Conectar
This study aimed (1) to examine the association between patient engagement with a bidirectional, semiautomated postdischarge texting programme and Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) survey outcomes, readmissions and revisit rates in a large health system and (2) to describe operational and clinical flow considerations for implementing a postdischarge texting programme.
The study involved 1 main academic hospital (beds: 2500+) and 6 community hospitals (beds: 190–400, averaging 300 beds per hospital) in Houston, Texas.
Retrospective, observational cohort study between non-engaged patients (responded with 0–2 incoming text messages) and engaged patients (responded with 3+ incoming, patient-initiated text messages) between December 2022 and May 2023. We used the two-tailed t-test for continuous variables and 2 test for categorical variables to compare the baseline characteristics between the two cohorts. For the binary outcomes, such as the revisit (1=yes, vs 0=no) and readmissions (1=yes vs 0=no), we constructed mixed effect logistic regression models with the random effects to account for repeated measurements from the hospitals. For the continuous outcome, such as the case mix index (CMI), a generalised linear quantile mixed effect model was built. All tests for significance were two tailed, using an alpha level of 0.05, and 95% CIs were provided. Significance tests were performed to evaluate the CMI and readmissions and revisit rates.
From 78 883 patients who were contacted over the course of this pilot implementation, 49 222 (62.4%) responded, with 39 442 (50%) responded with 3+ incoming text messages. The engaged cohort had higher HCAHPS scores in all domains compared with the non-engaged cohort. The engaged cohort used significantly fewer 30-day acute care resources, experiencing 29% fewer overall readmissions and 20% fewer revisit rates (23% less likely to revisit) and were 27% less likely to be readmitted. The results were statistically significant for all but two hospitals.
This study builds on the few postdischarge texting studies, and also builds on the patient engagement literature, finding that patient engagement with postdischarge texting can be associated with fewer acute care resources. To our knowledge, this is the only study that documented an association between a text-based postdischarge programme and HCAHPS scores, perhaps owing to the bidirectionality and ease with which patients could interact with nurses. Future research should explore the texting paradigms to evaluate their associated outcomes in a variety of postdischarge applications.
by Akira Obana, Kibo Ote, Yuko Gohto, Hidenao Yamada, Fumio Hashimoto, Shigetoshi Okazaki, Ryo Asaoka
PurposeMeasurements of macular pigment optical density (MPOD) using the autofluorescence spectroscopy yield underestimations of actual values in eyes with cataracts. Previously, we proposed a correction method for this error using deep learning (DL); however, the correction performance was validated through internal cross-validation. This cross-sectional study aimed to validate this approach using an external validation dataset.
MethodsMPODs at 0.25°, 0.5°, 1°, and 2° eccentricities and macular pigment optical volume (MPOV) within 9° eccentricity were measured using SPECTRALIS (Heidelberg Engineering, Heidelberg, Germany) in 197 (training dataset inherited from our previous study) and 157 eyes (validating dataset) before and after cataract surgery. A DL model was trained to predict the corrected value from the pre-operative value using the training dataset, and we measured the discrepancy between the corrected value and the actual postoperative value. Subsequently, the prediction performance was validated using a validation dataset.
ResultsUsing the validation dataset, the mean absolute values of errors for MPOD and MPOV corrected using DL ranged from 8.2 to 12.4%, which were lower than values with no correction (P Conclusion
The usefulness of the DL correction method was validated. Deep learning reduced the error for a relatively good autofluorescence image quality. Poor-quality images were not corrected.
Chronic wounds contribute to significant healthcare and economic burden worldwide. Wound assessment remains challenging given its complex and dynamic nature. The use of artificial intelligence (AI) and machine learning methods in wound analysis is promising. Explainable modelling can help its integration and acceptance in healthcare systems. We aim to develop an explainable AI model for analysing vascular wound images among an Asian population. Two thousand nine hundred and fifty-seven wound images from a vascular wound image registry from a tertiary institution in Singapore were utilized. The dataset was split into training, validation and test sets. Wound images were classified into four types (neuroischaemic ulcer [NIU], surgical site infections [SSI], venous leg ulcers [VLU], pressure ulcer [PU]), measured with automatic estimation of width, length and depth and segmented into 18 wound and peri-wound features. Data pre-processing was performed using oversampling and augmentation techniques. Convolutional and deep learning models were utilized for model development. The model was evaluated with accuracy, F1 score and receiver operating characteristic (ROC) curves. Explainability methods were used to interpret AI decision reasoning. A web browser application was developed to demonstrate results of the wound AI model with explainability. After development, the model was tested on additional 15 476 unlabelled images to evaluate effectiveness. After the development on the training and validation dataset, the model performance on unseen labelled images in the test set achieved an AUROC of 0.99 for wound classification with mean accuracy of 95.9%. For wound measurements, the model achieved AUROC of 0.97 with mean accuracy of 85.0% for depth classification, and AUROC of 0.92 with mean accuracy of 87.1% for width and length determination. For wound segmentation, an AUROC of 0.95 and mean accuracy of 87.8% was achieved. Testing on unlabelled images, the model confidence score for wound classification was 82.8% with an explainability score of 60.6%. Confidence score was 87.6% for depth classification with 68.0% explainability score, while width and length measurement obtained 93.0% accuracy score with 76.6% explainability. Confidence score for wound segmentation was 83.9%, while explainability was 72.1%. Using explainable AI models, we have developed an algorithm and application for analysis of vascular wound images from an Asian population with accuracy and explainability. With further development, it can be utilized as a clinical decision support system and integrated into existing healthcare electronic systems.
The aim of this study was to systematically consolidate evidence on perspectives and thoughts of women living with HIV regarding the peer support they have encountered during pregnancy and after childbirth.
Mixed studies systematic review.
PubMed, EMBASE, Cochrane, PsycINFO, CINAHL, Scopus and ProQuest were sourced from 1981 to January 2022.
A convergent qualitative synthesis approach was used to analyse the data. Quality appraisal was performed using the Mixed Methods Appraisal Tool.
A total of 12 studies were included, involving 1596 pregnant women and 1856 new mothers living with HIV. An overarching theme, ‘From One Mother to Another: The Supportive Journey of Pregnant Women and New Mothers Living with HIV’, and two themes were identified: (1) Emotional support buddies and extended networks and (2) Link bridge to healthcare support and self-empowerment.
Peer support played an indispensable role in the lives of women living with HIV and served as a complementary support system to professional and family support.
What problem did the study address? Pregnant women and new mothers living with HIV face preconceived stigma and discrimination.
What were the main findings? Peer support was perceived to be beneficial in enhancing emotional support among women living with HIV and was well-accepted by them.
Where and on whom will the research have an impact? Healthcare providers and community social workers could develop or enhance peer support educational programmes tailored to pregnant women and new mothers living with HIV. Policymakers and administrators can leverage public awareness, advocacy and political will to formulate and implement policies and campaigns aimed at fostering awareness and receptivity towards peer support interventions.
Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA).
No patient or public contribution.
This study aims to explore the overall and specific aspects of the functioning of medical laboratory professionals (MLPs) in Ontario, Canada during the COVID-19 pandemic.
A cross-sectional analysis where a questionnaire was used to assess the mental status of MLPs.
An online questionnaire administered in Ontario, Canada.
632 MLPs (medical laboratory technologists, technicians and assistants) were included.
We employed the WHO Disability Assessment Schedule V.2.0 (WHODAS V.2.0) Questionnaire to assess functioning/disability and Copenhagen Psychosocial Questionnaire, third edition for psychosocial workplace factors. Multiple regression analysis examined the relationship between overall and specific domain functioning scores and psychosocial workplace factors.
Of the total 632 participants, the majority were female gender and Caucasian. It was found that health (β=2.25, p
This study provides preliminary evidence of the overall and specific aspects of functioning among the MLPs during the COVID-19 pandemic. Besides, these findings can support and guide the improvement of workplace practices and policies among MLPs in the future.
FreshRSS 