Healthcare Performance & Patient Safety Analytics

A KPI-Driven Study of Lowell General Hospital

Business Context :

Lowell General Hospital places patient well-being at the core of its care delivery model. While maintaining a strong reputation for quality and safety, hospital leadership recognizes that patient dissatisfaction can arise from multiple operational and clinical factors.
To ensure patients continue to receive safe, responsive, and high-quality care, management initiated a data-driven analysis of key healthcare performance and patient safety metrics to better understand their impact on patient experience.

Problem Statement :

Despite a strong commitment to patient-centric care, variations in hospital operations and safety outcomes may influence patient satisfaction.
A structured analysis of critical KPIs is required to:
Identify performance trends
Evaluate relationships between operational efficiency, staff responsiveness, and patient safety
Detect potential areas of risk impacting patient experience

Project Objectives :

• Assess the impact of key healthcare KPIs on patient experience and safety
• Analyze correlations between operational and safety metrics
• Benchmark hospital performance against defined standards
• Provide data-driven recommendations to enhance patient satisfaction and care quality

Key Metrics Analyzed :

• Average Licensed Bed Occupancy Rate
• Staff Responsiveness – Top Box Score
• Unassisted Fall Rate per 1,000 Patient Days

Dataset Overview :

Data Source
Lowell General Hospital – Healthcare Performance Dataset

• Dataset Scope
• Records: 60 rows × 4 columns
• Granularity: Monthly data
• Timeframe: January 2020 – December 2024
• Coverage: 5 years of hospital performance data

Key Variables Analyzed

• Month
• Average Licensed Bed Occupancy Rate
• Unassisted Fall Rate per 1,000 Patient Days
• Staff Responsiveness – Domain Top Box Score

Dataset preview :

Understanding KPIs :

1. Average Licensed Bed Occupancy Rate :

Purpose: Measures hospital capacity utilization and operational efficiency
Captured internally and reported as a percentage
Indicates how effectively licensed beds are being utilized over time
High occupancy may impact staff workload and patient experience
Calculation:

2. Staff Responsiveness –Top Box Score

Purpose: Measures patient perception of staff responsiveness and quality of care
Derived from the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey
Collected post-discharge from patients
Based on responses to two key questions:
Timeliness of help after pressing the call button
Timeliness of assistance with bathroom use or bedpan
Top Box Definition:
Percentage of patients who responded “Always” to both questions
Calculation:

3. Unassisted Patient Falls per 1,000 Patient Days

Purpose: Measures patient safety and fall-prevention effectiveness
Sourced from the National Database of Nursing Quality Indicators (NDNQI)
A fall is defined as an unplanned descent to the floor, with or without injury
Unassisted falls occur when no staff member is present to help the patient
Calculation:

Metrics

Relationship Between KPIs :

Correlation Analysis Summary

• Bed Occupancy Rate vs. Unassisted Fall Rate (per 1,000 Patient Days)
• Correlation: +0.70 (Strong Positive Relationship)
• Higher bed occupancy levels are associated with an increase in unassisted patient falls, suggesting potential strain on staff capacity during periods of high utilization.
• Bed Occupancy Rate vs. Staff Responsiveness – Top Box Score
• Correlation: −0.37 (Moderate Negative Relationship)
• As bed occupancy increases, staff responsiveness scores tend to decline, indicating possible operational pressure impacting timely patient assistance.
• Unassisted Fall Rate (per 1,000 Patient Days) vs. Staff Responsiveness – Top Box Score
• Correlation: −0.79 (Strong Negative Relationship)
• Lower staff responsiveness is strongly associated with higher unassisted fall rates, highlighting staff responsiveness as a critical driver of patient safety outcomes.
• 💡 Key Insight
• High bed occupancy may indirectly affect patient safety by reducing staff responsiveness, leading to increased unassisted falls.

Visualization using PowerBi :

Tools & Technology Used

• Python: Pandas, NumPy
• Visualization: Matplotlib, Seaborn &
• Power Bi – Dashboarding
• Analysis: Pearson correlation for linear relationships,
• Spearman correlation for monotonic relationships
• Environment: Jupyter Notebook

Summary of Insights & Findings :

• Analysis of key KPIs at Lowell General Hospital highlights strong interdependencies between bed occupancy, staff responsiveness, and patient safety. A strong positive correlation (r = +0.70) indicates that higher bed occupancy is associated with increased unassisted fall rates, suggesting operational strain during high-demand periods.
• Bed occupancy also shows a moderate negative relationship with staff responsiveness (r = −0.37), implying that rising patient volumes may impact staff availability.
• Staff responsiveness demonstrates a strong negative correlation with unassisted falls (r = −0.79), confirming its critical role in patient safety outcomes.
• Findings show consistently high bed occupancy, responsiveness challenges during peak periods, and increased fall rates with higher occupancy.

Key Recommendations :

Optimized Staff-to-Patient Ratios –

• Maintaining an appropriate staff-to-patient ratio is essential to prevent staff overload, ensure timely patient assistance, and reduce the likelihood of unassisted falls—particularly during periods of high bed occupancy.

Targeted Staff Training & Responsiveness Enhancement –

• Focused training programs aimed at improving staff alertness, responsiveness, and proactive patient monitoring—especially during peak occupancy periods—can significantly enhance patient safety and lower fall risks.

Adaptive Operational Planning –

• Staffing levels should be reviewed and adjusted dynamically based on real-time bed occupancy trends to maintain consistent care quality, reduce operational strain, and support patient safety across all demand levels.

Conclusion :

Sustaining optimal bed occupancy levels, supported by responsive and well-trained staff, is critical to improving patient safety and overall hospital performance. Long-term success relies on continuous optimization of staffing strategies, ongoing training initiatives, and proactive operational planning aligned with patient demand.