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DOI: 10.3414/ME9245
A Three-layered Model of Nursing Based on Hospital Observation Data
Analysis of Nursing Care with Ubiquitous SensorsPublication History
05 November 2009
Publication Date:
17 January 2018 (online)
Summary
Objectives: Our aim is to investigate causes of medical incidents and construct a knowledge base for preventing malpractice based on monitored data.
Methods: To monitor nursing care, we developed an observing system of nursing activities with a ubiquitous sensor network and detecting errors in nursing care. This system is composed of a voice-recording device, mobile sensors and environmental setting type sensors. In cooperation with a hospital in western Japan, we have collected nursing activity data of nurses engaged at a combined ward, including ophthalmology, otolaryngology, and internal medicine for diabetes. After analyzing intravenous drip injection procedure (IVDI procedure) data, we introduce a three-layered model of nursing to understand nursing activities based on observed data. This model consists of three layers, 1) nursing care classification layer: Class, 2) nursing care step layer: Step, and 3) nursing care action layer: Action. This model is designed to take consistency with existing nursing care workflows.
Results: We implemented a detection system and succeeded in comprehending the work-flow of IVDI procedure at the rate of over 95%. This system also can distinguish IVDI workflows performed in parallel by at least two or several nurses. We implemented a picture showing interface of IVDI workflows which can show each patient with a specific color and distinct nurses.
Conclusions: Our system succeeded in verification of nursing care steps in IVDI procedure in ratios of more than 95%. Detection errors are due to the sensor system, so it is necessary to use or develop more precise devices.
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References
- 1 Project to Collect Medical Near-Miss/Adverse Event Information. 2006 Annual Report, Center for Medical Adverse Event Prevention, Japan Council for Quality Healthcare; July 18, 2007. ( http://www2.jcqhc.or.jp/html/documents/pdf/med-safe/year_report_english_2006.pdf ).
- 2 Reason J. Human Error. Cambridge University Press; 1992
- 3 Bogner MS, Hilsdale NJ. editors. Human error in medicine. Lawrence Erlbaum Associates; 1994
- 4 Bates DW, Cullen DJ. et al. Incidence of adverse drug events and potential adverse drug events. JAMA 1995; 274: 29-34.
- 5 Leape LL. et al. System analysis of adverse drug events. JAMA 1995; 274: 35-43.
- 6 Kohn LT, Corrigan JM, Donaldson MS. editors. To Err Is Human: a Safer Health System. The National Academy Press; 2000
- 7 Ohno Y. Introduction to Time and Motion Study in Health Care Service. KANGO-KENKYU 2004; 37: 289-295. (in Japanese).
- 8 Kogure K. Introduction to the E-Nightingale Project. ITE Technical Report 2006; 30: 17-22. (in Japanese).
- 9 Ohboshi N. et al. Development and Evaluation of Error Detecting System in Nursing Workflow. Japan Journal of Medical Informatics 2007; 27: 57-65 (in Japanese).
- 10 Kuwahara N. et al. Verifying Nursing Activities based on Nursing Workflow Model for Detecting Errors. Context-Aware Computing and Self-Managing Systems, Chapter 2: pp 15-41. CRC Press; 2009
- 11 Ozaku IH. et al. Nursing Spoken Corpora for Understanding Nursing Assignments. The Ninth International Congress on Nursing Information (NI2006); 2006
- 12 Naya F. et al. Workers’ Routine Activity Recognition using Body Movements and Location Information. Tenth IEEE International Symposium on Wearable Computers (ISWC2006); 2006 pp 105-108.
- 13 Resource Description Framework (RDF). http://www.w3.org/RDF/. W3C.
- 14 Japan Academy of Nursing Science. Classification of Nursing care. Japan Academy of Nursing Science; 2005 (ISBN 4-8180-1142-8) (in Japanese).