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Learn how AI can help HCOs reduce the incidence of HACs and HAIs, avoid adverse outcomes related to HACs, and meet HAC reduction goals to avoid financial penalties. Discover how AI can help to identify patients at high risk for developing HACs prior to admission, predict potential treatment-related complications (e.g., lengthy catheter utilization for patients at increased CAUTI risk), and indicate potential need for monitoring and evaluation based on distinct patient risk factors.

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

Elizabeth Joye
74-Year-Old Female
Risk of HAI if admitted in the next 12 months
Risk Score Percentile
Impact on Risk  |  Contributing Factor
+19% | Diagnosis of GI Disorder (12M)
July 6, 2020
+16% | Diagnosis of Bacterial Infection (12M)
Sept 19, 2020
+13% | Increase in # of HCCs (12M)
4 to 7
+9% | Level of Social Support

What Are HACs/HAIs?

It’s natural to expect that when a patient is admitted to the hospital, they will get better, not sicker. But patients can develop hospital-acquired conditions (HACs)—undesirable complications or medical conditions that were not present on admission and developed during their hospital stay. Unfortunately, HACs are common and costly. Approximately 2.5 million HACs occur annually in the U.S. among all inpatients over the age of 18.¹ Each year, Medicare levies substantial penalties on hospitals under the Hospital-Acquired Conditions Reduction Program—estimated at approximately $360 million.² 

Hospital-acquired infections (HAIs) represent a significant portion of all HACs and are among the leading causes of death in the United States.³ At any given time, one in every 31 hospitalized patients has a HAI, there are approximately 680,000 HAIs in U.S. acute care hospitals annually, and nearly 70,000 of these patients will die during their hospitalization.⁴𝄒⁵  HAIs are also extremely costly and are responsible for between $28 and $33 billion in potentially preventable healthcare expenditures annually.³

Why It Matters

Patients with HAIs are also at increased risk for sepsis—the leading cause of both inpatient death and readmissions.⁶ Each year, at least 1.7 million adults in America develop sepsis, and nearly 270,000 die as a result.⁷ The AHRQ lists sepsis as the most expensive condition treated in the U.S., and the HHS recently estimated that healthcare costs associated with sepsis total more than $60 billion annually.⁸ 

AI Presents an Opportunity

Ensuring patient health and safety is the number one priority for hospitals. In addition to maximizing prevention efforts to reduce the incidence of HACs, hospitals can leverage predictive analytics to identify patients likely to be at high risk for HACs. Identification of these patients can enable key interventions that may include patient education, antimicrobial stewardship, and consistent monitoring. This insight can lower costs and save lives.

Did You Know…

  • 11% of patients with HAIs die during their hospitalization⁵
  • 2.5 million HACs occur annually in the U.S. in inpatients over the age of 18¹
  • 774 hospitals will face Medicare payment cuts in fiscal year 2021 under the HACRP⁹
  • $30 billion is the potentially preventable annual expenditure attributed to HAIs³
Citations & Footnotes

1. Agency for Healthcare Research and Quality, “AHRQ National Scorecard on Hospital-Acquired Conditions Updated Baseline Rates and Preliminary Results 2014–2017.” Agency for Healthcare Research and Quality, https://www.ahrq.gov/sites/default/files/wysiwyg/professionals/quality-patient-safety/pfp/hacreport-2019.pdf. Accessed 2 Mar. 2021. 

2. Sankaran, Roshun, et al. “A Comparison of Estimated Cost Savings from Potential Reductions in Hospital-Acquired Conditions to Levied Penalties under the CMS Hospital-Acquired Condition Reduction Program.” The Joint Commission Journal on Quality and Patient Safety, vol. 46, no. 8, Aug. 2020, pp. 438–447, DOI:https://doi.org/10.1016/j.jcjq.2020.05.002. Accessed 2 Mar. 2021.

3. “National HAI Action Plan | Health.Gov.” Health.Gov, U.S. Dept. of Health and Human Services, 2018, health.gov/our-work/health-care-quality/health-care-associated-infections/national-hai-action-plan#actionplan_development. Accessed 25 Feb. 2020.

4. CDC Data Portal. “Healthcare-associated infections.” Centers for Disease Control and Prevention, 11 Nov. 2020, www.cdc.gov/hai/data/portal/index.html. Accessed 2 Mar. 2021.

5. Magill, Shelley S., et al. “Changes in Prevalence of Health Care–Associated Infections in U.S. Hospitals.” New England Journal of Medicine, vol. 379, no. 18, Nov. 2018, pp. 1732–1744, DOI: 10.1056/NEJMoa1801550. Accessed 2 Mar. 2021.

6. “Sepsis.” National Institute of General Medical Sciences, 10 Sep. 2020, Accessed 2 Mar. 2021.

7. CDC. “Sepsis: Clinical Information.” Centers for Disease Control and Prevention, 7 Dec. 2020, https://www.cdc.gov/sepsis/clinicaltools/index.html#:~:text=Each%20year%2C%20at%20least%201.7,in%20a%20hospital%20has%20sepsis. Accessed 2 Mar. 2021. 

8. HHS.gov. “Largest Study of Sepsis Cases among Medicare Beneficiaries Finds Significant Burden.” U.S. Department of Health and Human Services, 14 Feb. 2020, www.hhs.gov/about/news/2020/02/14/largest-study-sepsis-cases-among-medicare-beneficiaries-finds-significant-burden.html. Accessed 2 Mar. 2021.

9. “Map: The Hospitals Facing 2021 Penalties for Hospital-Acquired Conditions.” Advisory Board, 2021, www.advisory.com/daily-briefing/2021/02/23/hac-penalties. Accessed 2 Mar. 2021.

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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