Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of death in the U.S., affecting nearly 16 million Americans. In 2018, chronic lower respiratory diseases—primarily COPD—were responsible for over 150,000 deaths. But despite the remarkable prevalence and lethality of COPD, awareness is alarmingly low; more than 50% of adults with low pulmonary function are unaware of their COPD.
Learn how AI can help HCOs promote early diagnosis of COPD, slow COPD progression, and avoid adverse events and complications. Discover how AI can help to identify undiagnosed or early-stage COPD, identify opportunities for improved patient engagement, and predict individuals likely to experience potentially preventable hospitalization and other adverse events.
Remote monitoring data capture key vital signs and health behaviors (e.g. blood pressure, heart rate, blood glucose, activity levels, etc.).
Self-reported data that identify an individual's specific needs and the acute social and economic challenges they are experiencing.
EHR data with comprehensive patient histories of vital signs and symptoms, problem lists and chief complaints, tests results, diagnoses and procedures, and prescriptions.
ClosedLoop generates explainable predictions using
thousands of auto-generated, clinically relevant contributing factors
Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of death in the United States, affecting nearly 16 million Americans.¹ In 2018, chronic lower respiratory diseases—primarily COPD—were responsible for over 150,000 deaths.² But despite the remarkable prevalence and lethality of COPD, awareness is alarmingly low; more than 50% of adults with low pulmonary function were not aware that they had COPD.³
Barriers to awareness and timely diagnosis of COPD stem from a number of sources: COPD may be inaccurately diagnosed as another respiratory disease or obscured by potential comorbidities.⁴ However, accurately identifying COPD early and staging proactive interventions is critical to both improving quality of life for patients and reducing the significant $32 Billion economic burden COPD poses.⁵
AI-based models are ideally suited to identify patients at high-risk for COPD. Organizations can leverage predictive analytics to enroll these patients in self-management programs that prioritize patient engagement and continuity of care. Self-management interventions in COPD patients are beneficial for reducing hospitalizations, and patients with higher continuity of care have a lower likelihood of avoidable hospitalization.⁶𝄒⁷ Ultimately, reducing hospitalizations for COPD patients corresponds to improved health outcomes and reduced cost. With proper management, most people with COPD can achieve good symptom control and quality of life, as well as reduce their risk of other associated conditions including heart disease and lung cancer.
1. Wheaton, Anne G., et al. “Employment and activity limitations among adults with chronic obstructive pulmonary disease — United States, 2013.” Morbidity and Mortality Weekly Report, vol. 64, no. 11, Mar. 2015, pp. 289–295. DOI: PMC4584881
2. CDC. “Chronic Obstructive Pulmonary Disease (COPD) Includes: Chronic Bronchitis and Emphysema.” Centers for Disease Control and Prevention, 30, Oct. 2020, https://www.cdc.gov/nchs/fastats/copd.htm. Accessed 26 Nov. 2020.
3. Mannino DM, et al. “Obstructive lung disease and low lung function in adults in the United States: data from the National Health and Nutrition Examination Survey 1988-1994.” Arch Intern Med, vol. 160, no. 11, Jun. 2000, pp.1683–1689. DOI: 10.1001/archinte.160.11.1683
4. Yawn, Barbara, and Wollan, Peter. “Knowledge and attitudes of family medical professionals coming to COPD continuing medical education.” International Journal of Chronic Obstructive Pulmonary Disease, vol. 3, no. 2, Jun. 2008, pp. 311–317. DOI: 10.2147/copd.s2486
5. Ford, Earl S., et al. "Total and state-specific medical and absenteeism costs of COPD among adults aged 18 years in the United States for 2010 and projections through 2020." Chest vol. 147, no. 1, Jan. 2015, pp. 31-45. DOI: 10.1378/chest.14-0972
6. Jonkman, Nini, et al. “Do Self-Management Interventions in COPD Patients Work and Which Patients Benefit Most? An Individual Patient Data Meta-Analysis.” International Journal of Chronic Obstructive Pulmonary Disease, vol. Volume 11, Aug. 2016, pp. 2063–2074, 10.2147/copd.s107884.
7. Lin, I.-P. et al. "Continuity Of Care And Avoidable Hospitalizations For Chronic Obstructive Pulmonary Disease (COPD)". The Journal Of The American Board Of Family Medicine, vol 28, no. 2, 2015, pp. 222-230. American Board Of Family Medicine (ABFM), doi:10.3122/jabfm.2015.02.140141.
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.
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.
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.
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.
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.
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.
Using predictive analytics to supplement clinician-driven referrals has helped me identify more patients more quickly for complex case management. I have greater assurance knowing this tool is helping me find patients most in need of my care.
ClosedLoop stood out from other AI firms in that they offered not only a usable, flexible analytics platform but extensive healthcare expertise.