Predictive Staffing: Using Order-Volume History to Cut Verification Backlogs

In over 75% of US hospitals, pharmacists now provide clinical services to most inpatients, and the 2024 ASHP National Survey put a price on that reach: in large hospitals, drug-shortage management alone consumes staff time equal to 1.5 full-time pharmacists [ASHP National Survey, 2024]. Every hour spent firefighting is an hour pulled off the verification queue. So when orders pile up at 9 PM, the instinct is to call it a staffing problem. It usually isn't. It's a forecasting problem.

The queue is predictable

Order volume doesn't arrive at random. It tracks admission patterns, rounding schedules, and discharge cycles that repeat week over week. Time-series machine learning already forecasts hospital discharge volume with usable accuracy [JAMA Network Open, 2018]. The same order-volume history sitting in every EHR can flag when the verification queue will spike, hours before the first order lands in it.

Most departments still staff to an average. They set a fixed evening or overnight complement and absorb the variance as wait time. That's why a Tuesday 6 PM admission wave becomes a 9 PM backlog. The data to prevent it was already in the system.

The backlog that forms at 9 PM was visible in the order data at 3 PM. Staff to a daily average and you guarantee you miss it.

From headcount to forecast

EHR-embedded workload tools move the unit of planning from headcount to predicted demand. Verified orders and clinical interventions are the metrics administrators already use to justify pharmacist positions [Webb et al., 2023]. Feed those same numbers forward instead of just counting them after the fact, and coverage becomes scheduled rather than reactive. You add a remote verifier for the two-hour window the model flags, not for a full shift you're guessing at.

That distinction is the whole point. Reactive staffing pays for idle capacity most of the night and still misses the peak. Forecast-driven staffing puts the pharmacist where the orders are.

The inputs are modest. Hourly order counts by unit, day of week, and a flag for the known schedule drivers like OR block time and ED admission peaks. That history alone supports a defensible 24-hour forecast. The model doesn't need to be exotic to beat a flat staffing grid, it just needs to be pointed at data the department already collects and ignores.

NetlinkRx is designed around this kind of transparency. The model is built so partner hospitals see the same real-time dashboard the clinical team uses, with verification times, queue depth, and intervention rates visible as they occur rather than summarized in a monthly recap. When the order-volume model predicts an evening surge, coverage is built to scale into that window so the queue stays flat on the dashboard the director sees. That's what transparent performance should mean in practice: no black-box reporting, no waiting until month-end to learn the backlog happened.

Pull your last 90 days of order-verification timestamps and plot volume by hour and weekday. The surge windows will be obvious. Staff to those, not to the average.

References

1. Pedersen CA, et al. ASHP National Survey of Pharmacy Practice in Hospital Settings: Clinical Services and Workforce, 2024. American Journal of Health-System Pharmacy. 2025. Link
2. McCoy TH Jr, Pellegrini AM, Perlis RH. Assessment of Time-Series Machine Learning Methods for Forecasting Hospital Discharge Volume. JAMA Network Open. 2018;1(7):e184087. Link
3. Webb AJ, Carver B, Rowe S, Sikora A. The use of electronic health record embedded MRC-ICU as a metric for critical care pharmacist workload. JAMIA Open. 2023;6(4):ooad101. Link

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