Using medication administration and double-check data to infer social network influence in intensive care units
- 8 Downloads
Identifying individuals with positive patient safety influence can be challenging and often involve time-consuming surveys. However, as healthcare systems increase their integration of health information technologies with clinical workflow, more social and behavioral data can be obtained automatically. Physician referrals, surgical team schedules, and medication double-check logs are examples of behavioral data that reflect a degree of individual choice, preference, and influence. In this paper, we explored data from medication double-check interactions in the context of nursing influence. Survey responses and double-check data from 86 nurses in three intensive care units were collected. There was on average 21.8% overlap between influence and double-check networks. Medication in-degree scores tended to correlate more with influence, especially when it came to trust proxies for two units (0.79 and 0.47). While results varied by unit and medication class, this work provides a lens for exploring influence on units using unobtrusive measures.
KeywordsInfluence Intensive care unit Patient safety Double-check Passive data Unobtrusive measure
AF, EF, and SP were involved in the project formulation and data collection associated with this manuscript. AF and TK were involved in the data analysis associated with the manuscript. Each author was involved in the writing, editing and review of the manuscript.
This work was supported by the Robert Wood Johnson Foundation under Grant #72004.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interests.
- Flin R, Winter J, Sarac C, Raduma M (2009) Human factors in patient safety: review of topics and tools. Report for methods and measures working group of WHO patient safety. World Health Organization, GenevaGoogle Scholar
- Flodgren G, Parmelli E, Doumit G, Gattellari M, O’Brien MA, Grimshaw J, Eccles MP (2010) Local opinion leaders: effects on professional practice and health care outcomes. Cochrane Database Syst Rev. https://doi.org/10.1002/14651858.cd000125.pub4.local CrossRefGoogle Scholar
- Ueno M (2004) Data mining and text mining technologies for collaborative learning in an ILMS “Samurai,” pp 4–5Google Scholar
- Zhou Y (2008) Mining organizational emails for social networks with application to Enron corpus. Rensselaer Polytechnic Institute, TroyGoogle Scholar