Case Study: How Healthcare Providers Can Leverage Statistical Insights to Improve Operational Efficiency & Patient Outcomes

Healthcare Analytics Case Study — Etiquette Technologies
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Executive summary

Statistical analysis turns operational hospital data into actionable decisions. This case study presents calculations and KPIs you can implement today — from patient wait-time percentiles to ARIMA forecasting for inventory. Use the interactive sections to copy snippets for implementation.

Avg. OPD Wait Time
38 min ▲ 8%
Median: 24 min • P95: 94 min
Bed Occupancy Rate
78% vs target 85%
ALOS: 4.6 days
30-day Readmission
9.2% ▼ 2%
High risk: Diabetes cohort

1. Patient wait time — measurements & actions

Patient wait time is one of the most visible metrics for patients. Calculate mean, median and percentiles (P75, P90, P95). Use time-series decomposition to identify weekly daily patterns and queueing models to design service capacity.

Sample KPIs

  • Average wait time (mean)
  • Median wait time
  • P90 and P95 wait times (for SLA monitoring)
  • Throughput (patients/hour)

Recommended actions

  • Introduce appointment bucketing to smooth arrivals
  • Fast-track lanes for known follow-ups
  • Real-time dashboards showing queue lengths
  • Trigger extra staff if P90 > threshold

2. Bed occupancy & capacity planning

Track Bed Occupancy Rate (occupied beds / total beds). Use ALOS and arrival distributions to forecast bed demand. Apply scenario analysis for seasonal surges (e.g. dengue, influenza).

MetricValueAction
Bed Occupancy Rate78%Monitor weekly; open surge beds at 90%
Average Length of Stay4.6 daysDischarge planning to reduce ALOS
Turnover Interval2.4 hoursImprove cleaning throughput

3. Readmission prediction & cohort analysis

Use logistic regression or gradient boosting models to compute readmission risk. Feature examples: comorbidities, medication adherence, LOS, discharge destination.

Deploy: risk score > threshold → schedule tele-follow-ups; med adherence program.

4. Staff optimization

Match staffing to predicted patient arrivals using regression or queuing simulation. Key outputs: predicted required nurses per shift, overtime forecasts, burnout risk index.

5. Infection control & outbreak detection

Track moving averages of infection incidence per ward; use control charts to detect out-of-control signals. Spatial heatmaps help detect clustering.

6. Pharmacy forecasting & inventory

Forecast drug demand with ARIMA/Holt-Winters. Implement ABC/VED classification to prioritize stock. Alerts for reorder point based on lead time and safety stock.

Detailed metrics & sample SQL / pseudocode

Sample KPIs table

KPIFormulaTarget
Avg Waitmean(wait_minutes)< 30 mins
P95 Waitpercentile(wait_minutes,95)< 90 mins
Bed Occupancyoccupied / total75%-85%
30d Readmissioncount(readmit within 30d)/discharges< 10%

Next steps

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