The AI tool aims to solve Tanzania's high maternal mortality rate (MMR)

The problem involves four key delays: reaching facilities, seeking care, treatment, and arrival

Key complications if not addressed include continued high maternal mortality rates, which conflicts with SDG targets to reduce global MMR to less than 70 per 100k births. Additional challenges include transport issues, absence of health workers, high caseloads, and limited infrastructure/diagnostic ability

It addresses diagnosis and management of maternal health complications including abortion, anemia, antepartum issues, UTI, postpartum complications, and pregnancy-induced hypertension

Indicators include various delays in care and treatment

Traditional handling appears to be manual assessment by healthcare workers

AI tool aims to improve through early detection and timely management of risks

Global target (SDG) is to reduce MMR to less than 70 per 100,000 births

Specific to Tanzania, which has one of the highest MMR rates

N/A

Early detection and management of maternal health risks

Developed as part of AI4D Africa's Anglophone Multidisciplinary Research Lab

Uses text-based machine learning/LLM technology

Patient-oriented attributes (medical history, complaints, etc.)

Diagnostic attributes (doctor's plans, observations, examinations)

N/A

Workflow: Login/Register → Input Text → Get Results (2-5s)

Medical professionals/Doctors

Patient explanations, doctor's observations, test results

Classification of complications (abortion, anaemia, antepartum, UTI, postpartum, pregnancy-induced hypertension)

Provides validation based on confidence levels

Overall accuracy: 80%

Average sensitivity: 64%

Specific sensitivity rates vary by condition (33%-86%)

Processing time: 2-5 seconds

N/A

N/A

N/A

Online Links, not open source

Currently not available

Limited demographic scope (Amana Regional Referral Hospital only)

N/A

N/A

N/A

N/A

Clinical Accuracy and Reliability

• Why do sensitivity rates vary so dramatically across conditions (from 33% for UTI to 86% for postpartum)?
• How does the tool's 80% overall accuracy rate compare to human clinician diagnoses?

Healthcare Delivery Impact

• How does the 2-5 second processing time affect real-world patient care compared to traditional diagnostic approaches?
• Does the tool actually reduce the identified delays (seeking care, reaching facilities, treatment, arrival)?
• Are early referrals based on the tool's recommendations proving appropriate and timely?

Resource Utilization

• Does the tool demonstrably reduce healthcare worker caseload?
• How cost-effective is the implementation compared to traditional care methods?

Equity and Bias

• How does the tool's performance vary across different patient demographics?
• Does the single-hospital training data create biases in predictions?
• Are there specific patient populations for whom the tool is less effective?

System Integration

• How well does the tool integrate into existing hospital workflows?
• Are healthcare workers able to effectively interpret and act on the tool's recommendations?
• Does the confidence level validation system appropriately filter unreliable predictions?

N/A

Several technical issues help explain the varying performance levels seen across different maternal health conditions in the AI system. The most significant is data imbalance, where some conditions like pregnancy-induced hypertension have abundant training samples (567) while others like postpartum complications have very few (61). Additionally, the system's reliance on data from a single hospital limits its ability to generalize across different healthcare settings and patient populations.

The tool's monitoring framework centers around a 50% confidence threshold for validation, suggesting a balanced approach between sensitivity and specificity

It enables rapid detection of maternal complications, processes cases in 2-5 seconds, and helps manage high patient caseloads efficiently, By providing quick diagnostic support that enhances healthcare workers' capacity to deliver timely care.

The tool offers valuable support for strategic healthcare planning and resource allocation.

Increased satisfaction for clinicians, patients, and community

Proper resource and medication use

Shortened time for management

Improved maternal and child health awareness

Reduced maternal and perinatal death

Yes

Plans to extend project scope

Treatment recommendation features planned

Specific partnerships with different health care providers