Primary care consultation length by deprivation and multimorbidity in England

Göpfert, Anya; Deeny, Sarah R; Fisher, Rebecca

doi:10.1101/2020.01.27.20018960

Cited by 10 publications

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Value of using artificial intelligence derived clusters by health and social care need in Primary Care: A qualitative interview study

Holt,

Simpson,

Santer

et al. 2024

Preprint

View full text Add to dashboard Cite

Purpose People living with MLTCs attending consultations in primary care frequently have unmet social care needs (SCNs), which can be challenging to identify and address. Artificial intelligence (AI) derived clusters could help to identify patients at risk of SCNs. Understanding the views of people living with MLTCs and those involved in their care can help inform the design of effective interventions informed by AI-derived clusters to address SCNs. Methods Qualitative study using semi-structured online and telephone interviews with 24 people living with MLTCs and 20 people involved in the care of MLTCs. Interviews were analysed using Reflexive Thematic Analysis. Results Primary care was viewed as an appropriate place to have conversations about SCNs. However, participants felt health care professionals lack capacity to have these conversations and to identify sources of support. AI was perceived as a tool that could potentially increase capacity for this but only when supplemented with effective, clinical conversations. Interventions harnessing AI should be brief, be easy to use and remain relevant over time, to ensure no additional burden on clinical capacity. Interventions must allow flexibility to be used by multidisciplinary teams within primary care, frame messages positively and facilitate conversations that remain patient centered. Conclusion Our findings suggest that AI-derived clusters to identify and support SCNs in primary care have perceived value, but there were some concerns including the need to consider personal context. AI derived clusters can be used as a tool to inform and prioritise effective clinical conversations.

show abstract

Value of using artificial intelligence derived clusters by health and social care need in Primary Care: A qualitative interview study

Holt,

Simpson,

Santer

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Population, workforce, and organisational characteristics affecting appointment rates: a retrospective cross-sectional analysis in primary care

2023

View full text Add to dashboard Cite

Background: The recent publication of data on appointment volumes for all practices in England has enabled representative analysis of factors affecting appointment activity rates for the first time. Aim: To identify population, workforce, and organisational predictors of practice variations in appointment volume. Design and Setting: Cross-sectional analysis of 6,284 GP practices in England in August to October 2022. Method: Multivariable regression analysis relating population age and deprivation, numbers of GPs, nurses and other care professionals and organisation characteristics, to numbers of appointments by staff type and to proportions of appointments on the same or next day after booking. Results: Appointment levels were higher at practices serving rural areas. Practices serving more deprived populations had more appointments with other care professionals but not GPs. One additional full-time equivalent (FTE) GP was associated with an extra 175 appointments over three months. Additional FTEs of other staff types were associated with larger differences in appointment rates (367 appointments per additional nurse and 218 appointments per additional other care professional over three months). There was evidence of substitution between staff types in appointment provision. Levels of staffing were not associated with proportions of same or next day appointments. Conclusion: Higher staffing levels are associated with more appointment provision, but not speed of appointment availability. New information on activity levels shows evidence of substitution between GPs and other care professionals in appointment provision and demonstrates additional workload for practices serving deprived and rural areas.

show abstract

Exploring the barriers to and facilitators of implementing CanRisk in primary care: a qualitative thematic framework analysis

Zhou¹,

Donoso

Carver

et al. 2023

Br J Gen Pract

View full text Add to dashboard Cite

Background: The CanRisk tool enables the collection of risk factor information and calculation of estimated future breast cancer risks based on the multifactorial Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) model. Despite BOADICEA being recommended in NICE guidelines and CanRisk being freely available for use, the CanRisk tool has not yet been implemented widely in primary care. Aim: The aim of this study was to explore the barriers and facilitators to the implementation of the CanRisk tool in primary care. Design and setting: A multi-methods study, conducted with primary care practitioners (PCPs) in the UK. Methods: Three methods were employed. Participants completed two vignette-based case studies, a semi-structured interview and a questionnaire. Results: 16 PCPs completed the study. The main barriers to implementation were: the time needed to complete the tool, competing priorities, IT infrastructure, and PCPs’ lack of confidence and knowledge to use the tool. The main facilitators included: easy navigation of the tool, its potential clinical impact, and the increasing availability of and expectation to use risk prediction tools. Conclusion: This more developed understanding of the barriers and facilitators to the use of CanRisk in primary care highlights that future implementation activities should focus on reducing the time needed to complete a CanRisk calculation, integrating the CanRisk tool into existing IT infrastructure, and identifying appropriate contexts in which to conduct a CanRisk calculation. PCPs may also benefit from information about cancer risk assessment and CanRisk specific training.

show abstract

Primary care consultation length by deprivation and multimorbidity in England

Cited by 10 publications

References 19 publications

Value of using artificial intelligence derived clusters by health and social care need in Primary Care: A qualitative interview study

Value of using artificial intelligence derived clusters by health and social care need in Primary Care: A qualitative interview study

Population, workforce, and organisational characteristics affecting appointment rates: a retrospective cross-sectional analysis in primary care

Exploring the barriers to and facilitators of implementing CanRisk in primary care: a qualitative thematic framework analysis

Contact Info

Product

Resources

About