Artificial intelligence technologies and compassion in healthcare: A systematic scoping review

Frontiers in Psychology, Journal Year: 2023, Volume and Issue: 13

Published: Jan. 17, 2023

Advances in artificial intelligence (AI) technologies, together with the availability of big data society, creates uncertainties about how these developments will affect healthcare systems worldwide. Compassion is essential for high-quality and research shows prosocial caring behaviors benefit human health societies. However, possible association between AI technologies compassion under conceptualized underexplored.The aim this scoping review to provide a comprehensive depth balanced perspective emerging topic compassion, inform future practice. The questions were: How discussed relation healthcare? are being used enhance What gaps current knowledge unexplored potential? key areas where could support healthcare?A systematic following five steps Joanna Briggs Institute methodology. Presentation conforms PRISMA-ScR (Preferred Reporting Items Systematic reviews Meta-Analyses extension Scoping Reviews). Eligibility criteria were defined according 3 concept constructs (AI healthcare) developed from literature informed by medical subject headings (MeSH) words electronic searches. Sources evidence Web Science PubMed databases, articles published English language 2011-2022. Articles screened title/abstract using inclusion/exclusion criteria. Data extracted (author, date publication, type article, aim/context healthcare, relevant findings, country) was charted tables. Thematic analysis an inductive-deductive approach generate code categories data. A multidisciplinary team assessed themes resonance relevance practice.Searches identified 3,124 articles. total 197 included after screening. number has increased over 10 years (2011, n = 1 2021, 47 Jan-Aug 2022 35 articles). Overarching related (1) Developments debates (7 themes) Concerns ethics, jobs, loss empathy; Human-centered design healthcare; Optimistic speculation address care gaps; Interrogation what it means be care; Recognition potential patient monitoring, virtual proximity, access Calls curricula development professional education; Implementation applications wellbeing workforce. (2) (10 Empathetic awareness; response relational behavior; Communication skills; Health coaching; Therapeutic interventions; Moral learning; Clinical clinical assessment; Healthcare quality bond therapeutic alliance; Providing information advice. (3) Gaps (4 Educational effectiveness AI-assisted Patient diversity technologies; education practice settings; Safety technologies. (4) Key (3 Enriching education, learning practice; Extending healing spaces; Enhancing relationships.There interest grown internationally last decade. In range contexts, empathetic communication moral findings reconceptualization as human-AI system intelligent comprising six elements: Awareness suffering (e.g., pain, distress, risk, disadvantage); Understanding (significance, context, rights, responsibilities etc.); Connecting verbal, physical, signs symbols); Making judgment (the need act); (5) Responding intention alleviate suffering; (6) Attention effect outcomes response. These elements can operate at individual (human or machine) collective level (healthcare organizations systems) cyclical different types suffering. New novel approaches enrich learning, extend relationships.In complex adaptive such implemented, not ideology, but through strategic choices, incentives, regulation, training, well joined up thinking caring. Research funders encourage into Educators, technologists, professionals themselves

Language: Английский

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Appeal of word of mouth: Influences of public opinions and sentiment on ports in corporate choice of import and export trade in the post-COVID-19 era

Ocean & Coastal Management, Journal Year: 2022, Volume and Issue: 225, P. 106239 - 106239

Published: June 1, 2022

Language: Английский

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Patient Satisfaction and Hospital Quality of Care Evaluation in Malaysia Using SERVQUAL and Facebook

Healthcare, Journal Year: 2021, Volume and Issue: 9(10), P. 1369 - 1369

Published: Oct. 14, 2021

Social media sites, dubbed patient online reviews (POR), have been proposed as new methods for assessing satisfaction and monitoring quality of care. However, the unstructured nature POR data derived from social creates a number challenges. The objectives this research were to identify service (SERVQUAL) dimensions automatically hospital Facebook using machine learning classifier, examine their associations with dissatisfaction. From January 2017 December 2019, empirical was conducted in which gathered official page Malaysian public hospitals. To find SERVQUAL POR, topic classification utilising supervised developed, study’s objective established logistic regression analysis. It discovered that 73.5% patients satisfied service, whereas 26.5% dissatisfied. identified 13.2% tangible, 68.9% reliability, 6.8% responsiveness, 19.5% assurance, 64.3% empathy. After controlling variables, all except tangible assurance shown be significantly related dissatisfaction (reliability, p < 0.001; = 0.016; empathy, 0.001). Rural hospitals had higher probability (p Therefore, assisted by technologies, provided pragmatic feasible way capturing perceptions care supplementing conventional surveys. findings offer critical information will assist healthcare authorities capitalising on evaluating services real time.

Language: Английский

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Exploring the Relationship between Urban Youth Sentiment and the Built Environment Using Machine Learning and Weibo Comments

International Journal of Environmental Research and Public Health, Journal Year: 2022, Volume and Issue: 19(8), P. 4794 - 4794

Published: April 15, 2022

As the relationship between built environment and sense of human experience becomes increasingly important, emotional geography has begun to focus on sentiments in space time improving quality urban construction from perspective public emotion mental health. While youth is a powerful force construction, there are no studies environment. With development Internet, social media provided large source data for metrics sentiment. Based more than 10,000 geolocated Sina Weibo comments posted over one week (from 19 25 July 2021) Shanghai using machine learning algorithm attention mechanism, this study calculates sentiment label intensity each comment. Ten elements five aspects were selected assess at different scales also explore correlations scales. The finds that overall tends be negative. Sentiment significantly associated with most smaller Urban have higher proportion both happy sad sentiments, within which closely related all elements. This uses deep improve accuracy classification confirms great impact research can help cities develop optimization measures policies create positive environments enhance well-being youth.

Language: Английский

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Comparative Analysis of Patient Satisfaction Surveys—A Crucial Role in Raising the Standard of Healthcare Services

Healthcare, Journal Year: 2023, Volume and Issue: 11(21), P. 2878 - 2878

Published: Nov. 1, 2023

(1) Background: The study aimed to assess the patients' perception of quality medical staff's care, hotel's services, and hospital's overall impression as well determine best rating scale through a comparative analysis patient satisfaction questionnaires. (2) Methods: A retrospective was performed based on questionnaires addressed patients hospitalized in Orthopedics Traumatology departments County Clinical Emergency Hospital Oradea between 2015 2019. Three different types were used during period, with number questions varying 30 (variant A) 37 C). evaluation done using Likert scales three, four, or five answer variables. (3) Results: items that we found be present all three categories surveys for which at least two questionnaire variants various variables chosen. In terms treatment given by staff, hotel hospital, perceive higher level quality. (4) Conclusions: general about hospital is strongly dependent care provided doctors specific conditions hospital. assessment binary more accurate.

Language: Английский

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Use of Artificial Intelligence with Ethics and Privacy for Personalized Customer Services

Springer eBooks, Journal Year: 2023, Volume and Issue: unknown, P. 231 - 257

Published: Jan. 1, 2023

Language: Английский

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Havayolu İşletmelerinde Yolcu Memnuniyetinin LOPCOW-AROMAN Modeliyle Analizi: Star Alliance Stratejik Ortaklığı Uygulaması

Dumlupınar Üniversitesi Sosyal Bilimler Dergisi, Journal Year: 2024, Volume and Issue: 81, P. 168 - 189

Published: July 26, 2024

Havacılık sektöründe müşteri memnuniyeti işletme başarısı üzerinde kritik bir role sahiptir. Pandemi sonrası tüketici beklentilerinin değişmesiyle birlikte, havayolu işletmelerinin performansının izlenmesi ve geliştirilmesi giderek daha önemli hale gelmiştir. Bu çalışma, Star Alliance stratejik ortaklığına odaklanarak yolcu bakımından performanslarını incelemeyi amaçlamaktadır. doğrultuda, memnuniyetini ölçmek için Skytrax’ın çevrimiçi değerlendirmelerinden elde edilen ikincil veriler kullanılmıştır. Çalışmada, memnuniyet kriterlerinin önem düzeylerini belirlemek LOPCOW yöntemi alternatiflerinin sıralamak AROMAN olmak üzere Çok Kriterli Karar Verme yöntemleri (ÇKKV) Bulgular, en sırasıyla yiyecek içecek, fiyat-fayda dengesi kabin ekibi hizmeti olduğunu ortaya koymuştur. Ayrıca, yüksek memnuniyetine sahip işletmesinin Air New Zealand olduğu belirlenmiştir. Son olarak, sıralamanın tutarlılığını test etmek amacıyla iki aşamalı duyarlılık analizi gerçekleştirilmiş büyük ölçüde tutarlı gözlemlenmiştir. işletmelerine pandemi dönemde değerlendirmek güçlü model sağlamaktadır.

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Providing a Framework for Evaluating the Quality of Health Care Services Using the HealthQual Model and Multi-Attribute Decision-Making Under Imperfect Knowledge of Data

Informatica, Journal Year: 2023, Volume and Issue: unknown, P. 85 - 120

Published: Jan. 1, 2023

Due to the increasing importance of evaluating quality health care services using patient-centred approach, this study aimed propose a novel framework by combining SERVQUAL model and multi-attribute decision-making (MADM) methods interval-valued triangular fuzzy numbers (IVTFN). In study, after an initial overview related work expert opinions, list most important dimensions indicators for measuring was extracted localized. Then, determine each identified factors, one MADM’s acceptable called step-wise weight assessment ratio analysis (SWARA) used. in order use developed comparing different centres ranking them, collecting evaluation data form linguistic variables, another practical method field MADM has been used, namely, Additive Ratio Assessment (ARAS) method. The sub-dimensions are, on hand, appropriate conditions case and, other findings from implementation show that among service quality, responsiveness then reliability highest rank case. Also, IVTFN, eliminates problems Likert scale reduces possibility facing imperfect knowledge which is common problem qualitative evaluations. Utilizing results can significantly help decision makers their choice strategies improve quality. Furthermore, improving play role promoting competitiveness performance providers patient satisfaction with received. as side effect, be used compare hospitals centres, well ranking.

Language: Английский

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Machine Learning for Evaluating Hospital Mobility: An Italian Case Study

Published: April 1, 2024

This study delves into hospital mobility, understood as an indicator of perceived service quality, across the Italian regions Apulia and Emilia Romagna, utilizing logistic regression among machine learning techniques. The focus is on how structural, operational, clinical variables impact patient perceptions influencing their healthcare choices. Through analysis mobility trends with learning, significant differences between were uncovered, highlighting influence regional context quality. integration SHAP (SHapley Additive exPlanations) values our provided deeper insights model, elucidating specific contribution each variable to incorporation underscores study&#039;s commitment employing advanced, explainable AI techniques enhance interpretability fairness evaluations. choice elucidated quality perception, offering essential for optimizing resource distribution underscoring importance data-driven strategies foster more equitable, efficient, patient-centred systems. Contributing understanding dynamics within context, research paves way further investigations enhancing accessibility leveraging a tool improving services efficiency in diverse settings.

Language: Английский

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Machine Learning for Evaluating Hospital Mobility: An Italian Case Study

Applied Sciences, Journal Year: 2024, Volume and Issue: 14(14), P. 6016 - 6016

Published: July 10, 2024

This study delves into hospital mobility within the Italian regions of Apulia and Emilia-Romagna, interpreting it as an indicator perceived service quality. Utilizing logistic regression alongside other machine learning techniques, we analyze impact structural, operational, clinical variables on patient perceptions quality, thus influencing their healthcare choices. The analysis trends has uncovered significant regional differences, emphasizing how context shapes To further enhance analysis, SHAP (SHapley Additive exPlanations) values have been integrated model. These quantify specific contributions each variable to quality service, significantly improving interpretability fairness evaluations. A methodological innovation this is use these scores weights in data envelopment (DEA), facilitating a comparative efficiency facilities that both weighted normative. combination SHAP-weighted DEA provides deeper understanding dynamics offers essential insights for optimizing distribution resources. approach underscores importance data-driven strategies develop more equitable, efficient, patient-centered systems. research contributes promotes investigations accessibility leveraging tool increase services across diverse settings. findings are pivotal policymakers system managers aiming reduce disparities promote responsive personalized service.

Language: Английский

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