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Healthcare

Healthcare is an area I am interested in exploring through operations research. These reading notes connect published studies on scheduling, capacity, and routing with questions from my work in logistics.

The studies below are work by other researchers. The application ideas are research interests; my delivered project experience is in logistics. Study findings depend on their setting and methods, with those limits explained alongside each result.

Research notes

Research with practical applications.

Three topics that interest me: imaging appointments, infusion schedules, and home health visits. Each note brings together a published study, a modeling idea, and questions about how the findings could translate to another setting.

Sources reviewed September 30, 2026.

01 / Imaging appointment scheduling

Imaging schedules and appointment changes.

The operational challenge

Missed appointments and late cancellations can leave scanners idle while other patients wait for an opening. Recovering that time requires matching available patients to the right equipment, staff and appointment length.

Modeling ideas

I would evaluate a scheduling workflow that combines early confirmations, cancellation detection and a ready-to-book waitlist. Matching patients to available slots would account for exam duration, equipment, preparation and authorization status.

Operational implications

Recovering otherwise empty slots could increase completed scans using existing equipment. The financial test is added contribution after staffing and reminder costs; recovered capacity is not automatically a cash saving.

Data and evaluation questions
Starting data
Appointment history, cancellations, attendance, exam lengths, scanner calendars and staffing costs at one center.
Operating constraints
Clinical priority, patient preparation, authorization, accessibility and patient choice of location.
Evaluation measures
Completed scans per staffed scanner-hour, empty slots, waiting time, overtime and incremental contribution after program costs.

02 / Infusion capacity & workforce scheduling

Treatment timing, chairs, and nursing capacity.

The operational challenge

Infusion treatments vary in duration and nursing needs. Concentrating too many starts at the same time can create queues and workload peaks, while longer treatments can push the working day into overtime.

Modeling ideas

I would model treatment start times against chair occupancy, nursing workload and pharmacy readiness, then compare alternative schedules with the current operation. The aim would be a smoother day while preserving clinical requirements.

Operational implications

Better sequencing could reduce paid overtime and late finishes or release capacity within current staffing. Savings would need to be measured against actual payroll and delivery costs, with patient waiting and care requirements held in view.

Data and evaluation questions
Starting data
Treatment timestamps, chair occupancy, nursing shifts, pharmacy release times and overtime records for one infusion unit.
Operating constraints
Clinician-approved treatment windows, staffing requirements, breaks, patient acuity and variable treatment durations.
Evaluation measures
Paid overtime, late finishes, patient waiting and treatments completed per staffed chair-day; validate the model before a supervised pilot.

03 / Home health routing & visit scheduling

Routing and continuity in home health visits.

The operational challenge

Home health teams must fit visits around patient availability, staff qualifications and travel. Planning assignments and routes together can help avoid unnecessary driving, uneven workloads and missed visit windows.

Modeling ideas

I would combine staff-to-visit assignments with daily route planning, accounting for skills, continuity of care, patient availability and travel. Dispatchers could compare feasible schedules and adjust them when visits change.

Operational implications

Shorter routes could lower mileage reimbursement and paid travel time; faster planning could reduce administrative work. Financial value depends on whether released time reduces paid hours or supports additional appropriate visits.

Data and evaluation questions
Starting data
Authorized visit locations, visit durations, staff skills, working hours, travel history and mileage reimbursement for one local agency.
Operating constraints
Continuity of care, patient time windows, required skills, breaks, service territory and realistic traffic buffers.
Evaluation measures
Miles and paid travel minutes per completed visit, dispatcher time, overtime and on-time completion, with missed visits and continuity monitored.

Questions of measurement and context.

A question that connects these studies is how a model performs outside its original setting. I am interested in the role of historical data, local constraints, and operational validation in understanding that transfer. Service quality and staff workload matter alongside cost.

Lower paid overtime or mileage can reduce expenses. Recovered appointment slots and staff time create capacity, which has financial value when it changes costs or supports additional care. Savings should be calculated from measured operational changes after implementation and ongoing operating costs.

More published research: surgery, inventory and staffing

Further studies by other researchers on improving operating room use, inventory management and nurse scheduling.

Operating Room Scheduling

Smarter scheduling lifted operating room usage into the high 90s

Challenge — Operating rooms routinely run under-used during peak morning hours, a chronic source of lost surgical capacity and idle staff time.

Approach — Researchers rolled out a smarter scheduling system alongside organizational changes, then compared results across two six-month periods.

87.5% → 98.1%operating room usage, 9–11 a.m. block

Source: Vladu et al., Healthcare (Basel), 2024

Pharmacy Inventory

A restocking simulation reduced average inventory cost by more than half

Challenge — An outpatient pharmacy was carrying excess drug inventory under old, static restocking rules, tying up capital and increasing waste risk.

Approach — Researchers built a computer model that tested different restocking rules against real demand patterns to find the best setting.

55%reduction in average inventory cost (68% less inventory on hand)

Source: Chen et al., Healthcare (Basel), 2022

Nurse Scheduling

Centralized, smarter scheduling can cut cost and raise satisfaction together

Challenge — Decentralized, manual nurse scheduling tends to force a tradeoff between labor cost and the quality of schedules nurses actually want.

Approach — Researchers built a smarter, centralized approach to nurse scheduling that balances cost against what nurses actually want.

Cost ↓schedule cost and nurse satisfaction improved together

Source: Wright & Mahar, Omega, 2013

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