Nursing Decision Support System Research Articles

Retracted: Design of Intelligent Nursing Decision Support System Based on Multiattribute Decision Model

Retracted: Design of Intelligent Nursing Decision Support System Based on Multiattribute Decision Model

Design of Intelligent Nursing Decision Support System Based on Multiattribute Decision Model.

Nursing medical histories are copied randomly, leading to the occurrence of incorrectly altered histories, and medical errors are on the rise year by year. Moreover, erroneous falsification of nursing medical history is a challenging issue that needs to be urgently addressed in domestic nursing medical history. In this manuscript, a caregiver association method, which is based on a multiattribute decision model, is proposed which is specifically designed for the caregiver association selection problem in intelligent caregiving decision making. In this mode, through the selection and modeling of key performance indicators and the design of combined weights, the purpose of regulating the priority of attributes is achieved. At the same time, a comprehensive evaluation index is designed to realize the joint optimization of multiple attributes. Simulation results show that the proposed nursing workstation can not only tradeoff the quality of nursing staff experience and system performance but also balance the distribution of nursing work and effectively reduce the association delay and system energy consumption．

Nursing decision support system: application in electronic health records†

Abstract The clinical decision support system makes electronic health records (EHRs) structured, intelligent, and knowledgeable. The nursing decision support system (NDSS) is based on clinical nursing guidelines and nursing process to provide intelligent suggestions and reminders. The impact on nurses’ work is mainly in shortening the recording time, improving the quality of nursing diagnosis, reducing the incidence of nursing risk events, and so on. However, there is no authoritative standard for the NDSS at home and abroad. This review introduces development and challenges of EHRs and recommends the application of the NDSS in EHRs, namely the nursing assessment decision support system, the nursing diagnostic decision support system, and the nursing care planning decision support system (including nursing intervene), hoping to provide a new thought and method to structure impeccable EHRs.

Design and application of nursing clinical decision support system based on structured electronic medical records

Objective Establishing intelligent nursing clinical decision support system to improve the safety, quality and efficiency of clinical nursing work. Methods Guided by the HIMSS EMRAM Analytics stage 7 evaluation standard, the nursing decision support system is continuously improved through the establishment of knowledge bases such as nursing plans and nursing conventions. Results By comparing the writing time of nursing documents, the correct rate of nursing diagnosis, and the incidence of nursing risk events before and after the used of the system, the results showed that after the used of the nursing decision-making support system, whole hospital′s writing time could save 222.5 hours per day, improved accuracy of nursing diagnosis from 68.33% (205/300)to 90.67% (272/300), the difference was statistical significace (χ2=45.907, P<0.05). Decreased incidents, hospital-wide, on falls from 0.127‰ (80/631 702) to 0.071‰ (45/638 715),and on pressure ulcer form 0.064‰ (41/631 702)to 0.028‰ (18/638 715), the difference was statistical significace (χ2=13.004~15.071, P<0.05). Conclusion Nursing clinical decision support system is the trend of hospital informatization and is worthy of clinical application. Key words: Structured electronic medical records; Information systems; Nursing clinical decision support system

Nurses' Responses to Differing Amounts and Information Content in a Diagnostic Computer-Based Decision Support Application

Nurses often need to make quick decisions with incomplete diagnostic information while they are under time pressure. The use of a data-driven, computerized decision support approach in daily work activities has great potential to facilitate precise and context-sensitive use of the information implicit in nursing diagnoses. This study explored optimal information amounts and levels of information content for designing and implementing a diagnostic nursing decision support system. Specifically, the use of probability data for likely nursing problems and the preferred number of displayed nursing problems were presented to expert and novice nurses. The study used a counterbalanced, repeated-measures, and factorial design. The authors developed two scenarios: (1) a pneumonia patient with diabetes mellitus complications and (2) a patient with controlled diabetes who also had a bone fracture. A previously developed prototype for a diagnostic nursing decision support system was used to display the information. Eighteen novice and expert nurses from two hospitals in Korea participated. Results for the differing levels of content did not differ significantly with level of expertise, but the preferred amount of information was significant for the two groups of nurses. The differing information needs of novices and experts should be considered when designing future computer-based decision support.

Encoding a clinical practice guideline using guideline interchange format: A case study of a depression screening and management guideline

Purpose Clinical practice guidelines (CPGs) are common tools for clinicians in daily practice. In order to use CPGs effectively at the point of care, representing CPGs into computer-interpretable format is essential. Since computer-interpretable guidelines (CIGs) have been reported to increase clinicians’ usage of guidelines and improve patient's outcomes, it is critical to assess health care knowledge translated from CPGs into CIGs. The overall goal of this study was to illustrate the steps involved in encoding a guideline in guideline interchange format-3 (GLIF3) through a case study of a depression screening and management CPG for a nursing decision support system (DSS). Methods This study consisted of five steps: (1) Selection of a CPG; (2) extraction and categorization in GLIF3 of concepts related to depression screening and management tasks; (3) converting GLIF3 steps to a flowchart; (4) creation of scenarios representing possible execution paths; (5) guideline execution engine (GLEE) execution of scenarios. Discussion This study contributes to the body of knowledge regarding creation of CIGs and the use of GLEE as an evaluation tool for the encoded CIG in GLIF format for a depression CPG. CPG representation using GLIF3 and its evaluation by GLEE are useful methods to prepare nursing CPGs for implementation in a DSS.