Can we increase the value of data from bladder diaries? International Consultation on Incontinence-Research Society 2023

BACKGROUND

Bladder diaries represent a fundamental component in the assessment of patients presenting with lower urinary tract symptoms. Nevertheless, their importance often remains underappreciated and undervalued within clinical practice. This paper aims to conduct a comprehensive review of the existing literature concerning the utility of bladder diaries, underscore the criticality of their precision, elucidate the factors contributing to noncompliance with bladder diary completion, and investigate potential strategies for enhancing patient compliance.

MATERIALS AND METHODS

A review of the English-language scientific literature available in the domains of Medline, Embase, Emcare, Midirs, and Cinahl was conducted. This was supplemented by discussion at the International Consultation on Incontinence Research Society Proposal session to define knowledge and identify gaps in knowledge surrounding the utility of bladder diaries. The existing evidence and outcome of the relevant discussion held in the meeting are presented.

RESULTS

Bladder diaries (BD) serve to characterize the nature and severity of storage lower urinary tract symptoms (LUTS) and provide an objective record of an individual's urination patterns. They aid in the refinement and customization of treatment strategies based on the clinical responses documented in the diary, optimizing treatment outcomes. Notably, both BD and urodynamic studies (UDS) play complementary yet distinct roles in LUTS evaluation. BD offers a more comprehensive and accessible approach to assessing specific storage LUTS, particularly due to their affordability and widespread availability, especially in resource-limited settings. Nevertheless, the absence of a standardized BD format across global healthcare systems presents a significant challenge. Despite being recognized as reliable, noninvasive, validated, and cost-effective tools for evaluating patients with LUTS, the implementation and completion of BD have proven to be complex. The introduction of automated bladder diaries heralds an era of precise, real-time data collection, potentially enhancing the patient-clinician relationship. Completion of bladder diaries depends on an array of individual, social, and healthcare-specific factors. Compliance with bladder diary completion could be enhanced with clear instructions, patient education, regular follow-ups and positive re-enforcement. This study has identified four critical areas for future research: Addressing healthcare disparities between affluent and developing nations, enhancing the current functionality and effectiveness of bladder diaries, exploring the feasibility of incorporating bladder diaries into the treatment and education process and improving the quality and functionality of existing bladder diaries.

CONCLUSION

Bladder diaries play a pivotal role in the evaluation and management of patients with LUTS, providing a holistic perspective. When their complete potential is harnessed, they have the capacity to revolutionize the paradigm of LUTS management, ushering in a patient-centered era of care.

Privacy-Conflict Resolution for Integrating Personal and Electronic Health Records in Blockchain-Based Systems

Integrating personal health records (PHRs) and electronic health records (EHRs) facilitates the provision of novel services to individuals, researchers, and healthcare practitioners. Simultaneously, integrating healthcare data leads to complexities arising from the structural and semantic heterogeneity within the data. The subject of healthcare data evokes strong emotions due to concerns surrounding privacy breaches. Blockchain technology is employed to address the issue of patient data privacy in inter-organizational processes, as it facilitates patient data ownership and promotes transparency in its usage. At the same time, blockchain technology creates new challenges for e-healthcare systems, such as data privacy, observability, and online enforceability. This article proposes designing and formalizing automatic conflict resolution techniques in decentralized e-healthcare systems. The present study expounds upon our concepts by employing a running case study centered around preventive and personalized healthcare domains.

Plain Language Summary

This paper suggests using blockchain technology for privacy concerns in integrating personal health records and electronic health records in decentralized e-healthcare systems. This report focuses on designing automatic conflict resolution techniques to ensure patient data ownership, transparency, and privacy in inter-organizational processes. This paper proposes designing automatic conflict resolution techniques in decentralized e-healthcare systems, which can improve inter-organizational processes in healthcare. Using blockchain technology to integrate personal and electronic health records can ensure patient data ownership and promote transparency in data usage, addressing privacy concerns in healthcare systems. This paper emphasizes the importance of data privacy and protection in healthcare systems, highlighting the need for compliance with laws and regulations. The research results, including the proof-of-concept prototype, can provide practical insights into implementing conflict resolution techniques in decentralized e-healthcare systems.

Forecasting User Interests Through Topic Tag Predictions in Online Health Communities

The increasing reliance on online communities for healthcare information by patients and caregivers has led to the increase in the spread of misinformation, or subjective, anecdotal and inaccurate or non-specific recommendations, which, if acted on, could cause serious harm to the patients. Hence, there is an urgent need to connect users with accurate and tailored health information in a timely manner to prevent such harm. This article proposes an innovative approach to suggesting reliable information to participants in online communities as they move through different stages in their disease or treatment. We hypothesize that patients with similar histories of disease progression or course of treatment would have similar information needs at comparable stages. Specifically, we pose the problem of predicting topic tags or keywords that describe the future information needs of users based on their profiles, traces of their online interactions within the community (past posts, replies) and the profiles and traces of online interactions of other users with similar profiles and similar traces of past interaction with the target users. The result is a variant of the collaborative information filtering or recommendation system tailored to the needs of users of online health communities. We report results of our experiments on two unique datasets from two different social media platforms which demonstrates the superiority of the proposed approach over the state of the art baselines with respect to accurate and timely prediction of topic tags (and hence information sources of interest).

Impact of Using the Internet of Medical Things on e-Healthcare Performance: Blockchain Assist in Improving Smart Contract

Background

This paper explores the use of blockchain technology and smart contracts in the Internet of Medical Things (IoMT). It aims to identify the challenges and benefits of implementing smart contracts based on blockchain technology in the IoMT. It provides solutions and evaluates the IoMT uses in e-healthcare performance.

Methods

A quantitative approach used an online survey from public and private hospital administrative departments in Dubai, United Arab Emirates (UAE). ANOVA, t-test, correlation, and regression analysis were performed to assess the e-healthcare performance with and without IoMT (smart contract based on blockchain).

Patients and Methods

A mixed method was used in this research, a quantitative approach for data analysis utilizing online surveys from public and private hospitals' administrative departments in Dubai, UAE. A correlation, regression through ANOVA, and independent two-sample t-test were performed to assess the e-healthcare performance with and without IoMT (smart contract based on blockchain).

Results

Blockchain application in smart contracts has proven to be significant in the healthcare sector. Results highlight the importance of integrating smart contracts and blockchain technology in the IoMT infrastructure to improve efficiency, transparency, and security. The study provides empirical evidence to support the implementation of smart contracts in the e-healthcare sector and suggests improved e-healthcare performance through this transition.

Conclusion

The emergence of e-healthcare systems with upgraded smart contracts and blockchain technology brings continuous health monitoring, time-effective operations, and cost-effectiveness to the healthcare sector.

Wearable Sensors and Smart Devices to Monitor Rehabilitation Parameters and Sports Performance: An Overview

A quantitative evaluation of kinetic parameters, the joint's range of motion, heart rate, and breathing rate, can be employed in sports performance tracking and rehabilitation monitoring following injuries or surgical operations. However, many of the current detection systems are expensive and designed for clinical use, requiring the presence of a physician and medical staff to assist users in the device's positioning and measurements. The goal of wearable sensors is to overcome the limitations of current devices, enabling the acquisition of a user's vital signs directly from the body in an accurate and non-invasive way. In sports activities, wearable sensors allow athletes to monitor performance and body movements objectively, going beyond the coach's subjective evaluation limits. The main goal of this review paper is to provide a comprehensive overview of wearable technologies and sensing systems to detect and monitor the physiological parameters of patients during post-operative rehabilitation and athletes' training, and to present evidence that supports the efficacy of this technology for healthcare applications. First, a classification of the human physiological parameters acquired from the human body by sensors attached to sensitive skin locations or worn as a part of garments is introduced, carrying important feedback on the user's health status. Then, a detailed description of the electromechanical transduction mechanisms allows a comparison of the technologies used in wearable applications to monitor sports and rehabilitation activities. This paves the way for an analysis of wearable technologies, providing a comprehensive comparison of the current state of the art of available sensors and systems. Comparative and statistical analyses are provided to point out useful insights for defining the best technologies and solutions for monitoring body movements. Lastly, the presented review is compared with similar ones reported in the literature to highlight its strengths and novelties.

Flexible wearable sensors - an update in view of touch-sensing

Nowadays, much of user interface is based on touch and the touch sensors have been common for displays, Internet of things (IoT) projects, or robotics. They can be found in lamps, touch screens of smartphones, or other wide arrays of applications as well. However, the conventional touch sensors, fabricated from rigid materials, are bulky, inflexible, hard, and hard-to-wear devices. The current IoT trend has made these touch sensors increasingly important when it added in the skin or clothing to affect different aspects of human life flexibly and comfortably. The paper provides an overview of the recent developments in this field. We discuss exciting advances in materials, fabrications, enhancements, and applications of flexible wearable sensors under view of touch-sensing. Therein, the review describes the theoretical principles of touch sensors, including resistive, capacitive, and piezoelectric types. Following that, the conventional and novel materials, as well as manufacturing technologies of flexible sensors are considered to. Especially, this review highlights the multidisciplinary approaches such as e-skins, e-textiles, e-healthcare, and e-control of flexible touch sensors. Finally, we summarize the challenges and opportunities that use is key to widespread development and adoption for future research.

Enabling Privacy-Assured Fog-Based Data Aggregation in E-Healthcare Systems

Wearable body area network is a key component of the modern-day e-healthcare system (e.g., telemedicine), particularly as the number and types of wearable medical monitoring systems increase. The importance of such systems is reinforced in the current COVID-19 pandemic. In addition to the need for a secure collection of medical data, there is also a need to process data in real-time. In this article, we design an improved symmetric homomorphic cryptosystem and a fog-based communication architecture to support delay- or time-sensitive monitoring and other-related applications. Specifically, medical data can be analyzed at the fog servers in a secure manner. This will facilitate decision making, for example, allowing relevant stakeholders to detect and respond to emergency situations, based on real-time data analysis. We present two attack games to demonstrate that our approach is secure (i.e., chosen-plaintext attack resilience under the computational Diffie-Hellman assumption), and evaluate the complexity of its computations. A comparative summary of its performance and three other related approaches suggests that our approach enables privacy-assured medical data aggregation, and the simulation experiments using Microsoft Azure further demonstrate the utility of our scheme.

Remote Pain Monitoring Using Fog Computing for e-Healthcare: An Efficient Architecture

The integration of medical signal processing capabilities and advanced sensors into Internet of Things (IoT) devices plays a key role in providing comfort and convenience to human lives. As the number of patients is increasing gradually, providing healthcare facilities to each patient, particularly to the patients located in remote regions, not only has become challenging but also results in several issues, such as: (i) increase in workload on paramedics, (ii) wastage of time, and (iii) accommodation of patients. Therefore, the design of smart healthcare systems has become an important area of research to overcome these above-mentioned issues. Several healthcare applications have been designed using wireless sensor networks (WSNs), cloud computing, and fog computing. Most of the e-healthcare applications are designed using the cloud computing paradigm. Cloud-based architecture introduces high latency while processing huge amounts of data, thus restricting the large-scale implementation of latency-sensitive e-healthcare applications. Fog computing architecture offers processing and storage resources near to the edge of the network, thus, designing e-healthcare applications using the fog computing paradigm is of interest to meet the low latency requirement of such applications. Patients that are minors or are in intensive care units (ICUs) are unable to self-report their pain conditions. The remote healthcare monitoring applications deploy IoT devices with bio-sensors capable of sensing surface electromyogram (sEMG) and electrocardiogram (ECG) signals to monitor the pain condition of such patients. In this article, fog computing architecture is proposed for deploying a remote pain monitoring system. The key motivation for adopting the fog paradigm in our proposed approach is to reduce latency and network consumption. To validate the effectiveness of the proposed approach in minimizing delay and network utilization, simulations were carried out in iFogSim and the results were compared with the cloud-based systems. The results of the simulations carried out in this research indicate that a reduction in both latency and network consumption can be achieved by adopting the proposed approach for implementing a remote pain monitoring system.

Intelligent Machine Learning Approach for Effective Recognition of Diabetes in E-Healthcare Using Clinical Data

Significant attention has been paid to the accurate detection of diabetes. It is a big challenge for the research community to develop a diagnosis system to detect diabetes in a successful way in the e-healthcare environment. Machine learning techniques have an emerging role in healthcare services by delivering a system to analyze the medical data for diagnosis of diseases. The existing diagnosis systems have some drawbacks, such as high computation time, and low prediction accuracy. To handle these issues, we have proposed a diagnosis system using machine learning methods for the detection of diabetes. The proposed method has been tested on the diabetes data set which is a clinical dataset designed from patient's clinical history. Further, model validation methods, such as hold out, K-fold, leave one subject out and performance evaluation metrics, includes accuracy, specificity, sensitivity, F1-score, receiver operating characteristic curve, and execution time have been used to check the validity of the proposed system. We have proposed a filter method based on the Decision Tree (Iterative Dichotomiser 3) algorithm for highly important feature selection. Two ensemble learning algorithms, Ada Boost and Random Forest, are also used for feature selection and we also compared the classifier performance with wrapper based feature selection algorithms. Classifier Decision Tree has been used for the classification of healthy and diabetic subjects. The experimental results show that the proposed feature selection algorithm selected features improve the classification performance of the predictive model and achieved optimal accuracy. Additionally, the proposed system performance is high compared to the previous state-of-the-art methods. High performance of the proposed method is due to the different combinations of selected features set and Plasma glucose concentrations, Diabetes pedigree function, and Blood mass index are more significantly important features in the dataset for prediction of diabetes. Furthermore, the experimental results statistical analysis demonstrated that the proposed method would effectively detect diabetes and can be deployed in an e-healthcare environment.

Smart Cupboard for Assessing Memory in Home Environment

Sensor systems for the Internet of Things (IoT) make it possible to continuously monitor people, gathering information without any extra effort from them. Thus, the IoT can be very helpful in the context of early disease detection, which can improve peoples' quality of life by applying the right treatment and measures at an early stage. This paper presents a new use of IoT sensor systems-we present a novel three-door smart cupboard that can measure the memory of a user, aiming at detecting potential memory losses. The smart cupboard has three sensors connected to a Raspberry Pi, whose aim is to detect which doors are opened. Inside of the Raspberry Pi, a Python script detects the openings of the doors, and classifies the events between attempts of finding something without success and the events of actually finding it, in order to measure the user's memory concerning the objects' locations (among the three compartments of the smart cupboard). The smart cupboard was assessed with 23 different users in a controlled environment. This smart cupboard was powered by an external battery. The memory assessments of the smart cupboard were compared with a validated test of memory assessment about face-name associations and a self-reported test about self-perceived memory. We found a significant correlation between the smart cupboard results and both memory measurement methods. Thus, we conclude that the proposed novel smart cupboard successfully measured memory.

A Review of Technology-based Interventions in Improving Type 2 Diabetes Management in Chinese Americans

Health disparities of type 2 diabetes (DM2) in America is an ongoing crisis. Despite this, technology has been helpful in managing DM2 in the non-Hispanic White, Hispanic, and African American populations and has been proven effective. Furthermore, it may be used to supplement health provider DM2 care through telemedicine to lower hemoglobin A1c (A1c), a gold standard DM2 measurement, and other DM2-related outcomes, such as glycated hemoglobin. The purpose of this study was to review current literature on the use of telemedicine in assisting DM2 management in racial ethnic minorities and to discuss how to adjust the telemedicine DM2 management program to be applied to Chinese Americans. In addition, it is worthy to note that the role of nurses makes a substantial difference in the effectiveness of technological management of DM2 from being culturally sensitive and sending catered messages to address specific patient needs.

Systemic Design Approach to a Real-Time Healthcare Monitoring System: Reducing Unplanned Hospital Readmissions

Following hospital discharge, millions of patients continue to recover outside formal healthcare organizations (HCOs) in designated transitional care periods (TCPs). Unplanned hospital readmissions of patients during TCPs adversely affects the quality and cost of care. In order to reduce the rates of unplanned hospital readmissions, we propose a real-time patient-centric system, built around applications, to assist discharged patients in remaining at home or in the workplace while being supported by care providers. Discrete-event system modeling techniques and supervisory control theory play fundamental roles in the system's design. Simulation results and analysis show that the proposed system can be effective in documenting a patient's condition and health-related behaviors. Most importantly, the system tackles the problem of unplanned hospital readmissions by supporting discharged patients at a lower cost via home/workplace monitoring without sacrificing the quality of care.

Role-based access control through on-demand classification of electronic health record

Electronic health records (EHR) provides convenient method to exchange medical information of patients between different healthcare providers. Access control mechanism in healthcare services characterises authorising users to access EHR records. Role Based Access Control helps to restrict EHRs to users in a certain role. Significant works have been carried out for access control since last one decade but little emphasis has been given to on-demand role based access control. Presented work achieved access control through physical data isolation which is more robust and secure. We propose an algorithm in which selective combination of policies for each user of the EHR database has been defined. We extend well known data mining technique 'classification' to group EHRs with respect to the given role. Algorithm works by taking various roles as class and defined their features as a vector. Here, features are used as a Feature Vector for classification to describe user authority.

Applying CHAID algorithm to investigate critical attributes of secured interoperable health data exchange

With enhanced interoperability in healthcare environment the exchange of electronic health records (EHRs), both intra and inter organisations, has increased manifold. Sharing of the EHR creates room for illegal disclosures and confidentiality breaches. Interoperable healthcare is a complex system with many independent components. To design a secured framework for such a system, one need to understand the most important security attributes and predict various dependencies among them. The security attributes selected for statistical analysis are taken from the real-time study of patient-doctor relationship existing in any hospital or clinic. Hospitals with functional EHR-systems are the prerequisite of this study. The dependencies in the obtained data are generated through classification technique, chi-squared automatic interaction detection (CHAID). The decision tree obtained is analysed and verified using regression. The paper enabled the identification of the salient feature controlling which would maximally reduce security threats while sharing EHRs in interoperable healthcare environment.

Macro influencers of electronic health records adoption

While adoption rates for electronic health records (EHRs) have improved, the reasons for significant geographical differences in EHR adoption within the USA have remained unclear. To understand the reasons for these variations across states, we have compiled from secondary sources a profile of different states within the USA, based on macroeconomic and macro health-environment factors. Regression analyses were performed using these indicator factors on EHR adoption. The results showed that internet usage and literacy are significantly associated with certain measures of EHR adoption. Income level was not significantly associated with EHR adoption. Per capita patient days (a proxy for healthcare need intensity within a state) is negatively correlated with EHR adoption rate. Health insurance coverage is positively correlated with EHR adoption rate. Older physicians (>60 years) tend to adopt EHR systems less than their younger counterparts. These findings have policy implications on formulating regionally focused incentive programs.