Patients, samples and batches flow through scarce beds, rooms, machines and clinicians. Pick the decision, such as which rule frees beds or which gate delays a pipeline, and test it before you change the schedule.
No patient-identifiable data is needed: models use aggregate times and counts.
Hospital bed and discharge flow
Decision: which single rule change frees beds soonest? The model follows each patient from admission to discharge through cleaning, equipment and transport, and each wait holds a bed. You read bed occupancy and the wait for a bed with confidence intervals.
What flows
Admitted patients, cleaning requests, transport requests
What is scarce
Beds, cleaning crews, porters, discharge nurses
Where it waits
Patients waiting for a bed, beds waiting for cleaning, patients waiting for transport
Questions you can test
Does an extra cleaning crew free more beds than an earlier discharge round?
How long do patients wait for a bed at the busiest hour of the day?
Which step holds a bed longest after the patient is ready to leave?
What happens to bed waits if arrivals rise on one weekday?
Should urgent admissions take priority over planned ones for the next free bed?
Operating room scheduling
Decision: which scheduling rule absorbs case overruns with the least overtime? The model runs pre-op, operating room and recovery together, with each case drawing its own duration. You read overtime and delay with confidence intervals.
Cases waiting for an operating room, cases waiting for the robot, patients waiting for a recovery bay
Questions you can test
Does placing the longest cases first reduce finishing times?
How often does a full recovery bay hold a case in the operating room?
What does a second robotic system change for the robotic list?
Would a float team absorb overruns better than a longer day?
How much does one equipment failure delay the rest of the day?
Virtual care capacity
Decision: how many clinician session slots cover both visit requests and device alerts? The model sends both kinds of demand to the same clinicians and records the alerts that wait unanswered. You read the wait for each kind with its confidence interval.
What flows
Visit requests, device alerts, escalated alerts
What is scarce
Clinicians, health coaches, video session slots
Where it waits
Visit requests waiting for a slot, alerts waiting for review, escalations waiting for a clinician
Questions you can test
Should clinician hours move to the evening to match visit demand?
How many alerts reach a clinician because a coach could not respond in time?
What share of visit requests are abandoned at each wait threshold?
Does adding coaches reduce clinician interruptions?
How does a spike in alerts affect visit waits?
Clinical lab and diagnostics turnaround
Decision: which wait do you attack first to shorten sample turnaround? The model sends each sample through accessioning, a wait for a full analyzer run and a review queue. You read turnaround and each stage wait with confidence intervals.
What flows
Routine samples, urgent samples, repeat tests
What is scarce
Accessioning staff, analyzers, reviewers
Where it waits
Samples waiting to be logged, samples waiting for a run, results waiting for review
Questions you can test
Do smaller analyzer runs shorten routine turnaround without hurting urgent samples?
Where does the largest wait occur between receipt and result?
How much does an analyzer failure delay results that day?
Would a second reviewer at midday change turnaround more than a third analyzer?
How does a courier round that arrives all at once affect the queue?
Vaccination and screening clinic
Decision: where do you place staff so no station backs up on clinic day? The model sends each visitor through check-in, screening, the procedure and an observation chair. You read the queue at each station with its confidence interval.
Check-in line, screening line, treatment line, wait for an observation chair
Questions you can test
How many observation chairs stop the treatment line from backing up?
Which station needs the extra nurse?
How many walk-ins can the day absorb before the average wait passes 30 minutes?
Does a separate lane for appointments shorten the queue?
How many people turn away at the busiest hour?
Clinical trial and drug pipeline
Decision: where does one more site or lab slot shorten the program most? The model moves work through lab slots, trial sites and monitors, and holds work at each gate until a decision. You read program duration with its confidence interval.
What flows
Candidate compounds, study participants, data review packages
What is scarce
Assay lab slots, trial sites, study monitors, review committee
Where it waits
Candidates waiting for an assay slot, candidates held at a stage gate, participants waiting for screening
Questions you can test
Does a second assay slot or an extra site shorten the path to the next gate?
How often does the review committee hold ready work?
How long does enrollment take under different screening failure rates?
Where do candidates wait longest between stages?
How does a delay at one site spread through the study?
Cell therapy and biomanufacturing
Decision: how many clean-room suites and technicians do you need before demand arrives? The model sends each patient-specific lot through clean rooms and steps that cannot be shortened. You read lot turnaround and suite utilization with confidence intervals.
What flows
Patient-specific lots, quality test samples, maintenance jobs
Lots waiting for a clean room, lots waiting for a technician, lots waiting for testing
Questions you can test
Does a new clean room or a second technician shift shorten the wait for a lot?
How much does one incubator failure delay the lots behind it?
How many lots can the facility start per week without a growing queue?
Which step sets the pace for the whole facility?
What is the effect of giving urgent patients priority?
Elective care and medical tourism scheduling
Decision: how many consult slots, procedure hours and recovery beds does a busy week need? The model sends each visiting patient through consult, procedure and recovery on dates the patient booked in advance. You read the number of patients left without a bed with its confidence interval.
