发表机构
British School in Colombo, Sri Lanka（斯里兰卡科伦坡英国学校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究分析斯里兰卡MediVerify平台150万次药品查询，揭示公众搜索行为与基本药物政策不匹配，并验证了低延迟、低能耗的高性能系统，为LMICs数字健康提供参考。
AI 中文摘要
国家级数字健康平台产生的使用数据可揭示人群的医疗保健需求。斯里兰卡在线药品信息平台MediVerify提供经国家药品监管局（NMRA）批准的药品信息，在运营第一年内记录了超过149万次查询。低收入和中等收入国家（LMICs）的大规模药品搜索行为仍缺乏充分描述。目标：描述药品信息寻求行为，识别公众需求与基本药物政策之间的不匹配，并评估技术性能。方法：我们回顾性分析了2024年7月至2025年7月期间提交的1,497,304次匿名查询。查询经规范化处理后，使用基于Levenshtein距离（阈值≤5）的模糊匹配映射到11,933种已批准药品。治疗类别采用基于解剖治疗化学（ATC）分类体系进行分配。分析了查询模式和系统性能，并根据处理时间估算了能耗。结果：维生素/矿物质（11.74%）、抗生素（10.57%）、抗糖尿病药（7.02%）和抗高血压药（6.84%）主导了搜索。前20种药品占查询的37.3%，而约40%-50%的注册药品从未被查询。零结果查询（约1%）表明存在未满足的信息需求。频繁搜索的药品与基本药物清单存在差异。中位延迟为8毫秒，查询解决率超过99%。估计能耗约为每百万次查询0.12千瓦时。结论：大规模药品搜索数据为医疗信息需求和政策一致性提供了洞察。MediVerify表明，国家级数字健康平台可以以较低的计算和环境成本实现高利用率。使用分析可以加强LMICs的药品政策和数字健康基础设施。
英文摘要
National-scale digital health platforms generate usage data that reveal population healthcare needs. MediVerify, Sri Lanka's online medicines information platform providing access to National Medicines Regulatory Authority (NMRA)-approved medicines, recorded over 1.49 million queries in its first year. Large-scale medicine search behaviour in low- and middle-income countries (LMICs) remains poorly characterised. Objectives: To characterise medicine information-seeking behaviour, identify mismatches between public demand and essential medicines policy, and evaluate technical performance. Methods: We retrospectively analysed 1,497,304 anonymised queries submitted between July 2024-2025. Queries were normalised and mapped to 11,933 approved medicines using fuzzy matching based on Levenshtein distance (threshold <=5). Therapeutic categories were assigned using an Anatomical Therapeutic Chemical (ATC)-aligned classification. Query patterns and system performance were analysed, and energy consumption was estimated from processing times. Results: Vitamins/minerals (11.74%), antibiotics (10.57%), anti-diabetes (7.02%), and antihypertensives (6.84%) dominated searches. The top 20 accounted for 37.3% of queries, while approximately 40%-50% of registered medicines were never queried. Zero-result queries (~ 1%) indicated unmet information needs. Frequently searched medicines diverged from essential medicines lists. Median latency was 8ms, with >99% query resolution. Estimated energy consumption was approximately 0.12 kWh per million queries. Conclusions: Large-scale medicine search data provide insights into healthcare information demand and policy alignment. MediVerify demonstrates that a national digital health platform can achieve high utilisation at low computational and environmental cost. Usage analytics could strengthen pharmaceutical policy and digital health infrastructure in LMICs.
Comments10 pages, 6 figures, Published in Frontiers in Digital Health
Journal refhttps://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2026.1855835/full
DOI：10.3389/fdgth.2026.1855835
