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 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.

The HoPE Model Architecture: a Novel Approach to Pregnancy Information Retrieval Based on Conversational Agents.

Conversational agents are used to communicating with humans in a friendly manner. To achieve the highest level of performance, agents need to respond assertively and fastly. Transformer architectures are shown to produce excellent performances on recent tasks; however, for tasks involving conversational agents, they may have a lower speed performance. The main goal of this study is to evaluate and propose a HoPE (Healthcare Obstetric in PrEgnancy) model that is tailored to pregnancy data. We carried out a dataset extraction and construction process based on collections of health documents related to breastfeeding, childcare, pregnant care, nutrition, risks, vaccines, exams, and physical exercises. We evaluated two pre-trained models in the Portuguese language for the conversational agent architecture proposal and chose the one with the best performance to compose the HoPE architecture. The BERTimbau model, which has been trained on data augmentation strategies, proves to be able to retrieve information quickly and most accurately than others. For the fine-tuning process, we achieved a Spearman correlation of 95.55 on BERTimbau augmented with a few pairs (1.500 pairs). The HoPE model architecture achieved an F1-Score of 0.89, outperforming other combinations tested in this study. We will evaluate this approach for clinical studies in future studies.

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.

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).

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.

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.

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.

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.

Analyzing the performance of a blockchain-based personal health record implementation.

BACKGROUND: The Personal Health Record (PHR) and Electronic Health Record (EHR) play a key role in more efficient access to health records by health professionals and patients. It is hard, however, to obtain a unified view of health data that is distributed across different health providers. In particular, health records are commonly scattered in multiple places and are not integrated. OBJECTIVE: This article presents the implementation and evaluation of a PHR model that integrates distributed health records using blockchain technology and the openEHR interoperability standard. We thus follow OmniPHR architecture model, which describes an infrastructure that supports the implementation of a distributed and interoperable PHR. METHODS: Our method involves implementing a prototype and then evaluating the integration and performance of medical records from different production databases. In addition to evaluating the unified view of records, our evaluation criteria also focused on non-functional performance requirements, such as response time, CPU usage, memory occupation, disk, and network usage. RESULTS: We evaluated our model implementation using the data set of more than 40 thousand adult patients anonymized from two hospital databases. We tested the distribution and reintegration of the data to compose a single view of health records. Moreover, we profiled the model by evaluating a scenario with 10 superpeers and thousands of competing sessions transacting operations on health records simultaneously, resulting in an average response time below 500 ms. The blockchain implemented in our prototype achieved 98% availability. CONCLUSION: Our performance results indicated that data distributed via a blockchain could be recovered with low average response time and high availability in the scenarios we tested. Our study also demonstrated how OmniPHR model implementation can integrate distributed data into a unified view of health records.

Toward a Model for Personal Health Record Interoperability.

Health information technology, applied to electronic health record (EHR), has evolved with the adoption of standards for defining patient health records. However, there are many standards for defining such data, hindering communication between different healthcare providers. Even with adopted standards, patients often need to repeatedly provide their health information when they are taken care of at different locations. This problem hinders the adoption of personal health record (PHR), with the patients' health records under their own control. Therefore, the purpose of this paper is to propose an interoperability model for PHR use. The methodology consisted prototyping an application model named OmniPHR, to evaluate the structuring of semantic interoperability and integration of different health standards, using a real database from anonymized patients. We evaluated health data from a hospital database with 38 645 adult patients' medical records processed using different standards, represented by openEHR, HL7 FHIR, and MIMIC-III reference models. OmniPHR demonstrated the feasibility to provide interoperability through a standard ontology and artificial intelligence with natural language processing (NLP). Although the first executions reached a 76.39% F1-score and required retraining of the machine-learning process, the final score was 87.9%, presenting a way to obtain the original data from different standards on a single format. Unlike other models, OmniPHR presents a unified, structural semantic and up-to-date vision of PHR for patients and healthcare providers. The results were promising and demonstrated the possibility of subsidizing the creation of inferences rules about possible patient health problems or preventing future problems.

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.

A Hospital Bed Allocation Hybrid Model Based on Situation Awareness.

Bed management is an important area of planning and control for hospitals, as it has the important role of maintaining the balance between patients from the emergency department, patients who have elective surgery or scheduled treatment, and patients who are discharged from the hospital, while maintaining high bed occupancy rates. Effective management of these resources has always been a challenge for managers. In the 1980s and 1990s, thousands of patients had operations canceled due to nonmedical reasons. Due to the constant uncertainty experienced by hospitals today, use of the cognitive model known as situation awareness has been increasing in healthcare. Situation awareness seeks to understand environmental context to design the future, using artificial intelligence techniques. In this context, this article contributes the use of situation awareness in bed management using a hybrid system that combines known techniques of artificial neural networks and multiattribute value theory for decision-making by automating the process of bed allocation. The system was evaluated in a hospital in Porto Alegre, Brazil, yielding a result of 93.5% similarity between the beds determined by the proposed model and those chosen by the hospital manager.

A Mapping Study on Mobile Games for Patients of Chronic Diseases.

There is a growing interest of using technologies to propose solutions for healthcare issues. One of such issues is the incidence of chronic diseases, which are responsible for a considerable proportion of worldwide mortality. It is possible to prevent the development of such diseases using tools and methods that instruct the population. To achieve this, mobile games provide a powerful environment for teaching different subjects to user, without them actively knowing that they are learning new concepts. Despite the growing interest of using mobile games in healthcare, more specifically by patients with chronic diseases, in the best of our knowledge there are no studies that address the current research being published in the area. To close this gap, we carried out a systematic mapping study to synthesize an overview of the area. Five databases were searched and more than 1200 studies were analyzed and filtered. Among them, 17 met the the inclusion and exclusion criteria defined in this work. The results show that there is still room for research in this area, since the studies focus on a younger audience rather than proposing solutions for all ages. Furthermore, the number of chronic conditions being addressed is still small, obesity and diabetes are prevalent. Besides, the full capacity of game features that foster learning through games are not being employed, the majority of games proposed by the articles encompass less than half of these features.

OmniPHR: A distributed architecture model to integrate personal health records.

The advances in the Information and Communications Technology (ICT) brought many benefits to the healthcare area, specially to digital storage of patients' health records. However, it is still a challenge to have a unified viewpoint of patients' health history, because typically health data is scattered among different health organizations. Furthermore, there are several standards for these records, some of them open and others proprietary. Usually health records are stored in databases within health organizations and rarely have external access. This situation applies mainly to cases where patients' data are maintained by healthcare providers, known as EHRs (Electronic Health Records). In case of PHRs (Personal Health Records), in which patients by definition can manage their health records, they usually have no control over their data stored in healthcare providers' databases. Thereby, we envision two main challenges regarding PHR context: first, how patients could have a unified view of their scattered health records, and second, how healthcare providers can access up-to-date data regarding their patients, even though changes occurred elsewhere. For addressing these issues, this work proposes a model named OmniPHR, a distributed model to integrate PHRs, for patients and healthcare providers use. The scientific contribution is to propose an architecture model to support a distributed PHR, where patients can maintain their health history in an unified viewpoint, from any device anywhere. Likewise, for healthcare providers, the possibility of having their patients data interconnected among health organizations. The evaluation demonstrates the feasibility of the model in maintaining health records distributed in an architecture model that promotes a unified view of PHR with elasticity and scalability of the solution.

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.