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Patient-generated health data (PGHD) is the person's health-related data collected outside the clinical environment. Integrating this data into the electronic health record (EHR) supports better patient-provider communication and shared decision-making, empowering patients to actively manage their health conditions. In this study, we investigated the essential features needed for patients and healthcare providers to effectively integrate PGHD functionality into the EHR system. Through our collaborative design approach involving healthcare professionals (HCPs) and patients, we developed a prototype and suggestion, using Estonia as a model, which is the ideal approach for collecting and integrating PGHD into the EHR.

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One approach to enriching the Learning Health System (LHS) is leveraging vital signs and data from wearable technologies. Blood oxygen, heart rate, respiration rates, and other data collected by wearables (like sleep and exercise patterns) can be used to monitor and predict health conditions. This data is already being collected and could be used to improve healthcare in several ways. Our approach will be health data interoperability with HL7 FHIR (for data exchange between different systems), openEHR (to store researchable data separated from software but connected to ontologies, external terminologies and code sets) and maintain the semantics of data. OpenEHR is a standard that has an important role in modelling processes and clinical decisions. The six pillars of Lifestyle Medicine can be a first attempt to change how patients see their daily decisions, affecting the mid to long-term evolution of their health. Our objective is to develop the first stage of the LHS based on a co-produced personal health recording (CoPHR) built on top of a local LLM that interoperates health data through HL7 FHIR, openEHR, OHDSI and terminologies that can ingest external evidence and produces clinical and personal decision support and, when combined with many other patients, can produce or confirm evidence.

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In recent years, the adoption of wearable gadgets such as Fitbit has revolutionized the way individuals track and monitor their personal activity data. These devices provide valuable in-sights into an individual's physical activity levels, sleep patterns, and overall health metrics. Integrating this data into healthcare informatics systems can offer significant benefits in terms of personalized healthcare delivery and improved patient outcomes. This paper explores the synergistic integration of Fitbit-generated personal activity data using the openEHR Reference Model in healthcare informatics as a practical case study in patient-generated health data (PGHD) integration based on health informatics standards as a framework for the representation and exchange of Electronic Health Records (EHRs). The synergistic integration of Fitbit-generated personal activity data through openEHR and FHIR standards models also covers the way for advanced analytics and population health management. By linking and analyzing data from various sources, including sensors and wearable devices, healthcare organizations can identify trends, patterns, and insights that can guide population health strategies, preventive care initiatives, and personalized treatment plans, in addition to aiding physicians in follow-up care.

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A As health technology advances, this study aims to develop an innovative nutritional intake management system that integrates artificial intelligence technology and social media software to achieve precise analysis of patient-generated data and comprehensive management in continuous care. Our system is built on the Line Bot platform, allowing users to easily and intuitively obtain detailed analyses of their individual nutritional intake by reporting dietary information. While users report their dietary habits through the Line Bot, our AI model conducts real-time analysis of nutrient intake, providing personalized nutritional recommendations. This instantaneous feedback not only enhances user engagement in nutritional management but also aids in establishing healthy habits. Additionally, through integration with social media software, our system facilitates information sharing and community support among users, promoting the exchange of nutritional knowledge and mutual assistance. This study further explores the specific needs of patients with chronic diseases, collecting individual data on chronic conditions and total nutritional intake. Based on the nutritional intake guidelines proposed by the Health Promotion Administration in Taiwan, more precise nutritional management recommendations are provided to meet the unique health needs of each patient. This study introduces a comprehensive, patient-generated data-based approach for precision nutrition management in continuous care. By integrating artificial intelligence, social media software, and data analysis, our system not only offers effective tools for monitoring and managing patients' nutritional intake but also fosters interaction and support among patients, driving the implementation of continuous care practices.

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This scoping review aimed to identify and synthesize the literature related to patient-generated health data (PGHD) among older adults with cancer in home setting. Of the 1,090 articles extracted through six databases searches, 53 were selected. Studies were published from 2007 to 2022 and the types of devices to generate PGHD included research-grade and consumer-grade wearable devices. PGHD was assessed for physical activity, vital signs, and sleep. PGHD utilization was categorized: 1) identification, monitoring, review, and analysis (100%); 2) feedback and information report (32.1%); 3) motivation (26.4%); and 4) education and coaching (17.0%). Our study reveals that various PGHDs from older adults with cancer are mainly collected passively, with limited use for interaction with healthcare providers. These results may provide valuable insights for healthcare providers into potential PGHD applications in geriatric cancer care.

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OBJECTIVE: To use workflow execution models to highlight new considerations for patient-centered clinical decision support policies (PC CDS), processes, procedures, technology, and expertise required to support new workflows. METHODS: To generate and refine models, we used (1) targeted literature reviews; (2) key informant interviews with 6 external PC CDS experts; (3) model refinement based on authors' experience; and (4) validation of the models by a 26-member steering committee. RESULTS AND DISCUSSION: We identified 7 major issues that provide significant challenges and opportunities for healthcare systems, researchers, administrators, and health IT and app developers. Overcoming these challenges presents opportunities for new or modified policies, processes, procedures, technology, and expertise to: (1) Ensure patient-generated health data (PGHD), including patient-reported outcomes (PROs), are documented, reviewed, and managed by appropriately trained clinicians, between visits and after regular working hours. (2) Educate patients to use connected medical devices and handle technical issues. (3) Facilitate collection and incorporation of PGHD, PROs, patient preferences, and social determinants of health into existing electronic health records. (4) Troubleshoot erroneous data received from devices. (5) Develop dashboards to display longitudinal patient-reported data. (6) Provide reimbursement to support new models of care. (7) Support patient engagement with remote devices. CONCLUSION: Several new policies, processes, technologies, and expertise are required to ensure safe and effective implementation and use of PC CDS. As we gain more experience implementing and working with PC CDS, we should be able to begin realizing the long-term positive impact on patient health that the patient-centered movement in healthcare promises.

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BACKGROUND: The increased pervasiveness of digital health technology is producing large amounts of person-generated health data (PGHD). These data can empower people to monitor their health to promote prevention and management of disease. Women make up one of the largest groups of consumers of digital self-tracking technology. OBJECTIVE: In this scoping review, we aimed to (1) identify the different areas of women's health monitored using PGHD from connected health devices, (2) explore personal metrics collected through these technologies, and (3) synthesize facilitators of and barriers to women's adoption and use of connected health devices. METHODS: Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for scoping reviews, we searched 5 databases for articles published between January 1, 2015, and February 29, 2020. Papers were included if they targeted women or female individuals and incorporated digital health tools that collected PGHD outside a clinical setting. RESULTS: We included a total of 406 papers in this review. Articles on the use of PGHD for women steadily increased from 2015 to 2020. The health areas that the articles focused on spanned several topics, with pregnancy and the postpartum period being the most prevalent followed by cancer. Types of digital health used to collect PGHD included mobile apps, wearables, websites, the Internet of Things or smart devices, 2-way messaging, interactive voice response, and implantable devices. A thematic analysis of 41.4% (168/406) of the papers revealed 6 themes regarding facilitators of and barriers to women's use of digital health technology for collecting PGHD: (1) accessibility and connectivity, (2) design and functionality, (3) accuracy and credibility, (4) audience and adoption, (5) impact on community and health service, and (6) impact on health and behavior. CONCLUSIONS: Leading up to the COVID-19 pandemic, the adoption of digital health tools to address women's health concerns was on a steady rise. The prominence of tools related to pregnancy and the postpartum period reflects the strong focus on reproductive health in women's health research and highlights opportunities for digital technology development in other women's health topics. Digital health technology was most acceptable when it was relevant to the target audience, was seen as user-friendly, and considered women's personalization preferences while also ensuring accuracy of measurements and credibility of information. The integration of digital technologies into clinical care will continue to evolve, and factors such as liability and health care provider workload need to be considered. While acknowledging the diversity of individual needs, the use of PGHD can positively impact the self-care management of numerous women's health journeys. The COVID-19 pandemic has ushered in increased adoption and acceptance of digital health technology. This study could serve as a baseline comparison for how this field has evolved as a result. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/26110.

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BACKGROUND: Mobile health (mHealth) uses mobile technologies to promote wellness and help disease management. Although mHealth solutions used in the clinical setting have typically been medical-grade devices, passive and active sensing capabilities of consumer-grade devices like smartphones and activity trackers have the potential to bridge information gaps regarding patients' behaviors, environment, lifestyle, and other ubiquitous data. Individuals are increasingly adopting mHealth solutions, which facilitate the collection of patient-generated health data (PGHD). Health care professionals (HCPs) could potentially use these data to support care of chronic conditions. However, there is limited research on real-life experiences of HPCs using PGHD from consumer-grade mHealth solutions in the clinical context. OBJECTIVE: This systematic review aims to analyze existing literature to identify how HCPs have used PGHD from consumer-grade mobile devices in the clinical setting. The objectives are to determine the types of PGHD used by HCPs, in which health conditions they use them, and to understand the motivations behind their willingness to use them. METHODS: A systematic literature review was the main research method to synthesize prior research. Eligible studies were identified through comprehensive searches in health, biomedicine, and computer science databases, and a complementary hand search was performed. The search strategy was constructed iteratively based on key topics related to PGHD, HCPs, and mobile technologies. The screening process involved 2 stages. Data extraction was performed using a predefined form. The extracted data were summarized using a combination of descriptive and narrative syntheses. RESULTS: The review included 16 studies. The studies spanned from 2015 to 2021, with a majority published in 2019 or later. Studies showed that HCPs have been reviewing PGHD through various channels, including solutions portals and patients' devices. PGHD about patients' behavior seem particularly useful for HCPs. Our findings suggest that PGHD are more commonly used by HCPs to treat conditions related to lifestyle, such as diabetes and obesity. Physicians were the most frequently reported users of PGHD, participating in more than 80% of the studies. CONCLUSIONS: PGHD collection through mHealth solutions has proven beneficial for patients and can also support HCPs. PGHD have been particularly useful to treat conditions related to lifestyle, such as diabetes, cardiovascular diseases, and obesity, or in domains with high levels of uncertainty, such as infertility. Integrating PGHD into clinical care poses challenges related to privacy and accessibility. Some HCPs have identified that though PGHD from consumer devices might not be perfect or completely accurate, their perceived clinical value outweighs the alternative of having no data. Despite their perceived value, our findings reveal their use in clinical practice is still scarce. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.2196/39389.

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Person-generated health data (PGHD) are valuable for studying outcomes relevant to everyday living, for obtaining information not otherwise available, for long-term follow-up, and in situations where decisions cannot wait for traditional clinical research to be completed. While there is no dispute that these data are subject to bias, insights gained may be better than having an information void, provided the biases are understood and addressed. People will share information known uniquely to them about exposures that may affect drug tolerance, safety, and effectiveness (eg, nonprescription and complementary medications, alcohol, tobacco, illicit drugs, exercise, etc). Patients may be the best source of safety information when long-term follow-up is needed (eg, the 5- to 15-year follow-up required for some gene therapies). Validation studies must be performed to evaluate what people can accurately report and when supplementary confirmation information is needed. However, PGHD has already proven valuable in quantifying and contrasting COVID-19 vaccine benefits and risks and for evaluating disease transmission and the accuracy of COVID-19 testing. Going forward, PGHD will be used for patient-measured and patient-relevant outcomes, including for regulatory purposes, and will be linked to broader health data networks using tokenization, becoming a mainstay for signals about risks and benefits for diverse populations. This article is part of a Special Collection on Pharmacoepidemiology.

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En la actualidad podría afirmarse que la mayor problemática existente en torno a los delitos de descubrimiento y revelación de secretos de empresa se encuentra en la indeterminación de su objeto material: el secreto de empresa. Esta indeterminación, que la reciente Ley 1/2019, de 20 de febrero, de Secretos Empresariales ayuda a solventar, ha llevado a los Tribunales de la jurisdicción penal a pronunciamientos dispares sobre la aplicación de los tipos penales relativos al descubrimiento y revelación de secretos de empresa, siendo uno de los supuestos más cuestionados en la práctica de nuestros Tribunales el tratamiento (o no) de un listado de clientes como un secreto de empresa. Si bien, hay muchas resoluciones que abogan por entender que dichos listados de clientes no forman parte de la información confidencial y reservada de una empresa –lo que impediría entenderla como un secreto de empresa–, encontramos también ejemplos de casos en los que se ha adoptado una solución contraria. Por medio del presente análisis, se pretende responder a la siguiente pregunta: ¿Puede un listado de pacientes ser considerado un secreto de empresa y, por tanto, dar lugar su descubrimiento y/o revelación a la comisión de un delito de los recogidos en el artículo 278 y siguientes del Código Penal? ¿Y si dicho listado de pacientes contuviera documentación clínica (con datos médicos) de cada uno de ellos? (AU)

Nowadays, the main problem with the offences of discovery and disclosure of trade secrets may lie in the indeterminacy of its material object: the business or trade secret. This indeterminacy, which the recent Law 1/2019, of 20 February, on Business Secrets helps to resolve, has led the Courts of the criminal jurisdiction to make disparate pronouncements on the application of criminal offences relating to the discovery and disclosure of business secrets, with one of the most questioned cases in the practice of our Courts being the treatment (or not) of a list of clients as a business secret. While there are many rulings that argue that such customer lists do not form part of the confidential and reserved information of a company –which would prevent it from being considered a trade secret–, there are also examples of cases in which the opposite solution has been adopted. This analysis aims to answer the following question: Can a list of patients be considered a business secret and, therefore, can its discovery and/or disclosure give rise to the commission of an offence under Article 278 et seq. of the Criminal Code? What if the list of patients contained clinical documentation (with medical data) for each of them? (AU)

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Quality of life (QoL) is affected by environmental influences and varies between patients. A combined measurement through Patient Reported Outcomes (PROs) and Patient Generated Data (PGD) may enhance the detection of QoL impairments by a longitudinal survey. Leveraging different approaches of QoL measurement techniques, the challenge is to combine data in a standardized, interoperable way. We developed an app (Lion-App) to semantically annotate data from sensor systems as well as PROs to be merged in an overall analysis of QoL. A FHIR implementation guide was defined for a standardized assessment. To access sensor data the interfaces of Apple Health or Google Fit are used instead of integrating various provider directly into the system. Since QoL cannot be collected exclusively via sensor values, a combination of PROs and PGD is necessary. PGD enable a progression of QoL which offers more insight into personal limitations whereas PROs give insight about personal burden. The use of FHIR enables structured exchange of data while personalized analyses might improve therapy and outcome.

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The adoption of digital health services in surgical care delivery is changing the patient experience. The goal of patient-generated health data monitoring incorporated with patient-centered education and feedback is to optimally prepare patients for surgery and personalize postoperative care to improve outcomes that matter to both patients and surgeons. Challenges include the need for the adoption of new methods for implementation and evaluation and equitable application of surgical digital health interventions, with considerations for accessibility as well as the development of new diagnostics and decision support that include the needs and characteristics of all populations served.

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Introducción: La adolescencia es una etapa esencial dentro del ciclo de vida humano. La presencia de enfermedades en esta etapa puede afectar la capacidad para crecer y desarrollarse a plenitud, sobre todo cuando son de índole física, psicológica y ocurren en contextos escolares. Objetivo: Evaluar la autopercepción de salud que tienen los estudiantes adolescentes matriculados en instituciones educativas en tiempos de pandemia por Covid-19. Cartagena 2021. Materiales y métodos: Estudio cuantitativo, de corte transversal, y correlacional. Población de 1188 estudiantes de 12-17 años de dos instituciones educativas en Cartagena (Colombia). Muestra estimada de 319 sujetos, seleccionados a través de muestreo aleatorio simple. Se aplicó encuesta sociodemográfica diseñada por el equipo investigador y validada por expertos y para el estado de salud percibido se usó el Cuestionario de Salud SF-36, la versión en español adaptada culturalmente al contexto colombiano por Lugo, García y Gómez la cual cuenta con alfas de Cronbach entre 0,7 y 0,94. Resultados: Los adolescentes fueron principalmente de 15 años (21,9%), de octavo (21,7%) y noveno (27,7%), se dedican a estudiar (95,9%), en sus familias se devengan menos del salario mínimo (42,3%) y entre 1-2 (40,8%). Viven con padre y madre, con o sin hermanos (72,7%), los padres están casados o en unión libre (60,5%) y han estudiado hasta bachillerato tanto madres (54,9%) como padres (49,2%). La autopercepción de la salud fue buena (32,6%) y excelente (33,9%). Se observaron correlaciones estadísticamente significativas (p < 0,05) entre dicha autopercepción y edad, grado, escolaridad del padre y valoración del rendimiento académico. Conclusión: Pese a la emergencia sanitaria ocasionada por la pandemia de covid-19, la autopercepción de salud que tienen un grupo de estudiantes adolescentes resulta ser favorable y positiva.

Introduction: Adolescence is an essential stage within the human life cycle. The presence of diseases at this stage can affect the ability to grow and develop to the fullest, especially when they are physical, psychological and occur in school contexts. Objective: To evaluate the self-perception of health of adolescent students enrolled in educational institutions in times of the Covid-19 pandemic. Cartagena 2021. Materials and methods: Quantitative, cross-sectional, correlational study. Population of 1188 students aged 12-17 years from two educational institutions in Cartagena (Colombia). Estimated sample of 319 subjects, selected through simple random sampling. A sociodemographic survey designed by the research team and validated by experts was applied and for perceived health status the SF-36 Health Questionnaire was used, the Spanish version culturally adapted to the Colombian context by Lugo, García and Gómez, which has Cronbach's alphas between 0.7 and 0.94. Results: The adolescents were mainly 15 years old (21.9%), in eighth grade (21.7%) and ninth grade (27.7%), they are dedicated to study (95.9%), in their families they earn less than the minimum wage (42.3%) and between 1-2 (40.8%). They live with father and mother, with or without siblings (72.7%), the parents are married or in union (60.5%) and both mothers (54.9%) and fathers (49.2%) have studied up to high school. Self-perception of health was good (32.6%) and excellent (33.9%). Statistically significant correlations (p < 0.05) were observed between self-perception and age, grade, father's schooling and assessment of academic performance. Conclusion: Despite the health emergency caused by the covid-19 pandemic, the self-perception of health of a group of adolescent students was favorable and positive.

Introdução: A adolescência é uma etapa essencial no ciclo de vida humana. A presença de doenças nesta fase pode afetar a capacidade de crescimento e desenvolvimento pleno, especialmente quando elas são físicas, psicológicas e ocorrem em contextos escolares. Objetivo: Avaliar a autopercepção da saúde entre os estudantes adolescentes matriculados em instituições educacionais em tempos da pandemia de Covid-19. Cartagena 2021. Materiais e métodos: Estudo quantitativo, transversal, correlacional. População de 1188 estudantes de 12-17 anos de duas instituições educacionais em Cartagena (Colômbia). Amostra estimada de 319 sujeitos, selecionados através de amostragem aleatória simples. Foi aplicada uma pesquisa sociodemográfica projetada pela equipe de pesquisa e validada por especialistas e para a percepção do estado de saúde foi utilizado o questionário de saúde SF-36, a versão espanhola culturalmente adaptada ao contexto colombiano por Lugo, García e Gómez que tem a alfabetização de Cronbach entre 0,7 e 0,94. Resultados: Os adolescentes tinham principalmente 15 anos (21,9%), na oitava série (21,7%) e na nona série (27,7%), dedicam-se aos estudos (95,9%), em suas famílias ganham menos do que o salário mínimo (42,3%) e entre 1-2 (40,8%). Eles vivem com ambos os pais, com ou sem irmãos (72,7%), os pais são casados ou em união (60,5%) e ambas as mães (54,9%) e os pais (49,2%) estudaram até o ensino médio. A auto-percepção da saúde foi boa (32,6%) e excelente (33,9%). Foram observadas correlações estatisticamente significativas (p < 0,05) entre autopercepção e idade, série, escolaridade do pai e avaliação do desempenho acadêmico. Conclusão: Apesar da emergência sanitária causada pela pandemia de covid-19, a autopercepção da saúde de um grupo de estudantes adolescentes é favorável e positiva.

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El dataísmo puede privar al individuo de su privacidad. Las personas reflexionan sobre el coste de oportunidad que supone ceder sus datos y otorgan mayor importancia a la efectividad en la lucha contra enfermedades y pandemias frente a su uso ilícito, ilegal o poco ético. El big data es un bien común de la humanidad, y compartir datos puede salvar vidas, pero aprovechémoslo aplicando correctamente la ética de los datos, donde los gobiernos y organizaciones estén implicados y se respete el derecho fundamental de protección de datos. (AU)

Dataism can deprive the individuals of their privacy. People are reflecting on the opportunity cost of giving away their data and are placing greater importance on the effectiveness of fighting diseases and pandemics than on its illicit, illegal or unethical use. Big data is a common good of humanity, and sharing data can save lives, but let’s harness it with the right application of data ethics, where governments and organisations are involved and the fundamental right to data protection is respected. (AU)

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BACKGROUND: People with rheumatic diseases experience troublesome fluctuations in fatigue. Debated causes include pain, mood and inflammation. To determine the relationships between these potential causes, serial assessments are required but are methodologically challenging. This mobile health (mHealth) study explored the viability of using a smartphone app to collect patient-reported symptoms with contemporaneous Dried Blood Spot Sampling (DBSS) for inflammation. METHODS: Over 30 days, thirty-eight participants (12 RA, 13 OA, and 13 FM) used uMotif, a smartphone app, to report fatigue, pain and mood, on 5-point ordinal scales, twice daily. Daily DBSS, from which C-reactive Protein (CRP) values were extracted, were completed on days 1-7, 14 and 30. Participant engagement was determined based on frequency of data entry and ability to calculate within- and between-day symptom changes. DBSS feasibility and engagement was determined based on the proportion of samples returned and usable for extraction, and the number of days between which between-day changes in CRP which could be calculated (days 1-7). RESULTS: Fatigue was reported at least once on 1085/1140 days (95.2%). Approximately 65% of within- and between-day fatigue changes could be calculated. Rates were similar for pain and mood. A total of 287/342 (83.9%) DBSS, were returned, and all samples were viable for CRP extraction. Fatigue, pain and mood varied considerably, but clinically meaningful (≥ 5 mg/L) CRP changes were uncommon. CONCLUSIONS: Embedding DBSS in mHealth studies will enable researchers to obtain serial symptom assessments with matched biological samples. This provides exciting opportunities to address hitherto unanswerable questions, such as elucidating the mechanisms of fatigue fluctuations.

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It is very important to ensure reliable performance of deep learning model for future dataset for healthcare. This is more pronounced in the case of patient generated health data such as patient reported symptoms, which are not collected in a controlled environment. Since there has been a big difference in influenza incidence since the COVID-19 pandemic, we evaluated whether the deep learning model can maintain sufficiently robust performance against these changes. We have collected 226,655 episodes from 110,893 users since June 2020 and tested the influenza screening model, our model showed 87.02% sensitivity and 0.8670 of AUROC. The results of COVID-19 pandemic are comparable to that of before COVID-19 pandemic.

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In this article, we provide an overview of remote monitoring of pediatric PGHD and family-generated health data, including its current uses, future opportunities, and implementation resources.

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