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Chronic wounds present significant challenges for patients and nursing care teams worldwide. Digital health tools offer potential for more standardised and efficient nursing care pathways but require further rigorous evaluation.
This retrospective matched cohort study aimed to compare the impacts of a digital tracking application for wound documentation versus traditional manual nursing assessments.
Data from 5236 patients with various wound types were analysed. Propensity score matching balanced groups, and bivariate tests, correlation analyses, linear regression, and Hayes' Process Macro Model 15 were utilised for a mediation-moderation model.
Digital wound tracking was associated with significantly shorter healing durations (15 vs. 35 days) and fewer clinic nursing visits (3 vs. 5.8 visits) compared to standard nursing monitoring. Digital tracking demonstrated improved wound size reduction over time. Laboratory values tested did not consistently predict healing outcomes. Digital tracking exhibited moderate negative correlations with the total number of nursing visits. Regression analysis identified wound complexity, hospitalizations, and initial wound size as clinical predictors for more nursing visits in patients with diabetes mellitus (p < .01). Digital tracking significantly reduced the number of associated nursing visits for patients with peripheral vascular disease.
These findings suggest that digital wound management may streamline nursing care and provide advantages, particularly for comorbid populations facing treatment burdens.
This study adhered to STROBE guidelines in reporting this observational research.
By streamlining documentation and potentially shortening healing times, digital wound tracking could help optimise nursing resources, enhance wound care standards, and improve patient experiences. This supports further exploration of digital health innovations to advance evidence-based nursing practice.
This study involved retrospective analysis of existing patient records and did not directly include patients or the public in the design, conduct, or reporting of the research.
Atrial fibrillation (AF) is a major public health issue and there is rationale for the early diagnosis of AF before the first complication occurs. Previous AF screening research is limited by low yields of new cases and strokes prevented in the screened populations. For AF screening to be clinically and cost-effective, the efficiency of identification of newly diagnosed AF needs to be improved and the intervention offered may have to extend beyond oral anticoagulation for stroke prophylaxis. Previous prediction models for incident AF have been limited by their data sources and methodologies.
We will investigate the application of random forest and multivariable logistic regression to predict incident AF within a 6-month prediction horizon, that is, a time-window consistent with conducting investigation for AF. The Clinical Practice Research Datalink (CPRD)-GOLD dataset will be used for derivation, and the Clalit Health Services (CHS) dataset will be used for international external geographical validation. Analyses will include metrics of prediction performance and clinical utility. We will create Kaplan-Meier plots for individuals identified as higher and lower predicted risk of AF and derive the cumulative incidence rate for non-AF cardio-renal-metabolic diseases and death over the longer term to establish how predicted AF risk is associated with a range of new non-AF disease states.
Permission for CPRD-GOLD was obtained from CPRD (ref no: 19_076). The CPRD ethical approval committee approved the study. CHS Helsinki committee approval 21-0169 and data usage committee approval 901. The results will be submitted as a research paper for publication to a peer-reviewed journal and presented at peer-reviewed conferences.
A systematic review to guide the overall project was registered on PROSPERO (registration number CRD42021245093). The study was registered on ClinicalTrials.gov (NCT05837364).
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