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英国普通实践中的实践清单大小、劳动力构成和绩效:潜在剖面分析。

Practice list size, workforce composition and performance in English general practice: a latent profile analysis.

机构信息

Wolfson Institute of Population Health, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, 4 Newark Street, Whitechapel, London, UK.

出版信息

BMC Prim Care. 2024 Jun 11;25(1):207. doi: 10.1186/s12875-024-02462-w.

Abstract

BACKGROUND

Following government calls for General Practices in England to work at scale, some practices have grown in size from traditionally small, General Practitioner (GP)-led organisations to large multidisciplinary enterprises. We assessed the effect of practice list size and workforce composition on practice performance in clinical outcomes and patient experience.

METHODS

We linked five practice-level datasets in England to obtain a single dataset of practice workforce, list size, proportion of registered patients ≥ 65 years of age, female-male sex ratio, deprivation, rurality, GP contract type, patient experience of care, and Quality and Outcomes Framework (QOF) and non-QOF clinical processes and outcomes. Latent Profile Analysis (LPA) was used to cluster general practices into groups based on practice list size and workforce composition. Bayesian Information Criterion, Akaike Information Criterion and deliberation within the research team were used to determine the most informative number of groups. One-way ANOVA was used to assess how groups differed on indicator variables and other variables of interest. Linear regression was used to assess the association between practice group and practice performance.

RESULTS

A total of 6024 practices were available for class assignment. We determined that a 3-class grouping provided the most meaningful interpretation; 4494 (74.6%) were classified as 'Small GP-reliant practices', 1400 (23.2%) were labelled 'Medium-size GP-led practices with a multidisciplinary team (MDT) input' and 131 (2.2%) practices were named 'Large multidisciplinary practices'. Small GP-reliant practices outperformed larger multidisciplinary practices on all patient-reported indicators except on confidence and trust where medium-size GP-led practices with MDT input appeared to do better. There was no difference in performance between small GP-reliant practices and larger multidisciplinary practices on QOF incentivised indicators except on asthma reviews where medium-size GP-led practices with MDT input performed worse than smaller GP-reliant practices and immunisation coverage where the same group performed better than smaller GP-reliant practices. For non-incentivised indicators, larger multidisciplinary practices had higher cancer detection rates than small GP-reliant practices.

CONCLUSION

Small GP-reliant practices were found to provide better patient reported access, continuity of care, experience and satisfaction with care. Larger multidisciplinary practices appeared to have better cancer detection rates but had no effect on other clinical processes and outcomes. As England moves towards larger multidisciplinary practices efforts should be made to preserve good patient experience.

摘要

背景

在英国政府呼吁全科医生(GP)大规模工作之后,一些实践已经从传统的小型 GP 领导组织发展为大型多学科企业。我们评估了实践清单规模和劳动力构成对临床结果和患者体验中实践绩效的影响。

方法

我们将英格兰的五个实践层面数据集联系起来,以获得一个包含实践劳动力、清单规模、≥65 岁注册患者比例、男女性别比、贫困、农村、GP 合同类型、患者护理体验以及质量和成果框架(QOF)和非 QOF 临床流程和结果的单一数据集。潜在剖面分析(LPA)用于根据实践清单规模和劳动力构成将全科医生聚类到不同的组中。贝叶斯信息准则、赤池信息量准则和研究小组内部的审议用于确定最具信息量的组数量。单因素方差分析用于评估组之间在指标变量和其他感兴趣变量上的差异。线性回归用于评估实践组与实践绩效之间的关联。

结果

共有 6024 个实践可用于分类。我们确定 3 类分组提供了最有意义的解释;4494 个(74.6%)被归类为“小型依赖 GP 的实践”,1400 个(23.2%)被标记为“中型 GP 领导的实践,有一个多学科团队(MDT)的投入”,131 个(2.2%)实践被命名为“大型多学科实践”。小型依赖 GP 的实践在所有患者报告指标上的表现均优于大型多学科实践,除了在信心和信任方面,中型 GP 领导的多学科实践似乎表现更好。在 QOF 激励指标上,小型依赖 GP 的实践和大型多学科实践之间的表现没有差异,除了在哮喘评估中,中型 GP 领导的多学科实践的表现不如小型依赖 GP 的实践,在免疫接种覆盖率方面,同一组的表现优于小型依赖 GP 的实践。对于非激励指标,大型多学科实践的癌症检出率高于小型依赖 GP 的实践。

结论

小型依赖 GP 的实践提供了更好的患者报告的可及性、连续性、护理体验和对护理的满意度。大型多学科实践似乎具有更好的癌症检出率,但对其他临床流程和结果没有影响。随着英国向大型多学科实践发展,应努力保持良好的患者体验。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3b03/11165807/6d697f5e5f5c/12875_2024_2462_Fig1_HTML.jpg

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