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Using medication administration and double-check data to infer social network influence in intensive care units

  • Allan FongEmail author
  • Tomilayo Komolafe
  • Ella Franklin
  • Sarah Henrickson Parker
Original Article
  • 8 Downloads

Abstract

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.

Keywords

Influence Intensive care unit Patient safety Double-check Passive data Unobtrusive measure 

Notes

Author’s contribution

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.

Funding

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.

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Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2019

Authors and Affiliations

  1. 1.National Center for Human Factors in HealthcareMedstar HealthWashingtonUSA
  2. 2.Virginia TechRoanokeUSA
  3. 3.Virginia Tech Carilion Research InstituteRoanokeUSA
  4. 4.Carilion Clinic Center for Simulation, Research and Patient SafetyRoanokeUSA

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