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Learn how AI can integrate RPM data to help HCOs manage the progression of chronic diseases, proactively avoid adverse events and complications, and promote patient engagement. Discover how AI can harness RPM data to surface specific patients and key changes in health, constantly assess risk outside of in-person interactions, and optimize proactive engagement.

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

Remote Monitoring Data

Remote monitoring data capture key vital signs and health behaviors (e.g. blood pressure, heart rate, blood glucose, activity levels, etc.).

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.

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.

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ClosedLoop generates explainable predictions using

thousands of auto-generated, clinically relevant contributing factors

Jasmine Walters
53-Year-Old Female
Risk of admission due to COPD exacerbation in the next 12 months
Risk Score Percentile
Impact on Risk  |  Contributing Factor
+27% | Decline in Average Oxygen Saturation (Spo2 Pct)
93% to 90%
+21% | # of Unplanned Admissions (12M)
+10% | Hemoglobin (g/dL)
+7%   |  Resting Heart Rate

What Is Remote Patient Monitoring?

Remote patient monitoring (RPM) presents a key opportunity to improve care management for chronic diseases—the leading cause of death and disability.¹ The potential impact on health and financial outcomes is immense. The United States spends roughly $3.4 trillion annually on people with chronic conditions, and a staggering 60% of adults have at least one chronic disease.¹𝄒² RPM’s ability to collect and transmit health data outside of a conventional care setting is ideal to help alleviate the burden of chronic conditions on healthcare organizations (HCOs). It provides the data necessary to consistently measure and respond to changes in health, enable better healthcare resource allocation, and foster improved patient engagement. 

Why It Matters

RPM facilitates regular assessment without relying on frequent appointments and can help to potentially prevent acute clinical events. Rather than having to delay examinations and treatment until a scheduled appointment, RPM provides sustained collection of vital data and behaviors, such as changes in blood pressure, heart rate, or activity levels. Armed with this information, care teams and patients can proactively take action before chronic conditions worsen to the point of requiring hospitalization or a visit to the emergency department. 

AI Presents an Opportunity

As a source of data, RPM has incredible potential to improve outcomes, but to fully realize its value, HCOs must integrate it with the right analytics capabilities. AI-based models can ingest this ongoing data stream and accurately predict which patients are most likely to experience adverse events, surface the specific risk factors assessed in making predictions, and optimize proactive engagement and interventions. For care teams, AI can display these insights in existing clinical workflows to streamline outreach. It can also promote patient engagement by providing them with far greater insight into their own health and recommendations that foster healthy behaviors. 

Leveraged proactively, RPM programs can significantly improve disease management. In a study from the national association of America’s Health Insurance Plans, an HCO reported their Medicare members enrolled in the program were 76% less likely to experience readmissions.³ Another HCO reported that they saved $3.30 for every $1 spent on implementation.

Did You Know…

  • $3.4 trillion is the total U.S. spending annually on chronic conditions¹
  • Up to 40% reduction in hospitalizations for some chronic diseases is achievable through RPM programs⁴
  • $6,500 is the estimated annual savings per chronic disease patient through RPM⁴
  • 60% of adults in the U.S. have at least one chronic disease²
Citations & Footnotes

1. “About Chronic Diseases.” Centers for Disease Control and Prevention, 2021, www.cdc.gov/chronicdisease/about/index.htm. Accessed 14 May 2021.

2. Buttorff, Christine, et al. “Multiple Chronic Conditions in the United States.” Rand Corporation, 2017, doi: https://doi.org/10.7249/TL221. Accessed 14 May 2021.

3. Coalition to Transform Advanced Care. “Leveraging Telehealth To Support Aging Americans.” America’s Health Insurance Plans, Oct. 2018. https://www.ahip.org/wp-content/uploads/2018/10/AHIP-CTAC_Report.pdf Accessed 14 May 2021. 

4. Hodin, Michael. “The Medical Technology That Could Save the US Billions Each Year.” The Fiscal Times, 3 Mar. 2017, https://www.thefiscaltimes.com/Columns/2017/03/03/Medical-Technology-Could-Save-US-Billions-Each-Year. Accessed 14 May 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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