Frailty occurs in approximately 25% of older adults and results in increased vulnerability to poor health outcomes, worsening mobility and disability, hospitalizations, and mortality. For older adults, it doubles the risk of in-hospital mortality and increases the risk of one-year mortality by 50%. It also greatly contributes to cost – estimated at more than $14,000 per patient annually.
Learn how AI can help HCOs to promote early identification of frailty, address functional decline in frail patients, and reduce the incidence of adverse events. Discover how AI can help to identify frailty in the absence of standardized measures, distinguish high risk patients in need of enhanced support, and predict adverse events and complications.
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.
Data from Admit, Discharge, and Transfer feeds and patient data notification services that synchronize patient demographic, diagnostic, and visit information across healthcare systems.
Self-reported data that identify an individual's specific needs and the acute social and economic challenges they are experiencing.
ClosedLoop generates explainable predictions using
thousands of auto-generated, clinically relevant contributing factors
Frailty is an aging-related state of decreased physiological reserve that results in increased vulnerability to poor health outcomes, worsening mobility and disability, hospitalizations, and mortality.¹ Long recognized within the field of geriatrics as a clinical syndrome, frailty occurs in approximately 25% of people aged 65 and over.² Frailty also contributes greatly to cost of care. The estimated annual cost directly attributed to frailty is more than $14,000 per patient after controlling for other variables.³
People who are frail have a decreased ability to maintain or return to homeostasis after stressful events or aggressive interventions. This often leads to loss of independence.⁴ Compared to non-frail patients, they are 15% more likely to develop inpatient complications, and in the year following treatments for critical illness, have 30% increased disability in daily activities.¹𝄒⁵ For older adults, frailty may be a better predictor of mortality than age. It doubles the risk of in-hospital mortality and increases the risk of one-year mortality by 50%.⁵𝄒⁶
Even though frailty is a measurable phenotype that can be identiﬁed with standardized measures, such as unintentional weight loss of 10 pounds in the past year, these performance measures are not routinely captured in clinical encounters.⁷ Frailty is also not captured in administrative claims databases. But, validated claims-based algorithms have shown good discrimination of frailty and high predictive ability with adverse health outcomes. These algorithms present a powerful tool to enable provider identification and risk assessment of frail patients.⁷𝄒⁸
Early identification of frailty via predictive analytics is vital, as the evidence shows that frailty can be managed and reduced. Interventions designed to improve nutrition, stimulate cognition, and promote physical activity can reduce frailty between 35 and 45%.⁹𝄒¹⁰ Frailty also has greater reversibility than disability, and prioritizing screening can provide critical information to identify poor prognosis and reversible risk factors.
1. Bellal, Joseph, et al. “Superiority of Frailty Over Age in Predicting Outcomes Among Geriatric Trauma Patients: A Prospective Analysis.” JAMA Surgery, vol. 8, no. 149, Aug. 2014, pp. 766–772, doi:10.1001/jamasurg.2014.296.
2. Muscedere, John, et al. “The Impact of Frailty on Intensive Care Unit Outcomes: A Systematic Review and Meta-Analysis.” Intensive Care Medicine, vol. 43, no. 8, 2017, pp. 1105–1122, 10.1007/s00134-017-4867-0.
3. Simpson, Kit N., et al. “Effect of frailty on resource use and cost for Medicare patients.” Future Medicine, vol. 7, no. 8, 29 May 2018. doi.org/10.2217/cer-2018-0029.
4. Hubbard, Ruth E., et al. “Frailty Status at Admission to Hospital Predicts Multiple Adverse Outcomes.” Age and Ageing, vol. 46, no. 5, 22 May 2017, pp. 801–806, 10.1093/ageing/afx081.
5. Brummel, Nathan E., et al. “Frailty and Subsequent Disability and Mortality among Patients with Critical Illness.” American Journal of Respiratory and Critical Care Medicine, vol. 196, no. 1, 2 Dec. 2016, pp. 64-72, doi:10.1164/rccm.201605-0939OC.
6. Bagshaw, Sean M., et al. “Association between frailty and short- and long-term outcomes among critically ill patients: a multicentre prospective cohort study.” Canadian Medical Association Journal, vol. 186, no. 2, 25 Nov. 2013, pp.95-102. doi:10.1503/cmaj.130639.
7. Segal, Jodi B, et al. “Development of a Claims-Based Frailty Indicator Anchored to a Well-Established Frailty Phenotype.” Medical Care, vol. 55, no. 7, 2017, pp. 716–722, 10.1097/MLR.0000000000000729.
8. Cuthbertson, Carmen C, et al. “Controlling for Frailty in Pharmacoepidemiologic Studies of Older Adults: Validation of an Existing Medicare Claims-Based Algorithm.” Epidemiology (Cambridge, Mass.), vol. 29, no. 4, 2018, pp. 556–561, 10.1097/EDE.0000000000000833
9. Apóstolo, João, et al. “Predicting Risk and Outcomes for Frail Older Adults: An Umbrella Review of Frailty Screening Tools.” JBI Database of Systematic Reviews and Implementation Reports, vol. 15, no. 4, 2017, pp. 1154–1208, 10.11124/JBISRIR-2016-003018.
10. Ng, Tze Pin, et al. “Nutritional, Physical, Cognitive, and Combination Interventions and Frailty Reversal Among Older Adults: A Randomized Controlled Trial.” The American Journal of Medicine, vol. 128, no. 11, 1 Nov. 2015, pp.1225-1236, doi:10.1016/j.amjmed.2015.06.017.
11. Castillo-Angeles, Manuel, et al. “Association of Frailty With Morbidity and Mortality in Emergency General Surgery By Procedural Risk Level.” JAMA Surgery, 25 Nov. 2020, 10.1001/jamasurg.2020.5397.
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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.
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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.