Developing Evidenced-Based, User-Friendly, Self-Directed, and Effective-Driven Nursing Assessment Decision-Assistive System---An Example of Fall Assessment for Hospital Inpatients

  • Chang, Wen-Yin (PI)
  • Hsu, Chien-Yeh (CoI)

Project: A - Government Institutionb - National Science and Technology Council

Project Details


Patient falls and fall-related injuries are commonly reported and may result in a serious significant problem in the hospitals. Thus, developing an effective and safe intervention system is needed to reduce the occurrence of fall for adult hospital inpatients. However, no reports of a fall nursing assessment decision-assistive system for nurses in acute care hospitals have been published. Therefore, the aims of this 3-year of the grant proposal will be (1) to analyze the existing data in nursing information system (NIS) and fall incident reporting system between 2011 and 2013 to understand the gaps between these two data, and to analyze reasons and factors related to patient falls and failure of intervention, (2) to develop an evidenced-based, user-friendly, self-directed, and effective-driven nursing assessment decision-assistive system (EUSENADS) and to compare outcomes differences before and after implementation of the EUSENADS system. This 3-year study will be secondary data analysis and pre- and post-intervention outcome evaluation design and conducted in three phases—system plan and secondary data analysis, system development and testing, and implementation of the system. This study will be conducted in a medical center in Taipei. All nurses on the medical and surgical wards will be invited to participate in this study. Data will be collected from NIS and incident reporting system database, and by questionnaire survey. Instruments used for this study will be included data collection tool, system perception scale, and satisfaction with the system questionnaire. Items of the satisfaction questionnaire will be designed using 4-point Likert scale from (1) strongly disagree to (4) strongly agree. Data will be analyzed using descriptive statistics such as number, percentage, mean, and standard deviation to analyze the characteristics of nurses, fall rates, severity of fall, fall assessment time per person, sensitivity and specificity of fall assessment scales, and costs of fall-related hospitalization. The inferential statistics such as independent t tests, chi-square tests, Kruskal-Wallis test, paired-t test, and logistic regression analysis will be employed to analyze differences in the fall assessment time per person, fall rates, severity of fall, and the scores of satisfaction with the system before and after implementation of the EUSENADS system. Finally, a generalized estimating equation (GEE) will be utilized to analyze relationships before and after implementation of the EUSENADS system. The results of this study not only serve as references for hospital administrators and nursing leaders to design fall prevention information system, but also to enhance nurses’ abilities in decision-making to select appropriate fall prevention strategies for inpatients who at high risk to prevent the occurrence of fall in the hospital settings.
Effective start/end date8/1/157/31/16


  • inpatient fall
  • fall prevention
  • nurses
  • health informatics technology


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