Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents. Hospital pharmacy directors and qualitative researchers face two problems: multisource text (surveys, interviews, plans) and the need for rapid synthesis to inform preparedness. The primary keyword for this article is hospital pharmacy crisis management, and this post shows how to extract actionable findings from a Finnish dataset using AI-enabled qualitative research methods. According to the dataset "Additional file 1 of Crisis management in Finnish hospital pharmacies during the COVID-19 pandemic" published on 5 July 2025, a national cross-sectional survey was sent to all 21 hospital pharmacy heads in October–November 2020 and was triangulated with six interviews conducted in March–May 2021, yielding concrete operational lessons for preparedness.
Key Takeaways
According to the dataset "Additional file 1 of Crisis management in Finnish hospital pharmacies during the COVID-19 pandemic" (Latonen et al., hosted at LSE), a survey sent to all 21 hospital pharmacy heads in October–November 2020 and six interviews in March–May 2021 show concrete gaps and fixes for supply-chain preparedness. The dataset reports a 57% survey response rate (n = 12) and documents that "The risk perception of a crisis concerning pharmaceutical supply chain rose from 58 to 100%" after the pandemic onset.
- According to Latonen et al. (2025), the national survey in October–November 2020 achieved a 57% response rate (n = 12) out of 21 invitees.
- According to Latonen et al. (2025), only 4 pharmacies (25%) had a pre-existing pandemic preparedness plan, while 7 pharmacies (58%) developed a new plan during the pandemic.
- According to Latonen et al. (2025), the study found operational changes in internal communication and management in 92% of respondents, and clinical pharmacy services changed in 67% of respondents.
- According to Latonen et al. (2025), the authors conclude that "Preparedness of hospital pharmacies could be improved with pre-established crisis teams and plans, and data management systems providing easily accessible information to support decision-making."
What Happened in the Finnish Hospital Pharmacy Study
The study combined a national cross-sectional survey sent to all 21 Finnish hospital pharmacy heads in October–November 2020 with six semi-structured interviews conducted in March–May 2021, according to Latonen et al. (2025).
The survey instrument was developed from crisis management process models, and descriptive statistics plus deductive content analysis were used to analyse open-ended responses, according to Latonen et al. (2025).
Key measured outcomes included response rate (57%, n = 12), changes in risk perception (from 58% to 100% following the pandemic onset), presence of preparedness plans (4 pharmacies, 25%), and establishment of pandemic crisis teams (4 pharmacies, 33%), as reported in the dataset.
Findings Snapshot
| Date | Metric | Value | Implication |
|---|---|---|---|
| October–November 2020 | Survey invitations | 21 hospital pharmacy heads invited | Census-style survey frame, response limitations require triangulation |
| October–November 2020 | Survey response rate | 57% (n = 12) | According to Latonen et al. (2025), sample size required interview triangulation for depth |
| March–May 2021 | Interviews | 6 semi-structured interviews | Provided qualitative depth and confirmation of survey themes |
| During pandemic | Perceived supply-chain crisis risk | Rose from 58% to 100% | Indicates rapid escalation of perceived vulnerability across sites |
| During pandemic | Pre-existing preparedness plans | 4 pharmacies (25%) | Gaps in baseline preparedness identified by Latonen et al. (2025) |
| During pandemic | Operational changes reported | Internal communication 92%, clinical services 67%, medicine supply 58% | High incidence of operational adaptation across functional areas |
Implications for qualitative researchers and hospital pharmacy leaders
For qualitative researchers, the Finnish study shows that mixed-source designs need explicit triangulation: survey breadth (57% response in October–November 2020) plus purposive interviews (March–May 2021) strengthened validity, according to Latonen et al. (2025).
For hospital pharmacy leaders, the dataset implies that pre-established crisis teams and plans reduce ad hoc decision-making: only 4 pharmacies (25%) had plans before the pandemic, and 7 pharmacies (58%) developed new plans during the crisis, according to Latonen et al. (2025).
For supply-chain analysts, the unequal distribution of medicines and crisis information reported by Latonen et al. (2025) highlights the need for coordinated data systems and cross-institution communication to ensure equitable access.
How Evidano Helps
What Evidano is and why it fits this problem
Evidano is an AI-powered qualitative data analysis platform that helps researchers analyze interviews, open-ended surveys, and documents.
Evidano ingests survey text, interview transcripts, plans, and reports and produces thematic and cross-segment analyses to accelerate synthesis for preparedness planning.
Problem: Small survey sample and dispersed interviews slow synthesis
Solution: Evidano automates thematic coding across documents, surfaces co-occurrence networks, and quantifies theme frequency so teams can compare survey responses (n = 12, October–November 2020) with interview narratives (March–May 2021) in hours rather than weeks.
Evidano supports transcription and translation workflows when interviews come from different languages or recording formats, reducing manual cleaning time.
Problem: Unequal information sharing and medicine distribution
Solution: Evidano creates cross-segment dashboards that map themes by hospital, function, and date so leaders can identify where communication breakdowns and stock inequalities occurred and prioritize interventions.
Evidano’s AI chat over your documents enables fast drill-downs so decision-makers can ask, for example, which hospitals reported decreased collaboration and why, using the same dataset.
Problem: Need for reproducible, auditable analysis for policy recommendations
Solution: Evidano produces exportable code hierarchies, frequency tables, and visualizations that document how themes were derived and how many respondents expressed each concern, supporting transparent recommendations to stakeholders.
For product details see the Evidano features page.
FAQ: hospital pharmacy crisis management qualitative analysis
How did the study collect and analyse qualitative data?
Answer: The study used a national cross-sectional survey plus six purposive interviews and analysed open-ended responses by deductive content analysis, according to Latonen et al. (2025).
Supporting detail: The survey was distributed in October–November 2020 and interviews occurred in March–May 2021; the authors triangulated descriptive statistics with interview data to confirm themes.
What are the most important numerical findings to extract for policy briefs?
Answer: Extract the response rate (57%, n = 12 in October–November 2020), preparedness plan coverage (4 pharmacies, 25%), and operational change rates (internal communication 92%, clinical services 67%), as reported by Latonen et al. (2025).
Supporting detail: These statistics provide both coverage and intensity measures that policy briefs can use to justify investments in crisis teams and data systems.
Can AI tools safely speed synthesis of these mixed qualitative sources?
Answer: Yes, AI-assisted platforms can accelerate coding, theme extraction, and cross-segment analysis when combined with human review for validity.
Supporting detail: The Finnish dataset required triangulation across small-n survey data and interviews, a pattern AI can support by rapidly surfacing concordant and discordant themes for human analysts to verify.
Are the study findings generalisable beyond Finland?
Answer: The findings are context-specific but illustrate common preparedness gaps (limited pre-existing plans and rapid rises in perceived supply-chain risk) that other systems should evaluate against local data.
Supporting detail: Latonen et al. (2025) emphasise the need for data systems and coordinated information sharing, recommendations that translate into implementation actions in other health systems.
Conclusion & Next Steps
The Finnish dataset reported by Latonen et al. (2025) provides concrete, dated measures (survey in October–November 2020, interviews March–May 2021, dataset published 5 July 2025) that demonstrate both gaps and practical fixes for hospital pharmacy crisis preparedness.
AI-enabled qualitative research can compress the work of triangulating small surveys and interviews into reproducible outputs that directly inform crisis team formation, stockpile strategy, and data-system investments.
To apply these methods to your own hospital pharmacy data, start by ingesting transcripts, surveys, and plans into an AI-assisted platform to produce themes, frequency counts, and cross-site comparisons; for a hands-on trial, Try Evidano for free.
Topics
- hospital pharmacy crisis management
- qualitative analysis hospital pharmacy
- AI qualitative research
- pandemic preparedness hospital pharmacy
- pharmaceutical supply chain crisis
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