • 20% of all Medicare spending occurs in the last 12 months of life⁵
  • Roughly 70% of adults do not have an advance directive on record⁷ 
  • $365 Billion was spent on end-of-life care in 2018⁶
Citations & Footnotes

1. Gorina, Yelena, et al. “Hospitalization, Readmission and Death Experience of Noninstitutionalized Medicare Fee-for-service Beneficiaries Aged 65 and Over.” National Health Statistics Reports, No. 84, Sep. 2015, pp. 1–23. 

2. Kass-Bartelmes, Barbara L, and Ronda Hughes. “Advance care planning: preferences for care at the end of life.” Journal of pain & palliative care pharmacotherapy vol. 18, no. 1, 2004, pp. 87–109.

3. Teno, Joan M., et al. “Medical care inconsistent with patients' treatment goals: association with 1-year Medicare resource use and survival.” Journal of the American Geriatrics Society vol. 50, no. 3, Mar. 2002, pp. 496–500. DOI:10.1046/j.1532-5415.2002.50116.x 

4. Cubanski, Juliette, et al. “Medicare Spending at the End of Life: A Snapshot of Beneficiaries Who Died in 2014 and the Cost of Their Care.” Kaiser Family Foundation, Jul. 2016. 

5. Duncan, Ian, et al. “Medicare Cost at the End of Life.” The American journal of Hospice and Palliative Care, vol 36, no. 8, Aug. 2019, pp. 705–710. DOI: 10.1177/1049909119836204

6. Abbott, Ellen. “Ten Percent of All Healthcare Spending in the U.S. Goes toward End-of-Life Care.” WRVO Public Media, 30 Sept. 2019, https://www.wrvo.org/post/ten-percent-all-healthcare-spending-us-goes-toward-end-life-care#stream/0 

7. “Strong Public Support for Right to Die: More Americans Discussing—and Planning—End-of-Life Treatment.” Pew Research Center for the People & the Press, Jan. 2006, pp. 1–44.

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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