Design and application of time series algorithm model in information assisted sensing system of nursing measurement in neurology. (1st October 2020)

• Record Type: Journal Article
• Authors: Liu, Meirong; Wang, Miaoxia; He, Quanyuan; Yin, Mingyuan
• Is Part Of: Measurement. Volume 162(2020)
• Publication Date: 2020-10-01
• Subjects: Time series algorithm model -- Neurology nursing work -- Workload statistics -- Information-assisted sensing system -- Human resource allocation; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2020.107894
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: This paper used time series algorithm model. This paper calculates the nursing workload of neurology department. The measurement index of nursing workload of neurology department were established. Abstract: Objective: This paper studies the measurement indicators of nursing workload in neurology department, discusses the development and development law of nursing workload, provides scientific basis for nursing staff deployment, and guarantees high-quality and safe nursing services. Methods: By using time series algorithm model, the paper calculates the nursing workload of neurology department and establishes the measurement index of nursing workload of neurology department. Improve the nursing workload information system, automatically generate and extract daily nursing workload, and construct a time series model of daily nursing workload. Results: Through literature search and on-site observation, preliminary measurement of nursing workload measurement indicators, consultation with expert meetings to develop an expert consultation form for nursing workload measurement indicators, and application of SPSS 19.0 for time series analysis to construct a time series model for daily nursing workload. Results: The best fit models of the time series of the two wards in neurology department were both exponential smoothing models. The predictive value of the ward A smoothing model for the total nursing workload from January 1 to 3, 2014 was 317.39. 316.14, 295.94 points (upper limit: 366.39, 375.95, 364.88 points; lower limit: 268.40, 256.33, 227.00 points). The prediction value of the B Ward Index Smoothing Model for the total nursing workload from January 1 to 3, 2014 is 450.03, 449.38, 445.58 points (upper limit: 503.76, 512.04, 515.05 points; lower limit: 396.30, 386.71, 375.11 points). Conclusion: Time series analysis can predict the nursing workload on the one hand, and adjust the number of nurses on the day of work according to the short-term predicted value of the nursing workload on the time series model; on the other hand, it can evaluate the rationality of the existing manpower allocation strategy.