Family Medicine and Community Health最新文献

The recent release of highly advanced generative artificial intelligence (AI) chatbots, including ChatGPT and Bard, which are powered by large language models (LLMs), has attracted growing mainstream interest over its diverse applications in clinical practice, including in health and healthcare. The potential applications of LLM-based programmes in the medical field range from assisting medical practitioners in improving their clinical decision-making and streamlining administrative paperwork to empowering patients to take charge of their own health. However, despite the broad range of benefits, the use of such AI tools also comes with several limitations and ethical concerns that warrant further consideration, encompassing issues related to privacy, data bias, and the accuracy and reliability of information generated by AI. The focus of prior research has primarily centred on the broad applications of LLMs in medicine. To the author's knowledge, this is, the first article that consolidates current and pertinent literature on LLMs to examine its potential in primary care. The objectives of this paper are not only to summarise the potential benefits, risks and challenges of using LLMs in primary care, but also to offer insights into considerations that primary care clinicians should take into account when deciding to adopt and integrate such technologies into their clinical practice.

Objectives: This study aimed to investigate the effectiveness of Goal Attainment Scaling (GAS) in assessing an intervention for pre-frail senior citizens. Additionally, the study aimed to explain how the GAS goals were established based on the International Classification of Functioning, Disability and Health (ICF) categories, including body function, activity and participation and environmental factors.

Methods: In this study, 220 pre-frail older adults were randomly selected to participate in a controlled trial. The intervention group engaged in multicomponent exercise three times a week, once at a community health service location and twice at home. The control group received advice on physical activity but did not have supervised exercise. Participants in both groups selected individualised GAS goals from 23 goals developed based on ICF by focus group discussion. The study used generalised estimating equations to analyse the differences between the groups.

Results: The study included 144 participants, 72 in the exercise group and 72 in the control group. The top three individualised goals for all participants were vestibular functions (53.5%), pain management (43.1%) and lifting and carrying objects (31.9%). Both groups saw a significant increase in GAS scores at week 8 and week 24 of the intervention (p<0.05), but the exercise group showed a more significant improvement (p<0.05). The participants living alone were associated with lower postintervention improvements in the GAS scores. In contrast, the participants who were using a smartphone were likely to get higher postintervention improvements in the GAS scores.

Conclusions: GAS can be a valuable tool for setting and evaluating individualised and meaningful goals in body functions, activity and participation and environmental factors. The multicomponent exercise interventions can help pre-frail older adults achieve their expected goals as measured by the GAS.

Objective: Cardiovascular diseases (CVD) are one of the most prevalent diseases in India amounting for nearly 30% of total deaths. A dearth of research on CVD risk scores in Indian population, limited performance of conventional risk scores and inability to reproduce the initial accuracies in randomised clinical trials has led to this study on large-scale patient data. The objective is to develop an Artificial Intelligence-based Risk Score (AICVD) to predict CVD event (eg, acute myocardial infarction/acute coronary syndrome) in the next 10 years and compare the model with the Framingham Heart Risk Score (FHRS) and QRisk3.

Methods: Our study included 31 599 participants aged 18-91 years from 2009 to 2018 in six Apollo Hospitals in India. A multistep risk factors selection process using Spearman correlation coefficient and propensity score matching yielded 21 risk factors. A deep learning hazards model was built on risk factors to predict event occurrence (classification) and time to event (hazards model) using multilayered neural network. Further, the model was validated with independent retrospective cohorts of participants from India and the Netherlands and compared with FHRS and QRisk3.

Results: The deep learning hazards model had a good performance (area under the curve (AUC) 0.853). Validation and comparative results showed AUCs between 0.84 and 0.92 with better positive likelihood ratio (AICVD -6.16 to FHRS -2.24 and QRisk3 -1.16) and accuracy (AICVD -80.15% to FHRS 59.71% and QRisk3 51.57%). In the Netherlands cohort, AICVD also outperformed the Framingham Heart Risk Model (AUC -0.737 vs 0.707).

Conclusions: This study concludes that the novel AI-based CVD Risk Score has a higher predictive performance for cardiac events than conventional risk scores in Indian population.

Trial registration number: CTRI/2019/07/020471.

Objectives: To our best knowledge, no study in France has comprehensively investigated the prehospital history of patients admitted for severe cases of COVID-19. 'Patients' voice is an excellent means to capture data on primary care pathways.We aimed to identify clusters of COVID-19 hospitalised patients with similar prehospital symptom sequences, and to test whether these clusters were associated with a higher risk of poor clinical outcomes.

Design: Cross-sectional online survey using life-event calendars.

Setting: All patients hospitalised for COVID-19 between September 2020 and May 2021 in the Infectious Disease Departments in Nice and in Marseilles in France.

Participants: 312 patients responded to the survey.

Main outcome measures: From the day of symptom onset to the day of hospitalisation, we defined a symptom sequence as the time-ordered vector of the successive symptom grades (grade 1, grade 2, grade 3). State sequence analysis with optimal matching was used to identify clusters of patients with similar symptom sequences. Multivariate logistic regressions were performed to test whether these clusters were associated with admission to intensive care unit (ICU) and COVID-19 sequelae after hospitalisation.

Results: Three clusters of symptom sequences were identified among 312 complete prehospital pathways. A specific group of patients (29%) experienced extended symptoms of severe COVID-19, persisting for an average duration of 7.5 days before hospitalisation. This group had a significantly higher probability of being admitted to ICU (adjusted OR 2.01). They were less likely to know a loved one who was a healthcare worker, and more likely to have a lower level of education. Similarly, this group of patients, who were more likely to have previously visited the emergency room without exhibiting severe symptoms at that time, may have been inclined to postpone reassessment when their health worsened.Their relatives played a decisive role in their hospitalisation.

Conclusion and relevance: This study highlights the negative impact of delayed hospitalisation on the health outcomes of French patients with severe COVID-19 symptoms during the first wave and underscores the influence of socioeconomic factors, such as lower education levels and limited connections to the medical field, on patients' experiences.

Background: Artificial intelligence (AI) has rapidly permeated various sectors, including healthcare, highlighting its potential to facilitate mental health assessments. This study explores the underexplored domain of AI's role in evaluating prognosis and long-term outcomes in depressive disorders, offering insights into how AI large language models (LLMs) compare with human perspectives.

Methods: Using case vignettes, we conducted a comparative analysis involving different LLMs (ChatGPT-3.5, ChatGPT-4, Claude and Bard), mental health professionals (general practitioners, psychiatrists, clinical psychologists and mental health nurses), and the general public that reported previously. We evaluate the LLMs ability to generate prognosis, anticipated outcomes with and without professional intervention, and envisioned long-term positive and negative consequences for individuals with depression.

Results: In most of the examined cases, the four LLMs consistently identified depression as the primary diagnosis and recommended a combined treatment of psychotherapy and antidepressant medication. ChatGPT-3.5 exhibited a significantly pessimistic prognosis distinct from other LLMs, professionals and the public. ChatGPT-4, Claude and Bard aligned closely with mental health professionals and the general public perspectives, all of whom anticipated no improvement or worsening without professional help. Regarding long-term outcomes, ChatGPT 3.5, Claude and Bard consistently projected significantly fewer negative long-term consequences of treatment than ChatGPT-4.

Conclusions: This study underscores the potential of AI to complement the expertise of mental health professionals and promote a collaborative paradigm in mental healthcare. The observation that three of the four LLMs closely mirrored the anticipations of mental health experts in scenarios involving treatment underscores the technology's prospective value in offering professional clinical forecasts. The pessimistic outlook presented by ChatGPT 3.5 is concerning, as it could potentially diminish patients' drive to initiate or continue depression therapy. In summary, although LLMs show potential in enhancing healthcare services, their utilisation requires thorough verification and a seamless integration with human judgement and skills.

Objective: Despite the established cancer screening programme for oral, breast and cervical cancer by the Government of India, the screening coverage remains inadequate. This study aimed to describe the determinants for oral, breast and cervical cancer prevention in a rural community at the primary care level of Northern India and its policy implications.

Design: This was a camp-based project conducted for 1 year, using oral visual examination, clinical breast examination and visual inspection of cervix by application of 5% acetic acid according to primary healthcare operational guidelines. During the project, screen-positive participants were followed through reverse navigation. Information about socio-demographic profile, clinical and behavioural history and screening were collected. Predictors for screen-positivity and follow-up compliance were identified through multivariable analysis.

Settings: Based on the aim of project, one of the remotely located and low socioeconomic rural blocks, having 148 villages (estimated population of 254 285) in Varanasi district, India was selected as the service site. There is an established healthcare delivery and referral system as per the National Health Mission of Government of India. Oral, breast, gallbladder and cervical cancers are the leading cancers in the district.

Participants: We invited all men and women aged 30-65 years residing in the selected block for the last 6 months for the screening camps. Unmarried women, women with active vaginal bleeding, those currently pregnant and those who have undergone hysterectomy were excluded from cervical cancer screening.

Results: A total of 14 338 participants were screened through 190 camps and the majority (61.9%) were women. Hindu religion, tobacco use, intention to quit tobacco and presence of symptoms were significantly associated with screen-positivity. Nearly one-third (220; 30.1%) of the screened-positives complied with follow-up. Young age and illiteracy were significantly associated with lower compliance.

Conclusion: Poor follow-up compliance, despite the availability of tertiary cancer care, patient navigation, free transportation and diagnostic services, calls for research to explore the role of contextual factors and develop pragmatic interventions to justify 'close the care gap'. Community cancer screening needs strengthening through cancer awareness, establishing referral system and integration with the National Tobacco Control and Cancer Registry Programmes.

Objectives: Australian guidelines recommend 50-70 years consider taking aspirin to reduce their bowel cancer risk. We trialled a decision aid in general practice to facilitate the implementation of these guidelines into clinical practice. This publication reports on the qualitative results from the process evaluation of the trial. We aimed to explore general practitioners' (GPs) and their patients' approach to shared decision-making (SDM) about taking aspirin to prevent bowel cancer and how the decision aids were used in practice.

Methods: Semistructured interviews were conducted with 17 participants who received the decision aid and 12 GPs who participated in the trial between June and November 2021. The interviews were coded inductively, and emerging themes were mapped onto the Revised Programme Theory for SDM.

Results: The study highlighted the dynamics of SDM for taking aspirin to prevent bowel cancer. Some participants discussed the decision aid with their GPs as advised prior to taking aspirin, others either took aspirin or dismissed it outright without discussing it with their GPs. Notably, participants' trust in their GPs, and participants' diverse worldviews played pivotal roles in their decisions. Although the decision aid supported SDM for some, it was not always prioritised in a consultation. This was likely impacted during the trial period as the COVID-19 pandemic was the focus for general practice.

Conclusion: In summary, this study illustrated the complexities of SDM through using a decision aid in general practice to implement the guidelines for low-dose aspirin to prevent bowel cancer. While the decision aid prompted some participants to speak to their GPs, they were also heavily influenced by their unwavering trust in the GPs and their different worldviews. In the face of the COVID-19 pandemic, SDM was not highly prioritised. This study provides insights into the implementation of guidelines into clinical practice and highlights the need for ongoing support and prioritisation of cancer prevention in general practice consultations.

Trial registration number: ACTRN12620001003965.