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Learn how AI can help to reduce readmissions, improve continuity of care following discharge, and reduce adverse events following a transition to a new care setting. Discover how AI can help to identify gaps in medication reconciliation and adherence, accurately assess patient care needs at discharge to ensure appropriate support, and predict which patients are at high risk for post-discharge complications.

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.

ADT Records

Data from Admit, Discharge, and Transfer feeds and patient data notification services that synchronize patient demographic, diagnostic, and visit information across healthcare systems.

Health Risk Assessments

Self-reported data from health questionnaires that assess a person’s individual medical history, health risks, lifestyle, health behaviors, and quality of life.

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ClosedLoop generates explainable predictions using

thousands of auto-generated, clinically relevant contributing factors

Heather Pederson
73-Year-Old Female
Risk of poor care transition and unplanned admission in the next 30 days
Risk Score Percentile
Impact on Risk  |  Contributing Factor
+23% | # of Unplanned Admissions (12M)
+17% | Hemoglobin (g/dL)
+12% | # of ER Visits (6M)
+9% | Level of Social Support

What Are Transitions of Care?

Being discharged from the hospital can be bad for your health. Nearly one in five adult patients experience an adverse event within three weeks of leaving the hospital, and roughly 20% of Medicare patients discharged from a hospital—approximately 2.6 million older adults—are rehospitalized within 30 days, at a cost of over $26 billion every year.¹𝄒² 

Managing “transitions of care”—when a patient moves from one care setting to another as their condition and needs change—is the key to avoiding many of these adverse events.  Adverse drug events are a common driver of negative outcomes; studies have shown nearly one in three discharges had medication discrepancies, with 51% having the potential for serious harm.³  In reality, multiple modifiable factors contribute to readmissions, including poor medication reconciliation, hospital-acquired infections, post-procedural complications, provider discontinuity, inaccurately assessing patients' abilities to care for themselves, and failing to enlist the necessary resources.³𝄒⁴  

Why It Matters

With nearly 20% of Medicare patients being rehospitalized within 30 days, preventing these events is a priority for healthcare organizations (HCOs).  As HCOs pursue new solutions, experts are keen to point out that patients are readmitted for reasons that vary on a case-by-case basis and stress that interventions must be carefully tailored to each patient’s individual circumstances.⁵ 

AI Presents an Opportunity

AI can help HCOs improve care transitions by identifying patients at risk for post-discharge adverse events before they are discharged. Armed with this insight, organizations can implement individually-targeted interventions to improve health outcomes. Patient-centered interventions that prioritize engagement, patient self-care education, and persistent caregiver relationships can cut hospital readmissions following discharge by more than 30%.⁶𝄒⁷ Additionally, comprehensive transitional care programs that include home follow-up visits have the potential to reduce average hospital costs by up to $14,150 per episode of care.⁸ 

Did You Know…

  • $58.6 billion was the total Medicare FFS spending in 2018 on post-acute care services⁹ 
  • 32% of patients in a clinical study of medication reconciliation were discharged with at least one medication-related error¹⁰ 
  • More than 50% of adverse events due to insufficient transitional care following discharge are drug related¹¹
Citations & Footnotes

1. Misky, Gregory J., et al. “Post-hospitalization transitions: examining the effects of timing of primary care provider follow-up.” Journal of Hospital Medicine, vol. 7, no. 5, Sep. 2010, pp: 392-397. doi.org/10.1002/jhm.666. 

2. “Community-based Care Transitions Program.” Centers for Medicare and Medicaid Services. https://innovation.cms.gov/innovation-models/cctp. Accessed on 12/09/2020.  

3. Kreckman, John et al. “Improving medication reconciliation at hospital admission, discharge and ambulatory care through a transition of care team.” BMJ open quality vol. 7, no. 2, Apr. 2018. DOIi: 10.1136/bmjoq-2017-000281. 

4. “Hot Topics in Health Care - Transitions of Care: The Need for a More Effective Approach to Continuing Patient Care.” The Joint Commission, vol. 8, 2012. Accessed 12/09/2020. 

5. “Readmissions and Adverse Events after Discharge.” Agency for Healthcare Research and Quality, 7 Sept. 2019, psnet.ahrq.gov/primer/readmissions-and-adverse-events-after-discharge#. Accessed 22 Mar. 2021.

6. Burton, Rachel. “Improving Care Transitions: Better coordination of patient transfers among care sites and the community could save money and improve the quality of care.” Health Affairs—Health Policy Brief, Sep. 2012. DOI: 10.1377/hpb20120913.327236. 

7. Labson, Margherita C. “Innovative and successful approaches to improving care transitions from hospital to home.” Home healthcare now vol. 33, no. 2, Feb. 2015, pp: 88-95. doi:10.1097/NHH.0000000000000182. 

8. Sezgin, Duygu et al. “The effectiveness of intermediate care including transitional care interventions on function, healthcare utilisation and costs: a scoping review.” European geriatric medicine vol. 11, no. 6, Aug. 2020, pp: 961-974. doi:10.1007/s41999-020-00365-4. 

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

10. Belda-Rustarazo, S et al. “Medication reconciliation at admission and discharge: an analysis of prevalence and associated risk factors.” International journal of clinical practice vol. 69, no. 11, Jul. 2015, pp: 1268-74. doi:10.1111/ijcp.12701. 

11. Farhat, Nada M., et al. “Evaluation of Interdisciplinary Geriatric Transitions of Care on Readmission Rates.” The American Journal of Managed Care, vol. 25, no. 7, Jul. 2019. Accessed on 12/09/2020. 

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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