Conectar
To identify implementation strategies that effectively facilitate the adoption of social needs care coordination activities using enabling technologies among care management teams serving patients in community-based health centres.
Modified Delphi process.
Discrete, feasible implementation strategies were identified through literature review and semi-structured interviews with care management staff and subject matter experts in clinical informatics, workflow redesign, and product engineering. A modified Delphi was conducted with eight subject matter experts and nine health centre care management staff. Iterative rounds of online surveys were used to achieve consensus on the most relevant implementation strategies and their delivery methods.
The modified Delphi process achieved consensus on nine discrete implementation strategies needed to advance care management teams' ability to screen, refer and track social needs. Prioritised strategies included developing champions, enhancing quality improvement capacity, training staff on using enabling technologies and providing tailored technical assistance for workflow refinement. Consensus was also reached on a monthly cadence for most of the implementation strategies.
Consensus was reached on strategies to enhance care management teams' implementation of social needs screening, referrals and tracking using enabling technologies. These strategies will comprise an intervention to be pilot tested, refined and assessed in a cluster randomised clinical trial.
Findings from this study will inform the development of strategies to further the adoption of enabling technologies to support social needs care coordination.
This work is key to the design of a type 2 hybrid implementation-effectiveness trial that will assess whether user-informed, evidence-based implementation strategies can improve care management teams' adoption of enabling technologies to facilitate social needs care coordination for patients.
The research team includes a patient advisor with community-based nursing expertise and a nurse practitioner-clinical informaticist leader who was involved in data collection and interpretation of findings.
Trial registration: Clinicaltrials.gov registration # NCT06489002. Registered July 5, 2024, https://clinicaltrials.gov/study/NCT06489002?term=NCT06489002&rank=1.
To characterise patient and medication-related patterns observed in drug-related pressure ulcers (DRPUs) and provide descriptive findings that may support future consensus-building.
Multicentre retrospective observational study.
20 hospitals across Japan participated in the study with hospital pharmacists specialised in PU care.
A total of 1113 hospitalised patients with existing PUs were included and classified into three groups (definite, probable and no-possibility of DRPUs) based on predefined criteria.
The primary outcome was the description of medication-related characteristics observed in each DRPU classification group, including polypharmacy, initiation of new medications and dose adjustments. Secondary outcomes included differences in ulcer characteristics and functional status across DRPU categories.
The definite group (n=128, 11.5%) showed a significantly higher prevalence of polypharmacy (83.6% vs 71.1% in the no-possibility group, p
Medication-related characteristics such as polypharmacy, initiation of new medications, dose modifications and use of antipsychotics were more frequently observed in the definite DRPU group. These descriptive findings may help characterise the clinical patterns of DRPUs and may inform future hypothesis generation.
There are challenges involving human resource management, as the selection and evaluation processes for nursing diagnostic labels are time-consuming, resulting in an excessive workload. This, in turn, can lead to insufficient attention being given to patients' medical issues. As a proof of concept, to solve challenges related to nursing diagnoses, we developed an artificial neural network model using progress records and evaluated its performance. Specifically, datasets were obtained from progress record data from the critical care department system in Japan between 2014 and 2019 and the corresponding nursing diagnosis data from electronic medical records. The model was trained, and its performance was evaluated. We compared several methods for vectorizing progress records and evaluated performance with and without oversampling for imbalanced data. We used a naive Bayes classifier for comparison. The model using term frequency–inverse document frequency achieved the highest values for both accuracy and the area under the precision-recall curve across all target nursing diagnoses (accuracy = 0.705–0.911; area under the precision-recall curve = 0.387–0.929). The artificial neural network model outperformed the naive Bayes classifier in both accuracy and area under the precision-recall curve, which indicated its superiority as a classifier. 
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