Real-Time Cuffless Continuous Blood Pressure Estimation Using 1D Squeeze U-Net Model: A Progress toward mHealth

Hypotension prediction index for the prevention of hypotension during surgery and critical care: A narrative review

Surgeons and anesthesia clinicians commonly face a hemodynamic disturbance known as intraoperative hypotension (IOH), which has been linked to more severe postoperative outcomes and increases mortality rates. Increased occurrence of IOH has been positively associated with mortality and incidence of myocardial infarction, stroke, and organ dysfunction hypertension. Hence, early detection and recognition of IOH is meaningful for perioperative management. Currently, when hypotension occurs, clinicians use vasopressor or fluid therapy to intervene as IOH develops but interventions should be taken before hypotension occurs; therefore, the Hypotension Prediction Index (HPI) method can be used to help clinicians further react to the IOH process. This literature review evaluates the HPI method, which can reliably predict hypotension several minutes before a hypotensive event and is beneficial for patients' outcomes.

Telemedicine in specialist outpatient care during COVID-19: a qualitative study

BACKGROUND

COVID-19 accelerated healthcare changes, introducing various telehealth services. Work is needed to determine the suitability of telemedicine in the post-pandemic era.

AIMS

To explore perceptions and experiences of telemedicine among patients and providers (clinicians and health administrators) who were involved in telemedicine appointments in hospital outpatient clinics in 2020-2022.

DESIGN, SETTING AND PARTICIPANTS

Qualitative study: semi-structured interviews were conducted with 37 participants (16 patients and 21 providers) in various hospital specialist outpatient clinics in a New South Wales local health district.

RESULTS

Patients were generally satisfied with telemedicine consultations, especially during COVID restrictions, because of the convenience of accessing care from home and minimising the risk of COVID exposure. However, patients considered that the inability to receive a physical examination was a significant disadvantage of telemedicine. Providers had ambivalent perceptions and expressed concerns about mis- and under-diagnoses because of the inability to conduct physical examinations. They considered telemedicine suitable for review appointments but noted an associated increased workload and stressed the need for sustainable funding models (Medicare items). Both patients and providers recognised the need for education/training and better integration of telemedicine platforms into existing infrastructure to facilitate an optimal hybrid model of care.

CONCLUSION

Despite expressing some concerns over its limitations, patients valued telemedicine for its convenience and for meeting their needs during the pandemic. While acknowledging that patients experienced some benefits from telemedicine, clinicians expressed concerns about potential missed diagnoses, uncertain clinical outcomes and lack of administrative and technological infrastructure. The ultimate test of telemedicine will be its impact on clinical outcomes versus longstanding models of in-person care.

Improved U-Net Model to Estimate Cardiac Output Based on Photoplethysmography and Arterial Pressure Waveform

We aimed to estimate cardiac output (CO) from photoplethysmography (PPG) and the arterial pressure waveform (ART) using a deep learning approach, which is minimally invasive, does not require patient demographic information, and is operator-independent, eliminating the need to artificially extract a feature of the waveform by implementing a traditional formula. We aimed to present an alternative to measuring cardiac output with greater accuracy for a wider range of patients. Using a publicly available dataset, we selected 543 eligible patients and divided them into test and training sets after preprocessing. The data consisted of PPG and ART waveforms containing 2048 points with the corresponding CO. We achieved an improvement based on the U-Net modeling framework and built a two-channel deep learning model to automatically extract the waveform features to estimate the CO in the dataset as the reference, acquired using the EV1000, a commercially available instrument. The model demonstrated strong consistency with the reference values on the test dataset. The mean CO was 5.01 ± 1.60 L/min and 4.98 ± 1.59 L/min for the reference value and the predicted value, respectively. The average bias was -0.04 L/min with a -1.025 and 0.944 L/min 95% limit of agreement (LOA). The bias was 0.79% with a 95% LOA between -20.4% and 18.8% when calculating the percentage of the difference from the reference. The normalized root-mean-squared error (RMSNE) was 10.0%. The Pearson correlation coefficient (r) was 0.951. The percentage error (PE) was 19.5%, being below 30%. These results surpassed the performance of traditional formula-based calculation methods, meeting clinical acceptability standards. We propose a dual-channel, improved U-Net deep learning model for estimating cardiac output, demonstrating excellent and consistent results. This method offers a superior reference method for assessing cardiac output in cases where it is unnecessary to employ specialized cardiac output measurement devices or when patients are not suitable for pulmonary-artery-catheter-based measurements, providing a viable alternative solution.

Factors Influencing the Aged in the Use of Mobile Healthcare Applications: An Empirical Study in China

Mobile healthcare applications are of significant potential value in the development of the aged-care industry due to their great convenience, high efficiency, and low cost. Since the cognition and utilization rates of mobile healthcare applications for the elderly are still low, this study explored the factors that affect the elderly's adoption of mobile healthcare applications. This study conducted a questionnaire survey on the elderly in China and received 365 valuable responses. This study combined the technology acceptance model, protection motivation theory, and perceived risk theory to build a research model of factors affecting the use of mobile healthcare applications by the elderly. The data were analyzed using a structural equation model. The results were as follows: according to the empirical research, (1) perceived usefulness and perceived ease of use positively affect the use attitude of the elderly; perceived usefulness and user attitude positively affect the behavior intention of the elderly; perceived ease of use positively affects perceived usefulness; (2) perceived severity has a significant positive correlation with use attitude; perceived susceptibility and attitude to use have no significant impact; (3) perceived risk is negatively correlated with the use attitude and behavioral intention. The above-mentioned factors should be taken into consideration during the development of mobile healthcare applications for the aged to upgrade the overall service quality of mobile healthcare applications, thus enhancing the operational level of mobile healthcare applications and the health literacy of the aged.