Nursing Decision Support Research Articles

Health Community 4.0: An Innovative Multidisciplinary Solution for Tailored Healthcare Assistance Management.

This paper presents a co-designed solution aimed at improving community healthcare assistance management. To enhance patients' self-awareness of their health status, encouraging active participation in care process, and to support nurses in delivering patient-centered care, we have developed an innovative platform with a highly customized app. This platform was designed using a multi-disciplinary, bottom-up approach. Patient data collection and processing facilitate the automation of care timeline planning, based on real-time patients' needs and the available resources. To achieve this goal, different components have been considered: real-time health data collection and processing, patient care planning, decision support for nurses, secure communication for data transmission, and user-friendly interfaces to ensure easy access to platform functionalities.

Prediction of The Patients at Risk for Development Hematoma After Percutaneous Coronary Angiography: A Nursing Decision Support Model Pilot Study

The study aimed to develop a nursing clinical decision support model using the machine learning method, which is one of the important fields today, to identify patients with risk of hematoma development after Percutaneous Coronary Intervention and to help plan appropriate nursing interventions. In this study, the data of 100 patients with myocardial infarction was used in the development of the decision support model. R open-source programming language was used for statistical analysis of the data and the random forest method, one of the machine learning methods was used for the development of the model. The result of this pilot study, a nursing decision support model with a sensitivity of 69% and a specificity of 64% was developed with the Random forest method using 24 features regarding the demographic, laboratory, and percutaneous coronary intervention procedures of the patients.

Prevalence and risk factors of the use of physical restraint and impact of a decision support tool: A before-and-after study.

Physical restraint is frequently used in intensive care units to prevent patients' life-threatening removal of indwelling devices. In France, their use is poorly studied. Therefore, to evaluate the need for physical restraint, we have designed and implemented a decision support tool. Besides describing the prevalence of physical restraint use, this study aimed to assess whether the implementation of a nursing decision support tool had an impact on restraint use and to identify the factors associated with this use. A large observational, multicentre study with a repeated one-day point prevalence design was conducted. All adult patients hospitalized in intensive care units were eligible for this study. Two study periods were planned: before (control period) and after (intervention period) the deployment of the decision support tool and staff training. A multilevel model was performed to consider the centre effect. During the control period, 786 patients were included, and 510 were in the intervention period. The prevalence of physical restraint was 28% (95% CI: 25.1%-31.4%) and 25% (95% CI: 21.5%-29.1%) respectively (χ2 = 1.35; p = .24). Restraint was applied by the nurse and/or nurse assistant in 96% of cases in both periods, mainly to wrists (89% vs. 83%, p = .14). The patient-to-nurse ratio was significantly lower in the intervention period (1:3.0 ± 1 vs. 1:2.7 ± 0.7, p < .001). In multivariable analysis, mechanical ventilation was associated with physical restraint (aOR [95% CI] = 6.0 [3.5-10.2]). The prevalence of physical restraint use in France was lower than expected. In our study, the decision support tool did not substantially impact physical restraint use. Hence, the decision support tool would deserve to be assessed in a randomized controlled trial. The decision to physically restrain a patient could be protocolised and managed by critical care nurses. A regular evaluation of the level of sedation could allow the most deeply sedated patients to be exempted from physical restraint.

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

Global output on artificial intelligence in the field of nursing: A bibliometric analysis and science mapping.

To analyze the AI research in the field of nursing, to explore the current situation, hot topics, and prospects of AI research in the field of nursing, and to provide a reference for researchers to carry out related studies. We used the VOSviewer 1.6.17, SciMAT, and CiteSpace 5.8.R3 to generate visual cooperation network maps for the country, organizations, authors, citations, and keywords and perform burst detection, theme evolution, and so forth. A total of 9318 articles were obtained from the Web of Science Core Collection database. Four hundred and thirty-one AI research related to the field of nursing was published by 855 institutions from 54 countries. CIN-Computers Informatics Nursing was the top productive journal. The United States was the dominant country. The transnational cooperation between authors from developed countries was closer than that between authors from developing countries. The main hot topics included nurse rostering, nursing diagnosis, nursing decision support, disease risk factor prediction, nursing big data management, expert system, support vector machine, decision tree, deep learning, natural language processing, and nursing education. Machine learning represented one of the cutting-edge and most applicable branches of artificial intelligence in the field of nursing, and deep learning was the hottest technology among many machine learning methods in recent years. One of the most cited papers was published by Burke in 2004 and cited 500 times, which critically evaluated AI methods to deal with nurse scheduling problems. Although AI has been paid more and more attention to the field of nursing, there is still a lack of high-yielding authors who have been engaged in this field for a long time. Most of the high contribution authors and institutions came from developed countries; therefore, more transnational and multi-disciplinary cooperation is needed to promote the development of AI in the nursing field. This bibliometric analysis not only provided a comprehensive overview to help researchers to understand the important articles, journals, potential collaborators, and institutions in this field but also analyzed the history, hot spots, and future trends of the research topic to provide inspiration for researchers to choose research directions.

Conducting a representative national randomized control trial of tailored clinical decision support for nurses remotely: Methods and implications

Clinical Decision Support (CDS) systems, patient specific evidence delivered to clinicians via the electronic health record (EHR) at the right time and in the right format, has the potential to improve patient outcomes. Unfortunately, outcomes of CDS research are mixed. A potential cause lies in its testing. Many CDS are implemented in practice without sufficient testing, potentially leading to patient harm. When testing is conducted, most research has focused on “what” evidence to provide with little attention to the impact of the CDS display format (e.g., textual, graphical) on the user.In an adequately powered randomized control trial with 220 hospital based registered nurses, we will compare 4 randomly assigned CDS format groups (text, text table, text graphs, tailored to subject's graph literacy score) for effects on decision time and simulated patient outcomes. We recruit using state based professional registries, which allows access to participants from multiple institutions across the nation. We use online survey software (REDCap) for efficient study workflow including screening, informed consent documentation, pre-experiment demographic data collection including a graph literacy questionnaire used in randomization. The CDS prototype is accessed via a web app and the simulation-based experiment is conducted remotely at a subject's local computer using video-conferencing software. Also included are 6 post intervention surveys to assess cognitive workload, usability, numeracy, format preference, CDS utilization rationale, and CDS interpretation. Our methods are replicable and scalable for testing of health information technologies and have the potential to improve the safety and effectiveness of these technologies across disciplines.

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．

Automation in nursing decision support systems: A systematic review of effects on decision making, care delivery, and patient outcomes.

The study sought to summarize research literature on nursing decision support systems (DSSs ); understand which steps of the nursing care process (NCP) are supported by DSSs, and analyze effects of automated information processing on decision making, care delivery, and patient outcomes. We conducted a systematic review in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement. PubMed, CINAHL, Cochrane, Embase, Scopus, and Web of Science were searched from January 2014 to April 2020 for studies focusing on DSSs used exclusively by nurses and their effects. Information about the stages of automation (information acquisition, information analysis, decision and action selection, and action implementation), NCP, and effects was assessed. Of 1019 articles retrieved, 28 met the inclusion criteria, each studying a unique DSS. Most DSSs were concerned with two NCP steps: assessment (82%) and intervention (86%). In terms of automation, all included DSSs automated information analysis and decision selection. Five DSSs automated information acquisition and only one automated action implementation. Effects on decision making, care delivery, and patient outcome were mixed. DSSs improved compliance with recommendations and reduced decision time, but impacts were not always sustainable. There were improvements in evidence-based practice, but impact on patient outcomes was mixed. Current nursing DSSs do not adequately support the NCP and have limited automation. There remain many opportunities to enhance automation, especially at the stage of information acquisition. Further research is needed to understand how automation within the NCP can improve nurses' decision making, care delivery, and patient outcomes.

Investigating the Role of Clinical Dashboards in Improving Nursing Care: A Systematic Review

Introduction: Advances and increasing technology adoption in the field of health have made it possible to implement tools such as clinical dashboards to assist nursing staff in providing better, more effective and safer care. The aim of this study was to investigate the role of clinical dashboards in providing nursing care.Material and Methods: This was a review study. For this purpose, the keywords Nursing, Nursing care, Clinical Dashboard, Health Dashboard, Evaluation were searched in the database of PubMed, Google Scholar, science direct. Criteria for inclusion in this study were studies that examined the role of clinical dashboards in the field of nursing and were published between 1990 and 2020. The necessary information was extracted using a researcher-made checklist and analyzed and reported in a descriptive manner.Results: A total of 2749 articles were retrieved. After reviewing by title, abstract and keywords, 7 studies that had appropriate content validity were selected for the present study. The intensive care unit had the highest frequency of dashboard use in nursing processes (n=3, 42%). The findings of this study showed that improving the quality of care, reducing medical errors and increasing patient safety are the most important benefits of using clinical dashboards in the field of nursing. Improving nurses' awareness of important patient issues and supporting clinical decisions were next in line.Conclusion: Clinical dashboards in the field of nursing care can reduce errors and possible negligence in the treatment by integration patient information and providing a comprehensive visual view of important patient information and as a suitable tool for evidence-based clinical and nursing decision support.

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

SIRA + P: Development and Testing

ObjectivesTo describe the development of a new skin risk assessment scale called Skin Injury Risk Assessment and Prevention (SIRA+P) and to establish initial reliability and validity of the scale among patients ranging in age from birth, including pre-term, to adulthood, regardless of age or acuity of illness. Study DesignThe single-site study was a retrospective chart review to evaluate the measurement properties of SIRA+P. Charts of 385 patients of all ages and in all units (including the neonatal and pediatric intensive care units) of a free-standing children's hospital were included. Concurrent validity was assessed with scales having previously established reliability and validity. For subjects <30days of age, the comparison scale was the Neonatal Skin Risk Assessment Scale (NSRAS); for subjects 31days through 17years, the Braden Q Scale (Braden Q) was used; and for subjects 18years and older, the Braden Scale (Braden) was used. Interrater reliability was examined using Intraclass Correlation Coefficients (ICC). Concurrent validity procedures compared SIRA+P with NSRAS, Braden Q, and Braden using Pearson Correlation Coefficients. ResultsInterrater reliability for SIRA+P was very high (0.878). SIRA+P strongly correlated with the NSRAS (0.725), the Braden Q (−0.634), and the Braden (−0.778). ConclusionSIRA+P is designed to be used within the EHR and includes nursing decision support to guide pressure injury prevention interventions for specific skin integrity risks. SIRA+P has good interrater reliability, is valid across all age groups and accounts for device-related pressure.

An Internationally Consented Standard for Nursing Process-Clinical Decision Support Systems in Electronic Health Records.

Nurses are accountable to apply the nursing process, which is key for patient care: It is a problem-solving process providing the structure for care plans and documentation. The state-of-the art nursing process is based on classifications that contain standardized concepts, and therefore, it is named Advanced Nursing Process. It contains valid assessments, nursing diagnoses, interventions, and nursing-sensitive patient outcomes. Electronic decision support systems can assist nurses to apply the Advanced Nursing Process. However, nursing decision support systems are missing, and no "gold standard" is available. The study aim is to develop a valid Nursing Process-Clinical Decision Support System Standard to guide future developments of clinical decision support systems. In a multistep approach, a Nursing Process-Clinical Decision Support System Standard with 28 criteria was developed. After pilot testing (N = 29 nurses), the criteria were reduced to 25. The Nursing Process-Clinical Decision Support System Standard was then presented to eight internationally known experts, who performed qualitative interviews according to Mayring. Fourteen categories demonstrate expert consensus on the Nursing Process-Clinical Decision Support System Standard and its content validity. All experts agreed the Advanced Nursing Process should be the centerpiece for the Nursing Process-Clinical Decision Support System and should suggest research-based, predefined nursing diagnoses and correct linkages between diagnoses, evidence-based interventions, and patient outcomes.

Toward a More Robust and Efficient Usability Testing Method of Clinical Decision Support for Nurses Derived From Nursing Electronic Health Record Data.

To develop methods for rapid and simultaneous design, testing, and management of multiple clinical decision support (CDS) features to aid nurse decision-making. We used quota sampling, think-aloud and cognitive interviews, and deductive and inductive coding of synchronized audio video data and archival libraries. Our methods and organizational tools allowed us to rapidly improve the usability, understandability, and usefulness of CDS in a generalizable sample of practicing nurses. The method outlined allows the rapid integration of nursing terminology based electronic health record data into routine workflow and holds strong potential for improving patient outcomes. The methods and organizational tools for development of multiple CDS system features can be used to translate knowledge into practice.

Clinical Decision Support for Nurses

Clinical Decision Support for Nurses

Clinical Decision Support for Nurses: A Fall Risk and Prevention Example.

Clinical decision support tools in electronic health records have demonstrated improvement with process measures and clinician performance, predominantly for providers. Clinical decision support tools could improve patient fall risk identification and prevention plans, a common concern for nursing. This quality-improvement project used clinical decision support to improve the rate of nurse compliance with documented fall risk assessments and, for patients at high risk, fall prevention plans of care in 16 adult inpatient units. Preintervention and postintervention data were compared using quarterly audits, retrospective chart review, safety reports, and falls and falls-with-injury rates. Documentation of fall risk assessments on the 16 units improved significantly according to quarterly audit data (P = .05), whereas documentation of the plans of care did not. Retrospective chart review on two units indicated improvement for admission fall risk assessment (P = .05) and a decrease in the documentation of the shift plan of care (P = .01); one unit had a statistically significant decrease in documentation of plans of care on admission (P = .00). Examination of safety reports for patients who fell showed all patients before and after clinical decision support had fall risk assessments documented. Falls and falls with injury did not change significantly before and after clinical decision support intervention.

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.

Integrating evidence into clinical information systems for nursing decision support

Purpose To illustrate approaches for providing decision support for evidence-based nursing practice through integration of evidence into clinical information systems (CISs) with examples from our experience at Columbia University Medical Center. Organizing construct Examples are organized according to three types of decision support functions: information management, focusing attention, and patient-specific consultation. Methods Three decision support tools that are integrated into three types of CISs are discussed: (1) infobuttons that provide context-specific access to digital sources of evidence; (2) automated Fall-Injury Risk Assessment; and (3) personal digital assistant-based screening reminders, screening assessments, and tailored documentation templates for the identification and management of obesity, depression, and tobacco cessation. The informatics infrastructure for implementing these decision support tools is described from the perspective of components identified in the published literature. Conclusions Efforts to facilitate application of evidence into nursing practice are unlikely to be successful unless the approaches used are integrated into the clinical workflow. Our approaches use a variety of informatics methods to integrate evidence into CISs as a mechanism for providing decision support for evidence-based practice in a manner consistent with nursing workflow.

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.

Clinical knowledge base development for preterm-birth risk assessment

The director of the National Center for Nursing Research (NCNR) challenged nurse scientists to develop substantive content in nursing knowledge domains and called for NCNR funding priorities to include both prevention of low-birth-weight infants and information system technologies (Hinshaw, 1989). To address this challenge, this study was the first in an ongoing program of informatics research in which artificial intelligence technology, called “machine learning,” was used to develop and describe a knowledge base for preterm-birth risk assessment. This research should improve outcomes for childbearing families through the development of a knowledge base that provides improved decision support for nurses' preterm risk assessment. Accurate assessment of preterm-birth risk will permit intervention with appropriate educational programs, bed rest, and early symptom management to prolong gestation. Review of the literature found no conceptual or theoretical models of preterm birth risk. The lack of conceptual and theoretical models of preterm-birth risk may account for poor reliability and validity of existing preterm-birth risk screening instruments (Nunnally, 1978). Existing preterm risk screening instruments include factors that are not valid predictors of preterm-birth risk and fail to include factors reported in the literature that may be valid predictors of preterm birth (Alexander, Weiss, Hulsey, & Papiernik, 1991; Lockwood et al., 1991; Woolery, 1992). Although existing instruments are not adequately predictive of preterm risk, current preterm birth prevention programs use these invalid, unreliable tools to intervene with pregnant women on a daily basis. The outcomes where a women is incorrectly assessed “low risk” for preterm birth may result in preterm birth and delivery of an infant that dies or that requires expensive high technology to survive. The outcomes where a women is assessed “high risk” but does not actually manifest preterm birth may result in extra prenatal visits and more contact with perinatal nurses and physicians to provide education and assessment. However, the expense involved in extra prenatal visits is minimal compared with the costs of high technology and infant morbidity and mortality, resulting in a trend that increasingly treats all pregnant women as if they were “high risk” for preterm birth. Solutions to this problem may be found using machine-learning technology.