A Clinical Decision Support System as a Tool to Improve the Accuracy of Nursing Diagnoses

Sudarmaji, Wikan Purwihantoro; Sholihin, Sholihin; Permana, Roby Aji; Soares, Agustina; Nugraha, Yanuar Aga

doi:10.20473/jn.v14i3.17171

Cited by 3 publications

(10 citation statements)

References 12 publications

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“…Adopting multiple clinical scenarios, the use of the NP-CDSS improved the accuracy of nursing diagnoses developed in comparison to the traditional approach, thus reinforcing the body of literature on how technology using electronic documentation systems can help students to learn, to use, and to refine clinical reasoning skills needed for nursing practice. 1,11,12,[19][20][21] The improvements in accuracy of nursing diagnoses that persisted for scenarios 1 (cancer) and 3 (stroke) even after controlling for demographic and academic factors suggest that Florence increased nursing diagnostic accuracy. The reason of this improvement was due to the beneficial characteristic of the NP-CDSS to "guide" nursing students through the nursing process stages, starting from the identification of the correct diagnostic indicators (as highlighted by the significant results obtained, as shown in Table 2), which are crucial elements to provide the final correct nursing diagnoses.…”

Section: Discussionmentioning

confidence: 99%

“…Nursing Process-Clinical Decision Support System is defined as an electronic system used to support nurses' diagnostic reasoning using data from practice and documenting it in electronic health records. According to an internationally standard consensus, this system should contain 25 criteria grouped in eight main categories that include the following: (1) Florence is an Italian NP-CDSS, produced by i-CEA (Casa Editrice Ambrosiana, Rozzano, Italy), designed to support nurses and nursing students in the decision-making process regarding diagnoses, interventions, and outcomes. In addition, Florence allows the creation of simulated clinical cases with real patients, allowing students to improve their clinical reasoning skills following the phases of the nursing process.…”

Section: Nursing Process-clinical Decision Support Systemmentioning

confidence: 99%

“…The lack of standardized nursing terminology use in nursing documentation can pose a risk to patients. 1 The use of terminologies and the identification of nursing diagnoses showed an impact on patient, organizational, and educational outcomes. [2][3][4][5][6] Within the nursing process, nursing diagnosis is defined as the nurse's clinical judgment about patient's response to health problems based on a comprehensive nursing assessment.…”

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confidence: 99%

“…In addition, these systems have been shown to help nurses identify "high-accuracy" nursing diagnoses that reflect a patient's clinical condition. 1,17,18 A variety of electronic documentation systems (such as PROCEnf-USP, WiseNurse, Plataforma PEnsinar, and Nurse Diagnostician software) have been created to assess nursing students' diagnostic accuracy and have been found to strengthen nurses' clinical reasoning. 11,12,[19][20][21] Peres and colleagues 21 examined the diagnostic accuracy of nursing students from a paper-based diagnostic approach compared with a CDSS diagnostic approach.…”

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confidence: 99%

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Nursing Diagnosis Accuracy in Nursing Education

Bertocchi,

Dante,

La Cerra

et al. 2023

CIN: Computers, Informatics, Nursing

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Computer-based technologies have been widely used in nursing education, although the best educational modality to improve documentation and nursing diagnostic accuracy using electronic health records is still under investigation. It is important to address this gap and seek an effective way to address increased accuracy around nursing diagnoses identification. Nursing diagnoses are judgments that represent a synthesis of data collected by the nurse and used to guide interventions and to achieve desirable patients' outcomes. This current investigation is aimed at comparing the nursing diagnostic accuracy, satisfaction, and usability of a computerized system versus a traditional paper-based approach. A total of 66 nursing students solved three validated clinical scenarios using the NANDA-International terminologies traditional paper-based approach and then the computer-based Clinical Decision Support System. Study findings indicated a significantly higher nursing diagnostic accuracy (P < .001) in solving cancer and stroke clinical scenarios, whereas there was no significant difference in acute myocardial infarction scenario. The use of the electronic system increased the number of correct diagnostic indicators (P < .05); however, the level of students' satisfaction was similar. The usability scores highlighted the need to make the electronic documentation systems more user-friendly.

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Section: Discussionmentioning

confidence: 99%

Section: Nursing Process-clinical Decision Support Systemmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Nursing Diagnosis Accuracy in Nursing Education

Bertocchi,

Dante,

La Cerra

et al. 2023

CIN: Computers, Informatics, Nursing

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“…Some of the benefits of EHR include increasing efficiency, increasing positive patient outcomes, and population health (Kruse et al, 2018). The EHR may also include a Decision Support System that provides up-todate medical knowledge, reminders or other measures that assist healthcare professionals in making decisions (Sudarmaji et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Meta-Analysis the Effect of Electronic Health Record Utilization on Mortality and Readmission

Syahbaniar

Tamtomo²,

Murti³

2022

HEALTH POLICY MANAGE

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Background:The development of technology shows a very rapid development, especially in the health sector. One example of the use of technology in the health sector is the electronic health record. Electronic health records provide several benefits, one of which can reduce mortality and readmission rates in patients. The purpose of this study was to estimate the effect of electronic health records on mortality and readmission rates by meta-analysis. Subjects and Method: This was a meta-analysis study using PRISMA flowchart guidelines. The article search process was carried out between 2011-2022 using databases from PubMed, Google Scholar, ProQuest, Science Direct and Scopus. The PICO formula used is P = patients with asthma. I= using internet-based self-management. C= without using internet-based self-management. O= asthma control. Article searches were performed using the keywords "mhealth" OR "mobile health" OR "telemedicine" AND "self management" AND "asthma control" OR "asthma treatment" AND "asthma control". The inclusion criteria were full paper articles with randomized controlled trial study design, articles using English, the intervention provided was the application of internet-based self-management, and the outcome was asthma control. Based on the database, there were 9 articles that met the inclusion criteria. The analysis was carried out using Revman 5.3 software. Results: A total of 13 articles spread across 2 continents, namely Asia (Taiwan, Singapore, and South Korea) and North America (South America). Articles reviewed in the meta-analysis showed that electronic health records had an effect on reducing mortality by 0.74 times compared to those without using electronic health records (aOR= 0.74; 95% CI= 0.64 to 0.86; p<0.001). In addition, it was also found that electronic health records had an effect on reducing readmission by 0.77 times compared to without using electronic health records (aOR= 0.77; CI 95%= 0.62 to 0.95; p= 0.010) Conclusion: The application of electronic health records has an effect on reducing mortality and readmission rates.

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Global Trends and Hotspots in Nursing Research on Decision Support Systems

Yangöz,

Turan Kavradım,

Özer

2024

CIN: Computers, Informatics, Nursing

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Decision support systems have been widely used in healthcare in recent years; however, there is lack of evidence on global trends and hotspots. This descriptive bibliometric study aimed to analyze bibliometric patterns of decision support systems in nursing. Data were extracted from the Web of Science Core Collection. Published research articles on decision support systems in nursing were identified. Co-occurrence and co-citation analysis was performed using CiteSpace version 6.1.R2. In total, 165 articles were analyzed. A total of 358 authors and 257 institutions from 20 countries contributed to this research field. The most productive authors were Andrew Johnson, Suzanne Bakken, Alessandro Febretti, Eileen S. O'Neill, and Kathryn H. Bowles. The most productive country and institution were the United States and Duke University, respectively. The top 10 keywords were “care,” “clinical decision support,” “clinical decision support system,” “decision support system,” “electronic health record,” “system,” “nursing informatics,” “guideline,” “decision support,” and “outcomes.” Common themes on keywords were planning intervention, national health information infrastructure, and methodological challenge. This study will help to find potential partners, countries, and institutions for future researchers, practitioners, and scholars. Additionally, it will contribute to health policy development, evidence-based practice, and further studies for researchers, practitioners, and scholars.

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A Clinical Decision Support System as a Tool to Improve the Accuracy of Nursing Diagnoses

Cited by 3 publications

References 12 publications

Nursing Diagnosis Accuracy in Nursing Education

Nursing Diagnosis Accuracy in Nursing Education

Meta-Analysis the Effect of Electronic Health Record Utilization on Mortality and Readmission

Global Trends and Hotspots in Nursing Research on Decision Support Systems

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