Xiaolian Jiang

Prof. Xiaolian Jiang

West China Hospital/West China School of Nursing, Sichuan University, Chengdu 610041, Sichuan, China.

Research Focus & Metrics

Academic Career Length

9Years (2018-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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4 January 2023 in BMJ Open
Verified
Journal Article
Open access
PDF file
CWChenxi Wu
Chenxi Wu
HXHuiqiong Xu
Huiqiong Xu
DBDingxi Bai
Dingxi Bai
XCXinyu Chen
Xinyu Chen
JGJING GAO
JING GAO
XJXiaolian Jiang
Xiaolian Jiang
Objectives Medical artificial intelligence (AI) has been used widely applied in clinical field due to its convenience and innovation. However, several policy and regulatory issues such as credibility, sharing of responsibility and ethics have raised concerns in the use of AI. It is therefore necessary to understand the general public’s views on medical AI. Here, a meta-synthesis was conducted to analyse and summarise the public’s understanding of the application of AI in the healthcare field, to provide recommendations for future use and management of AI in medical practice. Design This was a meta-synthesis of qualitative studies. Method A search was performed on the following databases to identify studies published in English and Chinese: MEDLINE, CINAHL, Web of science, Cochrane library, Embase, PsycINFO, CNKI, Wanfang and VIP. The search was conducted from database inception to 25 December 2021. The meta-aggregation approach of JBI was used to summarise findings from qualitative studies, focusing on the public’s perception of the application of AI in healthcare. Results Of the 5128 studies screened, 12 met the inclusion criteria, hence were incorporated into analysis. Three synthesised findings were used as the basis of our conclusions, including advantages of medical AI from the public’s perspective, ethical and legal concerns about medical AI from the public’s perspective, and public suggestions on the application of AI in medical field. Conclusion Results showed that the public acknowledges the unique advantages and convenience of medical AI. Meanwhile, several concerns about the application of medical AI were observed, most of which involve ethical and legal issues. The standard application and reasonable supervision of medical AI is key to ensuring its effective utilisation. Based on the public’s perspective, this analysis provides insights and suggestions for health managers on how to implement and apply medical AI smoothly, while ensuring safety in healthcare practice. PROSPERO registration number CRD42022315033.

1 March 2018 in International Journal of Nursing Studies
Verified
Journal Article
Closed access
RHRujun Hu
Rujun Hu
HGHuiming Gao
Huiming Gao
YYYansheng Ye
Yansheng Ye
ZNZhihong Ni
Zhihong Ni
NJNing Jiang
Ning Jiang
XJXiaolian Jiang
Xiaolian Jiang
1

1 December 2020 in Journal of Advanced Nursing
Verified
Journal Article
Closed access
PDF file
KYKaihan Yang
Kaihan Yang
HLHui Liu
Hui Liu
LMLihua Ma
Lihua Ma
SWSong Wang
Song Wang
YTYali Tian
Yali Tian
XJXiaolian Jiang
Xiaolian Jiang
1

1 December 2018 in Nurse Education Today
Verified
Journal Article
Closed access
YYYansheng Ye
Yansheng Ye
RHRujun Hu
Rujun Hu
ZNZhihong Ni
Zhihong Ni
NJNing Jiang
Ning Jiang
XJXiaolian Jiang
Xiaolian Jiang
1

29 March 2022 in BMC Geriatrics
Verified
Journal Article
Open access
PDF file
MZMeng Zhang
Meng Zhang
XZXuemei Zhang
Xuemei Zhang
LGLangli Gao
Langli Gao
JYJirong Yue
Jirong Yue
XJXiaolian Jiang
Xiaolian Jiang
Background: Delirium is a common complication that leads to poor health outcomes in older patients undergoing treatment. Due to severe consequences, early recognition of high-risk patients and risk factors for delirium are crucial in the prompt initiation of prevention measures. However, research in medically hospitalized patients aged ≥80 years remains limited. This study aimed to determine the incidence, predictors and health outcomes of delirium in very old (aged ≥80 years) hospitalized patients in China. Methods: A prospective study was conducted in individuals aged ≥80 years admitted to geriatric departments. Potential risk factors were assessed within 24 h after hospital admission. Screening for delirium was performed on admission and every 48 h thereafter for 14 days and assessed if acute mental status changes were observed. During hospitalization, health outcomes were recorded daily. Results: Incident delirium occurred in 109 of 637 very old hospitalized patients (17.1%). The independent predictors of delirium in hospitalized patients aged 80 and over were cognitive function impairment [OR 17.42, 95% CI:(7.47–40.64)], depression [OR 9.30, 95% CI: (4.59–18.84)], CCI ≥ 5 [OR 4.21, 95% CI: (1.48–12.01)], sleep deprivation [OR 3.89, 95% CI: (1.71–8.82)], infection [OR 3.33, 95% CI: (1.70–6.54)], polypharmacy (≥5 medications) [OR 2.85, 95% CI: (1.51–5.39)], constipation [OR 2.58, 95% CI: (1.33–5.02)], and emergency admission [OR 2.13, 95% CI: (1.02–4.45)]. Patients with delirium had significantly longer hospital stays(P < 0.001) and higher percentages of physical restraint use(P < 0.001) and falls (P = 0.001) than those without delirium,. Conclusion: The incidence of delirium was high in hospitalized patients aged ≥80 years admitted to the geriatric department and was associated with prolonged hospital stay and higher rates of physical restraint use and falls. In this population, the most important independent risk factors for incident delirium were cognitive function impairment and depression. Health care professionals should recognize and initiate interventions for delirium early in geriatric patients.
1

11 March 2021 in International Journal of Mental Health Nursing
Verified
Journal Article
Closed access
PDF file
KYKai‐Han Yang
Kai‐Han Yang
LWLei Wang
Lei Wang
HLHui Liu
Hui Liu
LLLin‐Xia Li
Lin‐Xia Li
XJXiaolian Jiang
Xiaolian Jiang
1