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How AI can improve LOS

By: Jessie Tobin


Reducing the length of stay (LOS) for a hospitalized patient is critical for the patient's well-being and the hospital's bottom line. Twenty percent of patients in the US (United States) end up returning to the hospital within one month after discharge (3). Why is this? Patients who are hospitalized longer are more likely to develop other illnesses and experience a slower recovery time. Hospital LOS (the number of days a patient remains in the hospital) has long been used to measure hospital care and efficiency. Its partner data point, readmissions, is often used to measure care effectiveness. A reduction in LOS is a measured outcome of improved operational workflows. These improved workflows mean enhanced quality of care, reduction in costs, and effective allocation of resources according to staff and patient needs (4). staffing and patient needs (4).


The impact of LOS

LOS is an essential indicator of the efficiency of hospital management. A reduction in the number of inpatient days for a patient can decrease the risk of infection, decrease medication side effects, and improve the quality of treatment. Shorter LOS’s also mean increased hospital profits through more efficient bed management (1).

One 2022 report from the Agency for Healthcare Research and Quality aimed to identify emerging concepts regarding systematic strategies that hospitals and health systems can implement to reduce LOS. Researchers interviewed key informants representing vulnerable patients, hospitals, health systems, and clinicians. Their findings described a few strategies for lowering LOS: discharge planning, clinical pathways, inter and multidisciplinary care, case management, and tele-health (5).

A recent article from CNN reported how the ripple effects of the pandemic are keeping beds full and patients away from the care they need. For example, nursing homes are limiting new patients because of staffing shortages, which is driving the average hospital stay up to be longer than it was pre-pandemic. In Washington, about 10% of patients currently in hospital beds no longer need hospital care (2).

How AI can improve LOS

Providers are often stressed by an overabundance of tasks that impact their ability to care for patients. These tasks can and should be shifted and dispersed to supporting team members. This is when artificial intelligence (AI) can have a significant impact in minimizing inefficiencies and ensuring more streamlined and cost-effective health ecosystems. Edgility's EdgeAI increases total system capacity by reducing LOS and accelerating discharges to virtually increase available beds. With our EdgeAI, health systems have reduced their LOS by fourteen percent and increased patient satisfaction by ten percent.


As the focus on patient satisfaction has increased through the decades, it’s become paramount that there exists cooperation between healthcare executives/administration, physicians, and nurses. The healthcare team members' attitudes impact sixty percent of patient experiences and their perception of their quality of care and service. In short, ensuring employee satisfaction will indirectly increase patient satisfaction (6).

A hospital should operate like a well-oiled machine. At Edgility the team focuses on creating AI that moves hospitals closer to this goal. Edgility’s AI improves the ecosystem of hospitals by improving the operations, such as tracking patients efficiently. This, in turn, allows doctors to focus on providing care and getting patients discharged on-time – increasing provider satisfaction and joy in practicing medicine.




Resources:

  1. Baek, H., Cho, M., Kim, S., Hwang, H., Song, M., & Yoo, S. (2018). Analysis of length of hospital stay using electronic health records: A statistical and data mining approach. PloS one, 13(4), e0195901. https://doi.org/10.1371/journal.pone.0195901

  2. McPhillips, D. (2022). Covid is still keeping hospitals back up, even as new admissions stay low. CNN.https://www.cnn.com/2022/06/24/health/hospital-discharge-delays-nursing-homes/index.html

  3. ​​Pollack, A. H., Backonja, U., Miller, A. D., Mishra, S. R., Khelifi, M., Kendall, L., & Pratt, W. (2016). Closing the Gap: Supporting Patients' Transition to Self-Management after Hospitalization. Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference, 2016, 5324–5336. https://doi.org/10.1145/2858036.2858240

  4. Siddique SM, Tipton K, Leas B, et al. Interventions to Reduce Hospital Length of Stay in High-risk Populations: A Systematic Review. JAMA Netw Open. 2021;4(9):e2125846. DOI: 10.1001/jamanetworkopen.2021.25846

  5. Stone K, Zwiggelaar R, Jones P, Mac Parthaláin N (2022) A systematic review of the prediction of hospital length of stay: Towards a unified framework. PLOS Digit Health 1(4): e0000017. https://doi.org/10.1371/journal.pdig.0000017

  6. Torres, A. The Business Of Healthcare: How Patient Satisfaction Plays A Role. Resident Student Organization. https://acoep-rso.org/the-fast-track/the-business-of-healthcare-how-patient-satisfaction-plays-a-role/