Barriers and Facilitators to the Implementation of an Electronic Patient-Reported Outcome System at Cancer Hospitals in Japan.

Background and objective Implementing electronic patient-reported outcomes (ePROs) in oncology practice has shown substantial clinical benefits. However, it can be challenging in routine practice, warranting strategies to adapt to different clinical contexts. In light of this, this study aimed to describe the implementation process of the ePRO system and elucidate the provider-level implementation barriers and facilitators to a novel ePRO system at cancer hospitals in Japan. Methods We implemented an ePRO system linked to electronic medical records at three cancer hospitals. Fifteen patients with solid cancers at the outpatient oncology unit were asked to regularly complete the Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE™) questionnaire and European Organization for Research and Treatment Core Quality of Life questionnaire (EORTC QLQ C30) by using the smartphone app between October 2021 and June 2022. Thirteen healthcare professionals were interviewed to identify implementation barriers and facilitators to the ePRO system by using the Consolidated Framework for Implementation Research framework. Results The healthcare professionals identified a lack of clinical resources and a culture and system that emphasizes treatment over care as the main barriers; however, the accumulation of successful cases, the leadership of managers, and the growing needs of patients can serve as facilitators to the implementation. Conclusions Our experience implementing an ePRO system in a few Japanese oncology practices revealed comprehensive barriers and facilitators. Further efforts are warranted to develop more successful implementation strategies.

Rupture progression model of stress integration for virtual reality ablation.

Virtual reality (VR) simulation is expected to be advantageous to surgical training for invasive operations such as incision and dissection that are unrepeatable in the real world. This study focuses on ablation in order to provide a simulation model for soft tissue rupture progression. To simulate soft tissue handling by two or more manipulators, the model must calculate the stress accumulation at the tip of a rupture. This stress accumulation is affected by the stress condition caused by the manipulators. Herein, the authors propose a method to integrate multiple stresses with two different hypotheses. The proposed model was evaluated through the simulation of primitive board ablation.

Investigation on factors related to poor CPAP adherence using machine learning: a pilot study.

To improve patients' adherence to continuous positive airway pressure (CPAP) therapy, this study aimed to clarify whether machine learning-based data analysis can identify the factors related to poor CPAP adherence (i.e., CPAP usage that does not reach four hours per day for five days a week). We developed a CPAP adherence prediction model using logistic regression and learn-to-rank machine learning with a pairwise approach. We then investigated adherence prediction performance targeting a 12-week period and the top ten factors correlating to poor CPAP adherence. The CPAP logs of 219 patients with obstructive sleep apnea (OSA) obtained from clinical treatment at Kyoto University Hospital were used. The highest adherence prediction accuracy obtained was an F1 score of 0.864. Out of the top ten factors obtained with the highest prediction accuracy, four were consistent with already-known clinical knowledge. The factors for better CPAP adherence indicate that air leakage should be avoided, mask pressure should be kept constant, and CPAP usage duration should be longer and kept constant. The results indicate that machine learning is an adequate method for investigating factors related to poor CPAP adherence.

Lower Limb Muscle Activity Control by using Jamming Footwear.

To change the lower limb muscle activities while walking, we propose a muscle activity controlling system that dynamically changes shoe insoles. We conducted a preliminary experiment on a prototype system to determine the system's validity. In this experiment, we analyzed how tibialis anterior and gastrocnemius muscle might change their activities depending on each insole condition. The results lead us to conclude that our system may affect changes in the duration percentage of tibialis anterior muscle activation when we change the heel part shape, and that it may change the maximum amplitude of gastrocnemius muscle activation when we change the arch part shape and hardness.

R-R Interval Outlier Exclusion Method Based on Statistical ECG Values Targeting HRV Analysis Using Wearable ECG Devices.

This paper describes an R-R Interval (RRI) outlier exclusion method that can exclude RRI outliers including misdetected R waves caused by artifacts, which are frequently observed when using wearable electrocardiogram (ECG) devices. The method distinguishes targeted misdetected R waves based on statistical ECG values that can reflect the occurrence of artifacts, and annotates all detected R waves with those results for evaluating RRI measurement status. Experimental results confirmed that the proposed method is effective to improve the accuracy of both time and frequency domain measures of heart rate variability than the conventional one.

Consideration of Calculation Process Assuming Heart Rate Variability Analysis Using Wearable ECG Devices.

Since many shirt-type wearable electrocardiogram (ECG) devices employ shirt-embedded dry electrodes that are easily affected by the daily activities of users, measured ECG often includes noise or artifacts. Experiments confirmed that a desirable HRV calculation process assuming these wearable ECG devices is a combination of accurate R wave detection, RRI outlier exclusion and missing RRI complement methods.

Reliability evaluation of R-R interval measurement status for time domain heart rate variability analysis with wearable ECG devices.

Electrocardiograms (ECGs) captured by wearable ECG devices easily contain artifacts because of measurement faults. Since the frequency characteristics of artifacts are quite similar to those of R waves, it may result in R-R interval (RRI) miscalculations. To enable accurate heart rate variability (HRV) analysis in daily life, this paper proposes a method to reliably evaluate RRI measurement status, one that uses the electric potential characteristics of the QRS complex. Initial evaluation results show that it has the potential to distinguish miscalculated RRIs. Also proposed is a new RRI outlier exclusion method that uses the aforementioned method. Time domain measures of HRV derived from the proposed RRI outlier exclusion method are found to be more accurate than those derived from the conventional one.

An analysis method for wearable electrocardiogram measurement based on non-orthogonal complex wavelet expansion.

An analysis method for wearable electrocardiogram (ECG) measurement is proposed, which is based on the non-orthogonal complex wavelet expansion. The standard continuous wavelet transform performs the signal filtering that corresponds to the orthogonal projection of the ECG signal onto each of the non-orthogonal wavelets. This causes feature leakage from the neighboring wavelet filterbanks and degrades the detection accuracy of the QRS complexes or T waves, even though the wavelets whose waveforms fit to the morphologies of the ECG signals are chosen. The proposed method involves a different signal filtering that corresponds to the non-orthogonal projection of the signal onto each wavelet so that the filter outputs can be the expansion coefficients of the wavelets to approximate the ECG signal. The concept of our proposed method is supported by simulation results.