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Learn how AI can help HCOs to reduce potentially preventable hospitalizations and improve care transitions that avoid adverse outcomes following discharge. Discover how AI can help to proactively identify patients at high risk for preventable inpatient stays, address gaps in chronic care management, and predict adverse outcomes (e.g., declines in ADL).

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.

e-Prescribing Data

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

Rx Claims

Data extracted from health insurance pharmacy claims with details about each medication and its type, fill dates, days supply, pharmacy location, and prescribing clinician.

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ClosedLoop generates explainable predictions using

thousands of auto-generated, clinically relevant contributing factors

Maya Leblanc
63-Year-Old Female
Risk of a potentially preventable hospitalization in the next 12 months
Risk Score Percentile
Impact on Risk  |  Contributing Factor
+28% | High LDL Cholesterol (mg/dL)
+19% | Rise in Blood Pressure (mmHg)
130/85 to 150/100
+12% | e-Prescription Filled by Patient (Y/N)
+7% | Increase in HTN Medication Dose (mg)
20 to 40

What Are Potentially Preventable Hospitalizations?

Potentially preventable hospitalizations are both common and costly. According to a 2020 study from the Agency for Healthcare Research and Quality (AHRQ), roughly 4.8 million potentially preventable inpatient stays account for nearly $34 billion in hospital costs each year.¹

Why It Matters

Potentially preventable hospitalizations occur more frequently in chronic conditions.  In recent years, chronic conditions accounted for 77% of potentially preventable adult stays and 61% of potentially preventable pediatric stays.¹ Such hospitalizations may often be preventable if chronic conditions are successfully managed in outpatient settings. The rate of potentially preventable stays is also significantly higher in older adults, as they are 12 times more likely to have chronic conditions than people aged 18–44.¹ For older adults in particular, successfully avoiding hospitalizations is vital; among older adults who are hospitalized, 30% experience a decline in their ability to perform activities of daily living (ADL) as a result of their hospitalization, and for many, it is permanent.²

AI Presents an Opportunity

By harnessing the power of predictive analytics, patients at high risk for preventable hospitalization can be identified in a timely manner. Healthcare providers can turn this insight into action by implementing targeted care management strategies, such as improved ambulatory care, enhanced access to effective treatment, or the adoption of healthy behaviors. Helping patients avoid hospitalizations can lead to better health outcomes and contribute significantly to decreasing healthcare costs.

Did You Know…

  • 13% of all hospital stays each year are thought to be preventable¹
  • 4.8 million hospitalizations are estimated to be potentially preventable annually¹
  • Two-Thirds Medicare is the expected primary payer of two-thirds of potentially preventable stays and associated costs¹
  • $34 billion is the total cost of preventable hospitalizations each year¹
Citations & Footnotes

1. AHRQ “Characteristics and Costs of Potentially Preventable Inpatient Stays, 2017 #259.” Agency for Healthcare Research and Quality, Jun. 2017, www.hcup-us.ahrq.gov/reports/statbriefs/sb259-Potentially-Preventable-Hospitalizations-2017.jsp. Accessed 26 Feb. 2021.

2. Wells, Elina U. et al. "Factors That Contribute To Recovery Of Community Mobility After Hospitalization Among Community-Dwelling Older Adults". Journal Of Applied Gerontology, vol 39, no. 4, 2018, pp. 435-441. SAGE Publications, doi:10.1177/0733464818770788.

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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