Tecnología aplicada al cuidado de enfermería: wereables, apps y robótica <span class="so-article-trans-title" dir="auto"> Translated title: Technology Applied to Nursing Care: Wereables, Apps and Robotics </span>

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Abstract

RESUMEN La tecnología y la inteligencia artificial ganan cada vez más espacio en el diagnóstico, tratamiento y seguimiento del paciente: los wearables, las apps mHealth y los dispositivos basados en la tecnología robótica son un apoyo para el personal de salud de enfermería. A través del método hermenéutico se analizaron los resultados de una revisión bibliográfica de documentos seleccionados en Pubmed, Scielo y ELSEVIER, para describir el estado del arte de las wearables, apps y dispositivos tecnológicos que pueden ser empleados en el cuidado enfermero; lo que constituyó el objetivo de la presente investigación. Se procesaron diecisiete artículos y tres informes. Los resultados se muestran en tres categorías donde se identificaron: tres wereables, ocho aplicaciones móviles y tres robots que se emplean en enfermería. Se concluye que los wereables, las aplicaciones móviles y la robótica tienen hoy día una presencia trascendente en los espacios de cuidado y atención de salud y que acercan al personal de enfermería a sus pacientes. La tecnología no suple el cuidado de enfermería, pero si constituye una herramienta de apoyo en su quehacer.

Translated abstract

ABSTRACT Technology and artificial intelligence are gaining more and more space in patient diagnosis, treatment and monitoring; wearables, mHealth apps and devices based on robotic technology are a support for nursing health personnel. Through the hermeneutical method, the bibliographic review was carried out with the analysis and interpretation of the selected documents in Pubmed, Scielo and ELSEVIER: seventeen articles and three reports were processed. The results are shown in three categories identified as: three wearable, eight mobile applications and three robots used in nursing. The conclusion is that wearables, mobile applications and robotics today have a significant presence in healthcare spaces nursing staff closer to their patients. Technology does not replace nursing care but it is a support tool in their work.

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Wearable Health Technology and Electronic Health Record Integration: Scoping Review and Future Directions

Catherine Dinh-Le, Rachel Chuang, Sara Chokshi … (2019)

Background Due to the adoption of electronic health records (EHRs) and legislation on meaningful use in recent decades, health systems are increasingly interdependent on EHR capabilities, offerings, and innovations to better capture patient data. A novel capability offered by health systems encompasses the integration between EHRs and wearable health technology. Although wearables have the potential to transform patient care, issues such as concerns with patient privacy, system interoperability, and patient data overload pose a challenge to the adoption of wearables by providers. Objective This study aimed to review the landscape of wearable health technology and data integration to provider EHRs, specifically Epic, because of its prevalence among health systems. The objectives of the study were to (1) identify the current innovations and new directions in the field across start-ups, health systems, and insurance companies and (2) understand the associated challenges to inform future wearable health technology projects at other health organizations. Methods We used a scoping process to survey existing efforts through Epic’s Web-based hub and discussion forum, UserWeb, and on the general Web, PubMed, and Google Scholar. We contacted Epic, because of their position as the largest commercial EHR system, for information on published client work in the integration of patient-collected data. Results from our searches had to meet criteria such as publication date and matching relevant search terms. Results Numerous health institutions have started to integrate device data into patient portals. We identified the following 10 start-up organizations that have developed, or are in the process of developing, technology to enhance wearable health technology and enable EHR integration for health systems: Overlap, Royal Philips, Vivify Health, Validic, Doximity Dialer, Xealth, Redox, Conversa, Human API, and Glooko. We reported sample start-up partnerships with a total of 16 health systems in addressing challenges of the meaningful use of device data and streamlining provider workflows. We also found 4 insurance companies that encourage the growth and uptake of wearables through health tracking and incentive programs: Oscar Health, United Healthcare, Humana, and John Hancock. Conclusions The future design and development of digital technology in this space will rely on continued analysis of best practices, pain points, and potential solutions to mitigate existing challenges. Although this study does not provide a full comprehensive catalog of all wearable health technology initiatives, it is representative of trends and implications for the integration of patient data into the EHR. Our work serves as an initial foundation to provide resources on implementation and workflows around wearable health technology for organizations across the health care industry.

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Application of Artificial Intelligence in Community-Based Primary Health Care: Systematic Scoping Review and Critical Appraisal

Samira Abbasgholizadeh Rahimi, France Légaré, Gauri Dutt Sharma … (2021)

Background Research on the integration of artificial intelligence (AI) into community-based primary health care (CBPHC) has highlighted several advantages and disadvantages in practice regarding, for example, facilitating diagnosis and disease management, as well as doubts concerning the unintended harmful effects of this integration. However, there is a lack of evidence about a comprehensive knowledge synthesis that could shed light on AI systems tested or implemented in CBPHC. Objective We intended to identify and evaluate published studies that have tested or implemented AI in CBPHC settings. Methods We conducted a systematic scoping review informed by an earlier study and the Joanna Briggs Institute (JBI) scoping review framework and reported the findings according to PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analysis-Scoping Reviews) reporting guidelines. An information specialist performed a comprehensive search from the date of inception until February 2020, in seven bibliographic databases: Cochrane Library, MEDLINE, EMBASE, Web of Science, Cumulative Index to Nursing and Allied Health Literature (CINAHL), ScienceDirect, and IEEE Xplore. The selected studies considered all populations who provide and receive care in CBPHC settings, AI interventions that had been implemented, tested, or both, and assessed outcomes related to patients, health care providers, or CBPHC systems. Risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST). Two authors independently screened the titles and abstracts of the identified records, read the selected full texts, and extracted data from the included studies using a validated extraction form. Disagreements were resolved by consensus, and if this was not possible, the opinion of a third reviewer was sought. A third reviewer also validated all the extracted data. Results We retrieved 22,113 documents. After the removal of duplicates, 16,870 documents were screened, and 90 peer-reviewed publications met our inclusion criteria. Machine learning (ML) (41/90, 45%), natural language processing (NLP) (24/90, 27%), and expert systems (17/90, 19%) were the most commonly studied AI interventions. These were primarily implemented for diagnosis, detection, or surveillance purposes. Neural networks (ie, convolutional neural networks and abductive networks) demonstrated the highest accuracy, considering the given database for the given clinical task. The risk of bias in diagnosis or prognosis studies was the lowest in the participant category (4/49, 4%) and the highest in the outcome category (22/49, 45%). Conclusions We observed variabilities in reporting the participants, types of AI methods, analyses, and outcomes, and highlighted the large gap in the effective development and implementation of AI in CBPHC. Further studies are needed to efficiently guide the development and implementation of AI interventions in CBPHC settings.

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The Use of Artificial Intelligence–Based Conversational Agents (Chatbots) for Weight Loss: Scoping Review and Practical Recommendations

Han Shi Jocelyn Chew (2022)

Background Overweight and obesity have now reached a state of a pandemic despite the clinical and commercial programs available. Artificial intelligence (AI) chatbots have a strong potential in optimizing such programs for weight loss. Objective This study aimed to review AI chatbot use cases for weight loss and to identify the essential components for prolonging user engagement. Methods A scoping review was conducted using the 5-stage framework by Arksey and O’Malley. Articles were searched across nine electronic databases (ACM Digital Library, CINAHL, Cochrane Central, Embase, IEEE Xplore, PsycINFO, PubMed, Scopus, and Web of Science) until July 9, 2021. Gray literature, reference lists, and Google Scholar were also searched. Results A total of 23 studies with 2231 participants were included and evaluated in this review. Most studies (8/23, 35%) focused on using AI chatbots to promote both a healthy diet and exercise, 13% (3/23) of the studies used AI chatbots solely for lifestyle data collection and obesity risk assessment whereas only 4% (1/23) of the studies focused on promoting a combination of a healthy diet, exercise, and stress management. In total, 48% (11/23) of the studies used only text-based AI chatbots, 52% (12/23) operationalized AI chatbots through smartphones, and 39% (9/23) integrated data collected through fitness wearables or Internet of Things appliances. The core functions of AI chatbots were to provide personalized recommendations (20/23, 87%), motivational messages (18/23, 78%), gamification (6/23, 26%), and emotional support (6/23, 26%). Study participants who experienced speech- and augmented reality–based chatbot interactions in addition to text-based chatbot interactions reported higher user engagement because of the convenience of hands-free interactions. Enabling conversations through multiple platforms (eg, SMS text messaging, Slack, Telegram, Signal, WhatsApp, or Facebook Messenger) and devices (eg, laptops, Google Home, and Amazon Alexa) was reported to increase user engagement. The human semblance of chatbots through verbal and nonverbal cues improved user engagement through interactivity and empathy. Other techniques used in text-based chatbots included personally and culturally appropriate colloquial tones and content; emojis that emulate human emotional expressions; positively framed words; citations of credible information sources; personification; validation; and the provision of real-time, fast, and reliable recommendations. Prevailing issues included privacy; accountability; user burden; and interoperability with other databases, third-party applications, social media platforms, devices, and appliances. Conclusions AI chatbots should be designed to be human-like, personalized, contextualized, immersive, and enjoyable to enhance user experience, engagement, behavior change, and weight loss. These require the integration of health metrics (eg, based on self-reports and wearable trackers), personality and preferences (eg, based on goal achievements), circumstantial behaviors (eg, trigger-based overconsumption), and emotional states (eg, chatbot conversations and wearable stress detectors) to deliver personalized and effective recommendations for weight loss.

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Author and article information

Journal

Journal ID (publisher-id): rcim

Title: Revista Cubana de Informática Médica

Abbreviated Title: RCIM

Publisher: Universidad de Ciencias Médicas de La Habana (Ciudad de la Habana, , Cuba )

ISSN (Electronic): 1684-1859

Publication date (Print and electronic): June 2023

Volume: 15

Issue: 1

Electronic Location Identifier: e567

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[1] Guaranda orgnameUniversidad Estatal de Bolívar Ecuador

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Publisher ID: S1684-18592023000100014 Publisher ID: S1684-1859(23)01500100014

SO-VID: e7df2b57-8493-4eb9-971c-0e9aa55d87ab

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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Date received : 29 September 2022

Date accepted : 04 January 2023

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Tecnología aplicada al cuidado de enfermería: wereables, apps y robótica Translated title: Technology Applied to Nursing Care: Wereables, Apps and Robotics

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Most cited references 25

Wearable Health Technology and Electronic Health Record Integration: Scoping Review and Future Directions

Application of Artificial Intelligence in Community-Based Primary Health Care: Systematic Scoping Review and Critical Appraisal

The Use of Artificial Intelligence–Based Conversational Agents (Chatbots) for Weight Loss: Scoping Review and Practical Recommendations

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