Towards a Patient-Centered Design of a Cancer Telerehabilitation System.

Successful implementation of telehealth platforms requires a detailed understanding of patient's needs, preferences, and attitudes toward a home-based platform. The goal of this study was to identify patient-centered characteristics of a cancer rehabilitation system based on cognitive evaluation of user interface and semi-structured qualitative interviews. Quantitative and qualitative feedback from 29 patients with metastatic urogenital cancer was collected after using a cancer telerehabilitation system. Heuristic evaluation, cognitive walkthrough, and analysis of qualitative interviews demonstrated a high level of support for the concept of home-based cancer telerehabilitation by cancer patients. Post-task surveys demonstrated sufficient usability and satisfaction scores from the participants. The patients provided valuable and insightful comments on how to further improve the functionality and interface of the platform. Further improvement of the system usability, consistency, and accessibility based on the patient-centered design principles will significantly facilitate the implementation of cancer telerehabilitation in clinical practice.

Pneumococcal Vaccination Lowers the Risk of Alzheimer's Disease: A Study Utilizing Data from the IBM® MarketScan® Database.

Previous studies demonstrated an association between influenza vaccination and the likelihood of developing Alzheimer's disease. This study was aimed at assessing whether pneumococcal vaccinations are associated with a lower risk of Alzheimer's disease based on analysis of data from the IBM® MarketScan® Database. Vaccinated and unvaccinated matched cohorts were generated using propensity-score matching with the greedy nearest-neighbor matching algorithm. The conditional logistic regression method was used to estimate the relationship between pneumococcal vaccination and the onset of Alzheimer's disease. There were 142,874 subjects who received the pneumococcal vaccine and 14,392 subjects who did not. The conditional logistic regression indicated that the people who received the pneumococcal vaccine had a significantly lower risk of developing Alzheimer's disease as compared to the people who did not receive any pneumococcal vaccine (OR=0.37; 95%CI: 0.33-0.42; P-value < .0001). Our findings demonstrated that the pneumococcal vaccine was associated with a 63% reduction in the risk of Alzheimer's disease among US adults aged 65 and older.

Deep Learning Approaches to Predict Exercise Exertion Levels Using Wearable Physiological Data.

Using physiological data from wearable devices, the study aimed to predict exercise exertion levels by building deep learning classification and regression models. Physiological data were obtained using an unobtrusive chest-worn ECG sensor and portable pulse oximeter from healthy individuals who performed 16-minute cycling exercise sessions. During each session, real-time ECG, pulse rate, oxygen saturation, and revolutions per minute (RPM) data were collected at three intensity levels. Subjects' ratings of perceived exertion (RPE) were collected once per minute. Each 16-minute exercise session was divided into eight 2-minute windows. The self-reported RPEs, heart rate, RPMs, and oxygen saturation levels were averaged for each window to form the predictive features. In addition, heart rate variability (HRV) features were extracted from the ECG for each window. Different feature selection algorithms were used to choose top-ranked predictors. The best predictors were then used to train and test deep learning models for regression and classification analysis. Our results showed the highest accuracy and F1 score of 98.2% and 98%, respectively in training the models. For testing the models, the highest accuracy and F1 score were 80%.

Identifying Determinants of Survival Disparities in Multiple Myeloma Patients Using Electronic Health Record Data.

Multiple myeloma (MM) is one of the most common hematological malignancies. The goal of this study was to analyze the sociodemographic, economic, and genetic characteristics of long-term and short-term survival of multiple myeloma patients using EHR data from an academic medical center in New York City. The de-identified analytical dataset comprised 2,111 patients with MM who were stratified based on the length of survival into two groups. Demographic variables, cancer stage, income level, and genetic mutations were analyzed using descriptive statistics and logistic regression. Age, race, and cancer stage were all significant factors that affected the length of survival of multiple myeloma patients. In contrast, gender and income level were not significant factors based on the multivariate adjusted analysis. Older adults, African American patients, and patients who were diagnosed with stage III of multiple myeloma were the people most likely to exhibit short-term survival after the MM diagnosis.

NLP-Assisted Differential Diagnosis of Chronic Obstructive Pulmonary Disease Exacerbation.

Chronic Obstructive Pulmonary Disease (COPD) frequently coincides with other comorbidities such as congestive heart failure, hypertension, coronary artery disease, or atrial fibrillation. The exhibition of overlapping sets of symptoms associated with these conditions prevents early identification of an acute exacerbation upon admission to a hospital. Early identification of the underlying cause of exacerbation allows timely prescription of an optimal treatment plan as well as allows avoiding unnecessary clinical tests and specialist consultations. The aim of this study was to develop a predictive model for early identification of COPD exacerbation by using the clinical notes generated within 24 hours of admission to the hospital. The study cohort included patients with a prior diagnosis of COPD. Four predictive models have been developed, among which the support vector machine showed the best performance based on the resulting 80% F1 score.

Assessing the Barriers and Facilitators to Pulmonary Rehabilitation Referrals Using the Consolidated Framework for Implementation Research (CFIR).

Chronic obstructive pulmonary disease (COPD) is a global health issue causing significant illness and death. Pulmonary Rehabilitation (PR) offers non-pharmacological treatment, including education, exercise, and psychological support which was shown to improve clinical outcomes. In both stable COPD and after an acute exacerbation, PR has been demonstrated to increase exercise capacity, decrease dyspnea, and enhance quality of life. Despite these benefits, referrals for PR for COPD treatment remain low. This study aims to evaluate the perceptions of healthcare providers for referring a COPD patient to PR. Semi-structured qualitative interviews were conducted with pulmonary specialists, hospitalists, and emergency department physicians. Domains and constructs from the Consolidated Framework for Implementation Research (CFIR) were applied to the qualitative data to organize, analyze, and identify the barriers and facilitators to referring COPD patients. The findings from this study will help guide strategies to improve the referral process for PR.

Extraction of Substance Use Information From Clinical Notes: Generative Pretrained Transformer-Based Investigation.

BACKGROUND: Understanding the multifaceted nature of health outcomes requires a comprehensive examination of the social, economic, and environmental determinants that shape individual well-being. Among these determinants, behavioral factors play a crucial role, particularly the consumption patterns of psychoactive substances, which have important implications on public health. The Global Burden of Disease Study shows a growing impact in disability-adjusted life years due to substance use. The successful identification of patients' substance use information equips clinical care teams to address substance-related issues more effectively, enabling targeted support and ultimately improving patient outcomes. OBJECTIVE: Traditional natural language processing methods face limitations in accurately parsing diverse clinical language associated with substance use. Large language models offer promise in overcoming these challenges by adapting to diverse language patterns. This study investigates the application of the generative pretrained transformer (GPT) model in specific GPT-3.5 for extracting tobacco, alcohol, and substance use information from patient discharge summaries in zero-shot and few-shot learning settings. This study contributes to the evolving landscape of health care informatics by showcasing the potential of advanced language models in extracting nuanced information critical for enhancing patient care. METHODS: The main data source for analysis in this paper is Medical Information Mart for Intensive Care III data set. Among all notes in this data set, we focused on discharge summaries. Prompt engineering was undertaken, involving an iterative exploration of diverse prompts. Leveraging carefully curated examples and refined prompts, we investigate the model's proficiency through zero-shot as well as few-shot prompting strategies. RESULTS: Results show GPT's varying effectiveness in identifying mentions of tobacco, alcohol, and substance use across learning scenarios. Zero-shot learning showed high accuracy in identifying substance use, whereas few-shot learning reduced accuracy but improved in identifying substance use status, enhancing recall and F1-score at the expense of lower precision. CONCLUSIONS: Excellence of zero-shot learning in precisely extracting text span mentioning substance use demonstrates its effectiveness in situations in which comprehensive recall is important. Conversely, few-shot learning offers advantages when accurately determining the status of substance use is the primary focus, even if it involves a trade-off in precision. The results contribute to enhancement of early detection and intervention strategies, tailor treatment plans with greater precision, and ultimately, contribute to a holistic understanding of patient health profiles. By integrating these artificial intelligence-driven methods into electronic health record systems, clinicians can gain immediate, comprehensive insights into substance use that results in shaping interventions that are not only timely but also more personalized and effective.

Association of Diagnostic Discrepancy with Length of Stay and Mortality in Congestive Heart Failure Patients Admitted to the Emergency Department.

The goal of this study was to analyze diagnostic discrepancies between emergency department (ED) and hospital discharge diagnoses in patients with congestive heart failure admitted to the ED. Using a synthetic dataset from the Department of Veterans Affairs, the patients' primary diagnoses were compared at two levels: diagnostic category and body system. With 12,621 patients and 24,235 admission cases, the study found a 58% mismatch rate at the category level, which was reduced to 30% at the body system level. Diagnostic categories associated with higher levels of mismatch included aplastic anemia, pneumonia, and bacterial infections. In contrast, diagnostic categories associated with lower levels of mismatch included alcohol-related disorders, COVID-19, cardiac dysrhythmias, and gastrointestinal hemorrhage. Further investigation revealed that diagnostic mismatches are associated with longer hospital stays and higher mortality rates. These findings highlight the importance of reducing diagnostic uncertainty, particularly in specific diagnostic categories and body systems, to improve patient care following ED admission.

Assessing Patient Perspectives on Pulmonary Telerehabilitation Using Thematic Analysis of Semi-Structured Qualitative Interviews.

The study was aimed at exploring patients' experiences after the completion of a 12-month pulmonary telerehabilitation (PR) program. Semi-structured qualitative interviews were conducted with 16 COPD patients. The interviews were analyzed using a thematic approach to identify patterns and themes. The patients exhibited high acceptability and satisfaction with the remote PR program and provided valuable input for its improvement. These insights will be used for the implementation of a patient-centered COPD telerehabilitation system.

Telemedicine Applications for Cancer Rehabilitation: A Scoping Review.

BACKGROUND: Cancer is a significant public health issue worldwide. Treatments such as surgery, chemotherapy, and radiation therapy often cause psychological and physiological side effects, affecting patients' ability to function and their quality of life. Physical activity is crucial to cancer rehabilitation, improving physical function and quality of life and reducing cancer-related fatigue. However, many patients face barriers to accessing cancer rehabilitation due to socioeconomic factors, transportation issues, and time constraints. Telerehabilitation can potentially overcome these barriers by delivering rehabilitation remotely. OBJECTIVE: To identify how telemedicine is used for the rehabilitation of patients with cancer. METHODS: This scoping review followed recognized frameworks. We conducted an electronic literature search on PubMed for studies published between January 2015 and May 2023. Inclusion criteria were studies reporting physical therapy telerehabilitation interventions for cancer patients, including randomized and non-randomized controlled trials, feasibility studies, and usability studies. Twenty-one studies met the criteria and were included in the final review. RESULTS: Our search yielded 37 articles, with 21 included in the final review. Randomized Controlled Trials comprised 47.6% (10/21) of the studies, with feasibility studies at 33.3% (7/21) and usability studies at 19.0% (4/21). Sample sizes were typically 50 or fewer participants in 57.1% (12/21) of the reports. Participants were generally aged 65 or younger (81.0%, 17/21), with a balanced gender distribution. Organ-specific cancers were the focus of 66.7% (14/21) of the articles, while 28.6% (6/21) included post-treatment patients. Web-based systems were the most used technology (61.9%, 13/21), followed by phone call/SMS-based systems (42.9%, 9/21) and mobile applications (23.8%, 5/21). Exercise programs were mainly home-based (90.5%, 19/21) and included aerobic (90.5%, 19/21), resistance (61.9%, 13/21), and flexibility training (33.3%, 7/21). Outcomes included improvements in functional capacity, cognitive functioning, and quality of life (47.6%, 10/21); reductions in pain and hospital length of stay; and enhancements in fatigue, physical and emotional well-being, and anxiety. Positive effects on feasibility (14.3%, 3/21), acceptability (38.1%, 8/21), and cost-effectiveness (9.5%, 2/21) were also noted. Functional outcomes were frequently assessed (71.4%, 19/21) with tools like the 6-Minute Walk Test and grip strength tests. CONCLUSIONS: Telerehabilitation for cancer patients is beneficial and feasible, with diverse approaches in study design, technologies, exercises, and outcomes. Future research should focus on developing standardized methodologies, incorporating objective measures, and exploring emerging technologies like virtual reality, wearable or non-contact sensors, and artificial intelligence to optimize telerehabilitation interventions. Addressing these areas can enhance clinical practice and improve outcomes for remote rehabilitation patients.

Immersive Technologies for Depression Care: Scoping Review.

BACKGROUND: Depression significantly impacts quality of life, affecting approximately 280 million people worldwide. However, only 16.5% of those affected receive treatment, indicating a substantial treatment gap. Immersive technologies (IMTs) such as virtual reality (VR) and augmented reality offer new avenues for treating depression by creating immersive environments for therapeutic interventions. Despite their potential, significant gaps exist in the current evidence regarding the design, implementation, and use of IMTs for depression care. OBJECTIVE: We aim to map the available evidence on IMT interventions targeting depression treatment. METHODS: This scoping review followed a methodological framework, and we systematically searched databases for studies on IMTs and depression. The focus was on randomized clinical trials involving adults and using IMTs. The selection and charting process involved multiple reviewers to minimize bias. RESULTS: The search identified 16 peer-reviewed articles, predominantly from Europe (n=10, 63%), with a notable emphasis on Poland (n=9, 56%), which contributed to more than half of the articles. Most of the studies (9/16, 56%) were conducted between 2020 and 2021. Regarding participant demographics, of the 16 articles, 5 (31%) exclusively involved female participants, and 7 (44%) featured participants whose mean or median age was >60 years. Regarding technical aspects, all studies focused on VR, with most using stand-alone VR headsets (14/16, 88%), and interventions typically ranging from 2 to 8 weeks, predominantly in hospital settings (11/16, 69%). Only 2 (13%) of the 16 studies mentioned using a specific VR design framework in planning their interventions. The most frequently used therapeutic approach was Ericksonian psychotherapy, used in 56% (9/16) of the studies. Notably, none of the articles reported using an implementation framework or identified barriers and enablers to implementation. CONCLUSIONS: This scoping review highlights the growing interest in using IMTs, particularly VR, for depression treatment but emphasizes the need for more inclusive and comprehensive research. Future studies should explore varied therapeutic approaches and cost-effectiveness as well as the inclusion of augmented reality to fully realize the potential of IMTs in mental health care.

Immersive Virtual Reality Use in Medical Intensive Care: Mixed Methods Feasibility Study.

BACKGROUND: Immersive virtual reality (VR) is a promising therapy to improve the experience of patients with critical illness and may help avoid postdischarge functional impairments. However, the determinants of interest and usability may vary locally and reports of uptake in the literature are variable. OBJECTIVE: The aim of this mixed methods feasibility study was to assess the acceptability and potential utility of immersive VR in critically ill patients at a single institution. METHODS: Adults without delirium who were admitted to 1 of 2 intensive care units were offered the opportunity to participate in 5-15 minutes of immersive VR delivered by a VR headset. Patient vital signs, heart rate variability, mood, and pain were assessed before and after the VR experience. Pre-post comparisons were performed using paired 2-sided t tests. A semistructured interview was administered after the VR experience. Patient descriptions of the experience, issues, and potential uses were summarized with thematic analysis. RESULTS: Of the 35 patients offered the chance to participate, 20 (57%) agreed to partake in the immersive VR experience, with no difference in participation rate by age. Improvements were observed in overall mood (mean difference 1.8 points, 95% CI 0.6-3.0; P=.002), anxiety (difference of 1.7 points, 95% CI 0.8-2.7; P=.001), and pain (difference of 1.3 points, 95% CI 0.5-2.1; P=.003) assessed on 1-10 scales. The heart rate changed by a mean of -1.1 (95% CI -0.3 to -1.9; P=.008) beats per minute (bpm) from a baseline of 86.1 (SD 11.8) bpm and heart rate variability, assessed by the stress index (SI), changed by a mean of -5.0 (95% CI -1.5 to -8.5; P=.004) seconds-2 from a baseline SI of 40.0 (SD 23) seconds-2. Patients commented on the potential for the therapy to address pain, lessen anxiety, and facilitate calmness. Technical challenges were minimal and there were no adverse effects observed. CONCLUSIONS: Patient acceptance of immersive VR was high in a mostly medical intensive care population with little prior VR experience. Patients commented on the potential of immersive VR to ameliorate cognitive and emotional symptoms. Investigators can consider integrating minimally modified commercial VR headsets into the existing intensive care unit workflow to further assess VR's efficacy for a variety of endpoints.

Machine Learning Approaches for Exercise Exertion Level Classification Using Data from Wearable Physiologic Monitors.

This research aimed to develop a model for real-time prediction of aerobic exercise exertion levels. ECG signals were registered during 16-minute cycling exercises. Perceived ratings of exertion (RPE) were collected each minute from the study participants. Based on the reported RPE, each consecutive minute of the exercise was assigned to the "high exertion" or "low exertion" class. The characteristics of heart rate variability (HRV) in time and frequency domains were used as predictive features. The top ten ranked predictive features were selected using the minimum redundancy maximum relevance (mRMR) algorithm. The support vector machine demonstrated the highest accuracy with an F1 score of 82%.

Exercise Exertion Level Prediction Using Data from Wearable Physiologic Monitors.

This study aims to develop machine learning (ML) algorithms to predict exercise exertion levels using physiological parameters collected from wearable devices. Real-time ECG, oxygen saturation, pulse rate, and revolutions per minute (RPM) data were collected at three intensity levels during a 16-minute cycling exercise. Parallel to this, throughout each exercise session, the study subjects' ratings of perceived exertion (RPE) were gathered once per minute. Each 16-minute exercise session was divided into a total of eight 2-minute windows. Each exercise window was labeled as "high exertion," or "low exertion" classes based on the self-reported RPEs. For each window, the gathered ECG data were used to derive the heart rate variability (HRV) features in the temporal and frequency domains. Additionally, each window's averaged RPMs, heart rate, and oxygen saturation levels were calculated to form all the predictive features. The minimum redundancy maximum relevance algorithm was used to choose the best predictive features. Top selected features were then used to assess the accuracy of ten ML classifiers to predict the next window's exertion level. The k-nearest neighbors (KNN) model showed the highest accuracy of 85.7% and the highest F1 score of 83%. An ensemble model showed the highest area under the curve (AUC) of 0.92. The suggested method can be used to automatically track perceived exercise exertion in real-time.

Telemonitoring of Home-Based Biking Exercise: Assessment of Wireless Interfaces.

BACKGROUND: Telerehabiliation has been shown to have great potential in expanding access to rehabilitation services, enhancing patients' quality of life, and improving clinical outcomes. Stationary biking exercise can serve as an effective aerobic component of home-based physical rehabilitation programs. Remote monitoring of biking exercise provides necessary safeguards to ensure exercise adherence and safety in patients' homes. The scalability of the current remote monitoring of biking exercise solutions is impeded by the high cost that limits patient access to these services, especially among older adults with chronic health conditions. OBJECTIVE: The aim of this project was to design and test two low-cost wireless interfaces for the telemonitoring of home-based biking exercise. METHODS: We designed an interactive biking system (iBikE) that comprises a tablet PC and a low-cost bike. Two wireless interfaces to monitor the revolutions per minute (RPM) were built and tested. The first version of the iBikE system uses Bluetooth Low Energy (BLE) to send information from the iBikE to the PC tablet, and the second version uses a Wi-Fi network for communication. Both systems provide patients and their clinical teams the capability to monitor exercise progress in real time using a simple graphical representation. The bike can be used for upper or lower limb rehabilitation. We developed two tablet applications with the same graphical user interfaces between the application and the bike sensors but with different communication protocols (BLE and Wi-Fi). For testing purposes, healthy adults were asked to use an arm bike for three separate subsessions (1 minute each at a slow, medium, and fast pace) with a 1-minute resting gap. While collecting speed values from the iBikE application, we used a tachometer to continuously measure the speed of the bikes during each subsession. Collected data were later used to assess the accuracy of the measured data from the iBikE system. RESULTS: Collected RPM data in each subsession (slow, medium, and fast) from the iBikE and tachometer were further divided into 4 categories, including RPM in every 10-second bin (6 bins), RPM in every 20-second bin (3 bins), RPM in every 30-second bin (2 bins), and RPM in each 1-minute subsession (60 seconds, 1 bin). For each bin, the mean difference (iBikE and tachometer) was then calculated and averaged for all bins in each subsession. We saw a decreasing trend in the mean RPM difference from the 10-second to the 1-minute measurement. For the 10-second measurements during the slow and fast cycling, the mean discrepancy between the wireless interface and tachometer was 0.67 (SD 0.24) and 1.22 (SD 0.67) for the BLE iBike, and 0.66 (SD 0.48) and 0.87 (SD 0.91) for the Wi-Fi iBike system, respectively. For the 1-minute measurements during the slow and fast cycling, the mean discrepancy between the wireless interface and tachometer was 0.32 (SD 0.26) and 0.66 (SD 0.83) for the BLE iBike, and 0.21 (SD 0.21) and 0.47 (SD 0.52) for the Wi-Fi iBike system, respectively. CONCLUSIONS: We concluded that a low-cost wireless interface provides the necessary accuracy for the telemonitoring of home-based biking exercise.