Leveraging AI for Analysis and Interpretation

Artificial intelligence (AI) can be a powerful tool to enhance the efficiency, depth, and accessibility of data analysis and interpretation. It can support analysts in rapidly exploring datasets, identifying patterns, summarizing trends, and generating initial visualizations and insights. AI can also help structure written explanations, translate technical findings for different audiences, and synthesize relevant evidence from large volumes of information. When used well, AI can enable analysts to spend less time on routine or time-intensive tasks and more time on critical thinking, such as contextualizing findings, building shared understanding, and tailoring messages to decision-makers’ needs.

However, AI must be used thoughtfully, with sound professional judgment and clear accountability. It should not replace the analyst’s role in interpreting results, assessing data limitations, or making context-specific judgments. AI-generated outputs may contain inaccuracies, overlook important nuances, or reflect biases in the underlying data. Therefore, outputs must always be reviewed, validated and adapted.

Collaboration between data producers, analysts, and data users to interpret findings is also essential to build shared understanding and ownership. Final interpretations, conclusions and recommendations should always remain grounded in human expertise, subject-matter expertise, and an understanding of the local context. Therefore, AI should be considered a support tool that amplifies and strengthens analysis and interpretation, but is not a substitute for critical thinking, judgement and shared decision-making.

Effective AI Prompts

What Makes an Effective Prompt

• Be clear on the purpose – e.g., identify trends, show anomalies, interpret trends.
• Define the audience – e.g., analyst deep dive versus policymaker summary.
• Specify geography, time, and scope – e.g., country, administrative level, indicators, time period.
• Provide interpretation guidance – e.g., highlight gender and equity gaps, suggest possible explanations.
• Specify the output format – e.g., a slide deck, short narrative, comparison table.
• Set guardrails – e.g., stick to the data shown; flag uncertainty or data quality issues; avoid speculation beyond evidence; note assumptions and inferences.

AI Prompts and Gender analysis

Using AI prompts to support gender analysis and interpretation Below is an example of a prompt that you can adapt and paste into the Datasuite Assistant to generate an initial gender analysis and interpretation. Please incorporate your country’s data into the prompt.

The AI output is designed to provide a starting point only. It will highlight key assumptions and suggest reflection questions that your team can use to guide your discussions. All AI outputs should be carefully reviewed and refined based on your team’s knowledge, expertise, and country context, focusing on the findings that are most relevant for your intended audience.

These prompts can be used to generate findings for your CAM analytical report or used to create a short summary for the chartbook.

Please reach out to Lior Idit Miller (lmiller9@worldbank.org) and/or Elizabeth Hazel (ehazel1@jhu.edu) for questions/support at the CAM or afterwards.
More details on the MAGE project can be found here .

Prompt for Country Analytical Teams

Using only the data points below, generate a gender-lens interpretation of maternal health in this country. The AI should provide an initial interpretation that requires country team review, validation and refinement.

The interpretation should be brief and concise. For each section, the AI should flag assumptions and pose reflective questions for teams to consider based on their contextual knowledge.

Country data:

• Country name: [Add country name here]
• Demand for family planning satisfied by modern contraceptives: [Add country data here]
• Unmet need for family planning: [Add country data here]
• ANC 4+ visits coverage: [Add country data here]
• ANC first trimester coverage: [Add country data here]
• Institutional delivery coverage: [Add country data here]
• Postnatal care coverage: [Add country data here]
• C-section rate: [Add country data here]
• Urban/rural institutional delivery gap (percentage points): [Add country data here]
• Maternal education institutional delivery gap (percentage points): [Add country data here]

Output format:

Create a bold heading for this section called “Draft Interpretation for Country Team Review”. Include this opening text: Access to essential maternal health services reflects broader gender inequities that shape women’s autonomy over their reproductive health and their ability to seek care.

Organize the findings by subheading: Family planning; Antenatal care; Delivery and postnatal care; Equity. Then for each subheading include “Interpretation”, “Assumptions for team validation” and “Reflection questions”. Reflection questions should be limited to the specific assumptions mentioned and no more than 3.

Generate Policy Message Options:

Based on the data provided, generate 4-5 bullet point illustrative policy messages that country teams can use as starting points for discussion. These should be:

• Directly linked to specific data points provided
• Policy-relevant and potentially actionable
• Framed through a gender and equity lens
• Concise (1-2 sentences each)

Each bullet should identify the data-driven finding and its gender/equity implication. Present these as suggestions rather than recommendations.

Include this closing text: These are illustrative examples intended to spark discussion. Country teams should validate for accuracy and relevance to current policy priorities, adapt language to local context, or develop alternative messages that better reflect their strategic objectives. Teams may select, combine, revise, or replace these options entirely.

Example of an AI output from prompt above using Malawi 2025 CAM data – FOR ILLUSTRATIVE PURPOSES ONLY

Draft Interpretation for Country Team Review

Access to essential maternal health services reflects broader gender inequities that shape women’s autonomy over their reproductive health and their ability to seek care.

Family Planning

Interpretation: Malawi demonstrates relatively strong performance in modern contraceptive uptake (80.6% demand satisfied), suggesting progress in women’s access to fertility control. However, the persistent 20% unmet need indicates that one in five women who wish to delay or prevent pregnancy cannot do so. This gap may reflect gendered barriers including limited decision-making power, partner opposition, or restricted access for certain groups such as adolescents and unmarried women.

Assumptions for Team Validation:

• The unmet need figure captures women across all age groups and marital statuses equally
• High contraceptive satisfaction rates reflect genuine choice rather than method limitations
• Partner/spousal consent is not a significant barrier to contraceptive access

Reflection Questions:

1. Does the 20% unmet need concentrate among specific populations (adolescents, rural women, unmarried women) who face additional gender-related access barriers?
2. To what extent does male partner involvement—either supportive or restrictive—influence contraceptive uptake in Malawi?
3. Are current family planning services designed to accommodate women with limited autonomy in household decision-making?

Antenatal Care

Interpretation: The significant gap between first trimester ANC coverage (27.6%) and ANC 4+ visits (62.4%) suggests women are entering care late in pregnancy. This pattern may reflect gendered constraints including women’s limited control over household resources for transport, time poverty due to domestic responsibilities, or cultural norms around pregnancy disclosure. Late ANC initiation also occurs due to limited pregnancy diagnosis options at both individual and health system level. Late ANC initiation reduces opportunities for early risk detection and birth preparedness.

Assumptions for Team Validation:

• Late ANC initiation is primarily driven by demand-side barriers rather than supply-side availability
• Women have agency to seek care independently once pregnancy is confirmed
• The ANC 4+ figure reflects the recommended contact schedule quality

Reflection Questions:

1. What gender-specific barriers (financial dependence, time constraints, need for permission) delay women’s first ANC visit in Malawi?
2. Are there cultural practices around pregnancy concealment that disproportionately affect women’s early care-seeking?
3. How do women’s domestic and caregiving responsibilities influence their ability to attend multiple ANC visits?

Delivery and Postnatal Care

Interpretation: Malawi’s high institutional delivery rate (95.2%) represents a significant achievement in ensuring women access skilled birth attendance. The C-section rate of 9.6% falls slightly below the WHO-recommended ranges (10-15%), suggesting somewhat appropriate access to emergency obstetric care without over-medicalization. Strong postnatal care coverage (84.1%) indicates continued engagement with the health system post-delivery. These figures suggest health system investments have successfully addressed major access barriers for facility-based care.

Assumptions for Team Validation:

• High institutional delivery rates reflect genuine access and quality rather than coercive policies
• The C-section rate is equitably distributed across wealth and geographic groups
• Postnatal care includes meaningful content addressing maternal and newborn health needs

Reflection Questions:

1. Does the high institutional delivery rate mask variations in quality or respectful maternity care practices?
2. Is the C-section rate equitably available to women in rural areas facing obstetric emergencies?
3. Does PNC adequately address women’s own health needs, or does it focus predominantly on newborn care?

Equity

Interpretation: The 25 percentage-point urban/rural gap in institutional delivery reveals significant geographic inequity, likely reflecting rural women’s compounded disadvantages including distance to facilities, transport costs, and limited infrastructure. The 15 percentage-point education gap demonstrates how women’s educational attainment—itself shaped by gender norms—influences maternal health outcomes. Less-educated women may face greater barriers including limited health literacy, reduced autonomy, and economic constraints that intersect with gender-based inequities.

Assumptions for Team Validation:

• The urban/rural gap primarily reflects supply-side factors (facility availability) rather than demand-side preferences
• Educational attainment serves as a proxy for women’s empowerment and decision-making capacity
• These gaps have remained stable and are not already being addressed by recent interventions

Reflection Questions:

1. For rural women who deliver outside institutions, what are the primary gendered barriers—distance, cost, permission, or preference for traditional care?
2. How does low educational attainment intersect with other dimensions of marginalization (poverty, age, parity) to compound barriers for specific groups of women?
3. Are existing programs specifically designed to reach women facing multiple intersecting disadvantages?

Suggested Policy Message Options

Based on the data provided, the following illustrative policy messages are offered as starting points for country team discussion:

• Closing the unmet need gap: With 20% of women unable to access desired family planning despite 80.6% demand satisfaction, targeted interventions addressing gendered barriers—including partner engagement and adolescent-friendly services—could accelerate progress toward full reproductive autonomy.
• Early ANC as a gender equity priority: The 35 percentage-point gap between first trimester ANC (27.6%) and ANC 4+ coverage (62.4%) suggests women face early pregnancy barriers; addressing women’s financial dependence and time poverty could improve early care initiation.
• Sustaining delivery gains while addressing quality: Malawi’s 95.2% institutional delivery rate demonstrates strong access; the next frontier involves ensuring respectful, woman-centered care that upholds dignity and autonomy during childbirth.
• Rural women’s access imperative: The 25 percentage-point urban/rural delivery gap indicates rural women face compounded geographic and gender barriers; community-based strategies and transport solutions could address these intersecting disadvantages.
• Education-sensitive approaches: The 15 percentage-point education gap in institutional delivery suggests less-educated women require tailored outreach that accounts for limited health literacy and potential constraints on autonomous decision-making.

These are illustrative examples intended to spark discussion. Country teams should validate for accuracy and relevance to current policy priorities, adapt language to local context, or develop alternative messages that better reflect their strategic objectives. Teams may select, combine, revise, or replace these options entirely.