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Every year, there are more than 35.7 million hospital stays in the U.S., totaling over $415 billion in annual healthcare spending. The average length of stay (LOS) is 4.6 days. If it can be safely reduced, in addition to curbing excess spending, eliminating unnecessary hospital days has the potential to significantly improve patient health outcomes.
Learn how AI can help HCOs improve resource management planning, reduce patient vulnerability, and optimize the patient discharge process. Discover how AI can help to accurately predict patient LOS, assess patient-specific risk factors to help triage and streamline care, and surface individual patients that are appropriate for discharge.
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.
Data with details from CMS Hospital Compare and other quality measures related to timely and effective care, complications, and readmissions and deaths.
ClosedLoop generates explainable predictions using
thousands of auto-generated, clinically relevant contributing factors
Every year, there are more than 35.7 million hospital stays in the U.S., totaling over $415 billion in annual healthcare spending.¹ The average length of stay (LOS) is 4.6 days. If it can be safely reduced, in addition to curbing excess spending, eliminating unnecessary hospital days has the potential to significantly improve patient health outcomes. Excessively long stays may lead to unforeseen complications that impact care, including increased risk for hospital-acquired conditions (HACs) and infections, reduced ability to provide immediate care to other patients due to reduced capacity, and an inability to efficiently allocate healthcare resources.
Delays in discharge often lead to prolonged LOS and create clinical and operational burdens on providers. As long as patients continue to occupy beds while awaiting discharge, clinical personnel must attend to them, reducing the amount of time they can spend with other patients that may require more intensive care. This leads to greater scarcity of beds and delays operational processes, such as sanitizing rooms and medical equipment before subsequent use. Further, extended LOS can increase risk for HACs in more vulnerable patients, and may also result in “access block”—a situation in which patients requiring admission are forced to wait for more than eight hours in the emergency department due to lack of available inpatient beds.² Access block occurs for approximately 8% of patients and perpetuates extended LOS; it is associated with nearly a day of increased LOS on average.
The impact of prolonged LOS on health outcomes is especially pronounced in the ICU setting and is associated with greater incidence of adverse events for vulnerable patients, such as older adults. Elderly ICU patients generally require more resource-intensive treatment, and roughly 55% that experience a prolonged LOS die within six months of discharge.³ These patients also incur approximately seven times the cost of patients that do not experience a prolonged LOS.
AI-based models are ideally suited to help healthcare organizations (HCOs) reduce LOS and improve health and financial outcomes. Leveraging AI, HCOs can accurately predict patient LOS, surface individual patients that are appropriate for discharge, and assess patient-specific risk factors to help triage and streamline care. Teams across the organization can use AI-driven insights to improve resource management planning, reduce patient vulnerability, optimize hand-off procedures and communication, and accelerate the discharge process.
1. Freeman, William, et al. “Statistical Brief #246: Overview of U.S. Hospital Stays in 2016: Variation by Geographic Region.” Agency for Healthcare Research and Quality: Healthcare Cost and Utilization Project, Dec. 2018, https://www.hcup-us.ahrq.gov/reports/statbriefs/sb246-Geographic-Variation-Hospital-Stays.jsp. Accessed 29 Jun. 2021.
2. Bashkin, Osnat, et al. “Organizational Factors Affecting Length of Stay in the Emergency Department: Initial Observational Study.” Israel Journal of Health Policy Research, vol. 4, no. 38, 15 Oct. 2015, DOI: https://doi.org/10.1186/s13584-015-0035-6. Accessed 29 Jun. 2021.
3. Abd-Elrazek, Merhan A., et al. “Predicting Length of Stay in Hospitals Intensive Care Unit Using General Admission Features.” Ain Shams Engineering Journal, 20 Apr. 2021, DOI: https://doi.org/10.1016/j.asej.2021.02.018. Accessed 29 Jun. 2021.
