A Clinical Decision Support System for Assessing the Risk of Cervical Cancer: Development and Evaluation Study

Development and evaluation of a clinical nursing decision support system for the prevention of neonatal hypoglycaemia

BACKGROUND

Hypoglycaemia is one of the most common complications during the neonatal period. Recurrent hypoglycaemia episodes can result in neurodevelopmental deficits and even sudden death. Available evidence indicates that healthcare professionals ought to promptly assess the risk of hypoglycaemia in newborns immediately following birth and formulate the most suitable preventive strategies. Consequently, this study was designed to develop a clinical nursing decision support system for neonatal hypoglycaemia prevention based on the prediction model for neonatal hypoglycaemia risk that was developed in a previous study, and to evaluate its efficacy.

METHODS

Nursing process as the theoretical framework, based on evidence-based nursing, standardized nursing language, and clinical decision support technology, the neonatal hypoglycaemia prevention nursing decision support system was developed.This system was implemented in the neonatology department of a tertiary grade A general hospital from September 1st to 30th, 2023.The application efficacy of the system was assessed and compared through the examination of the incidence of neonatal hypoglycemia, adverse outcomes associated with neonatal hypoglycemia, and the experiences of nurses following the implementation of the system.

RESULTS

The incidence of neonatal hypoglycaemia decreased after the system was implemented, and the difference was statistically significant (X2 = 4.522, P = 0.033). None of the neonates experienced adverse outcomes during hospitalization. The rate of hypoglycaemia risk assessment in neonates after system implementation was 92.16%. The total Clinical Nursing Information System Effectiveness Evaluation Scale score was 104.36 ± 1.96.

CONCLUSION

The neonatal hypoglycaemia prevention nursing decision support system realizes neonatal hypoglycaemia risk assessment, intelligent decision-making, and effect evaluation, effectively diminishes the incidence of neonatal hypoglycaemia, and enhances the standardization of neonatal hypoglycaemia management.

Developing a clinical decision support system software prototype that assists in the management of patients with self-harm in the emergency department: protocol of the PERMANENS project

BACKGROUND

Self-harm presents a significant public health challenge. Emergency departments (EDs) are crucial healthcare settings in managing self-harm, but clinician uncertainty in risk assessment may contribute to ineffective care. Clinical Decision Support Systems (CDSSs) show promise in enhancing care processes, but their effective implementation in self-harm management remains unexplored.

METHODS

PERMANENS comprises a combination of methodologies and study designs aimed at developing a CDSS prototype that assists clinicians in the personalized assessment and management of ED patients presenting with self-harm. Ensemble prediction models will be constructed by applying machine learning techniques on electronic registry data from four sites, i.e., Catalonia (Spain), Ireland, Norway, and Sweden. These models will predict key adverse outcomes including self-harm repetition, suicide, premature death, and lack of post-discharge care. Available registry data include routinely collected electronic health record data, mortality data, and administrative data, and will be harmonized using the OMOP Common Data Model, ensuring consistency in terminologies, vocabularies and coding schemes. A clinical knowledge base of effective suicide prevention interventions will be developed rooted in a systematic review of clinical practice guidelines, including quality assessment of guidelines using the AGREE II tool. The CDSS software prototype will include a backend that integrates the prediction models and the clinical knowledge base to enable accurate patient risk stratification and subsequent intervention allocation. The CDSS frontend will enable personalized risk assessment and will provide tailored treatment plans, following a tiered evidence-based approach. Implementation research will ensure the CDSS' practical functionality and feasibility, and will include periodic meetings with user-advisory groups, mixed-methods research to identify currently unmet needs in self-harm risk assessment, and small-scale usability testing of the CDSS prototype software.

DISCUSSION

Through the development of the proposed CDSS software prototype, PERMANENS aims to standardize care, enhance clinician confidence, improve patient satisfaction, and increase treatment compliance. The routine integration of CDSS for self-harm risk assessment within healthcare systems holds significant potential in effectively reducing suicide mortality rates by facilitating personalized and timely delivery of effective interventions on a large scale for individuals at risk of suicide.