The promise of big data for palliative and end-of-life care research

The 2023-2026 Hospice and Palliative Nurses Association Research Agenda

The Hospice and Palliative Nursing Association established the triannual research agenda to ( a ) provide focus for researchers to conduct meaningful scientific and quality improvement initiatives and inform evidence-based practice, ( b ) guide organizational funding, and ( c ) illustrate to other stakeholders the importance of nursing research foci. HPNA Research Agendas are developed to give direction for future research to continue advancing expert care in serious illness and ensure equitable delivery of hospice and palliative care.

The use of natural language processing in palliative care research: A scoping review

BACKGROUND

Natural language processing has been increasingly used in palliative care research over the last 5 years for its versatility and accuracy.

AIM

To evaluate and characterize natural language processing use in palliative care research, including the most commonly used natural language processing software and computational methods, data sources, trends in natural language processing use over time, and palliative care topics addressed.

DESIGN

A scoping review using the framework by Arksey and O'Malley and the updated recommendations proposed by Levac et al. was conducted.

SOURCES

PubMed, Web of Science, Embase, Scopus, and IEEE Xplore databases were searched for palliative care studies that utilized natural language processing tools. Data on study characteristics and natural language processing instruments used were collected and relevant palliative care topics were identified.

RESULTS

197 relevant references were identified. Of these, 82 were included after full-text review. Studies were published in 48 different journals from 2007 to 2022. The average sample size was 21,541 (median 435). Thirty-two different natural language processing software and 33 machine-learning methods were identified. Nine main sources for data processing and 15 main palliative care topics across the included studies were identified. The most frequent topic was mortality and prognosis prediction. We also identified a trend where natural language processing was frequently used in analyzing clinical serious illness conversations extracted from audio recordings.

CONCLUSIONS

We found 82 papers on palliative care using natural language processing methods for a wide-range of topics and sources of data that could expand the use of this methodology. We encourage researchers to consider incorporating this cutting-edge research methodology in future studies to improve published palliative care data.

Effectiveness and cost-effectiveness of palliative care in natural experiments: a systematic review

CONTEXT

Investigators in palliative care rely heavily on routinely collected data, which carry risk of unobserved confounding and selection bias. 'Natural experiments' offer opportunities to generate credible causal treatment effect estimates from observational data.

OBJECTIVES

We aimed first to review studies that employed 'natural experiments' to evaluate palliative care, and second to consider implications for expanding use of these methods.

METHODS

We searched systematically seven databases to identify studies using 'natural experiments' to evaluate palliative care's effect on outcomes and costs. We searched three grey literature repositories, and hand-searched journals and prior systematic reviews. We assessed reporting using the Strengthening the Reporting of Observational Studies in Epidemiology checklist and a bespoke methodological quality tool, using two reviewers at each stage. We combined results in a narrative synthesis.

RESULTS

We included 17 studies, which evaluated a wide range of interventions and populations. Seven studies employed a difference-in-differences design; five each used instrumental variables and interrupted time series analysis. Outcomes of interest related mostly to healthcare use. Reporting quality was variable. Most studies reported lower costs and improved outcomes associated with palliative care, but a third of utilisation and place of death evaluations found no effect.

CONCLUSION

Among the large number of observational studies in palliative care, a small minority have employed causal mechanisms. High-volume routine data collection, the expansion of palliative care services worldwide and recent methodological advances offer potential for increased use of 'natural experiments'. Such studies would improve the quality of the evidence base.

Hotspot Mining in the Field of Library and Information Science under the Environment of Big Data

Currently, with the implementation of big data strategies in countries all over the world, big data has achieved vigorous development in various fields. Big data research and application practices have also rapidly attracted the attention of the library and information field. Objective. The study explored the current state of research and research hotspots of big data in the library and information field and further discussed the future research trends. Methods. In the CNKI database, 16 CSSCI source journals in the discipline of library information and digital library were selected as data sources, and the relevant literature was retrieved with the theme of “big data.” The collected literature was excluded and expanded according to the citation relationship. Then, with the help of Bicomb and SPSS, co-word analysis and cluster analysis would be carried out on these literature results. Results. According to the findings of the data analysis, the research hotspots on the topic mainly focus on five major research themes, namely, big data and smart library, big data and intelligence research, data mining and cloud computing, big data and information analysis, and library innovation and services. Limitations. At present, the research scope and coverage on this topic are wide, which leads to the research still staying at the macro level. Conclusions. Big data research will remain one of the hotspots in the future. However, the most study is still limited to the perspective of library and information and has not yet analyzed the research status, research hotspots, and development trends in this field from the perspective of big data knowledge structure. Moreover, machine learning, artificial intelligence, knowledge services, AR, and VR may be new directions for future attention and development.