Digital Health Research Priorities

Jacennik, Barbara

doi:10.56226/ihr.v1i1.13

Cited by 11 publications

(1 citation statement)

References 2 publications

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“…There has been an increase in research and research needs discussion highlighting possibilities for the future development of artificial intelligence in nursing care. However, the importance of collaborative, interdisciplinary research has been generally underscored (Ampavathi & Vijaya Saradhi, 2022 ; Apell & Eriksson, 2021 ; Jacennik, 2022 ).…”

Section: Artificial Intelligence Priorities For Nursing Management: T...mentioning

confidence: 99%

Is there a gap between artificial intelligence applications and priorities in health care and nursing management?

Chen

Moreira

Liu

et al. 2022

J Nursing Management

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Aim The article aims to outline a contrast between three priorities for nursing management proposed a decade ago and key features of the following 10 years of developments on artificial intelligence for health care and nursing management. This analysis intends to contribute to update the international debate on bridging the essence of health care and nursing management priorities and the focus of artificial intelligence developers. Background Artificial intelligence research promises innovative approaches to supporting nurses' clinical decision‐making and to conduct tasks not related to patient interaction, including administrative activities and patient records. Yet, even though there has been an increase in international research and development of artificial intelligence applications for nursing care during the past 10 years, it is unclear to what extent the priorities of nursing management have been embedded in the devised artificial intelligence solutions. Evaluation Starting from three priorities for nursing management identified in 2011 in a special issue of the Journal Nursing Management, we went on to identify recent evidence concerning 10 years of artificial intelligence applications developed to support health care management and nursing activities since then. Key Issue The article discusses to what extent priorities in health care and nursing management may have to be revised while adopting artificial intelligence applications or, alternatively, to what extent the direction of artificial intelligence developments may need to be revised to contribute to long acknowledged priorities of nursing management. Conclusion We have identified a conceptual gap between both sets of ideas and provide a discussion on the need to bridge that gap, while admitting that there may have been recent field developments still unreported in scientific literature. Implications for Nursing Management Artificial intelligence developers and health care nursing managers need to be more engaged in coordinating the future development of artificial intelligence applications with a renewed set of nursing management priorities.

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Section: Artificial Intelligence Priorities For Nursing Management: T...mentioning

confidence: 99%

Is there a gap between artificial intelligence applications and priorities in health care and nursing management?

Chen

Moreira

Liu

et al. 2022

J Nursing Management

View full text Add to dashboard Cite

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Bringing researchers to the consumer table: The process and outcomes of a consumer roundtable on telehealth

et al. 2023

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Introduction Despite the significant expansion and rapid uptake of telehealth services as a COVID-19 response, the pandemic restricted opportunities to involve health consumers in telehealth research. Authentic consumer and community involvement in research begins with engagement in priority-setting. We report here on the process and outcomes of a consumer-led event intended to support involvement of consumers, from early in the research process. Methods In 2022, The University of Queensland's Consumer and Community Network hosted a Consumer Roundtable to ‘bring researchers to the consumer table’ and explore emerging issues and priorities for future research. The event used World Café Method, with three 20-min rounds of small group discussion centred on questions about telehealth experiences, followed by a facilitated harvest discussion about future research directions. Participants’ notes from small group discussions were subjected to conventional inductive content analysis, and a visual record was created in real-time by a graphic artist. Results Twenty-eight consumers and 22 researchers took part. Content analysis identified three main foci from discussions: person-centred care, better access to better care, the (unrealised) potential of telehealth. Research questions prioritised by consumer vote focussed on marginalised groups and stigmatised conditions; differences between telehealth and face-to-face healthcare delivery; and the experience of conveying and receiving compassion via telehealth. Discussion The Consumer Roundtable created early engagement between health consumer representatives and telehealth researchers, which has yielded ongoing partnerships. World Café method proved particularly useful for seeding relationships between researchers and consumers. However, there was limited opportunity to generate consensus about research priorities.

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Evaluating Patients’ Experiences with Healthcare Services: Extracting Domain and Language-Specific Information from Free-Text Narratives

Jacennik¹,

Zawadzka-Gosk

Moreira

et al. 2022

IJERPH

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Evaluating patients’ experience and satisfaction often calls for analyses of free-text data. Language and domain-specific information extraction can reduce costly manual preprocessing and enable the analysis of extensive collections of experience-based narratives. The research aims were to (1) elicit free-text narratives about experiences with health services of international students in Poland, (2) develop domain- and language-specific algorithms for the extraction of information relevant for the evaluation of quality and safety of health services, and (3) test the performance of information extraction algorithms’ on questions about the patients’ experiences with health services. The materials were free-text narratives about health clinic encounters produced by English-speaking foreigners recalling their experiences (n = 104) in healthcare facilities in Poland. A linguistic analysis of the text collection led to constructing a semantic–syntactic lexicon and a set of lexical-syntactic frames. These were further used to develop rule-based information extraction algorithms in the form of Python scripts. The extraction algorithms generated text classifications according to predefined queries. In addition, the narratives were classified by human readers. The algorithm-based and the human readers’ classifications were highly correlated and significant (p < 0.01), indicating an excellent performance of the automatic query algorithms. The study results demonstrate that domain-specific and language-specific information extraction from free-text narratives can be used as an efficient and low-cost method for evaluating patient experiences and satisfaction with health services and built into software solutions for the quality evaluation in health care.

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Digital Health Research Priorities

Cited by 11 publications

References 2 publications

Is there a gap between artificial intelligence applications and priorities in health care and nursing management?

Is there a gap between artificial intelligence applications and priorities in health care and nursing management?

Bringing researchers to the consumer table: The process and outcomes of a consumer roundtable on telehealth

Evaluating Patients’ Experiences with Healthcare Services: Extracting Domain and Language-Specific Information from Free-Text Narratives

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