Department of BioHealth Informatics, Indiana University School of Informatics and Computing, Indiana University-Purdue University Indianapolis, 410 W, St,, Suite 2000, Indianapolis, IN 46202, USA.
BMC Med Inform Decis Mak. 2014 Apr 10;14:31. doi: 10.1186/1472-6947-14-31.
A cloud-based clinical decision support system (CDSS) was implemented to remotely provide evidence-based guideline reminders in support of preventative health. Following implementation, we measured the agreement between preventive care reminders generated by an existing, local CDSS and the new, cloud-based CDSS operating on the same patient visit data.
Electronic health record data for the same set of patients seen in primary care were sent to both the cloud-based web service and local CDSS. The clinical reminders returned by both services were captured for analysis. Cohen's Kappa coefficient was calculated to compare the two sets of reminders. Kappa statistics were further adjusted for prevalence and bias due to the potential effects of bias in the CDS logic and prevalence in the relative small sample of patients.
The cloud-based CDSS generated 965 clinical reminders for 405 patient visits over 3 months. The local CDSS returned 889 reminders for the same patient visit data. When adjusted for prevalence and bias, observed agreement varied by reminder from 0.33 (95% CI 0.24 - 0.42) to 0.99 (95% CI 0.97 - 1.00) and demonstrated almost perfect agreement for 7 of the 11 reminders.
Preventive care reminders delivered by two disparate CDS systems show substantial agreement. Subtle differences in rule logic and terminology mapping appear to account for much of the discordance. Cloud-based CDSS therefore show promise, opening the door for future development and implementation in support of health care providers with limited resources for knowledge management of complex logic and rules.
为了远程提供基于循证的指南提醒以支持预防保健,我们实施了一个基于云的临床决策支持系统(CDSS)。在实施之后,我们测量了现有本地 CDSS 和新的基于云的 CDSS 在处理相同患者就诊数据时生成的预防保健提醒之间的一致性。
将同一组在初级保健中就诊的患者的电子健康记录数据发送到基于云的 Web 服务和本地 CDSS。捕获这两个服务返回的临床提醒以进行分析。计算 Cohen's Kappa 系数以比较这两组提醒。进一步调整 Kappa 统计数据,以考虑到 CDS 逻辑中的偏差和相对较小患者样本中的患病率的潜在影响,对偏差和患病率进行调整。
基于云的 CDSS 在 3 个月内为 405 次就诊的患者生成了 965 个临床提醒。本地 CDSS 为相同的患者就诊数据返回了 889 个提醒。在调整了偏差和患病率后,观察到的一致性因提醒而异,从 0.33(95%CI 0.24-0.42)到 0.99(95%CI 0.97-1.00)不等,对于 11 个提醒中的 7 个,几乎达到了完美的一致性。
由两个不同的 CDSS 系统提供的预防保健提醒具有显著的一致性。规则逻辑和术语映射方面的细微差异似乎解释了大部分差异。基于云的 CDSS 因此具有很大的潜力,为支持资源有限的医疗保健提供者在复杂逻辑和规则的知识管理方面的未来发展和实施开辟了道路。