Evidence map›Paper›PMID 40129115›Full record

ArticleJournal of advanced nursing2026

An AI-Enabled Nursing Future With no Documentation Burden: A Vision for a New Reality.

Martin Michalowski, Maxim Topaz, Laura Maria Peltonen

Abstract readConsensus Statement
In one paragraph

Article in Journal of advanced nursing, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Review
  11. Article
  12. Article
  13. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Martin MichalowskiSchool of Nursing, University of Minnesota, Minneapolis, Minnesota, USA.ORCID https://orcid.org/0000-0003-2060-5878
Maxim TopazSchool of Nursing, Columbia University, New York, New York, USA.
Laura Maria PeltonenDepartment of Health and Social Management, University of Eastern Finland, Wellbeing Services County of North Savo, Kuopio, Finland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimsTo explore the potential of multimodal large language models in alleviating the documentation burden on nurses while enhancing the quality and efficiency of patient care.

designThis position paper is informed by expert discussions and a literature review.

methodsWe extensively reviewed nursing documentation practices and advanced technologies, such as multimodal large language models. We analysed key challenges, solutions and impacts to propose a futuristic multimodal large language model-driven model for nursing documentation.

resultsMultimodal large language models offer transformative capabilities by integrating multimodal audio, video and text data during patient encounters to dynamically update patient records in real time. This reduces manual data entry, enabling nurses to focus more on direct patient care. These systems also enhance care personalisation through predictive analytics and interoperability, which support seamless workflows and better patient outcomes. While predictive analytics could improve patient care by identifying trends and risk factors from nursing documentation, further research is required to validate its accuracy and clinical utility in real-world settings. Ethical, legal and practical challenges, including privacy concerns and biases in artificial intelligence models, require careful consideration for successful implementation.

conclusionTransitioning to multimodal large language model-driven documentation systems can significantly reduce administrative burdens, improve nurse satisfaction and enhance patient care. However, successful integration demands interdisciplinary collaboration, robust ethical frameworks and technological advancements. IMPLICATIONS FOR THE PROFESSION AND PATIENT CARE: Implementing multimodal large language models could alleviate professional burnout, improve nurse-patient interactions, and provide dynamic, up-to-date patient records that facilitate informed decision making. These advancements align with the goals of patient-centred care by enabling more meaningful engagement between nurses and patients. IMPACT: The problem being addressed is the administrative burden of nursing documentation. We suggest that multimodal large language models minimise manual documentation, enhance patient care quality and significantly impact nurses and patients in diverse healthcare settings globally.

Indexed as

Artificial IntelligenceDocumentationNursing RecordsElectronic Health RecordsHumansartificial intelligence in healthcarehealthcare innovationmultimodal large language modelsnurse burnoutnursingnursing documentationpatient‐centered care

Identifiers

PMID40129115
PMCPMC12721931

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.