Background: Thailand has a shortage of community health nurses for supporting the self-management of type 2 diabetes, which is prevalent and poorly controlled.Aim: This study examined the feasibility and acceptability of a self-care assistance programme for poorly controlled type 2 diabetes mellitus. The SukapapNet programme consisted of automated interactive voice response calls to patients and automated follow-up email notifications to their nurses.Design: Single-arm pre-post trial.Methods: Six nurses and 35 type 2 diabetes patients were recruited from primary care settings in suburban provinces in Thailand. The study was conducted from June 2017 to November 2017. We assessed patients before and after 12 weeks of the SukapapNet intervention.Results: Mean glycated haemoglobin decreased by 0.9%. Patients reported reduced carbohydrate consumption, increased physical activity, increased medication adherence, improved sleep quality, and more frequent foot care. Patients and nurses both recommended using the intervention, although nurses expressed concerns regarding increased workload. Conclusions:The study programme could improve outcomes in Thai type 2 diabetes patients. Further study of the impact of technology upon nurses' workload is warranted. Female; no. (%) 26 (74.3)

This study aimed to review systematically all available prediction tools identifying adult hospitalized patients at risk of drug-related problems, and to synthesize the evidence on performance and clinical usefulness.Methods: PubMed, Scopus, Web of Science, Embase, and CINAHL databases were searched for relevant studies. Titles, abstracts and full-text studies were sequentially screened for inclusion by two independent reviewers. The Prediction Model Risk of Bias Assessment Tool (PROBAST) and the Revised Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) checklists were used to assess risk of bias and applicability of prediction tools. A narrative synthesis was performed.Results: A total of 21 studies were included, 14 of which described the development of new prediction tools (four risk assessment tools and ten clinical prediction models) and six studies were validation based and one an impact study. There were variations in tool development processes, outcome measures and included predictors. Overall, tool performance had limitations in reporting and consistency, with the discriminatory ability based on area under the curve receiver operating characteristics (AUROC) ranging from poor to good (0.62-0.81), sensitivity and specificity ranging from 57.0% to 89.9% and 30.2% to 88.0%, respectively. The Medicines Optimisation Assessment tool and Assessment of Risk tool were prediction tools with the lowest risk of bias and low concern for applicability. Studies reporting external validation and impact on patient outcomes were scarce. Conclusion: Most prediction tools have limitations in development and validationprocesses, as well as scarce evidence of clinical usefulness. Future studies should attempt to either refine currently available tools or apply a rigorous process capturing evidence of acceptance, usefulness, performance and outcomes.

Drug-related problems (DRPs) are a major health concern. A better understanding of the characteristics of DRPs throughout the hospital stay may help to tailor pharmaceutical care services (PCS). This study aims to describe the characteristics of DRPs and to compare DRP pattern in different stages of hospital stay. DRPs were identified by clinical pharmacists as part of their routine services. Pharmacist assessed causality, severity and preventability of DRP. A total of 316 preventable DRPs occurred in 257 patients with the median of 1 (rang 1–3) DRPs per patient. 46.8% of DRPs occurred at discharge than at other stages. The most frequent cause of DRP was no drug treatment in spite of existing indication, accounting for 32.3% of all DRPs. No drug treatment with existing indication was detected frequently at discharge (56.1%) compared with other stages (p-value < 0.001). The common intervention to physician was starting a drug (34.0%) and the acceptance rate was 95.8%. DRPs in hospitalized patients occur at any stage of the hospital stay. Systematic identification of DRP characteristics enables pharmacists to tailor optimal type of PCS required and hence improve patient safety.

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