Digital health in smart cities: Rethinking the remote health monitoring architecture on combining edge, fog, and cloud

Purpose

Smart cities that support the execution of health services are more and more in evidence today. Here, it is mainstream to use IoT-based vital sign data to serve a multi-tier architecture. The state-of-the-art proposes the combination of edge, fog, and cloud computing to support critical health applications efficiently. However, to the best of our knowledge, initiatives typically present the architectures, not bringing adaptation and execution optimizations to address health demands fully.

Methods

This article introduces the VitalSense model, which provides a hierarchical multi-tier remote health monitoring architecture in smart cities by combining edge, fog, and cloud computing.

Results

Although using a traditional composition, our contributions appear in handling each infrastructure level. We explore adaptive data compression and homomorphic encryption at the edge, a multi-tier notification mechanism, low latency health traceability with data sharding, a Serverless execution engine to support multiple fog layers, and an offloading mechanism based on service and person computing priorities.

Conclusions

This article details the rationale behind these topics, describing VitalSense use cases for disruptive healthcare services and preliminary insights regarding prototype evaluation.

NT5E DNA methylation in papillary thyroid cancer: Novel opportunities for precision oncology

The ectoenzyme CD73, encoded by the NT5E gene, has emerged as a potential prognostic and therapeutic marker for papillary thyroid carcinoma (PTC), which has increased in incidence in recent decades. Here, from The Cancer Genome Atlas Thyroid Cancer (TCGA-THCA) database, we extracted and combined clinical features, levels of NT5E mRNA, and DNA methylation of PTC samples and performed multivariate and random forest analyses to evaluate the prognostic relevance and the potential of discriminating between adjacent non-malignant and thyroid tumor samples. As a result, we revealed that lower levels of methylation at the cg23172664 site were independently associated with BRAF-like phenotype (p = 0.002), age over 55 years (p = 0.012), presence of capsule invasion (p = 0.007) and presence of positive lymph node metastasis (LNM) (p = 0.04). The methylation levels of cg27297263 and cg23172664 sites showed significant and inversely correlations with levels of NT5E mRNA expression (r = -0.528 and r = -0.660, respectively), and their combination was able to discriminate between adjacent non-malignant and tumor samples with a precision of 96%-97% and 84%-85%, respectively. These data suggest that combining cg23172664 and cg27297263 sites may bring new insights to reveal new subsets of patients with papillary thyroid carcinoma.

Automatic documentation of professional health interactions: A systematic review

Electronic systems are increasingly present in the healthcare system and are often related to improved medical care. However, the widespread use of these technologies ended up building a relationship of dependence that can disrupt the doctor-patient relationship. In this context, digital scribes are automated clinical documentation systems that capture the physician-patient conversation and then generate the documentation for the appointment, enabling the physician to engage with the patient entirely. We have performed a systematic literature review on intelligent solutions for automatic speech recognition (ASR) with automatic documentation during a medical interview. The scope included only original research on systems that could detect speech and transcribe it in a natural and structured fashion simultaneously with the doctor-patient interaction, excluding speech-to-text-only technologies. The search resulted in a total of 1995 titles, with eight articles remaining after filtering for the inclusion and exclusion criteria. The intelligent models mainly consisted of an ASR system with natural language processing capability, a medical lexicon, and structured text output. None of the articles had a commercially available product at the time of the publication and reported limited real-life experience. So far, none of the applications has been prospectively validated and tested in large-scale clinical studies. Nonetheless, these first reports suggest that automatic speech recognition may be a valuable tool in the future to facilitate medical registration in a faster and more reliable manner. Improving transparency, accuracy, and empathy could drastically change how patients and doctors experience a medical visit. Unfortunately, clinical data on the usability and benefits of such applications is almost non-existent. We believe that future work in this area is necessary and needed.

Development and Validation of Conversational Agent to Pregnancy Safe-education

Pregnant women constantly need some information to support nutritional decisions during pregnancy, and many do not receive such assistance at all. This study aims to present a conversational agent to provide reliable information to pregnant women, focusing on nutritional education and evaluating the perception of pregnant women and health professionals about the agent. As a scientific contribution, this article developed and implemented a conversational agent in a real environment capable of generating reliable responses on the basis of a set of health documents. We proposed an intervention study with 25 women and 10 healthcare providers through a survey to measure the perceptions of these groups towards conversational agents. The results show that the intended design could ensure positive support for pregnant women, clarify certain issues for the public, and remove some knowledge barriers. The results showed no significant difference between the groups (p-value = 0.713). Depending on the perception of the pregnant group, the conversational agent model can teach new knowledge during the prenatal period (Mean = 4.56). The model presented for health professionals could already be indicated as a support tool for pregnant women (Mean = 4.7).

A Blockchain-Based End-to-End Data Protection Model for Personal Health Records Sharing: A Fully Homomorphic Encryption Approach

Personal health records (PHR) represent health data managed by a specific individual. Traditional solutions rely on centralized architectures to store and distribute PHR, which are more vulnerable to security breaches. To address such problems, distributed network technologies, including blockchain and distributed hash tables (DHT) are used for processing, storing, and sharing health records. Furthermore, fully homomorphic encryption (FHE) is a set of techniques that allows the calculation of encrypted data, which can help to protect personal privacy in data sharing. In this context, we propose an architectural model that applies a DHT technique called the interplanetary protocol file system and blockchain networks to store and distribute data and metadata separately; two new elements, called data steward and shared data vault, are introduced in this regard. These new modules are responsible for segregating responsibilities from health institutions and promoting end-to-end encryption; therefore, a person can manage data encryption and requests for data sharing in addition to restricting access to data for a predefined period. In addition to supporting calculations on encrypted data, our contribution can be summarized as follows: (i) mitigation of risk to personal privacy by reducing the use of unencrypted data, and (ii) improvement of semantic interoperability among health institutions by using distributed networks for standardized PHR. We evaluated performance and storage occupation using a database with 1.3 million COVID-19 registries, which showed that combining FHE with distributed networks could redefine e-health paradigms.

A rapid review of machine learning approaches for telemedicine in the scope of COVID-19

The COVID-19 pandemic has rapidly spread around the world. The rapid transmission of the virus is a threat that hinders the ability to contain the disease propagation. The pandemic forced widespread conversion of in-person to virtual care delivery through telemedicine. Given this gap, this article aims at providing a literature review of machine learning-based telemedicine applications to mitigate COVID-19. A rapid review of the literature was conducted in six electronic databases published from 2015 through 2020. The process of data extraction was documented using a PRISMA flowchart for inclusion and exclusion of studies. As a result, the literature search identified 1.733 articles, from which 16 articles were included in the review. We developed an updated taxonomy and identified challenges, open questions, and current data types. Our taxonomy and discussion contribute with a significant degree of coverage from subjects related to the use of machine learning to improve telemedicine in response to the COVID-19 pandemic. The evidence identified by this rapid review suggests that machine learning, in combination with telemedicine, can provide a strategy to control outbreaks by providing smart triage of patients and remote monitoring. Also, the use of telemedicine during future outbreaks could be further explored and refined.

Federated Learning for Healthcare: Systematic Review and Architecture Proposal

The use of machine learning (ML) with electronic health records (EHR) is growing in popularity as a means to extract knowledge that can improve the decision-making process in healthcare. Such methods require training of high-quality learning models based on diverse and comprehensive datasets, which are hard to obtain due to the sensitive nature of medical data from patients. In this context, federated learning (FL) is a methodology that enables the distributed training of machine learning models with remotely hosted datasets without the need to accumulate data and, therefore, compromise it. FL is a promising solution to improve ML-based systems, better aligning them to regulatory requirements, improving trustworthiness and data sovereignty. However, many open questions must be addressed before the use of FL becomes widespread. This article aims at presenting a systematic literature review on current research about FL in the context of EHR data for healthcare applications. Our analysis highlights the main research topics, proposed solutions, case studies, and respective ML methods. Furthermore, the article discusses a general architecture for FL applied to healthcare data based on the main insights obtained from the literature review. The collected literature corpus indicates that there is extensive research on the privacy and confidentiality aspects of training data and model sharing, which is expected given the sensitive nature of medical data. Studies also explore improvements to the aggregation mechanisms required to generate the learning model from distributed contributions and case studies with different types of medical data.

Method for evaluating roughness and valley areas coefficients of surfaces acquired by laser scanner

The quantitative determination of average roughness parameters, from the determination of height variations of the surface points, is frequently used to estimate the adhesion between an adhesive and the surface of a substrate. However, to determine the interaction between an adhesive and a surface of a heterogeneous material, such as a red ceramic, it is essential to define other roughness parameters. This work proposes a method for determining the roughness of red ceramic blocks from a three-dimensional evaluation, with the objective of estimating the contact area that the ceramic substrate can provide for a cementitious matrix. The study determines the average surface roughness from multiple planes and proposes the adoption of 2 more roughness parameters, the valley area index and the average valley area. The results demonstrate that there are advantages in using the proposed multiple plane method for roughness computation and that the valley area parameters are efficient to estimate the extent of adhesion between the materials involved.

Semantic interoperability in health records standards: a systematic literature review

The integration and exchange of information among health organizations and system providers are currently regarded as a challenge. Each organization usually has an internal ecosystem and a proprietary way to store electronic health records of the patient’s history. Recent research explores the advantages of an integrated ecosystem by exchanging information between the different inpatient care actors. Many efforts seek quality in health care, economy, and sustainability in process management. Some examples are reducing medical errors, disease control and monitoring, individualized patient care, and avoiding duplicate and fragmented entries in the electronic medical record. Likewise, some studies showed technologies to achieve this goal effectively and efficiently, with the ability to interoperate data, allowing the interpretation and use of health information. To that end, semantic interoperability aims to share data among all the sectors in the organization, clinicians, nurses, lab, the entire hospital. Therefore, avoiding data silos and keep data regardless of vendors, to exchange the information across organizational boundaries. This study presents a comprehensive systematic literature review of semantic interoperability in electronic health records. We searched seven databases of articles published between 2010 to September 2020. We showed the most chosen scenarios, technologies, and tools employed to solve interoperability problems, and we propose a taxonomy around semantic interoperability in health records. Also, we presented the main approaches to solve the exchange problem of legacy and heterogeneous data across healthcare organizations.

Tracking machine learning models for pandemic scenarios: a systematic review of machine learning models that predict local and global evolution of pandemics

This systematic review aims to study and classify machine learning models that predict pandemics' evolution within affected regions or countries. The advantage of this systematic review is that it allows the health authorities to decide what prediction model fits best depending upon the region's criticality and optimize hospitals' approaches to preparing and anticipating patient care. We searched ACM Digital Library, Biomed Central, BioRxiv+MedRxiv, BMJ, Computers and Applied Sciences, IEEEXplore, JMIR Medical Informatics, Medline Daily Updates, Nature, Oxford Academic, PubMed, Sage Online, ScienceDirect, Scopus, SpringerLink, Web of Science, and Wiley Online Library between 1 January 2020 and 31 July 2022. We divided the interventions into similarities between cumulative COVID-19 real cases and machine learning prediction models' ability to track pandemics trending. We included 45 studies that rated low to high risk of bias. The standardized mean differences (SMD) for the two groups were 0.18, 95% CI, with interval of [0.01, 0.35], I 2 =0, and p value=0.04. We built a taxonomic analysis of the included studies and determined two domains: pandemics trending prediction models and geolocation tracking models. We performed the meta-analysis and data synthesis and got low publication bias because of missing results. The level of certainty varied from very low to high. By submitting the 45 studies on the risk of bias, the levels of certainty, the summary of findings, and the statistical analysis via the forest and funnel plots assessments, we could determine the satisfactory statistical significance homogeneity across the included studies to simulate the progress of the pandemics and help the healthcare authorities to take preventive decisions.

First and second COVID-19 waves in Brazil: A cross-sectional study of patients' characteristics related to hospitalization and in-hospital mortality

Background

Background The second wave of the COVID-19 pandemic was more aggressive in Brazil compared to other countries around the globe. Considering the Brazilian peculiarities, we analyze the in-hospital mortality concerning socio-epidemiological characteristics of patients and the health system of all states during the first and second waves of COVID-19.

Methods

We performed a cross-sectional study of hospitalized patients with positive RT-PCR for SARS-CoV-2 in Brazil. Data was obtained from the Influenza Epidemiological Surveillance Information System (SIVEP-Gripe) and comprised the period from February 25, 2020, to April 30, 2021, separated in two waves on November 5, 2020. We performed a descriptive study of patients analyzing socio-demographic characteristics, symptoms, comorbidities, and risk factors stratified by age. In addition, we analyzed in-hospital and intensive care unit (ICU) mortality in both waves and how it varies in each Brazilian state.

Findings

Between February 25, 2020 and April 30, 2021, 678 235 patients were admitted with a positive RT-PCR for SARS-CoV-2, with 325 903 and 352 332 patients for the first and second wave, respectively. The mean age of patients was 59·65 (IQR 48·0 - 72·0). In total, 379 817 (56·00%) patients had a risk factor or comorbidity. In-hospital mortality increased from 34·81% in the first to 39·30% in the second wave. In the second wave, there were more ICU admissions, use of non-invasive and invasive ventilation, and increased mortality for younger age groups. The southern and southeastern regions of Brazil had the highest hospitalization rates per 100 000 inhabitants. However, the in-hospital mortality rate was higher in the northern and northeastern states of the country. Racial differences were observed in clinical outcomes, with White being the most prevalent hospitalized population, but with Blacks/Browns (Pardos) having higher mortality rates. Younger age groups had more considerable differences in mortality as compared to groups with and without comorbidities in both waves.

Interpretation

We observed a more considerable burden on the Brazilian hospital system throughout the second wave. Furthermore, the north and northeast of Brazil, which present lower Human Development Indexes, concentrated the worst in-hospital mortality rates. The highest mortality rates are also shown among vulnerable social groups. Finally, we believe that the results can help to understand the behavior of the COVID-19 pandemic in Brazil, helping to define public policies, allocate resources, and improve strategies for vaccination of priority groups.

Funding

Coordinating Agency for Advanced Training of Graduate Personnel (CAPES) (C.F. 001), and National Council for Scientific and Technological Development (CNPq) (No. 309537/2020-7).

Segmentation of Masses on Mammograms Using Data Augmentation and Deep Learning

The diagnosis of breast cancer in early stage is essential for successful treatment. Detection can be performed in several ways, the most common being through mammograms. The projections acquired by this type of examination are directly affected by the composition of the breast, which density can be similar to the suspicious masses, being a challenge the identification of malignant lesions. In this article, we propose a computer-aided detection (CAD) system to aid in the diagnosis of masses in digitized mammograms using a model based in the U-Net, allowing specialists to monitor the lesion over time. Unlike most of the studies, we propose the use of an entire base of digitized mammograms using normal, benign, and malignant cases. Our research is divided into four stages: (1) pre-processing, with the removal of irrelevant information, enhancement of the contrast of 7989 images of the Digital Database for Screening Mammography (DDSM), and obtaining regions of interest. (2) Data augmentation, with horizontal mirroring, zooming, and resizing of images; (3) training, with tests of six-based U-Net models, with different characteristics; (4) testing, evaluating four metrics, accuracy, sensitivity, specificity, and Dice Index. The tested models obtained different results regarding the assessed parameters. The best model achieved a sensitivity of 92.32%, specificity of 80.47%, accuracy of 85.95% Dice Index of 79.39%, and AUC of 86.40%. Even using a full base without case selection bias, the results obtained demonstrate that the use of a complete database can provide knowledge to the CAD expert.

Making the COVID-19 Pandemic a Driver for Digital Health: Brazilian Strategies

The COVID-19 outbreak exposed several problems faced by health systems worldwide, especially concerning the safe and rapid generation and sharing of health data. However, this pandemic scenario has also facilitated the rapid implementation and monitoring of technologies in the health field. In view of the occurrence of the public emergency caused by SARS-CoV-2 in Brazil, the Department of Informatics of the Brazilian Unified Health System created a contingency plan. In this paper, we aim to report the digital health strategies applied in Brazil and the first results obtained during the fight against COVID-19. Conecte SUS, a platform created to store all the health data of an individual throughout their life, is the center point of the Brazilian digital strategy. Access to the platform can be obtained through an app by the patient and the health professionals involved in the case. Health data sharing became possible due to the creation of the National Health Data Network (Rede Nacional de Dados em Saúde, RNDS). A mobile app was developed to guide citizens regarding the need to go to a health facility and to assist in disseminating official news about the virus. The mobile app can also alert the user if they have had contact with an infected person. The official numbers of cases and available hospital beds are updated and published daily on a website containing interactive graphs. These data are obtained due to creating a web-based notification system that uses the RNDS to share information about the cases. Preclinical care through telemedicine has become essential to prevent overload in health facilities. The exchange of experiences between medical teams from large centers and small hospitals was made possible using telehealth. Brazil took a giant step toward digital health adoption, creating and implementing important initiatives; however, these initiatives do not yet cover the entire health system. It is expected that the sharing of health data that are maintained and authorized by the patient will become a reality in the near future. The intention is to obtain better clinical outcomes, cost reduction, and faster and better services in the public health network.

A Survey About Real-Time Location Systems in Healthcare Environments

Every year healthcare organizations suffer from several issues, such as unapropriated workflow, thousands of deaths caused by medical errors, counterfeit drugs, and increasing costs. To offer better patient care and increase profit, hospitals could adopt solutions that help remedy these problems. Real-Time Location Systems have the potential to deal with many of these issues, as well as offering means for developing new and intelligent solutions. This kind of system enables tracking assets and people, allowing several improvements. Even though the benefits of such solutions are well known and desired by healthcare providers, their large scale adoption is still distant. In this article, we surveyed Real-Time Location Systems usage in hospitals. While developing this survey, we observed a need for organizing important aspects of healthcare-oriented Real-Time Location Systems. Therefore, we analyzed challenges regarding this topic and a taxonomy proposed. This survey offers researchers and developers ways to comprehend the challenges surrounding this area while proposing a classification of aspects that a Real-Time Location System for healthcare environments must assess for it to be successful.

Exploring publish/subscribe, multilevel cloud elasticity, and data compression in telemedicine

BACKGROUND AND OBJECTIVE

Multiple medical specialties rely on image data, typically following the Digital Imaging and Communications in Medicine (DICOM) ISO 12052 standard, to support diagnosis through telemedicine. Remote analysis by different physicians requires the same image to be transmitted simultaneously to different destinations in real-time. This scenario poses a need for a large number of resources to store and transmit DICOM images in real-time, which has been explored using some cloud-based solutions. However, these solutions lack strategies to improve the performance through the cloud elasticity feature. In this context, this article proposes a cloud-based publish/subscribe (PubSub) model, called PS2DICOM, which employs multilevel resource elasticity to improve the performance of DICOM data transmissions.

METHODS

A prototype is implemented to evaluate PS2DICOM. A PubSub communication model is adopted, considering the coexistence of two classes of users: (i) image data producers (publishers); and (ii) image data consumers (subscribers). PS2DICOM employs a cloud infrastructure to guarantee service availability and performance through resource elasticity in two levels of the cloud: (i) brokers and (ii) data storage. In addition, images are compressed prior to the transmission to reduce the demand for network resources using one of three different algorithms: (i) DEFLATE, (ii) LZMA, and (iii) BZIP2. PS2DICOM employs dynamic data compression levels at the client side to improve network performance according to the current available network throughput.

RESULTS

Results indicate that PS2DICOM can improve transmission quality, storage capabilities, querying, and retrieving of DICOM images. The general efficiency gain is approximately 35% in data sending and receiving operations. This gain is resultant from the two levels of elasticity, allowing resources to be scaled up or down automatically in a transparent manner.

CONCLUSIONS

The contributions of PS2DICOM are twofold: (i) multilevel cloud elasticity to adapt the computing resources on demand; (ii) adaptive data compression to meet the network quality and optimize data transmission. Results suggest that the use of compression in medical image data using PS2DICOM can improve the transmission efficiency, allowing the team of specialists to communicate in real-time, even when they are geographically distant.

Use of Internet of Things With Data Prediction on Healthcare Environments

Internet of Things (IoT) is a constantly growing paradigm that promises to revolutionize healthcare applications and could be associated with several other techniques. Data prediction is another widely used paradigm, where data captured over time is analyzed in order to identify and predict problematic situations that may happen in the future. After research, no surveys that address IoT combined with data prediction in healthcare area exist in the literature. In this context, this work presents a systematic literature review on Internet of Things applied to healthcare area with a focus on data prediction, presenting twenty-three papers about this theme as results, as well as a comparative analysis between them. The main contribution for literature is a taxonomy for IoT systems with data prediction applied to healthcare. Finally, this article presents the possibilities and challenges of exploration in the study area, showing the existing gaps for future approaches.

Electronic health records in a Blockchain: A systematic review

Blockchain could reinvent the way patient’s electronic health records are shared and stored by providing safer mechanisms for health information exchange of medical data in the healthcare industry, by securing it over a decentralized peer-to-peer network. Intending to support and ease the understanding of this distributed ledger technology, a solid Systematic Literature Review was conducted, aiming to explore the recent literature on Blockchain and healthcare domain and identify existing challenges and open questions, guided by the raise of research questions regarding EHR in a Blockchain. More than 300 scientific studies published in the last ten years were surveyed, resulting in an up-to-date taxonomy creation, challenges and open questions identified, and the most significant approaches, data types, standards and architectures regarding the use of Blockchain for EHR were assessed and discussed.

On Providing Multi-Level Quality of Service for Operating Rooms of the Future

The Operating Room (OR) plays an important role in delivering vital medical services to patients in hospitals. Such environments contain several medical devices, equipment, and systems producing valuable information which might be combined for biomedical and surgical workflow analysis. Considering the sensibility of data from sensors in the OR, independently of processing and network loads, the middleware that provides data from these sensors have to respect applications quality of service (QoS) demands. In an OR middleware, there are two main bottlenecks that might suffer QoS problems and, consequently, impact directly in user experience: (i) simultaneous user applications connecting the middleware; and (ii) a high number of sensors generating information from the environment. Currently, many middlewares that support QoS have been proposed by many fields; however, to the best of our knowledge, there is no research on this topic or the OR environment. OR environments are characterized by being crowded by persons and equipment, some of them of specific use in such environments, as mobile x-ray machines. Therefore, this article proposes QualiCare, an adaptable middleware model to provide multi-level QoS, improve user experience, and increase hardware utilization to middlewares in OR environments. Our main contributions are a middleware model and an orchestration engine in charge of changing the middleware behavior to guarantee performance. Results demonstrate that adapting middleware parameters on demand reduces network usage and improves resource consumption maintaining data provisioning.

Internet of Health Things: Toward intelligent vital signs monitoring in hospital wards

BACKGROUND

Large amounts of patient data are routinely manually collected in hospitals by using standalone medical devices, including vital signs. Such data is sometimes stored in spreadsheets, not forming part of patients' electronic health records, and is therefore difficult for caregivers to combine and analyze. One possible solution to overcome these limitations is the interconnection of medical devices via the Internet using a distributed platform, namely the Internet of Things. This approach allows data from different sources to be combined in order to better diagnose patient health status and identify possible anticipatory actions.

METHODS

This work introduces the concept of the Internet of Health Things (IoHT), focusing on surveying the different approaches that could be applied to gather and combine data on vital signs in hospitals. Common heuristic approaches are considered, such as weighted early warning scoring systems, and the possibility of employing intelligent algorithms is analyzed.

RESULTS

As a result, this article proposes possible directions for combining patient data in hospital wards to improve efficiency, allow the optimization of resources, and minimize patient health deterioration.

CONCLUSION

It is concluded that a patient-centered approach is critical, and that the IoHT paradigm will continue to provide more optimal solutions for patient management in hospital wards.

Personal Health Records: A Systematic Literature Review

Background

Information and communication technology (ICT) has transformed the health care field worldwide. One of the main drivers of this change is the electronic health record (EHR). However, there are still open issues and challenges because the EHR usually reflects the partial view of a health care provider without the ability for patients to control or interact with their data. Furthermore, with the growth of mobile and ubiquitous computing, the number of records regarding personal health is increasing exponentially. This movement has been characterized as the Internet of Things (IoT), including the widespread development of wearable computing technology and assorted types of health-related sensors. This leads to the need for an integrated method of storing health-related data, defined as the personal health record (PHR), which could be used by health care providers and patients. This approach could combine EHRs with data gathered from sensors or other wearable computing devices. This unified view of patients’ health could be shared with providers, who may not only use previous health-related records but also expand them with data resulting from their interactions. Another PHR advantage is that patients can interact with their health data, making decisions that may positively affect their health.

Objective

This work aimed to explore the recent literature related to PHRs by defining the taxonomy and identifying challenges and open questions. In addition, this study specifically sought to identify data types, standards, profiles, goals, methods, functions, and architecture with regard to PHRs.

Methods

The method to achieve these objectives consists of using the systematic literature review approach, which is guided by research questions using the population, intervention, comparison, outcome, and context (PICOC) criteria.

Results

As a result, we reviewed more than 5000 scientific studies published in the last 10 years, selected the most significant approaches, and thoroughly surveyed the health care field related to PHRs. We developed an updated taxonomy and identified challenges, open questions, and current data types, related standards, main profiles, input strategies, goals, functions, and architectures of the PHR.

Conclusions

All of these results contribute to the achievement of a significant degree of coverage regarding the technology related to PHRs.