Data for this publication are extracted each month as a snapshot in time from the Primary Care Registration database within the NHAIS (National Health Application and Infrastructure Services) system. This release is an accurate snapshot as at 1 April 2024. This publication also includes monthly data outputs from the Personal Demographic Service, which will become the data source for this publication from May 2024. More information about the data source change can be found in the Data Quality Statement. GP Practice; Primary Care Network (PCN); Sub Integrated Care Board Locations (SICBL); Integrated Care Board (ICB) and NHS England Commissioning Region level data are released in single year of age (SYOA) and 5-year age bands, both of which finish at 95+, split by gender. In addition, organisational mapping data is available to derive PCN; SICBL; ICB and Commissioning Region associated with a GP practice and is updated each month to give relevant organisational mapping. Quarterly publications in January, April, July and October will include Lower Layer Super Output Area (LSOA) populations.

Data for this publication are extracted each month as a snapshot in time from the Primary Care Registration database within the PDS (Patient Demographic Service) system. This release is an accurate snapshot as at 1 August 2024. GP Practice; Primary Care Network (PCN); Sub Integrated Care Board Locations (SICBL); Integrated Care Board (ICB) and NHS England Commissioning Region level data are released in single year of age (SYOA) and 5-year age bands, both of which finish at 95+, split by gender. In addition, organisational mapping data is available to derive PCN; SICBL; ICB and Commissioning Region associated with a GP practice and is updated each month to give relevant organisational mapping. Quarterly publications in January, April, July and October will include Lower Layer Super Output Area (LSOA) populations.

Practice demographic data are extracted as a quarterly snapshot in time from the GP Payments system maintained by the Health and Social Care Information Centre (HSCIC).

Data for GP Practices with 100 or fewer registered patients has been suppressed due to possible identification of individuals when data are linked to other data sets.

These releases are an accurate snapshot as at each date.

From April 2017, following a consultation, the frequency of this release has changed to monthly, and file structure has changed - there are now three files per release: Males by practice, Females by practice and all persons by commissioning region/region/CCG.

This dataset covers 86% of the population of Wales and 83% of GP practices in Wales. It is linkable with anonymised fields for individuals and GPs to other datasets, including bespoke project specific cohorts. Each GP practice uses a clinical information system to maintain an electronic health record for each of their patients; capturing the signs, symptoms, test results, diagnoses, prescribed treatment, referrals for specialist treatment and social aspects relating to the patients home environment.

The majority of the data is entered by the clinician during the patient consultation. Test results are electronically transferred from secondary care systems.

There are no standard rules for recording data within primary care clinical information systems. Therefore, each individual clinician can record information in their own way. The majority use Read Code Terminology, however, sometimes this is applied behind the scenes by the clinical system and sometimes local codes are used. Read codes are not as precise as ICD 10 or OPCS codes.

Coding standards have been agreed on for conditions monitored by the QOF (Quality Outcomes Framework) returns. Since the implementation of QOF these conditions have been coded in a more consistent way.

Time coverage varies between each practice.

A link to the number of GP practices per local health board in this dataset can be found in the Associated media.

Dataset Description:

This dataset comprises transcriptions of conversations between doctors and patients, providing valuable insights into the dynamics of medical consultations. It includes a wide range of interactions, covering various medical conditions, patient concerns, and treatment discussions. The data is structured to capture both the questions and concerns raised by patients, as well as the medical advice, diagnoses, and explanations provided by doctors.

Key Features:

• Doctor and Patient Roles: Each conversation is annotated with the role of the speaker (doctor or patient), making it easy to analyze communication patterns.
• Medical Context: The dataset includes diverse scenarios, from routine check-ups to more complex medical discussions, offering a broad spectrum of healthcare dialogues.
• Natural Language: The conversations are presented in natural language, allowing for the development and testing of NLP models focused on healthcare communication.
• Applications: This dataset can be used for various applications, such as building dialogue systems, analyzing communication efficacy, developing medical NLP models, and enhancing patient care through better understanding of doctor-patient interactions.

Potential Use Cases:

• NLP Model Training: Train models to understand and generate medical dialogues.
• Healthcare Communication Studies: Analyze communication strategies between doctors and patients to improve healthcare delivery.
• Medical Chatbots: Develop intelligent medical chatbots that can simulate doctor-patient conversations.
• Patient Experience Enhancement: Identify common patient concerns and doctor responses to enhance patient care strategies.

This dataset is a valuable resource for researchers, data scientists, and healthcare professionals interested in the intersection of technology and medicine, aiming to improve healthcare communication through data-driven approaches.

How the number of patients per doctor and nurse at GP practices in England has changed over time, and how it differs across age, region and deprivation.

The Dispensing Practice Name and Address dataset is produced monthly and provides the name and address details of dispensing practices for each primary care organisation at Sub Integrated Care Board Level (SICBL). It also includes: • how many GPs are in each practice and how many of them are dispensing GPs • practice name • practice address A dispensing practice is defined as a practice with at least one active dispensing GP. The information NHS Prescription Services hold on practices is supplied to NHS Prescription Services by primary care organisations (PCOs). You can view all definitions for the fields included in the dataset in the Dispensing Practice Name and Address data dictionary (XLSX: 12KB) This data was previously published on the NHSBSA Information Services Portal. Changes have been made to the data since migration to the Open Data Portal and you can read about these changes in the Dispensing Practice Name and Address migration changes documentation (ODT: 217KB).

This dataset presents the footprint of the percentage of general practitioner (GP) attendances and associated Medicare benefits expenditure per person. GP attendance has been calculated with the total services from eligible claims (excluding any bulk-billed incentive items or other top-up items), divided by the Estimated Resident Population (ERP) as at 30 June 2016. GP expenditure has been calculated with the total benefit paid for eligible claims, divided by the ERP as at 30 June 2016. The data spans the financial years of 2010-2017 and is aggregated to Statistical Area Level 3 (SA3) geographic areas from the 2011 Australian Statistical Geography Standard (ASGS). The data is sourced from the Medicare Benefits Schedule (MBS) claims data, which are administered by the Australian Government Department of Health. These claims data are derived from administrative information on services that qualify for a Medicare benefit under the Health Insurance Act 1973 and for which a claim is processed by the Department of Human Services. For further information about this dataset visit the data source: Australian Institute of Health and Welfare - Medicare Benefits Schedule GP and Specialist Attendances and Expenditure in 2016-17 Data Tables. Please note: AURIN has spatially enabled the original data.

This dataset presents the footprint of the percentage of patients with GP costs, and out-of-pocket cost per GP attendance at the 25th, 50th, 75th and 90th percentile. The data spans the financial year of 2016-2017 and is aggregated to Statistical Area Level 3 (SA3) geographic areas from the 2016 Australian Statistical Geography Standard (ASGS). The data is sourced from the Medicare Benefits Schedule (MBS) claims data, which are administered by the Australian Government Department of Health. The claims data are derived from administrative information on services that qualify for a Medicare benefit under the Health Insurance Act 1973 and for which a claim has been processed by the Department of Human Services. Data are reported for claims processed between 1 July 2016 and 30 June 2017. The data also contains the results from the ABS 2016-17 Patient Experience Survey, collected between 1 July 2016 and 30 June 2017. The Patient Experience Survey is conducted annually by the Australian Bureau of Statistics (ABS) and collects information from a representative sample of the Australian population. The Patient Experience Survey is one of several components of the Multipurpose Household Survey, as a supplement to the monthly Labour Force Survey. The Patients' spending on Medicare Services data accompanies the Patients' out-of-pocket spending on Medicare services 2016-17 Report. For further information about this dataset, visit the data source:Australian Institute of Health and Welfare - Patients' out-of-pocket spending on Medicare services Data Tables. Please note: AURIN has spatially enabled the original data.

These data, analysed in Cressie, Perrin, and Thomas-Agnan (2005) and Cressie and Wikle (2011) Section 4.2, represent the average doctor-prescription amounts per consultation in cantons of the Midi-Pyrenees Department in southwest France. There are 268 cantons in the Midi-Pyrenees where average doctor-prescription amounts (per consultation) were reported for the period January 1, 1999-December 31, 1999. There are 32 "missing cantons" in the Midi-Pyrenees (MP) with no doctor-prescription data. Not all of the "missing cantons" in the files correspond to actual cantons; see below. There are two datasets created from the original ones sent by Christine Thomas-Agnan: "Canton_neighbor.csv" and "Canton_vertex.csv". "Canton_neighbor.csv" is the dataset with cantons and their neighbours in the Midi-Pyrenees Region of France; note that only cantons with prescription data are included. "Canton_vertex.csv" is the dataset with cantons and vertices of the polygons; note that all cantons from the Midi-Pyrenees region are included, whether or not there were prescription data associated with them. The first 268 cantons in the dataset are those with prescription data. The same canton in both "Canton_neighbor.csv" and "Canton_vertex.csv" has the same value of "No.". The explanation of variables associated with each dataset is given as follows: Format: Canton_neighbor.csv Variable Variable Explanation No.: ID number from 1 to 268 (number of cantons with prescription data) INSEE_ID: INSEE code CANTON_NAME: Canton name X: X-coordinate of the centroid (in meters according to NTF) Y: Y-coordinate of the centroid (in meters according to NTF) Z: Variable Z (average prescription amount per consultation in 1999, in French Francs) X2: Variable X2 (percentage of patients 70 or older) X1: Variable X1 (per-capita income) E: Variable E (number of consultations in 1999) N1-N12: INSEE_IDs of the neighbour cantons (up to 12 neighbors) Canton_vertex.csv Variable Variable Explanation No.: ID number from 1 to 300 (number of all cantons in Midi-Pyrenees Region) INSEE_ID: INSEE code CANTON_NAME: Canton name X: X-coordinate of the centroid (in meters according to NTF) Y: Y-coordinate of the centroid (in meters according to NTF) X1,Y1 - X76,Y76: (X,Y) coordinates of vertices of the corresponding CANTON (up to 76 vertices) In addition to Cressie et al. (2005) and Section 4.2 of Cressie and Wikle (2011), these data have been analysed and modeled in Kang et al. (2009). Canton_neighbor.csv: Doctor-Prescription Amounts per Consultation and other covariates in Midi-Pyrenees cantons. Also included are the cantons' neighbors. Note: only the 268 cantons with prescription data are included in this file. Canton_vertex.csv: Verticies of the polygons describing all cantons, including "missing cantons"; the first 268 cantons in the dataset are those with doctor-prescription data. Usage:

This dataset presents the footprint of the number of general practitioner (GP) attendances in residential aged-care facilities per patient who received at least one GP attendance in a facility. This has been calculated with the total services from eligible claims (not including any bulk-billed incentive items or other top-up items), divided by the count of patients where the sum of GP attendances in residential aged-care facilities is greater than or equal to 1. The data spans the financial years of 2013-2017 and is aggregated to 2015 Department of Health Primary Health Network (PHN) areas, based on the 2011 Australian Statistical Geography Standard (ASGS). The data is sourced from the Medicare Benefits Schedule (MBS) claims data, which are administered by the Australian Government Department of Health. These claims data are derived from administrative information on services that qualify for a Medicare benefit under the Health Insurance Act 1973 and for which a claim is processed by the Department of Human Services. For further information about this dataset visit the data source: Australian Institute of Health and Welfare - Medicare Benefits Schedule GP and Specialist Attendances and Expenditure in 2016-17 Data Tables. Please note: AURIN has spatially enabled the original data using the Department of Health - PHN Areas. MBS claims data do not include services that were provided free of charge to public patients in hospitals or were subsidised by the Department of Veterans' Affairs, compensation arrangements or through other publicly funded programs including jurisdictional salaried GP services provided in remote outreach clinics. A claim is classified as a GP attendance in a residential aged-care facility if the Medicare service is one of the following codes: 00020, 00035, 00043, 00051, 00092, 00093, 00095, 00096, 05010, 05028, 05049, 05067, 05260, 05263, 05265, 05267, 00731, 00903, 02125, 02138, 02179, 02220. GP attendances in residential aged-care facilities comprise only GP attendances provided to patients in a residential aged-care facility.

ObjectiveEvaluate general practitioner (GP) management of tennis elbow (TE) in Australia.MethodsData about the management of TE by GPs from 2000 to 2015 were extracted from the Bettering the Evaluation of Care of Health program database. Patient and GP characteristics and encounter management data were classified by the International Classification of Primary Care, version 2, and reported using descriptive statistics with point estimates and 95% confidence intervals.ResultsTE was managed by GPs 242,000 times per year on average. Patients were mainly female (52.3%), aged between 35 and 64 years (mean: 49.3 yrs), had higher relative risks of concomitant disorders (e.g. carpal tunnel syndrome and other tendonitis) and their TE was 10 times more likely to be work related than problems managed for patients who did not have TE. Use of diagnostic tests was low, implying a clinical examination based diagnosis of TE. Management was by procedural treatments (36 per 100 TE problems), advice, education or counselling (25 per 100), and referral to other health care providers (14 per 100, mainly to physiotherapy). The rate of local injection did not change over the 15 years and was performed at similar rates as physiotherapy referral.ConclusionThe high risk of comorbidities and work relatedness and no abatement in the reasonably high rate of local injections (which is contrary to the evidence from clinical trials) provides support for the development and dissemination of TE clinical guidelines for GPs.

Leeds GP registered patients living inside Leeds. Counts per 5 year ageband for MSOAs, Wards, Community Committees, and Leeds.

Data has been collected annually since 2004/05. A new GMS Contract was introduced in April 2004 and a fundamental funding stream called the Quality and Outcomes Framework (QOF) was introduced at that time. QOF is a reward framework of indicators designed to remunerate general practices for providing good quality care to their patients. An important feature of QOF is the maintenance of registers which allow prevalence of a number of long-term conditions to be calculated. Register counts and prevalence per 1,000 GP registered population are published. Where registers are age-specific, prevalence per 1,000 age-specific population are also published.

The aim of the publication is to inform users about activity and usage of GP appointments historically and how primary care is impacted by seasonal pressures, such as winter. NHS England publishes this information to support winter preparedness and provide information about some activity within primary care. The publication covers historic appointments, marked as attended or did not attend, from national to practice level coverage. The aim is to inform users, who range from a healthcare professional to an inquiring citizen, about appointments within primary care. The publication includes data from participating practices and Primary Care Networks (PCNs) using EMIS, TPP, Eva Health formerly known as Microtest (up until February 2021), Informatica (up until December 2024), Cegedim (up until January 2025), Babylon (up until December 2024) and Evergreen Life GP systems. NHS England produce this information monthly, containing information about the most recent month and previous months. The publication includes important information, however it does not show the totality of GP activity/workload. The data presented only contains information which was captured on the GP practice and PCN appointment systems. This limits the activity reported on and does not represent all work happening within a primary care setting or assess the complexity of activity. No patient identifiable information has been collected or is included in this release. Between December 2020 and present the data contained in this publication will no longer contain covid-19 vaccination activity collected from GP System Suppliers as part of the General Practice Appointments Data. These appointments have been removed using the methodology outlined in the supporting information. In order to gain a more complete picture of general practice activity we will publish covid-19 vaccination activity carried out by PCN’s or GP Practice’s from the NIMS (National Immunisation Management Service) vaccination dataset. This publication now includes statistics on the duration of appointments, SDS role and the recorded national category, service setting and context type of the appointment. Both HCP Type and SDS role are currently presented for comparison purposes, but moving forward the intention is to only publish SDS Role Groups and remove HCP Type. Further information can be found in the supporting guidance below. Appointments recorded in Primary Care Network (PCN) appointment systems are included within this publication at national level from June 2023.

The dataset contains a time series of weekly incidence of general practitioner (GP) consultation by patients with influenza-like illness (ILI) in the Netherlands. The data was retrieved from the NIVEL Primary Care Database, Sentinel Practices. The catchment population of the sentinel GP network is approximately 1% of the entire Dutch population. The time series spans a period of 45 years, from January 5, 1970, to June 16, 2014, and is stratified into 10 age classes. In addition to weekly ILI incidence, the total catchment population size per age class is given as well. The data is used in our study about the estimation of age-specific susceptibility to influenza in the Netherlands, and its relation to loss of CD8+ T-cell memory.

BackgroundAnkylosing spondylitis (AS) is a chronic inflammatory arthritis which typically begins in early adulthood and impacts on healthcare resource utilisation and the ability to work. Previous studies examining the cost of AS have relied on patient-reported questionnaires based on recall. This study uses a combination of patient-reported and linked-routine data to examine the cost of AS in Wales, UK.MethodsParticipants in an existing AS cohort study (n = 570) completed questionnaires regarding work status, out-of-pocket expenses, visits to health professionals and disease severity. Participants gave consent for their data to be linked to routine primary and secondary care clinical datasets. Health resource costs were calculated using a bottom-up micro-costing approach. Human capital costs methods were used to estimate work productivity loss costs, particularly relating to work and early retirement. Regression analyses were used to account for age, gender, disease activity.ResultsThe total cost of AS in the UK is estimated at £19016 per patient per year, calculated to include GP attendance, administration costs and hospital costs derived from routine data records, plus patient-reported non-NHS costs, out-of-pocket AS-related expenses, early retirement, absenteeism, presenteeism and unpaid assistance costs. The majority of the cost (>80%) was as a result of work-related costs.ConclusionThe major cost of AS is as a result of loss of working hours, early retirement and unpaid carer’s time. Therefore, much of AS costs are hidden and not easy to quantify. Functional impairment is the main factor associated with increased cost of AS. Interventions which keep people in work to retirement age and reduce functional impairment would have the greatest impact on reducing costs of AS. The combination of patient-reported and linked routine data significantly enhanced the health economic analysis and this methodology that can be applied to other chronic conditions.

The purpose of the study: to analyse Lithuanian residents opinion about peculiarities of communication between doctors and patients. Major investigated questions: respondents were asked when was the last time they went to see a doctor. It respondents visited a doctor in the last 12 months, they were asked to indicate how long was the time period between registration to see a doctor and actual visit. It was analysed how long respondents had to wait at their last visit from the time appointed or from the time when they got to a health institution to the time a doctor got to them. It was questioned what was the main purpose for respondents last visit to a doctor. After question block was presented, respondents were asked for what complains, illnesses they visited a doctor the last time. Respondents were asked to evaluate a doctor whom they visited the last time. After question block was presented, peculiarities of doctor interaction with respondents (patients) at the time of their last visit was analysed. Respondents were asked if they think that a doctor can take care of their health. It was questioned how likely is that respondents would search for information about health using electronic tools. Respondents were asked if they are currently using medicines which were prescribed by a doctor to whom they visited the last time. Respondents, who uses prescribed medicines, were asked to indicate how often they use them exactly like doctor prescribed. Socio-demographic characteristics: gender, age, monthly family income per one family member, employment.

Large language models in healthcare can generate informative patient summaries while reducing the documentation workload of healthcare professionals. However, these models are prone to producing hallucinations, that is, generating unsupported information, which is problematic in the sensitive healthcare domain. To better characterize unsupported facts in medical texts, we developed a rigorous labeling protocol. Following this protocol, two medical experts annotated unsupported facts in 100 doctor-written summaries from the MIMIC-IV-Note Discharge Instructions and hallucinations 100 LLM-generated patient summaries. Here, we are releasing two datasets based on these annotations: Hallucinations-MIMIC-DI and Hallucinations-Generated-DI. We find that using these datasets to train on hallucination-free examples effectively reduces hallucinations for both Llama 2 (2.60 to 1.55 hallucinations per summary) and GPT-4 (0.70 to 0.40). Furthermore, we created a preprocessed version of the MIMIC-IV-Notes Discharge Instructions, releasing both a full-context version (MIMIC-IV-Note-Ext-DI) and a version that only uses the Brief Hospital Course for context (MIMIC-IV-Note-Ext-DI-BHC).

The latest asthma register data can be downloaded from: https://digital.nhs.uk/data-and-information/publications/statistical/quality-and-outcomes-framework-achievement-prevalence-and-exceptions-data/2021-22#resources This gives the number of patients on the asthma register at GP practice level, which can then be rolled up to national / ICB levels, although this could cause double counting of patients. For example, if a patient was registered at more than one practice in the year they may be reported against both. The QOF technical annex may also be helpful- please contact NHS Digital for more information on this: https://digital.nhs.uk/data-and-information/publications/statistical/quality-and-outcomes-framework-achievement-prevalence-and-exceptions-data/2021-22/technical-annex#definitions To replicate the montelukast indicator you would then need to submit a new FOI to request patient counts for inhaler prescribing. We would be able to supply this data by the type of inhaler but without the clinical indication.