AI in Hospital Patient Flow Management: C...

A patient walks into a hospital's emergency department with severe abdominal pain. The doctor examines them, orders a few tests and decides that they need to be admitted for further treatment. Everything has gone reasonably well up to this point. The patient has been assessed, the diagnosis is becoming clearer and the clinical team knows what needs to happen next. Except there's a problem. There isn't a bed available in the ward, so the patient remains in the emergency department for several more hours.

Meanwhile, another patient in the hospital has completed their treatment and is medically ready to leave. Their discharge summary is still pending, the pharmacy needs to prepare their medication and someone needs to coordinate transport home. Elsewhere, an operating theatre is waiting for a patient who hasn't completed the required preoperative checks. A ward has a bed that appears occupied in the system, although the patient has already left and the room is being prepared for the next admission.

Every one of these situations contributes to the same problem. The hospital has patients who need care, clinical teams ready to provide it and infrastructure that could potentially accommodate more activity. But the different parts of the organisation aren't moving at the same pace.

This is where AI in hospital patient flow management becomes interesting. Hospitals already collect enormous amounts of information through electronic health records, admission systems, operating theatre schedules and discharge processes. AI can help connect this information and predict where capacity problems are likely to emerge, allowing operational teams to act before those problems affect patients.

The scale of the challenge is significant. According to NHS England's Hospital Accident & Emergency Activity report for 2025–26, emergency departments in England recorded 28 million attendances during the financial year, an increase of 2.2% over the previous year. Only 74.9% of attendances were completed within four hours, against the national standard of 95%.

Hospitals clearly need more capacity in some areas, and staffing shortages remain an important constraint. But there's also an opportunity to examine how existing capacity is being used. That's the part of the problem AI could help address.

Hospital Waiting Times Are Connected to Everything Happening Behind the Scenes

When patients experience long waiting times, it's easy to assume that the emergency department needs more doctors or additional beds. Sometimes that's exactly what is required. However, emergency departments depend on the rest of the hospital functioning efficiently, and a delay elsewhere can create congestion that eventually reaches the waiting room.

Consider what happens when a hospital's inpatient wards are full. Patients who require admission remain in the emergency department because there is nowhere appropriate to transfer them. These patients continue occupying treatment spaces and require attention from emergency staff. As more patients arrive, the department has fewer resources available to assess and treat them.

This situation is commonly called emergency department boarding, and it has become a serious operational challenge across healthcare systems.

The UK's National Audit Office reported that average hospital bed occupancy had increased from 87% to 92%, leaving fewer beds available in the right locations when patients needed them. Its analysis also found that the average length of stay for patients admitted through A&E had risen from 7.3 days in 2019–20 to 8.1 days in 2025–26, an increase of approximately 11%.

The pressure on hospital capacity

28M – A&E attendances in England, 2025–26
74.9% – Completed within four hours
92% – Average hospital bed occupancy
8.1 days – Average stay for A&E admissions

Sources: NHS England 2025–26 A&E statistics and the National Audit Office's analysis of emergency care performance.

At occupancy levels this high, hospitals have very little flexibility when demand increases unexpectedly. A sudden influx of emergency admissions, an increase in patients requiring isolation or delays in discharging existing patients can quickly create problems across multiple departments.

This is an important consideration in our learning under Phaneesh Murthy, particularly when we discuss how technology can improve the performance of complex organisations. A hospital has multiple departments, and each department may be operating efficiently according to its own metrics. But patients move between these departments, which means overall performance depends on how well the individual processes work together.

AI could help hospital administrators understand these dependencies and identify where a small operational delay is likely to create a much larger problem later in the day.

Predictive AI Could Help Hospitals Know Which Beds Will Become Available

One of the most practical applications of AI in hospitals is predicting patient discharge.

Hospital administrators need to know how many patients are likely to leave during the next several hours so they can plan admissions and allocate beds. Traditionally, these estimates depend on information gathered from ward teams, scheduled discharge dates and manual updates. The difficulty is that a patient's discharge depends on several factors, including their clinical condition, outstanding tests and whether the required arrangements have been completed.

Machine learning models can analyse historical patient records and identify patterns associated with discharge readiness. These systems can estimate the probability that an individual patient will be discharged within a particular period and combine those estimates to help predict how many beds may become available.

There is already meaningful research supporting this application.

A 2024 study published in Communications Medicine examined electronic health record data from Oxfordshire, UK, covering February 2017 to January 2020. Researchers developed machine learning models to predict whether patients would be discharged within the following 24 hours.

The models achieved an area under the receiver operating characteristic curve, or AUROC, of 0.87 for elective admissions and 0.86 for emergency admissions. When the researchers combined individual predictions to estimate daily discharge totals, the mean absolute percentage errors were 8.9% for elective admissions and 4.9% for emergency admissions.

These results are promising, although they come from a retrospective study and do not establish that implementing the models will automatically reduce hospital waiting times. Hospitals would still need to validate performance using their own patient populations and evaluate whether the predictions actually improve operational decisions.

The opportunity is fairly straightforward. If a hospital can estimate that a particular ward is likely to discharge several patients before the afternoon, it can begin preparing for incoming admissions earlier. Bed management teams can coordinate cleaning and transfers, while clinical staff can identify which discharge-related tasks need attention.

Of course, the decision to discharge a patient must remain with the clinical team. AI can provide an estimate and help coordinate the work around it, but it cannot determine that a patient is medically ready to leave simply because the hospital needs the bed.

Discharge Delays Are Consuming Capacity Hospitals Already Have

Discharge planning deserves particular attention because it affects the entire patient journey.

A patient may have completed their treatment but remain in hospital because they are waiting for medication, community support or a suitable care arrangement. Sometimes the delay is relatively straightforward to resolve. In other situations, it involves complex coordination with social care providers, family members and external organisations.

The impact on hospital capacity can be substantial.

A December 2025 analysis by the Health Foundation found that the proportion of hospital bed days used by patients experiencing delayed discharge in England increased from 10.1% in July–September 2024 to 11.0% in July–September 2025. That represented approximately 19,000 additional delayed bed days over the period. The analysis also reported that the proportion of beds occupied by patients ready for discharge peaked at 14% during winter 2024–25.

These figures help explain why improving discharge coordination could make a meaningful difference to hospital operations.

AI could help identify patients who are approaching discharge readiness and alert the relevant teams to outstanding tasks. If a patient is likely to leave the following day, the system could prompt staff to review medication requirements or confirm whether external care arrangements are needed. Where workflows permit, it could also help prioritise administrative tasks that routinely delay departure.

The important part is connecting the prediction to an action.

During our mentorship under Phaneesh Murthy, we have learned to look closely at what happens between one step of a process and the next. In many organisations, delays occur because one team has completed its work but the next team hasn't received the information it needs. Hospitals face similar challenges, except that the consequences can directly affect patient care.

AI-assisted discharge planning could help reduce some of these handoff delays. However, it cannot independently create social care capacity, arrange unavailable community services or solve staffing shortages. Hospitals need to understand which delays can be improved through better coordination and which require broader changes to healthcare infrastructure.

Operating Theatres Could Become Easier to Schedule

Operating theatres are among the most resource-intensive parts of a hospital. A surgical procedure requires an appropriately staffed theatre, specialist equipment, an available recovery area and a patient who is ready for surgery. If any of these elements are unavailable, the procedure may be delayed or cancelled.

Scheduling becomes difficult because procedure durations vary. Some operations take longer than expected, while others finish early. Emergency cases may need to be accommodated, and postoperative patients may require beds that are already occupied.

AI could help hospitals develop more realistic surgical schedules using historical procedure times, patient characteristics and resource availability. Predictive models can estimate how long particular procedures are likely to take and help planners understand the probability of delays. Hospitals could also use simulation to examine how different scheduling decisions might affect theatre utilisation and recovery capacity.

This could become particularly valuable for healthcare systems trying to reduce elective surgery backlogs.

According to an October 2026 Health Foundation analysis, England's elective treatment waiting list stood at approximately 7.33 million pathways in July 2026. The proportion of waits within 18 weeks had improved from 61.3% to 65.4% over the previous year, but the analysis warned that progress remained insufficient to meet the government's longer-term target without further improvements.

A hospital with limited theatre capacity needs to use that capacity carefully. If AI can help reduce avoidable gaps between procedures or identify scheduling conflicts earlier, it may enable more patients to receive treatment using existing facilities.

However, theatre utilisation should never become the only objective. Hospitals need to preserve sufficient flexibility for emergencies and avoid creating unrealistic schedules that place additional pressure on clinical staff. A system that maximises the number of scheduled procedures but regularly causes overruns would create more problems than it solves.

AI Could Give Hospital Administrators a Better View of the Entire Day

Hospitals already use dashboards to monitor admissions, bed occupancy and operational performance. The limitation is that many dashboards show the current situation without explaining what is likely to happen next.

An AI-powered patient flow system could combine current operational information with forecasts for the next several hours. It could estimate expected emergency admissions, likely discharges and potential pressure on particular wards. Hospital managers would then have an opportunity to examine possible responses before capacity becomes critical.

For example, a hospital may anticipate an unusually busy afternoon based on historical demand patterns and current emergency department activity. At the same time, the discharge prediction system may indicate that fewer patients than expected are likely to leave a particular ward. This combination could prompt the operations team to investigate whether there are outstanding discharge tasks or whether suitable capacity exists elsewhere.

The benefit comes from coordinating the response across departments.

A bed management team might need to work with ward nurses, discharge coordinators and housekeeping. If a patient requires a particular type of bed or isolation facility, the system also needs to account for those constraints. The objective is to help staff understand what resources will be available and which actions could improve patient movement.

This is where the enterprise transformation lessons we have learned from Phaneesh Murthy become particularly relevant. A hospital may already possess the information required to improve an operational decision, but that information is often distributed across different applications. AI becomes useful when it helps connect those systems and makes the resulting information available to the people responsible for acting on it.

Of course, the system needs to be designed around the hospital's existing workflows. If administrators must constantly switch between applications or manually reconcile conflicting information, the technology may add to their workload. The strongest implementations will be those that fit into daily operational routines and make the next action clear.

Healthcare Workforce Shortages Make Better Coordination Even More Important

Hospitals cannot improve patient flow without considering the people responsible for delivering care. Nurses, physicians and support staff already work under considerable pressure, and operational inefficiencies often increase that burden.

The World Health Organization projects a global shortage of approximately 11 million health workers by 2030, with the greatest shortages concentrated in low- and lower-middle-income countries.

This is a major challenge for healthcare systems, particularly as populations age and demand for medical services continues to grow.

AI could help hospitals use their available workforce more effectively by reducing avoidable administrative work and improving the coordination of patient movement. Staff may spend less time searching for information about bed availability or repeatedly checking whether a discharge task has been completed.

Predictive systems could also help managers anticipate periods of higher demand and adjust staffing arrangements where flexibility exists. However, staffing decisions require more than a forecast of patient numbers. Hospitals need to consider clinical acuity, staff qualifications and safe staffing requirements.

There is also a risk that AI could be used primarily to increase throughput without addressing workforce wellbeing. If hospitals interpret every predicted improvement as an opportunity to assign additional work, staff may experience greater pressure despite the technology's operational benefits.

During our discussions under the mentorship of Phaneesh Murthy, we have learned that technology should be evaluated according to how it changes the work people actually perform. In healthcare, a successful system should help professionals spend more time on patient care and less time navigating administrative processes.

That means involving clinical staff in the design of AI-assisted workflows and evaluating whether the technology genuinely improves their working conditions.

Better Patient Flow Could Also Improve Patient Safety

Hospital waiting times are frequently discussed as a measure of convenience or service quality. But delays can have more serious consequences, particularly when patients remain in emergency departments while waiting for inpatient beds.

A 2026 systematic review of emergency department boarding examined 21 studies, including 17 focused on patient outcomes and four examining staff impacts. The review found associations between prolonged boarding and poorer outcomes, including treatment delays, medication errors and increased length of stay. It also identified additional risks for vulnerable patient groups and increased workload for clinical staff.

These findings help explain why patient flow is an important clinical issue.

When patients remain in the emergency department after the decision to admit them, they may receive care in an environment that was designed for assessment and emergency treatment rather than prolonged inpatient management. Emergency teams also need to divide their attention between newly arriving patients and those waiting for transfer.

AI-assisted bed management could potentially reduce some of these pressures by improving the timing of transfers and helping hospital teams anticipate capacity shortages. However, hospitals would need to demonstrate that the technology improves patient outcomes rather than relying solely on operational metrics.

A shorter average waiting time is encouraging, but patient safety measures should remain central to evaluating performance. Hospitals need to understand whether changes in patient flow affect adverse events, clinical deterioration or other important outcomes.

Hospitals Need to Measure the Right Outcomes

Introducing AI into hospital operations requires a clear understanding of what success looks like.

A hospital might begin by deploying a discharge prediction model in a limited number of wards. The organisation could compare the accuracy of AI-generated forecasts against existing planning methods and examine whether staff find the predictions useful.

Once the model performs reliably, the hospital could connect those predictions to operational workflows. The next evaluation would focus on whether the system helps staff complete discharge preparations earlier or reduces delays in allocating available beds.

Several measures would be relevant, including the time patients spend waiting for inpatient beds, delays between clinical discharge decisions and actual departure, avoidable theatre cancellations and the proportion of beds that remain unavailable because of administrative processes.

These outcomes also need to be interpreted alongside patient safety and staff experience. A hospital that reduces waiting times by creating unsafe pressure to discharge patients earlier would have failed to improve care.

The technology itself also requires ongoing monitoring. Models trained using historical hospital activity may become less reliable when patient populations, staffing arrangements or clinical practices change. Hospitals therefore need processes for validating predictions and identifying when model performance deteriorates.

This is another principle that has come up repeatedly during our learning under Phaneesh Murthy. Organisations need to establish how a technology investment will improve an actual business or operational outcome. In hospitals, that means measuring whether AI helps patients receive appropriate care sooner while preserving clinical quality.

Could AI Help Hospitals Reduce Waiting Times Without Building More Beds?

Healthcare systems will continue to need investment in infrastructure, workforce capacity and community services. AI cannot eliminate those requirements, and it cannot solve every cause of hospital congestion.

But the available evidence suggests that operational coordination deserves serious attention.

England recorded 28 million emergency department attendances during 2025–26, while hospitals operated with average bed occupancy of approximately 92%. Delayed discharges continue to consume a substantial share of inpatient capacity, and elective treatment waiting lists remain large despite improvements in activity.

Research also shows that machine learning can generate useful predictions about patient discharge, although hospitals still need evidence that deploying these models improves real-world performance.

The opportunity is to connect these capabilities into a more coordinated approach to hospital operations. AI could help administrators anticipate demand, identify potential discharge delays and allocate available capacity more effectively. Clinical teams would remain responsible for patient care decisions, while operational staff would receive better information about where their attention is needed.

Through our mentorship under Phaneesh Murthy, we have learned to examine technology through the processes it can improve and the people who depend on those processes. That perspective is particularly useful in healthcare, where a small improvement in coordination can affect patients waiting for treatment and the professionals responsible for delivering it.

A hospital that can predict tomorrow's discharge activity, anticipate this afternoon's emergency demand and coordinate its available beds more effectively may be able to treat more patients with the resources it already has.

That is a practical opportunity for AI in hospital patient flow management, and one worth exploring carefully as healthcare systems continue to face growing demand.

About the Authors

This article was written by professionals & students mentored by Phaneesh Murthy. A veteran in the field of marketing Phaneesh Murthy acts as a guide & mentor to professionals seeking to upskill & grow in their careers. You may reach out to the team via this website if you would like to take up Phaneesh Murthy's mentorship