A framework for the comparison of mobile patient monitoring systems

Home Monitoring Programs for Patients Testing Positive for SARS-CoV-2: An Integrative Literature Review

BACKGROUND

The severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) pandemic threatened to oversaturate hospitals worldwide, necessitating rapid patient discharge to preserve capacity for the most severe cases. This need, as well as the high risk of SARS-CoV-2 transmission, led many hospitals to implement remote patient monitoring (RPM) programs for SARS-CoV-2 positive patients in an effort to provide care that was safe and preserve scarce resources.

OBJECTIVE

The aim of this study is to provide an integrative review of peer-reviewed literature on different RPM programs that were implemented for SARS-CoV-2 positive patients including their strengths and challenges.

METHODS

A search was conducted for peer reviewed literature using PubMed, CINAHL, OVID, and Google Scholar. Peer-reviewed studies written in English or Spanish and published between 2019 and 2021 on RPM of SARS-CoV-2-positive patients were considered. Information was extracted according to a qualitative content analysis method, informed by the Comparison of Mobile Patient Monitoring Systems Framework.

RESULTS

Of 57 retrieved articles, 10 publications were included. The sample sizes ranged from 75 to 48,290 and the monitoring length ranged from 7 to 30 days. Information regarding the comparison framework was summarized. Main strengths of using RPM for SARS-CoV-2 positive patients was participant acceptance, feasibility, safety, and resource conservation. Main limitations were the lack of information on patient data security measures, robust outcomes testing, and identification of the most effective biomarkers to track SARS-CoV-2 decompensation.

CONCLUSION

Different RPM programs for SARS-CoV-2 were implemented, from sending home participants with a pulse oximeter and collecting readings via call to modifying existing mobile applications and sending holistic health questionnaires to participants. This review determined that RPM is beneficial to SARS-CoV-2 positive patients; however, its effectiveness can be improved by further research. Mainly, identifying what patient data are most effective at tracking SARS-CoV-2 decompensation by utilizing advanced technology already in the market.

Effectiveness of remote monitoring and feedback on objective compliance with a mandibular advancement device for treatment of obstructive sleep apnea

Compliance with a mandibular advancement device is important for the optimal treatment of obstructive sleep apnea. Recent advances in information and communication technology-based monitoring and intervention for chronic diseases have enabled continuous monitoring and personalized management. Self-evaluation and self-regulation through objective monitoring and feedback may improve compliance. The aim of this study was to evaluate the effects of information and communication technology-based remote monitoring and feedback services, using a smartphone application, on the objective compliance with a mandibular advancement device in patients with obstructive sleep apnea. Forty individuals who were diagnosed with obstructive sleep apnea by polysomnography were randomly assigned to groups A and B. During an initial 6-week evaluation period, the mandibular advancement device-wearing time was monitored with the smartphone application in group B, but not in group A. The two groups then switched the monitoring procedures during the second 6-week period (the smartphone application was then used by group B, but not by group A). If no input data were indicated on the cloud server of the smartphone application during the monitored period, push notifications were provided twice daily. Objective compliance, monitored by a micro-recorder within the mandibular advancement device, was noted and compared based on whether the monitoring service was provided. The number of mandibular advancement device-wearing days was significantly higher in the monitored period than in the unmonitored period. The mandibular advancement device-wearing time did not differ significantly between the two groups. In conclusion, information and communication technology-based remote monitoring and feedback services demonstrated a potential to increase the objective measures of compliance with mandibular advancement devices.

Anytime ECG Monitoring through the Use of a Low-Cost, User-Friendly, Wearable Device

Every year cardiovascular diseases kill the highest number of people worldwide. Among these, pathologies characterized by sporadic symptoms, such as atrial fibrillation, are difficult to be detected as state-of-the-art solutions, e.g., 12-leads electrocardiogram (ECG) or Holter devices, often fail to tackle these kinds of pathologies. Many portable devices have already been proposed, both in literature and in the market. Unfortunately, they all miss relevant features: they are either not wearable or wireless and their usage over a long-term period is often unsuitable. In addition, the quality of recordings is another key factor to perform reliable diagnosis. The ECG WATCH is a device designed for targeting all these issues. It is inexpensive, wearable (size of a watch), and can be used without the need for any medical expertise about positioning or usage. It is non-invasive, it records single-lead ECG in just 10 s, anytime, anywhere, without the need to physically travel to hospitals or cardiologists. It can acquire any of the three peripheral leads; results can be shared with physicians by simply tapping a smartphone app. The ECG WATCH quality has been tested on 30 people and has successfully compared with an electrocardiograph and an ECG simulator, both certified. The app embeds an algorithm for automatically detecting atrial fibrillation, which has been successfully tested with an official ECG simulator on different severity of atrial fibrillation. In this sense, the ECG WATCH is a promising device for anytime cardiac health monitoring.

eHealthChain-a blockchain-based personal health information management system

Medical IoT devices that use miniature sensors to collect patient's bio-signals and connected medical applications are playing a crucial role in providing pervasive and personalized healthcare. This technological improvement has also created opportunities for the better management of personal health information. The Personal Health Information Management System (PHIMS) supports activities such as acquisition, storage, organization, integration, and privacy-sensitive retrieval of consumer's health information. For usability and wide acceptance, the PHIMS should follow the design principles that guarantee privacy-aware health information sharing, individual information control, integration of information obtained from multiple medical IoT devices, health information security, and flexibility. Recently, blockchain technology has emerged as a lucrative option for the management of personal health information. In this paper, we propose eHealthChain-a blockchain-based PHIMS for managing health data originating from medical IoT devices and connected applications. The eHealthChain architecture consists of four layers, which are a blockchain layer for hosting a blockchain database, an IoT device layer for obtaining personal health data, an application layer for facilitating health data sharing, and an adapter layer, which interfaces the blockchain layer with an application layer. Compared to existing systems, eHealthChain provides complete control to the user in terms of personal health data acquisition, sharing, and self-management. We also present a detailed implementation of a Proof of Concept (PoC) prototype of eHealthChain system built using Hyperledger Fabric platform.

EDDAMAP: efficient data-dependent approach for monitoring asymptomatic patient

Background

A pandemic affects healthcare delivery and consequently leads to socioeconomic complications. During a pandemic, a community where there lives an asymptomatic patient (AP) becomes a potential endemic zone. Assuming we want to monitor the travel and/or activity of an AP in a community where there is a pandemic. Presently, most monitoring algorithms are relatively less efficient to find a suitable solution as they overlook the continuous mobility instances and activities of the AP over time. Conversely, this paper proposes an EDDAMAP as a compelling data-dependent technique and/or algorithm towards efficient continuous monitoring of the travel and/or activity of an AP.

Methods

In this paper, it is assumed that an AP is infected with a contagious disease in which the EDDAMAP technique exploits a GPS-enabled mobile device by tagging it to the AP along with its travel within a community. The technique further examines the Spatio-temporal trajectory of the AP to infer its spatial time-bounded activity. The technique aims to learn the travels of the AP and correlates them to its activities to derive some classes of point of interests (POIs) in a location. Further, the technique explores the natural occurring POIs via modelling to identify some regular stay places (SP) and present them as endemic zones. The technique adopts concurrent object feature localization and recognition, branch and bound formalism and graph theory to cater for the worst error-guaranteed approximation to obtain a valid and efficient query solution and also experiments with a real-world GeoLife dataset to confirm its performance.

Results

The EDDAMAP technique proofs a compelling technique towards efficient monitoring of an AP in case of a pandemic.

Conclusions

The EDDAMAP technique will promote the discovery of endemic zones and hence some public healthcare facilities can rely on it to facilitate the design of patient monitoring system applications to curtail a global pandemic.

Postoperative Remote Monitoring with a Transcutaneous Biosensing Patch: Preliminary Evaluation of Data Collection

Introduction. Connected systems transmitting vital parameters could well represent a tool to shorten postoperative hospital stay while providing continuous remote patient monitoring and potentially detect the onset of complications. Our aim was to analyze the functionality of a transcutaneous biosensing data collection patch in morbidly obese patients. Materials and Methods. An adhesive patch (The HealthPatch MD™) was applied to patients' chests postoperatively. The patch was connected to a tablet via a bluetooth network to collect the heart rate, respiratory rate, skin temperature, and posture recognition data. The tablet conveyed data to a secure health data central server by means of a WiFi or 3G/4G transmission. Data were stored in a digital health platform to which health care professionals could connect. The evaluation focused on the volume, quality, and security of data transmission. A pilot phase involved 10 patients. Thirty-three additional patients undergoing bariatric surgery were included in the experimental phase. Results. The mean length of stay was 2.28 days (range: 2-5 days). The mean time of patch application was 51 ± 25.2 hours per patient (range: 19-139 hours), totalizing 1,683 hours of recording for the 33 patients included. During this time, a total of 7.562.531 data measurement points were collected and transmitted to the e-health platform via the patch. Two total disconnections and two partial disconnections were observed. The acquisition of patient postural data was unreliable. Conclusions. Connected telemetry for remote postoperative monitoring is promising. However, it is still limited by data transmission problems.

Comparison of Features of a Mobile Application to Report School Violence Through Benchmarking

BACKGROUND

At a time and educational context favorable to technologies and innovations, we took the initiative to mobile phone applications (apps) relevant to school violence in an effort to be responsive to a growing public health problem in multiple countries.

METHODS

In this study, we compared and verified the features of 10 apps for reporting violence in schools. We used a benchmarking tool for integrative review of these mobile apps.

RESULTS

There were 13 apps on the Google Play platform, 11 apps in the App Store, and 5 apps on both platforms, totaling 19 apps. We selected 10 apps for comparative analysis.

CONCLUSIONS

Through benchmarking, we determined the relevant features of the apps with recommendations for improving app development in the future.

Mobile-Based Patient Monitoring Systems: A Prioritisation Framework Using Multi-Criteria Decision-Making Techniques

This study presents a prioritisation framework for mobile patient monitoring systems (MPMSs) based on multicriteria analysis in architectural components. This framework selects the most appropriate system amongst available MPMSs for the telemedicine environment. Prioritisation of MPMSs is a challenging task due to (a) multiple evaluation criteria, (b) importance of criteria, (c) data variation and (d) unmeasurable values. The secondary data presented as the decision evaluation matrix include six systems (namely, Yale-National Aeronautics and Space Administration (NASA), advanced health and disaster aid network, personalised health monitoring, CMS, MobiHealth and NTU) as alternatives and 13 criteria (namely, supported number of sensors, sensor front-end (SFE) communication, SFE to mobile base unit (MBU) communications, display of biosignals on the MBU, storage of biosignals on the MBU, intra-body area network (BAN) communication problems, extra-BAN communication problems, extra-BAN communication technology, extra-BAN communication protocols, back-end system communication technology, intended geographic area of use, end-to-end security and reported trial problems) based on the architectural components of MPMSs. These criteria are adopted from the most relevant studies and are found to be applicable to this study. The prioritisation framework is developed in three stages. (1) The unmeasurable values of the MPMS evaluation criteria in the adopted decision evaluation matrix based on expert opinion are represented by using the best-worst method (BWM). (2) The importance of the evaluation criteria based on the architectural components of the MPMS is determined by using the BWM. (3) The VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method is utilised to rank the MPMSs according to the determined importance of the evaluation criteria and the adopted decision matrix. For validation, mean ± standard deviation is used to verify the similarity of systematic prioritisations objectively. The following results are obtained. (1) The BWM represents the unmeasurable values of the MPMS evaluation criteria. (2) The BWM is suitable for weighing the evaluation criteria based on the architectural components of the MPMS. (3) VIKOR is suitable for solving the MPMS prioritisation problem. Moreover, the internal and external VIKOR group decision making are approximately the same, with the best MPMS being 'Yale-NASA' and the worst MPMS being 'NTU'. (4) For the objective validation, remarkable differences are observed between the group scores, which indicate the similarity of internal and external prioritisation results.

Mobile Patient Monitoring Systems from a Benchmarking Aspect: Challenges, Open Issues and Recommended Solutions

This paper presents comprehensive insights into mobile patient monitoring systems (MPMSs) from evaluation and benchmarking aspects on the basis of two critical directions. The current evaluation criteria of MPMSs based on the architectural components of MPMSs and possible solutions are discussed. This review highlights four serious issues, namely, multiple evaluation criteria, criterion importance, unmeasurable criteria and data variation, in MPMS benchmarking. Multicriteria decision-making (MCDM) analysis techniques are proposed as effective solutions to solve these issues from a methodological aspect. This methodological aspect involves a framework for benchmarking MPMSs on the basis of MCDM to rank available MPMSs and select a suitable one. The benchmarking framework is discussed in four steps. Firstly, pre-processing and identification procedures are presented. Secondly, the procedure of weight calculation based on the best-worst method (BWM) is described. Thirdly, the development of a benchmark framework by using the VIKOR method is introduced. Lastly, the proposed framework is validated.

A mobile health monitoring-and-treatment system based on integration of the SSN sensor ontology and the HL7 FHIR standard

BACKGROUND

Mobile health (MH) technologies including clinical decision support systems (CDSS) provide an efficient method for patient monitoring and treatment. A mobile CDSS is based on real-time sensor data and historical electronic health record (EHR) data. Raw sensor data have no semantics of their own; therefore, a computer system cannot interpret these data automatically. In addition, the interoperability of sensor data and EHR medical data is a challenge. EHR data collected from distributed systems have different structures, semantics, and coding mechanisms. As a result, building a transparent CDSS that can work as a portable plug-and-play component in any existing EHR ecosystem requires a careful design process. Ontology and medical standards support the construction of semantically intelligent CDSSs.

METHODS

This paper proposes a comprehensive MH framework with an integrated CDSS capability. This cloud-based system monitors and manages type 1 diabetes mellitus. The efficiency of any CDSS depends mainly on the quality of its knowledge and its semantic interoperability with different data sources. To this end, this paper concentrates on constructing a semantic CDSS based on proposed FASTO ontology.

RESULTS

This realistic ontology is able to collect, formalize, integrate, analyze, and manipulate all types of patient data. It provides patients with complete, personalized, and medically intuitive care plans, including insulin regimens, diets, exercises, and education sub-plans. These plans are based on the complete patient profile. In addition, the proposed CDSS provides real-time patient monitoring based on vital signs collected from patients' wireless body area networks. These monitoring include real-time insulin adjustments, mealtime carbohydrate calculations, and exercise recommendations. FASTO integrates the well-known standards of HL7 fast healthcare interoperability resources (FHIR), semantic sensor network (SSN) ontology, basic formal ontology (BFO) 2.0, and clinical practice guidelines. The current version of FASTO includes 9577 classes, 658 object properties, 164 data properties, 460 individuals, and 140 SWRL rules. FASTO is publicly available through the National Center for Biomedical Ontology BioPortal at https://bioportal.bioontology.org/ontologies/FASTO .

CONCLUSIONS

The resulting CDSS system can help physicians to monitor more patients efficiently and accurately. In addition, patients in rural areas can depend on the system to manage their diabetes and emergencies.

An Energy Efficient Wearable Smart IoT System to Predict Cardiac Arrest

Recently, many people have become more concerned about having a sudden cardiac arrest. With the increase in popularity of smart wearable devices, an opportunity to provide an Internet of Things (IoT) solution has become more available. Unfortunately, out of hospital survival rates are low for people suffering from sudden cardiac arrests. The objective of this research is to present a multisensory system using a smart IoT system that can collect Body Area Sensor (BAS) data to provide early warning of an impending cardiac arrest. The goal is to design and develop an integrated smart IoT system with a low power communication module to discreetly collect heart rates and body temperatures using a smartphone without it impeding on everyday life. This research introduces the use of signal processing and machine-learning techniques for sensor data analytics to identify predict and/or sudden cardiac arrests with a high accuracy.

How to Develop IoT Cloud e-Health Systems Based on FIWARE: A Lesson Learnt

Nowadays, the penetration of sensors and actuators in different application fields is revolutionizing all aspects of our daily life. One of the major sectors that is taking advantage of such cutting-edge cheap smart devices is healthcare. In this context, Remote Patient Monitoring (RPM) at home represents a tempting opportunity for hospitals to reduce clinical costs and to improve the quality of life of both patients and their families. It allows patients to be monitored remotely by means networks of Internet of Things (IoT) medical devices equipped with sensors and actuators that collect healthcare data from patients and send them to a Cloud-based Hospital Information System (HIS) for processing. Up to now, many different proprietary software systems have been developed as stand-along expensive solutions, presenting interoperability, extensibility, and scalability issues. In recent years, the European Commission (EC) has promoted the wide adoption of FIWARE technology, launching 16 Industrial Accelerators focusing on different application fields. One of these, i.e., FICHe, is specialized in healthcare, providing the guidelines on how to develop eHealth systems. This paper focuses on how to compose new cutting-edge IoT and Cloud-based Cyber Physical Health Sytem (CPHS) services and applications interconnected with remote medical sensors and actuators using FIWARE technology in the context envisioned by FICHe. In particular, we discuss the design and development of an RPM system implemented through the collaboration between the Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) “Bonino Pulejo” (i.e., a clinical and research healthcare centre specialized in the treatment of neuro lesions), University of Messina, IBM Research, Telefónica, and the University of the Western Cape in South Africa. The description of our best practice provides a model and guidelines for the development of lightweight and low cost RPM services for rural and isolated areas, with the expectation of expanding healthcare to the developing world and in general allows us to outline how to deal with the real adoption of the FIWARE technology in an e-health project.

A fuzzy logic-based warning system for patients classification

Typically acute deterioration in sick people is preceded by subtle changes in the physiological parameters such as pulse and blood pressure. The Modified Early Warning Score is a scoring system developed to assist hospital staff in gauging these physiological changes and identifying patients in need of urgent medical care to avoid catastrophic deterioration. This work discusses the design and implementation of an equivalent warning system that utilizes fuzzy logic techniques to categorize patients' status. The system is implemented and tested in Rashid Centre for Diabetes and Research in UAE. Results are compared with those obtained using the Modified Early Warning System that is currently used in practice. We demonstrate that the implemented system provides reliable results that are in agreement with the current Modified Early Warning Score system, with the added benefit of a scoring scheme that provides a better insight into the status or medical condition of each patient.

Survey on Monitoring and Quality Controlling of the Mobile Biosignal Delivery

A Mobile Patient Monitoring System (MPMS) acquires patient's biosignals and transmits them using wireless network connection to the decision-making module or healthcare professional for the assessment of patient's condition. A variety of wireless network technologies such as wireless personal area networks (e.g., Bluetooth), mobile ad-hoc networks (MANET), and infrastructure-based networks (e.g., WLAN and cellular networks) are in practice for biosignals delivery. The wireless network quality-of-service (QoS) requirements of biosignals delivery are mainly specified in terms of required bandwidth, acceptable delay, and tolerable error rate. An important research challenge in the MPMS is how to satisfy QoS requirements of biosignals delivery in the environment characterized by patient mobility, deployment of multiple wireless network technologies, and variable QoS characteristics of the wireless networks. QoS requirements are mainly application specific, while available QoS is largely dependent on QoS provided by wireless network in use. QoS provisioning refers to providing support for improving QoS experience of networked applications. In resource poor conditions, application adaptation may also be required to make maximum use of available wireless network QoS. This survey paper presents a survey of recent developments in the area of QoS provisioning for MPMS. In particular, our contributions are as follows: (1) overview of wireless networks and network QoS requirements of biosignals delivery; (2) survey of wireless networks' QoS performance evaluation for the transmission of biosignals; and (3) survey of QoS provisioning mechanisms for biosignals delivery in MPMS. We also propose integrating end-to-end QoS monitoring and QoS provisioning strategies in a mobile patient monitoring system infrastructure to support optimal delivery of biosignals to the healthcare professionals.

New algorithms for processing time-series big EEG data within mobile health monitoring systems

BACKGROUND AND OBJECTIVES

Recent advances in miniature biomedical sensors, mobile smartphones, wireless communications, and distributed computing technologies provide promising techniques for developing mobile health systems. Such systems are capable of monitoring epileptic seizures reliably, which are classified as chronic diseases. Three challenging issues raised in this context with regard to the transformation, compression, storage, and visualization of big data, which results from a continuous recording of epileptic seizures using mobile devices.

METHODS

In this paper, we address the above challenges by developing three new algorithms to process and analyze big electroencephalography data in a rigorous and efficient manner. The first algorithm is responsible for transforming the standard European Data Format (EDF) into the standard JavaScript Object Notation (JSON) and compressing the transformed JSON data to decrease the size and time through the transfer process and to increase the network transfer rate. The second algorithm focuses on collecting and storing the compressed files generated by the transformation and compression algorithm. The collection process is performed with respect to the on-the-fly technique after decompressing files. The third algorithm provides relevant real-time interaction with signal data by prospective users. It particularly features the following capabilities: visualization of single or multiple signal channels on a smartphone device and query data segments.

RESULTS

We tested and evaluated the effectiveness of our approach through a software architecture model implementing a mobile health system to monitor epileptic seizures. The experimental findings from 45 experiments are promising and efficiently satisfy the approach's objectives in a price of linearity. Moreover, the size of compressed JSON files and transfer times are reduced by 10% and 20%, respectively, while the average total time is remarkably reduced by 67% through all performed experiments.

CONCLUSIONS

Our approach successfully develops efficient algorithms in terms of processing time, memory usage, and energy consumption while maintaining a high scalability of the proposed solution. Our approach efficiently supports data partitioning and parallelism relying on the MapReduce platform, which can help in monitoring and automatic detection of epileptic seizures.

Patient Posture Monitoring System Based on Flexible Sensors

Monitoring patients using vision cameras can cause privacy intrusion problems. In this paper, we propose a patient position monitoring system based on a patient cloth with unobtrusive sensors. We use flexible sensors based on polyvinylidene fluoride, which is a flexible piezoelectric material. Theflexiblesensorsareinsertedintopartsclosetothekneeandhipoftheloosepatientcloth. We measure electrical signals from the sensors caused by the piezoelectric effect when the knee and hip in the cloth are bent. The measured sensor outputs are transferred to a computer via Bluetooth. We use a custom-made program to detect the position of the patient through a rule-based algorithm and the sensor outputs. The detectable postures are based on six human motions in and around a bed. The proposed system can detect the patient positions with a success rate over 88 percent for three patients.

SME2EM: Smart mobile end-to-end monitoring architecture for life-long diseases

Monitoring life-long diseases requires continuous measurements and recording of physical vital signs. Most of these diseases are manifested through unexpected and non-uniform occurrences and behaviors. It is impractical to keep patients in hospitals, health-care institutions, or even at home for long periods of time. Monitoring solutions based on smartphones combined with mobile sensors and wireless communication technologies are a potential candidate to support complete mobility-freedom, not only for patients, but also for physicians. However, existing monitoring architectures based on smartphones and modern communication technologies are not suitable to address some challenging issues, such as intensive and big data, resource constraints, data integration, and context awareness in an integrated framework. This manuscript provides a novel mobile-based end-to-end architecture for live monitoring and visualization of life-long diseases. The proposed architecture provides smartness features to cope with continuous monitoring, data explosion, dynamic adaptation, unlimited mobility, and constrained devices resources. The integration of the architecture׳s components provides information about diseases׳ recurrences as soon as they occur to expedite taking necessary actions, and thus prevent severe consequences. Our architecture system is formally model-checked to automatically verify its correctness against designers׳ desirable properties at design time. Its components are fully implemented as Web services with respect to the SOA architecture to be easy to deploy and integrate, and supported by Cloud infrastructure and services to allow high scalability, availability of processes and data being stored and exchanged. The architecture׳s applicability is evaluated through concrete experimental scenarios on monitoring and visualizing states of epileptic diseases. The obtained theoretical and experimental results are very promising and efficiently satisfy the proposed architecture׳s objectives, including resource awareness, smart data integration and visualization, cost reduction, and performance guarantee.

Data mining with Random Forests as a methodology for biomedical signal classification

As the contribution of specific parameters is not known and significant intersubject variability is expected, a decision system allowing adaptation for subject and environment conditions has to be designed to evaluate biomedical signal classification. A decision support system has to be trained in its desirable functionality prior to being used for patient monitoring evaluation. This paper describes a decision system based on data mining with Random Forests, allowing the adaptation for subject and environment conditions. This methodology may lead to specific system scoring by an artificial intelligence-supported patient monitoring evaluation system, which may help find a way of making decisions concerning future treatment and have influence on the quality of patients’ life.

Performance assessment of a closed-loop system for diabetes management

Telemedicine systems can play an important role in the management of diabetes, a chronic condition that is increasing worldwide. Evaluations on the consistency of information across these systems and on their performance in a real situation are still missing. This paper presents a remote monitoring system for diabetes management based on physiological sensors, mobile technologies and patient/doctor applications over a service-oriented architecture that has been evaluated in an international trial (83,905 operation records). The proposed system integrates three types of running environments and data engines in a single service-oriented architecture. This feature is used to assess key performance indicators comparing them with other type of architectures. Data sustainability across the applications has been evaluated showing better outcomes for full integrated sensors. At the same time, runtime performance of clients has been assessed spotting no differences regarding the operative environment.

Cancer care management through a mobile phone health approach: key considerations

Greater use of mobile phone devices seems inevitable because the health industry and cancer care are facing challenges such as resource constraints, rising care costs, the need for immediate access to healthcare data of types such as audio video texts for early detection and treatment of patients and increasing remote aids in telemedicine. Physicians, in order to study the causes of cancer, detect cancer earlier, act in prevention measures, determine the effectiveness of treatment and specify the reasons for the treatment ineffectiveness, need to access accurate, comprehensive and timely cancer data. Mobile devices provide opportunities and can play an important role in consulting, diagnosis, treatment, and quick access to health information. There easy carriage make them perfect tools for healthcare providers in cancer care management. Key factors in cancer care management systems through a mobile phone health approach must be considered such as human resources, confidentiality and privacy, legal and ethical issues, appropriate ICT and provider infrastructure and costs in general aspects and interoperability, human relationships, types of mobile devices and telecommunication related points in specific aspects. The successful implementation of mobile-based systems in cancer care management will constantly face many challenges. Hence, in applying mobile cancer care, involvement of users and considering their needs in all phases of project, providing adequate bandwidth, preparation of standard tools that provide maximum mobility and flexibility for users, decreasing obstacles to interrupt network communications, and using suitable communication protocols are essential. It is obvious that identifying and reducing barriers and strengthening the positive points will have a significant role in appropriate planning and promoting the achievements of mobile cancer care systems. The aim of this article is to explain key points which should be considered in designing appropriate mobile health systems in cancer care as an approach for improving cancer care management.

Electronic Health Record (EHR) As a Vehicle for Successful Health Care Best Practice

Objective:

application of EHR in journey toward the development and adaptation of best practice approach in health care has particular importance. The aim of this review article is survey of successful best practice through EHR.

Methods:

In this literature review articles were searched with keywords like Electronic Health Record, Best Practice in Science Direct, Google Scholar and Pub Med databases since 1999.

Results:

best practice in health care through some services like utilization management, case management, and information technology tools can perform. Utilization management in combination with evidence based medicine facilitate determine best decision. Health records are based on evidence medicine and be the richest source of health information. Definitely use of EHR has play pivotal role in journey toward the development and adaptation of best practice approach.

Conclusion:

Because of potential capabilities, EHR can be regarded as a main core and fundamental element in best practice approach. Success implementation of EHR relies on many factors that should be considered. Some critical success factors for EHR implementation that should be noted are change management, Physicians, nurses and key stakeholders involvement, leadership, provide reliable information technology infrastructure, system design, privacy and security, right budget, support high level management, clear communicate, determine goals and user needs, and define roles and responsibilities, interoperability standards.

Demonstrating the accuracy of an in-hospital ambulatory patient monitoring solution in measuring respiratory rate

This paper presents clinical testing conducted to evaluate the accuracy of Aingeal, a wireless in-hospital patient monitor, in measuring respiration rate via impedance pneumography. Healthy volunteers were invited to simultaneously wear a CE Marked Aingeal vital signs monitor and a capnograph, the current gold standard in respiration rate measurement. During the test, participants were asked to undergo a series of defined breathing protocols which included normal breathing, paced breathing between 8-23 breaths per minute (bpm) and a recovery period following moderate exercise. Statistical analysis of the data collected shows a mean difference of -0.73, a standard deviation of 1.61, limits of agreement of -3.88 and +2.42 bpm and a P-value of 0.22. This testing demonstrates comparable performance of the Aingeal device in measuring respiration rate with a well-accepted and widely used alternative method.

Design and customization of telemedicine systems

In recent years, the advances in information and communication technology (ICT) have resulted in the development of systems and applications aimed at supporting rehabilitation therapy that contributes to enrich patients' life quality. This work is focused on the improvement of the telemedicine systems with the purpose of customizing therapies according to the profile and disability of patients. For doing this, as salient contribution, this work proposes the adoption of user-centered design (UCD) methodology for the design and development of telemedicine systems in order to support the rehabilitation of patients with neurological disorders. Finally, some applications of the UCD methodology in the telemedicine field are presented as a proof of concept.