Nationally, patients are “no-shows” for 30% of their scheduled appointments, representing a $150 billion financial burden on the healthcare system annually. In addition to financial strain, missing appointments can lead to poor continuity of care, increased acute care utilization, and declines in health that could have been mitigated or prevented with earlier diagnosis and treatment.
Learn how AI can help HCOs to increase appointment attendance rates, reduce the financial burden of no-shows, and improve long-term patient health outcomes. Discover how AI can help to predict which patients are most likely to miss appointments, surface the most significantly contributing factors, identify patient-specific barriers to care (e.g., lack of transportation) from complex clinical data and social determinants of health, and predict adverse events due to missed appointments.
Data extracted from health insurance pharmacy claims with details about each medication and its type, fill dates, days supply, pharmacy location, and prescribing clinician.
Self-reported data that identify an individual's specific needs and the acute social and economic challenges they are experiencing.
Data from health plans, clinics and service providers that capture details about service interactions, billing and payment, technical issues, and complaints.
ClosedLoop generates explainable predictions using
thousands of auto-generated, clinically relevant contributing factors
Across the nation, patients are “no-shows” for 30% of their scheduled appointments. Such a high rate of missed appointments creates a financial burden for clinics and a potential health burden for individuals. For clinics, each missed appointment costs an average of $200, accumulating to an annual amount that can exceed $250,000 per clinic and $150 billion across the health system nationally.¹𝄒²
For individuals, missed appointments can lead to poor continuity of care, increased acute care utilization, and declines in health that could have been mitigated or prevented with earlier diagnosis and treatment.³𝄒⁴ For some patients, such as those with chronic conditions, repeatedly missed appointments can even lead to an increased risk of mortality. This is particularly notable for patients with long-term mental health conditions—among these patients, people that miss more than two appointments annually increase their risk of mortality by eight times that of similar patients who do not miss appointments.⁵
Predictive analytics and AI can help healthcare organizations (HCOs) increase appointment attendance rates, reduce the financial burden of no-shows, and improve the health outcomes of their patients. Using AI-based models, HCOs can predict which patients are most likely to no-show, identify the most significant contributing factors (e.g. long lead times, no-show history, lack of private insurance) and integrate these factors with distinct social determinants of health and complex patient data.⁶ HCOs can use these insights to proactively address patient-specific barriers in ways that promote care continuity and lead to better health outcomes.
1. Gier, Jamie. “Missed appointments cost the U.S. healthcare system $150B each year.” Healthcare Innovation, 26 Apr. 2017, https://www.hcinnovationgroup.com/clinical-it/article/13008175/missed-appointments-cost-the-us-healthcare-system-150b-each-year#:~:text=The%20total%20cost%20of%20missed,%24150%20billion%20figure%20is%20reached. Accessed 17 Mar. 2021.
2. Guzek, Lindsay M., et al. “The Estimated Cost of ‘No-Shows’ in an Academic Pediatric Neurology Clinic.” Pediatric Neurology, vol. 52, no. 2, Feb. 2015, pp. 198–201, doi: 10.1016/j.pediatrneurol.2014.10.020. Accessed 18 Mar. 2021.
3. Hwang, Andrew S., et al. “Appointment ‘No-Shows’ Are an Independent Predictor of Subsequent Quality of Care and Resource Utilization Outcomes.” Journal of General Internal Medicine, vol. 30, no. 10, 17 Mar. 2015, pp. 1426–1433, doi:10.1007/s11606-015-3252-3. Accessed 17 Mar. 2021.
4. Marbouh, Dounia, et al. “Evaluating the Impact of Patient No-Shows on Service Quality.” Risk Management and Healthcare Policy, vol. 13, 4 Jun. 2020, pp. 509–517, doi:10.2147/rmhp.s232114. Accessed 17 Mar. 2021.
5. McQueenie, Ross, et al. “Morbidity, Mortality and Missed Appointments in Healthcare: A National Retrospective Data Linkage Study.” BMC Medicine, vol. 17, no. 1, 11 Jan. 2019, doi:10.1186/s12916-018-1234-0. Accessed 17 Mar. 2021.
6. Kullgren, Jeffrey T., et al. “Nonfinancial Barriers and Access to Care for U.S. Adults.” Health Services Research, vol. 47, no. 1, 22 Aug. 2011, pp. 462–485, doi:10.1111/j.1475-6773.2011.01308.x. Accessed 18 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.
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.