There are nearly 800,000 strokes in the United States each year, and more than 50% of stroke patients are readmitted or die within one year of discharge. Up to 90% of stroke survivors experience residual functional deficits that significantly impact their quality of life, yet it is estimated that up to 40% of all strokes can be prevented with good blood pressure control.
Learn how AI can help healthcare organizations (HCOs) reduce the incidence of initial strokes, improve secondary stroke prevention, and improve care planning and rehabilitation efforts. Discover how AI can help to proactively identify high-risk individuals, surface modifiable risk factors for stroke survivors, and anticipate and address functional outcomes and adverse events.
EHR data with comprehensive patient histories of vital signs and symptoms, problem lists and chief complaints, tests results, diagnoses and procedures, and prescriptions.
Data capturing the most important problems facing a patient, when it occurred and when it was resolved, and lists other illnesses, injuries and factors that affect their health.
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.
ClosedLoop generates explainable predictions using
thousands of auto-generated, clinically relevant contributing factors
Cerebrovascular disease is the fourth leading cause of death among women, the fifth leading cause of death among men, and is a leading cause of serious long-term disability.¹ There are nearly 800,000 strokes in the United States each year, and more than 50% of stroke patients are readmitted or die within one year of discharge.²𝄒³ The incidence of stroke increases with age. Approximately 50% occur in people older than 75, and stroke is the second leading cause of hospital admission among the elderly.¹𝄒⁴ Notably, mortality from cerebrovascular disease is increasing significantly among younger adults and has risen by 36% in recent years.¹
The total cost associated with stroke is immense, averaging approximately $50 billion annually.⁵ Despite recent improvements in acute stroke treatment, more than one-third of patients are functionally dependent or have died within three months of a stroke. Moreover, up to 90% of stroke survivors are left with residual functional deficits that significantly impact quality of life and may increase risk of further adverse outcomes.² For example, immobility limits cardiovascular exercise and increases the risk for recurrent stroke and cardiovascular illness. Nearly one in four strokes occur in people who have previously had a stroke.⁵
Preventive interventions that account for modifiable risk factors can potentially reduce the incidence of stroke in high-risk patients. To this end, predictive analytics are critical for proactively and accurately identifying such patients and helping to facilitate proven interventions on an individual level. Interventions that address hypertension—a risk factor for 90% of all strokes—have proven to be effective, and it is estimated that up to 40% of all strokes can be prevented with good blood pressure control.⁶ Similarly, self-management interventions can significantly reduce risk. Smoking is associated with up to four times increased risk of stroke, and frequent exercise can reduce risk by up to half.⁶ For patients who have had a stroke, multidisciplinary rehabilitation programs remain the mainstay of treatment to improve outcomes and prevent secondary strokes.²
1. Tong, Xin, et al. “The Burden of Cerebrovascular Disease in the United States.” Preventing Chronic Disease, vol. 16, 25 Apr. 2019, www.ncbi.nlm.nih.gov/pmc/articles/PMC6733496/, 10.5888/pcd16.180411.
2. Winstein, Carolee J., et al. “Guidelines for Adult Stroke Rehabilitation and Recovery.” Stroke, vol. 47, no. 6, June 2016, 10.1161/str.0000000000000098.
3. Bates, Barbara E., et al. “One-Year All-Cause Mortality After Stroke: A Prediction Model.” PM&R, vol. 6, no. 6, 7 Nov. 2013, pp. 473–483, pubmed.ncbi.nlm.nih.gov/24211696/, 10.1016/j.pmrj.2013.11.006.
4. Bravata, Dawn M., et al. “Readmission and Death After Hospitalization for Acute Ischemic Stroke.” Stroke, vol. 38, no. 6, June 2007, pp. 1899–1904, www.ahajournals.org/doi/full/10.1161/STROKEAHA.106.481465, 10.1161/strokeaha.106.481465.
5. Virani, Salim S., et al. “Heart Disease and Stroke Statistics—2020 Update: A Report from the American Heart Association.” Circulation, vol. 141, no. 9, 3 Mar. 2020, pp. 139-596, doi/10.1161/CIR.0000000000000757.
6. Ayan Sabih, et al. “Stroke Prevention.” National Center for Biotechnology Information, StatPearls Publishing, 8 July 2020, www.ncbi.nlm.nih.gov/books/NBK470234/. Accessed 19 Feb. 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.
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.