Qualitative analysis of public health emergency systems

This post shows how the Zhuhai PHEIS modified e-Delphi converted expert input into compact operational indicators and how Evidano can automate transcription, thematic coding, and cross-segment analysis to reproduce that pipeline. The Zhuhai study (published June 5, 2026) delivered city/district and city/district/town indicator frameworks and supported handling 536 outbreaks between Jan 1 and Nov 30, 2024. Use a small pilot to check time savings and traceability before scaling.

• The researchers produced two frameworks: city/district (5 first-level, 21 second-level) and city/district/town (4 first-level, 17 second-level) from a modified e-Delphi process.
• The Delphi panel invited 41 experts and received 31 responses (75.6%), reporting an expert authority coefficient Cr = 0.72 and operational validation over 536 outbreaks (Jan 1–Nov 30, 2024).
• Evidano automates ingest, AI-assisted coding, and cross-segment frequency/co-occurrence analysis so teams can prioritize indicators and export traceable supporting quotes.

Researchers used a modified e-Delphi to build a public health emergency information system (PHEIS) framework for Zhuhai city, China. The study was published June 5, 2026 and reports a city/district framework (5 first-level and 21 second-level indicators) and a city/district/town framework (4 first-level and 17 second-level indicators. Source: PLOS ONE.

• Why researchers care: the paper converts stakeholder knowledge (41 invited experts; 31 respondents; Cr = 0.72) into a lean indicator set that supported handling 536 outbreaks between Jan 1 and Nov 30, 2024.
• Payoff for analysts: learn how to move from qualitative expert text and Delphi scores to prioritized modules (emergency response, preparedness, monitoring) and operational tasks you can automate with Evidano.

The Delphi converted a candidate indicator list into compact, operational indicator sets using a two-round modified e-Delphi. The team combined literature review and stakeholder workshops to build the candidate list, then ran a two-round modified e-Delphi using Excel questionnaires, keeping indicators with mean importance ≥ 3.5 and CV ≤ 0.25 while retaining a small number of conceptually critical items as documented exceptions.

• Inputs: policy documents, provincial standards, and frontline staff suggestions were used to form candidate indicators.
• Output: compact, operational indicator sets prioritized for emergency response, preparedness, and monitoring.
• Validation: the researchers measured expert authority (Cr = 0.72), used summary feedback between rounds so panelists could revise ratings, and validated the framework in live use during 2024 with 536 logged outbreaks.

This checklist gives seven steps to reproduce Zhuhai-style analysis using your Delphi rounds, meeting notes, and system logs.

• Step 1: Gather inputs, policy documents, Delphi ballots (Excel), free-text suggestions, and usage logs (for example 536 outbreaks).
• Step 2: Ingest into Evidano, upload spreadsheets, transcripts, and system logs, and apply custom dictionaries for domain terms.
• Step 3: Auto-code and map, run AI-assisted thematic coding, then align themes with Likert scores and CVs.
• Step 4: Run cross-segment analysis, compare city versus district versus street/township frequencies and co-occurrence patterns.
• Step 5: Produce traceable outputs, extract supporting quotes per retained indicator and generate one-page executive summaries.
• Step 6: Pilot operational changes, prioritize high-utility modules (emergency response, preparedness), and instrument metrics such as response time and data completeness.
• Step 7: Re-run Delphi or rapid feedback rounds, repeat rounds and use AI-avatar interviews to collect missing perspectives.

The Zhuhai study had IRB approval and a waiver of written consent where appropriate. When analyzing PHEIS data, treat outputs as research-only, redact PII, follow local ethics guidance, and use tools that support encrypted storage; Evidano supports PII redaction and encrypted storage.

To convert a Delphi study, meeting notes, or system logs into prioritized, auditable indicators, start with a small pilot that imports one Delphi round and corresponding usage logs into Evidano and produces a one-page decision brief. Run a two-week pilot to check time savings and signal clarity, and for hands-on help, Try Evidano for free or visit Evidano to request a demo or upload a sample Excel/CSV.