Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
Background: There has been increasing interest in the utilisation of health data analytics for decision support systems and prioritising pharmacy clinical work. Despite this potential, there remains limited evidence in the Australian context regarding the design and implementation of data-driven dashboards tailored specifically for pharmacists.
Objectives: We aimed to develop a disease state dashboard in an Australian hospital to assist clinicians identifying and prioritising the review of heart failure (HF) patients when admitted for other reasons, enabling timely optimisation of their care.
Practice Description: This project was undertaken to enhance clinical pharmacy services through the integration of health data analytics.
Practice Innovation: Using agile methodology to develop the dashboard to accurately identify HF patients regardless of their admission reason or specialty.
Evaluation Methods: A prospective validation was conducted to evaluate the sensitivity, specificity, and accuracy of the HF dashboard parameters. A random sample of 100 patients with confirmed HF diagnoses was reviewed by a clinical pharmacist to establish a reference standard. The same patient list was used to test the dashboard's ability to accurately identify HF cases using five key clinical indicators, including any form and dose of loop diuretics, intravenous loop diuretics, ICD-10 coding taxonomy, guidelines-directed medical therapy (GDMT), and BNP levels.
Results: A stakeholder group designed and built the dashboard from a set of 18 carefully chosen HF clinical and non-clinical parameters. Testing and validation of the dashboard demonstrated overall calculated accuracy to be >70% for the 5 HF main clinical parameters built in the dashboard. Positive predictive values for all parameters were also >80%, indicating low likelihood to identify incorrect patients.
Conclusion: The dashboard is scalable and transferable due to its flexible, parameter-driven design and use of standardized clinical data. Its agile development and real-time integration support expansion to other conditions and settings.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.japh.2025.102914 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Microfluidic paper-based analytical devices for food spoilage detection: emerging trends and future directions.
Talanta
September 2025
Faculty of Applied Sciences, Ton Duc Thang University, Ho Chi Minh City, Viet Nam. Electronic address:
Food spoilage poses a global challenge with far-reaching consequences for public health, economic stability, and environmental sustainability. Conventional analytical methods for spoilage detection though accurate are often cost-prohibitive, labor-intensive, and unsuitable for real-time or field-based monitoring. Microfluidic paper-based analytical devices (μPADs) have emerged as a transformative technology offering rapid, portable, and cost-effective solutions for food quality assessment.
View Article and Find Full Text PDFEvaluating the effectiveness of ChemoNurse: A mobile chemotherapy drug guide for cancer nurses - A randomized controlled trial.
Eur J Oncol Nurs
August 2025
Koç University Hospital, Faculty of Medicine, Department of Medical Oncology, Istanbul, Türkiye. Electronic address:
Purpose: This study aimed to evaluate the effectiveness of a mobile chemotherapy drug guide application, ChemoNurse, developed for cancer nurses, in improving their knowledge and attitudes toward chemotherapy practices.
Methods: A randomized controlled trial with a repeated-measures design was conducted with 59 nurses (29 intervention, 30 control) who participated. Nurses in the intervention group used the ChemoNurse mobile application for six months, while the control group received no additional intervention.
Symptoms of mental health difficulties in police cell detainees in Iceland: Is it an appropriate place of safety for patients? A mixed methods study.
Int J Law Psychiatry
September 2025
Wolfson Institute of Population Health, Centre for Psychiatry and Mental Health, Queen Mary University of London, UK. Electronic address:
Individuals with mental health difficulties in Iceland can be detained in police cells as a last resource place of safety when psychiatrists consider patients unsuitable for admission to a ward, for example, due to being under the influence of substances or being too violent. We aimed to investigate if detainment in a police cell affected symptoms of mental illnesses, and subsequently if police cells are an appropriate place of safety for patients in Iceland. We used a mixed methods approach utilising administrative records of detainees' visible symptoms of mental illnesses and ten semi-structured interviews with mental health professionals and police sergeants.
View Article and Find Full Text PDFBlurred boundaries: Community treatment orders as instruments of racial surveillance.
Int J Law Psychiatry
September 2025
School of Law, University of Edinburgh, Old College, South Bridge, Edinburgh EH8 9YL, United Kingdom. Electronic address:
The Mental Health Act 1983 (MHA) authorizes the compulsory detention and treatment of people with mental disorders who are perceived to pose a risk to themselves or to others. Since its enactment, there have been concerns that the coercive powers of the Act have been disproportionately used for Black people with mental disorders. This disproportionate impact of the MHA on Black people is most clearly seen in the excessive use of Community Treatment Orders (CTOs).
View Article and Find Full Text PDFApplications of Federated Large Language Model for Adverse Drug Reactions Prediction: Scoping Review.
J Med Internet Res
September 2025
Department of Information Systems and Cybersecurity, The University of Texas at San Antonio, 1 UTSA Circle, San Antonio, TX, 78249, United States, 1 (210) 458-6300.
Background: Adverse drug reactions (ADR) present significant challenges in health care, where early prevention is vital for effective treatment and patient safety. Traditional supervised learning methods struggle to address heterogeneous health care data due to their unstructured nature, regulatory constraints, and restricted access to sensitive personal identifiable information.
Objective: This review aims to explore the potential of federated learning (FL) combined with natural language processing and large language models (LLMs) to enhance ADR prediction.