Operational Audit of Sub-Contract Dispatch Efficiency at Kenyatta National Hospital Using Python

This project involved a process performance audit at Kenyatta National Hospital’s Medical Research Department, assessing the efficiency and timeliness in forwarding official sub-contracts and mail to the Senior Director Clinical Services (SDCS). Using Python for data analysis, the study extracted and analyzed dispatch records from March to June 2025, focusing on identifying operational lags and opportunities for improvement.

Objectives:
Measure the average time taken between mail receipt and forwarding to the SDCS

Identify trends, delays, and inconsistencies in dispatch logging

Generate recommendations to improve documentation accuracy and maintain optimal turnaround time

Data Source and Workflow:
Source: Departmental dispatch book records compiled into Excel

Fields: Incoming Date, Dispatch Date

Tool: Python (likely using pandas for time delta calculations and descriptive statistics)

Key Findings:
Total records analyzed: 37 mails/sub-contracts

Forwarded in <1 day: 51.4% (n = 19)

Forwarded in exactly 1 day: 24.3% (n = 9)

Forwarded in >1 day: 24.3% (n = 9)

Median time taken: 0 days (suggesting same-day processing)

Mean time: 1.30 days

Interquartile range (IQR): 1 day

Max delay: 9 days

Some dispatches appeared to be logged as processed before their incoming date, likely due to retrospective entry or data entry inconsistencies.

Recommendations:
Improve date-entry accuracy in dispatch records to reflect true operational timelines

Implement periodic internal audits on date logging fields

Sustain the high efficiency observed in majority of dispatches (<1 day turnaround)

Consider automating dispatch book entries with timestamped digital logs

Skills and Impact Demonstrated:
Applied Python-based data analysis to administrative process review

Interpreted operational data metrics (mean, median, IQR) in a real-world healthcare setting

Linked findings to actionable process optimization recommendations

Demonstrated the use of data science in health administration and research governance