Evidence map›Paper›PMID 37761715›Full record

ReviewHealthcare (Basel, Switzerland)2023

Enhancing Kidney Transplant Care through the Integration of Chatbot.

Oscar A Garcia Valencia, Charat Thongprayoon, Caroline C Jadlowiec, Shennen A Mao, Jing Miao, Wisit Cheungpasitporn

Abstract readReview
In one paragraph

Review in Healthcare (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 2 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

26 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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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

6 authors.

Oscar A Garcia ValenciaDivision of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA.ORCID 0000-0003-0186-9448
Charat ThongprayoonDivision of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA.
Caroline C JadlowiecDivision of Transplant Surgery, Department of Surgery, Mayo Clinic, Phoenix, AZ 85054, USA.ORCID 0000-0001-7860-9519
Shennen A MaoDivision of Transplant Surgery, Department of Transplantation, Mayo Clinic, Jacksonville, FL 32224, USA.ORCID 0000-0002-7571-2542
Jing MiaoDivision of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA.ORCID 0000-0003-0642-9740
Wisit CheungpasitpornDivision of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA.ORCID 0000-0001-9954-9711

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Kidney transplantation is a critical treatment option for end-stage kidney disease patients, offering improved quality of life and increased survival rates. However, the complexities of kidney transplant care necessitate continuous advancements in decision making, patient communication, and operational efficiency. This article explores the potential integration of a sophisticated chatbot, an AI-powered conversational agent, to enhance kidney transplant practice and potentially improve patient outcomes. Chatbots and generative AI have shown promising applications in various domains, including healthcare, by simulating human-like interactions and generating contextually appropriate responses. Noteworthy AI models like ChatGPT by OpenAI, BingChat by Microsoft, and Bard AI by Google exhibit significant potential in supporting evidence-based research and healthcare decision making. The integration of chatbots in kidney transplant care may offer transformative possibilities. As a clinical decision support tool, it could provide healthcare professionals with real-time access to medical literature and guidelines, potentially enabling informed decision making and improved knowledge dissemination. Additionally, the chatbot has the potential to facilitate patient education by offering personalized and understandable information, addressing queries, and providing guidance on post-transplant care. Furthermore, under clinician or transplant pharmacist supervision, it has the potential to support post-transplant care and medication management by analyzing patient data, which may lead to tailored recommendations on dosages, monitoring schedules, and potential drug interactions. However, to fully ascertain its effectiveness and safety in these roles, further studies and validation are required. Its integration with existing clinical decision support systems may enhance risk stratification and treatment planning, contributing to more informed and efficient decision making in kidney transplant care. Given the importance of ethical considerations and bias mitigation in AI integration, future studies may evaluate long-term patient outcomes, cost-effectiveness, user experience, and the generalizability of chatbot recommendations. By addressing these factors and potentially leveraging AI capabilities, the integration of chatbots in kidney transplant care holds promise for potentially improving patient outcomes, enhancing decision making, and fostering the equitable and responsible use of AI in healthcare.

Indexed as

AI-powered chatbotclinical decision supportdecision makingend-stage kidney diseaseethical considerationshealthcare professionalskidney transplantationoperational efficiencypatient communicationpatient outcomes

Identifiers

PMID37761715
PMCPMC10530762

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.