scholarly journals Identifying naturally occurring communities of primary care providers in the English National Health Service in London

BMJ Open ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. e036504
Author(s):  
Jonathan Clarke ◽  
Thomas Beaney ◽  
Azeem Majeed ◽  
Ara Darzi ◽  
Mauricio Barahona
Keyword(s):  
Primary Care ◽  
National Health Service ◽  
Health Service ◽  
National Health ◽  
Health Management ◽  
Lower Layer ◽  
Primary Care Providers ◽  
Care Providers ◽  
General Practices ◽  
Natural Communities

ObjectivesPrimary Care Networks (PCNs) are a new organisational hierarchy with wide-ranging responsibilities introduced in the National Health Service (NHS) Long Term Plan. The vision is that PCNs should represent ‘natural’ communities of general practices (GP practices) collaborating at scale and covering a geography that fits well with practices, other healthcare providers and local communities. Our study aims to identify natural communities of GP practices based on patient registration patterns using Markov Multiscale Community Detection, an unsupervised network-based clustering technique to create catchments for these communities.DesignRetrospective observational study using Hospital Episode Statistics - patient-level administrative records of attendances to hospital.SettingGeneral practices in the 32 Clinical Commissioning Groups of Greater LondonParticipantsAll adult patients resident in and registered to a GP practice in Greater London that had one or more outpatient encounters at NHS hospitals between 1st April 2017 and 31st March 2018.Main outcome measuresThe allocation of GP practices in Greater London to PCNs based on the registrations of patients resident in each Lower Layer Super Output Area (LSOA) of Greater London. The population size and coverage of each proposed PCN.Results3 428 322 unique patients attended 1334 GPs in 4835 LSOAs in Greater London. Our model grouped 1291 GPs (96.8%) and 4721 LSOAs (97.6%) into 165 mutually exclusive PCNs. Median PCN list size was 53 490, with a lower quartile of 38 079 patients and an upper quartile of 72 982 patients. A median of 70.1% of patients attended a GP within their allocated PCN, ranging from 44.6% to 91.4%.ConclusionsWith PCNs expected to take a role in population health management and with community providers expected to reconfigure around them, it is vital to recognise how PCNs represent their communities. Our method may be used by policymakers to understand the populations and geography shared between networks.

scholarly journals Postcode lottery? Hospital transfers from one London prison and responsible catchment area

2010 ◽  
Vol 34 (4) ◽  
pp. 140-142 ◽  
Author(s):  
Simon Wilson ◽  
Katrina Chiu ◽  
Janet Parrott ◽  
Andrew Forrester
Keyword(s):  
Primary Care ◽  
National Health Service ◽  
Health Service ◽  
National Health ◽  
Catchment Area ◽  
Clinical Implications ◽  
Hospital Unit ◽  
Transfer Data ◽  
Hospital Transfer

Aims and methodTo consider the link between responsible commissioner and delayed prison transfers. All hospital transfers from one London prison in 2006 were audited and reviewed by the prisoner's borough of origin.ResultsOverall, 80 prisoners were transferred from the audited prison to a National Health Service (NHS) facility in 2006: 26% had to wait for more than 1 month for assessment by the receiving hospital unit and 24% had to wait longer than 3 months to be transferred. These 80 individuals were the responsibility of 16 different primary care trusts. Of the delayed transfer cases (n=19), the services commissioned by three primary care trusts were responsible for the delays.Clinical implicationsThere are significant differences in performance between different primary care trusts related to hospital transfers of prisoners, with most hospitals able to admit urgent cases within 3 months. This suggests that a postcode lottery operates for prisoners requiring hospital transfer. Data from prison services may be useful in monitoring and improving the performance of local NHS services.

scholarly journals The Future of Health Care: Protocol for Measuring the Potential of Task Automation Grounded in the National Health Service Primary Care System (Preprint)

2018 ◽  
Author(s):  
Matthew Willis ◽  
Paul Duckworth ◽  
Angela Coulter ◽  
Eric T Meyer ◽  
Michael Osborne
Keyword(s):  
Machine Learning ◽  
Primary Care ◽  
Health Care ◽  
Primary Health Care ◽  
National Health Service ◽  
Health Service ◽  
National Health ◽  
Care Staff ◽  
Primary Health ◽  
The Future

BACKGROUND Recent advances in technology have reopened an old debate on which sectors will be most affected by automation. This debate is ill served by the current lack of detailed data on the exact capabilities of new machines and how they are influencing work. Although recent debates about the future of jobs have focused on whether they are at risk of automation, our research focuses on a more fine-grained and transparent method to model task automation and specifically focus on the domain of primary health care. OBJECTIVE This protocol describes a new wave of intelligent automation, focusing on the specific pressures faced by primary care within the National Health Service (NHS) in England. These pressures include staff shortages, increased service demand, and reduced budgets. A critical part of the problem we propose to address is a formal framework for measuring automation, which is lacking in the literature. The health care domain offers a further challenge in measuring automation because of a general lack of detailed, health care–specific occupation and task observational data to provide good insights on this misunderstood topic. METHODS This project utilizes a multimethod research design comprising two phases: a qualitative observational phase and a quantitative data analysis phase; each phase addresses one of the two project aims. Our first aim is to address the lack of task data by collecting high-quality, detailed task-specific data from UK primary health care practices. This phase employs ethnography, observation, interviews, document collection, and focus groups. The second aim is to propose a formal machine learning approach for probabilistic inference of task- and occupation-level automation to gain valuable insights. Sensitivity analysis is then used to present the occupational attributes that increase/decrease automatability most, which is vital for establishing effective training and staffing policy. RESULTS Our detailed fieldwork includes observing and documenting 16 unique occupations and performing over 130 tasks across six primary care centers. Preliminary results on the current state of automation and the potential for further automation in primary care are discussed. Our initial findings are that tasks are often shared amongst staff and can include convoluted workflows that often vary between practices. The single most used technology in primary health care is the desktop computer. In addition, we have conducted a large-scale survey of over 156 machine learning and robotics experts to assess what tasks are susceptible to automation, given the state-of-the-art technology available today. Further results and detailed analysis will be published toward the end of the project in early 2019. CONCLUSIONS We believe our analysis will identify many tasks currently performed manually within primary care that can be automated using currently available technology. Given the proper implementation of such automating technologies, we expect considerable staff resources to be saved, alleviating some pressures on the NHS primary care staff. INTERNATIONAL REGISTERED REPOR DERR1-10.2196/11232

Obstacles on the path to a primary-care led National Health Service: complexities of outpatient care

1999 ◽  
Vol 48 (2) ◽  
pp. 213-225 ◽  
Author(s):  
Maggie Somerset ◽  
Alex Faulkner ◽  
Alison Shaw ◽  
Liz Dunn ◽  
Deborah J Sharp
Keyword(s):  
Primary Care ◽  
National Health Service ◽  
Health Service ◽  
National Health ◽  
Outpatient Care

scholarly journals Understanding patterns of care for musculoskeletal patients using routinely collected National Health Service data from general practices in England

2020 ◽  
Vol 26 (4) ◽  
pp. 2470-2484
Author(s):  
Chris Smith ◽  
Jenny Hewison ◽  
Robert M West ◽  
Sarah R Kingsbury ◽  
Philip G Conaghan
Keyword(s):  
National Health Service ◽  
Health Service ◽  
National Health ◽  
Opioid Analgesics ◽  
Patterns Of Care ◽  
Long Term Conditions ◽  
The United Kingdom ◽  
Novel Approach ◽  
General Practices ◽  
Service Data

Musculoskeletal conditions are extremely common and represent a costly and growing problem in the United Kingdom. Understanding patterns of care and how they vary between individual patients and patient groups is necessary for effective and efficient disease management. In this article, we present a novel approach to understanding patterns of care for musculoskeletal patients in which trajectories are constructed from clinical and administrative data that are routinely collected by clinicians and healthcare professionals. Our approach is applied to routinely collected National Health Service data for musculoskeletal patients who were registered to a set of general practices in England and highlights both known and previously unreported variations in the prescribing of opioid analgesics by gender and presence of pre-existing depression. We conclude that the application of our approach to routinely collected National Health Service data can extend the dimensions over which patterns of care can be understood for musculoskeletal patients and for patients with other long-term conditions.

Policy Changes in the National Health Service: Implications and Opportunities for Occupational Therapists

2000 ◽  
Vol 63 (5) ◽  
pp. 218-224 ◽  
Author(s):  
Alice Godfrey
Keyword(s):  
Public Health ◽  
Primary Care ◽  
Health Promotion ◽  
National Health Service ◽  
Health Service ◽  
National Health ◽  
Occupational Therapists ◽  
Population Level ◽  
Policy Changes ◽  
Individual Health

Recent changes in the philosophy and structure of the National Health Service give greater emphasis to the prevention of ill health within locally defined communities. Occupational therapists, by virtue of their unique philosophy, have an opportunity to influence primary care strategy and practice by highlighting the links between environment, occupation and health. The recent changes in the structure of the National Health Service are described and the philosophy of occupational therapy is discussed in relation to these changes. This description provides the basis for recommendations as to how occupational therapists can work to build a recognition of the fundamental importance of adaptive occupation to individual health and, hence, to health at a community and population level. Working at a community and population level will require occupational therapists to strengthen links with health promotion and public health in order to help promote health through meaningful occupations within local settings.

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