Vol. 4 No. 5 (2024): May
Horizon Scans

Artificial Intelligence for Patient Flow

decorative image of the issue cover

Published May 1, 2024

Key Messages

Why Is This an issue?

  • Inefficient patient flow contributes to the overcrowding of health care settings and negative clinical outcomes and patient experiences downstream.
  • Patient flow management aims to achieve seamless patient movement through the health care system and between acute and long-term settings, ensuring timely access to quality care.

What Is the Technology?

  • Artificial intelligence (AI)-based patient flow management tools are interventions designed to forecast and monitor patient movement from admission to discharge as they progress through different care settings. AI-driven tools can leverage big data and digital information systems (e.g., electronic health records) to facilitate effective patient flow.
  • AI-based patient appointment scheduling tools, which can help improve patient flow, are created to automate appointment scheduling and optimize it by minimizing wait times and matching the demand for health services and hospital capacity.

What Is the Potential Impact?

  • AI tools for patient flow management can support volume forecasting of patients with various conditions, especially those experiencing chronic conditions that require different types of treatment or care in different settings over a long period of time.
  • These AI tools can predict admissions, patient movement from the emergency department to inpatient beds, discharge, and transfers to different health care settings. Evidence for their effectiveness in patients with emergency admissions and those transferred to tertiary and quaternary care, as well as inpatients from the general, cardiology, and mental health departments, was reported. In addition, evidence suggested that AI tools can optimize appointment scheduling in general outpatient settings and operating rooms.
  • In health care systems in Canada, AI tools are being used or investigated to enhance patient flow by predicting emergency admissions, transfers to alternate levels of care, and general inpatient discharges, as well as optimizing capacity planning for patients receiving oncology care. AI appointment scheduling tools are currently being used in some oncology care settings and operating rooms across Canada.
  • The implementation of AI systems generally requires an upfront investment of time and other resources in addition to the financial cost of the system itself for set-up, integration, and staff training. The goal of these systems is to improve efficiency and save money, time, and human resources in the long run.

What Else Do We Need to Know?

  • Patient privacy and data security issues are concerns regarding widespread implementation of AI tools trained on electronic health records systems and patient datasets.
  • AI algorithms trained on datasets lacking adequate representation of all relevant patients may not predict their flow accurately. Training datasets with sufficient data from all relevant patient groups can ensure the inputs and outputs of the algorithms accurately reflect patient care needs and mitigate potential bias.
  • To accurately predict the care needs of local patients, AI algorithms, once deployed, should be retrained on site-specific datasets containing data for the patient populations that are representative of the hospital or health system in which they are being used.
  • Not all institutions have the hardware or computing power available to adequately or efficiently process the large amounts of data that are required by these AI systems, or the infrastructure needed to use big data (e.g., from electronic health records), and may require additional resources for implementation, as well as for ongoing maintenance and updating of the systems.