Family Medicine and Community Health最新文献

The conversation about consciousness of artificial intelligence (AI) is an ongoing topic since 1950s. Despite the numerous applications of AI identified in healthcare and primary healthcare, little is known about how a conscious AI would reshape its use in this domain. While there is a wide range of ideas as to whether AI can or cannot possess consciousness, a prevailing theme in all arguments is uncertainty. Given this uncertainty and the high stakes associated with the use of AI in primary healthcare, it is imperative to be prepared for all scenarios including conscious AI systems being used for medical diagnosis, shared decision-making and resource management in the future. This commentary serves as an overview of some of the pertinent evidence supporting the use of AI in primary healthcare and proposes ideas as to how consciousnesses of AI can support or further complicate these applications. Given the scarcity of evidence on the association between consciousness of AI and its current state of use in primary healthcare, our commentary identifies some directions for future research in this area including assessing patients', healthcare workers' and policy-makers' attitudes towards consciousness of AI systems in primary healthcare settings.

Introduction: Pelvic floor disorders (PFDs) pose substantial physical and psychological burdens for a growing number of women. Given the ubiquity of these conditions and known patient reluctance to seek care, primary care providers (PCPs) have a unique opportunity to increase treatment and provide appropriate referrals for these patients.

Methods: An online survey was administered to PCPs to assess provider practices, knowledge, comfort managing and ease of referral for PFDs. Logistic regression was used to assess the association between demographic/practice characteristics of PCPs and two primary outcomes of interest: discomfort with management and difficulty with referral of PFDs.

Results: Of the 153 respondents to the survey, more felt comfortable managing stress urinary incontinence (SUI) and overactive bladder (OAB), compared with pelvic organ prolapse (POP) and faecal incontinence (FI) and were less likely to refer patients with urinary symptoms. Few providers elicited symptoms for POP and FI as compared with SUI and OAB. Provider variables that were significantly associated with discomfort with management varied by PFD, but tended to correlate with less exposure to PFDs (eg, those with fewer years of practice, and internal medicine and family physicians as compared with geriatricians); whereas the factors that were significantly associated with difficulty in referral, again varied by PFD, but were related to practice characteristics (eg, specialist network, type of practice, practice setting and quantity of patients).

Conclusion: These findings highlight the need to increase PCPs awareness of PFDs and develop effective standardised screening protocols, as well as collaboration with pelvic floor specialists to improve screening, treatment and referral for patients with PFDs.

This paper proposes the utilisation of twin studies as a novel and powerful methodological approach to investigate critical research questions pertaining to cancer prevention, screening, diagnosis, treatment and survivorship within primary care contexts. The inherent genetic similarity between monozygotic (MZ) (identical) twins provides a unique opportunity to disentangle genetic and environmental influences on cancer-related outcomes. MZ twins share virtually identical genetic makeup, offering a unique opportunity to discern the relative contributions of genetic and environmental factors to cancer-related outcomes. In contrast, dizygotic (DZ) twins, also known as fraternal twins, develop from two separate eggs fertilised by two different sperm and share on average 50% of their genetic material, the same level of genetic similarity found in non-twin siblings. Comparisons between MZ and DZ twins enable researchers to disentangle hereditary factors from shared environmental influences. This methodology has the potential to advance our understanding of the multifaceted interplay between genetic predisposition, lifestyle factors and healthcare interventions in the context of cancer care. This paper outlines the rationale, design considerations and potential applications of twin studies in primary care-based cancer research.

Cervical intraepithelial neoplasia grade 2 (CIN2) lesions may regress spontaneously, offering an alternative to immediate treatment, especially for women of childbearing age (15-45 years).We conducted a prospective multicentre study on conservative CIN2 management, with semiannual follow-up visits over 24 months, biomarkers' investigation and treatment for progression to CIN3+ or CIN2 persistence for more than 12 months. Here, we assess women's willingness to participate and adherence to the study protocol.The study was set in population-based organised cervical cancer screening.From April 2019 to October 2021, 640 CIN2 cases were diagnosed in women aged 25-64 participating in the screening programmes.According to our predefined inclusion and exclusion criteria, 228 (35.6%) women were not eligible; 93 (22.6%) of the 412 eligible refused, and 319 (77.4%) were enrolled. Refusal for personal reasons (ie, desire to become pregnant, anxiety, difficulty in complying with the study protocol) and external barriers (ie, residence elsewhere and language problems) accounted for 71% and 17%, respectively. Only 9% expressed a preference for treatment. The primary ineligibility factor was the upper age limit of 45 years. After enrolment, 12 (4%) women without evidence of progression requested treatment, 125 (39%) were lost to follow-up (mostly after 6-12 months) and 182 (57%) remained compliant. Remarkably, 40% of enrolees did not fully adhere to the protocol, whereas only 5% (20/412) of the eligible women desired treatment.Our study demonstrates a good acceptance of conservative management for CIN2 lesions by the women, supporting its implementation within cervical screening programmes.

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.