背景

研究者已开发数种衡量护理工作量的工具。常用的适用于所有类型的ICU患者的工具包括护理活动评分（NAS）和治疗干预评分系统（TISS）。既往研究显示，NAS可以解释实际护理时间的59％至81％，而治疗干预评分系统（TISS）仅描述实际护理时间的43％。并且在两个模型中，开发过程都不基于时间测量。

目标

本研究的目的是建立一个基于时间的模型，该模型可以更准确地评估与患者相关的护理工作量，并评估患者的特征是否影响护理时间，以及是否应将其包括在模型中。

设计

观察研究设计。

设置

荷兰的82家ICU参加了国家重症监护评估（NICE）质量注册。这些重症监护病房中有15个正在参加新实施的自愿护理能力模块。有七个ICU自愿参加了这项研究。

参加者

在整个轮班期间，观察员会跟踪由所选护士负责的患者。

方法

在七个荷兰重症监护病房中，以不同班次衡量每个患者每次护理活动所花费的时间。是使用内部开发的Web应用程序测量护理活动。建立了三种不同复杂程度的不同模型（1.仅护理活动；2.护理活动和病例混合校正；3.每个护理活动具有病例混合校正的复杂模型）来解释每位患者的护理时间总量。使用Pearson相关系数（R2），均方根预测误差（RMSPE），均值绝对预测误差（MAPE）和预测偏差建立模型。

结果

共观察371个班次的287位患者。与患者相关护理工作量相比，模型1的皮尔逊R值为0.89（95％CI 0.86-0.92），模型2为0.90（95％CI 0.88-0.93），模型3为0.64（95％CI 0.56-0.72）。

结论

在衡量护理工作量方面，新开发的护士操作工作量（NOW）模型优于现有模型，它减少了活动数量，从而减轻了注册负担。病例混合校正不能进一步提高此模型的性能。通过NOW测量的与患者相关的护理工作量可以洞悉患者所需的实际护理时间，因此可用于评估每位护士每名患者的平均工作量。

 

 

Background

Several instruments have been developed to measure nursing workload. The commonly used Nursing Activities Score (NAS) and Therapeutic Intervention Scoring System (TISS) are applied to all types of ICU patients. Former research showed that NAS explained 59 to 81% of actual nursing time, whereas the Therapeutic Intervention Scoring System (TISS) described only 43% of the actual nursing time. In both models the development was not based on time measurements.

Objectives

The aim of this study was to develop a time-based model which can assess patient related nursing workload more accurately and to evaluate whether patient characteristics influence nursing time and therefore should be included in the model.

Design

Observational study design.

Setting

All 82 Dutch ICUs participate in the National Intensive Care Evaluation (NICE) quality registry. Fifteen of these ICUs are participating in the newly implemented voluntary nursing capacity module. Seven of these ICUs voluntarily participated in this study.

Participants

The patient(s) that were under the responsibility of a chosen nurse were followed by the observer during the entire shift.

Methods

Time spent per nursing activity per patient was measured in different shifts in seven Dutch ICUs. Nursing activities were measured using an in-house developed web application. Three different models of varying complexity (1. nursing activities only; 2. nursing activities and case-mix correction; 3. complex model with case-mix correction per nursing activity) were developed to explain the total amount of nursing time per patient. The performance of the three models was assessed in 1000 bootstrap samples using the squared Pearson correlation coefficient (R2), Root Mean Squared Prediction Error (RMSPE), Mean Absolute Prediction Error (MAPE), and prediction bias.

Results

In total 287 unique patients have been observed in 371 shifts. Model one's Pearson's R was 0.89 (95%CI 0.86–0.92), model two with case-mix correction 0.90 (95%CI 0.88–0.93), and the third complex model 0.64 (95%CI 0.56–0.72) compared with the actual patient related nursing workload.

Conclusion

The newly developed Nurse Operation Workload (NOW) model outperforms existing models in measuring nursing workload, while it includes a lower number of activities and therewith lowers the registration burden. Case-mix correction does not further improve the performance of this model. The patient related nursing workload measured by the NOW gives insight in the actual nursing time needed by patients and can therefore be used to evaluate the average workload per patient per nurse.

原文链接：

https://www.sciencedirect.com/science/article/pii/S0020748920302662