Clinical Audit and Statistical Analysis of Maternal Mortality at KNH

This project presents a retrospective clinical audit and data-driven evaluation of 36 maternal deaths at the High-Dependency/Critical Care Obstetrics Unit (HCQ) of Kenyatta National Hospital. It combined public health analytics, clinical data science, and statistical computing in R to uncover patterns of preventable mortality, focusing on systemic gaps, timeliness of care, and referral quality.

The analysis aligns with WHO maternal mortality review guidelines and Kenya’s national frameworks for reproductive health improvement. Data were collected via structured Google Forms, cleaned and processed using the R programming language, and summarized with advanced reporting tools (gtsummary, tidyverse, gt).

Objectives:
To statistically examine demographic and clinical characteristics of maternal deaths

To evaluate referral pathways, delays in care, consultant involvement, and treatment timelines

To compare clinical and postmortem findings and assess institutional gaps in documentation and decision-making

To offer actionable recommendations based on structured clinical audits

Tools and Techniques Used:
Language: R (Version 4.4.3)

Libraries: gtsummary, janitor, tidyverse, gt

Data Source: Google Forms → CSV export

Analysis Workflow:

Structured data cleaning

Exploratory data analysis (EDA)

Statistical summaries of causes of death, timelines, delays

Stratified tables (demographics, risk assessments, referral sources)

Grouped cause-of-death analysis using ICD-based clinical categories

Key Findings:
Hypertensive disorders and hemorrhagic complications were the leading direct causes of death

55.6% of deaths occurred postnatally, reflecting missed opportunities in postpartum monitoring

75% of patients were referrals, often late and inadequately documented

Consultant involvement in critical decisions was low (only 33% during pre-delivery phases)

97% of the deaths lacked completed Maternal Death Review Forms (MDRFs), compromising audit quality

Postmortem reports were missing or unavailable in nearly half of the cases

Significance in Data Science and Health Analytics:
This project exemplifies how data science can bridge the gap between clinical evidence and systemic change in healthcare. By leveraging structured audit data and advanced R workflows, the team translated fragmented clinical records into clear, actionable insights for quality improvement. The audit not only identified patterns in maternal mortality but also modeled a replicable approach for healthcare quality assurance using open-source analytics.

Recommendations Generated from Analysis:
Standardize antenatal risk assessments and ensure follow-ups for high-risk pregnancies

Institutionalize consultant oversight in admissions, deliveries, and escalations

Digitize referral protocols and enforce communication with referring facilities

Implement mandatory MDRF filing and postmortem documentation for every case

Train staff on early warning signs and integrate continuous postpartum surveillance

Develop interactive dashboards for internal monitoring and stakeholder feedback

Impact & Future Directions:
This audit lays the foundation for a data-centric approach to maternal health system reform. Future phases may integrate predictive models to identify high-risk cases earlier, build referral scoring systems, and digitize mortality dashboards for real-time tracking and action.