The National Diabetes Audit (NDA) is part of the National Clinical Audit and Patient Outcomes Programme (NCAPOP) which is commissioned by the Healthcare Quality Improvement Partnership (HQIP) and funded by NHS England. The NDA is managed by NHS Digital in partnership with Diabetes UK. The NDA measures the effectiveness of diabetes healthcare against NICE Clinical Guidelines and NICE Quality Standards, in England and Wales. The NDA collects, analyses and reports data for use by primary care and specialist services, local and national commissioners to support change and improvement in the quality of services and health outcomes for people with diabetes. This data release includes the care process and treatment target measurements for 2019-20 (1st January 2019 – 31st March 2020). Data were collected during May and June 2020. The national report, scheduled for 2021, will contain commentary on the audit findings and recommendations. We will communicate to users when the publication date for this report has been finalised. GP practice participation in England and Wales has increased from 98.0 per cent in 2018-19 to 99.2 per cent in 2019-20. Diabetes specialist service participation stands at 98 services in 2019-20. For NDA 2019-20, Diabetes Eye Screening (DES) data has been collected directly from DES providers for the first time. All but one DES provider in England (Liverpool) successfully submitted data, although three providers made partial submissions. For Liverpool, eye examination information secondarily recorded in Primary Care systems has been used, which is likely to be incomplete. The new 'Retinal Screening' care process measure appears in the care process and treatment targets worksheets and also feeds into the new 'All Nine Care Processes' measure, which is reported in addition to the longstanding ‘All Eight Care Processes'. Please note that there is a potential issue with the SNOMED codes used to identify if a person has had their serum creatinine care process check. Two serum/plasma creatinine codes were removed from the NDA creatinine code set during the universal SNOMED code refresh. This has affected the measurement of creatinine care process completion in a small number of health economies, and thereby has the potential to influence the all eight/nine care process percentages for organisations/areas that still use these codes. To resolve the issue, the NDA business rules are currently being amended to add these codes back into future NDA data extractions.

Patient-drug-disease (PDD) Graph dataset, utilising Electronic medical records (EMRS) and biomedical Knowledge graphs. The novel framework to construct the PDD graph is described in the associated publication.PDD is an RDF graph consisting of PDD facts, where a PDD fact is represented by an RDF triple to indicate that a patient takes a drug or a patient is diagnosed with a disease. For instance, (pdd:274671, pdd:diagnosed, sepsis)Data files are in .nt N-Triple format, a line-based syntax for an RDF graph. These can be accessed via openly-available text edit software.diagnose_icd_information.nt - contains RDF triples mapping patients to diagnoses. For example:(pdd:18740, pdd:diagnosed, icd99592),where pdd:18740 is a patient entity, and icd99592 is the ICD-9 code of sepsis.drug_patients.nt- contains RDF triples mapping patients to drugs. For example:(pdd:18740, pdd:prescribed, aspirin),where pdd:18740 is a patient entity, and aspirin is the drug's name.Background:Electronic medical records contain multi-format electronic medical data that consist of an abundance of medical knowledge. Faced with patients' symptoms, experienced caregivers make the right medical decisions based on their professional knowledge, which accurately grasps relationships between symptoms, diagnoses and corresponding treatments. In the associated paper, we aim to capture these relationships by constructing a large and high-quality heterogenous graph linking patients, diseases, and drugs (PDD) in EMRs. Specifically, we propose a novel framework to extract important medical entities from MIMIC-III (Medical Information Mart for Intensive Care III) and automatically link them with the existing biomedical knowledge graphs, including ICD-9 ontology and DrugBank. The PDD graph presented in this paper is accessible on the Web via the SPARQL endpoint as well as in .nt format in this repository, and provides a pathway for medical discovery and applications, such as effective treatment recommendations.De-identificationIt is necessary to mention that MIMIC-III contains clinical information of patients. Although the protected health information was de-identifed, researchers who seek to use more clinical data should complete an on-line training course and then apply for the permission to download the complete MIMIC-III dataset: https://mimic.physionet.org/

Percentage of emergency admissions to any hospital in England occurring within 30 days of the last, previous discharge from hospital after admission: indirectly standardised by age, sex, method of admission and diagnosis/procedure. The indicator is broken down into the following demographic groups for reporting: ● All years and female only, male only and both male and female (persons). ● <16 years and female only, male only and both male and female (persons). ● 16+ years and female only, male only and both male and female (persons) ● 16-74 years and female only, male only and both male and female (persons) ● 75+ years and female only, male only and both male and female (persons) Results for each of these groups are also split by the following geographical and demographic breakdowns: ● Local authority of residence. ● Region. ● Area classification. ● NHS and private providers. ● NHS England regions. ● Deprivation (Index of Multiple Deprivation (IMD) Quintiles, 2019). ● Sustainability and Transformation Partnerships (STP) & Integrated Care Boards (ICB) from 2016/17. ● Clinical Commissioning Groups (CCG) & sub-Integrated Care Boards (sub-ICB). All annual trends are indirectly standardised against 2013/14.

In 2023, only half of LGBT adults in the United States reported feeling very comfortable asking their doctor questions about their health or treatment during visits in the past three years, while this was the case for 67 percent of non-LGBT adults. Furthermore, around 12 percent of LGBT adults surveyed reported not being comfortable asking questions during their healthcare visits, as opposed to seven percent of non-LGBT adults.

Deprecated report. This report was created early in the response to the COVID-19 pandemic. Increased reporting and quality in hospital data have rendered the estimated datasets obsolete. Updates to this report will be discontinued on July 29, 2021. The following dataset provides state-aggregated data for estimated patient impact and hospital utilization. The source data for estimation is derived from reports with facility-level granularity across two main sources: (1) HHS TeleTracking, and (2) reporting provided directly to HHS Protect by state/territorial health departments on behalf of their healthcare facilities. Estimates Basis: These files are representative estimates for each state and are updated weekly. These projections are based on the information we have from those who reported. As more hospitals report more frequently our projections become more accurate. The actual data for these data points are updated every day, once a day on healthdata.gov and these are the downloadable data sets.

Data on hospital admission, average length of stay, outpatient visits, and outpatient surgery in the United States, by type of ownership and size of hospital. Data are from Health, United States. SOURCE: American Hospital Association (AHA) Annual Survey of Hospitals, Hospital Statistics. Search, visualize, and download these and other estimates from over 120 health topics with the NCHS Data Query System (DQS), available from: https://www.cdc.gov/nchs/dataquery/index.htm.

A synthetic dataset of patient appointments, referrals, and journeys to a fictional service in the North East of England. The code can be adjusted to incorporate any area on mainland Great Britain. NI or the islands can be integrated too, however the structure of postcode, GP and OSA public data is different, and data input handlers will need to be adjusted.

The behaviour of the patients (visiting their nearby GP followed by attending a

specialist clinic), appointments (clinic appointments within 7day-6weeks of the referral (gp appointment)), and facilities (one major facility taking the load, along with minor facilities) is meant to mirror the real data used under Pilot 2 of the Track & Know Project.

Real postcodes, from Royal Mail, are used to generate the appointment population, real facilities are used based on the British Lung Foundations study of Obstructive Sleep Apnoea, and real GP's are used based on public data from the NHS.

This dataset contains the distribution of inpatient discharges by type of admission for each California hospital for years 2009-2015.

Note: After May 3, 2024, this dataset will no longer be updated because hospitals are no longer required to report data on COVID-19 hospital admissions, hospital capacity, or occupancy data to HHS through CDC’s National Healthcare Safety Network (NHSN). The related CDC COVID Data Tracker site was revised or retired on May 10, 2023.

Note: May 3,2024: Due to incomplete or missing hospital data received for the April 21,2024 through April 27, 2024 reporting period, the COVID-19 Hospital Admissions Level could not be calculated for CNMI and will be reported as “NA” or “Not Available” in the COVID-19 Hospital Admissions Level data released on May 3, 2024.

This dataset represents COVID-19 hospitalization data and metrics aggregated to county or county-equivalent, for all counties or county-equivalents (including territories) in the United States as of the initial date of reporting for each weekly metric. COVID-19 hospitalization data are reported to CDC’s National Healthcare Safety Network, which monitors national and local trends in healthcare system stress, capacity, and community disease levels for approximately 6,000 hospitals in the United States. Data reported by hospitals to NHSN and included in this dataset represent aggregated counts and include metrics capturing information specific to COVID-19 hospital admissions, and inpatient and ICU bed capacity occupancy.

Reporting information:

• As of December 15, 2022, COVID-19 hospital data are required to be reported to NHSN, which monitors national and local trends in healthcare system stress, capacity, and community disease levels for approximately 6,000 hospitals in the United States. Data reported by hospitals to NHSN represent aggregated counts and include metrics capturing information specific to hospital capacity, occupancy, hospitalizations, and admissions. Prior to December 15, 2022, hospitals reported data directly to the U.S. Department of Health and Human Services (HHS) or via a state submission for collection in the HHS Unified Hospital Data Surveillance System (UHDSS).
• While CDC reviews these data for errors and corrects those found, some reporting errors might still exist within the data. To minimize errors and inconsistencies in data reported, CDC removes outliers before calculating the metrics. CDC and partners work with reporters to correct these errors and update the data in subsequent weeks.
• Many hospital subtypes, including acute care and critical access hospitals, as well as Veterans Administration, Defense Health Agency, and Indian Health Service hospitals, are included in the metric calculations provided in this report. Psychiatric, rehabilitation, and religious non-medical hospital types are excluded from calculations.
• Data are aggregated and displayed for hospitals with the same Centers for Medicare and Medicaid Services (CMS) Certification Number (CCN), which are assigned by CMS to counties based on the CMS Provider of Services files.
• Full details on COVID-19 hospital data reporting guidance can be found here: https://www.hhs.gov/sites/default/files/covid-19-faqs-hospitals-hospital-laboratory-acute-care-facility-data-reporting.pdf

• County-level hospital data are derived using calculations performed at the Health Service Area (HSA) level. An HSA is defined by CDC’s National Center for Health Statistics as a geographic area containing at least one county which is self-contained with respect to the population’s provision of routine hospital care. Every county in the United States is assigned to an HSA, and each HSA must contain at least one hospital. Therefore, use of HSAs in the calculation of local hospital metrics allows for more accurate characterization of the relationship between health care utilization and health status at the local level.
• Data presented at the county-level represent admissions, hospital inpatient and ICU bed capacity and occupancy among hospitals within the selected HSA. Therefore, admissions, capacity, and occupancy are not limited to residents of the selected HSA.
• For all county-level hospital metrics listed below the values are calculated first for the entire HSA, and then the HSA-level value is then applied to each county within the HSA.
• For all county-level hospital metrics listed below the values are calculated first for the entire HSA, and then the HSA-level value is then applied to each county within the HSA.

• Time period: data for the previous MMWR week (Sunday-Saturday) will update weekly on Mondays as soon as they are reviewed and verified, usually before 8 pm ET. Updates will occur the following day when reporting coincides with a federal holiday. Note: Weekly updates might be delayed due to delays in reporting. All data are provisional. Because these provisional counts are subject to change, including updates to data reported previously, adjustments can occur. Data may be updated since original publication due to delays in reporting (to account for data received after a given Thursday publication) or data quality corrections.
• New hospital admissions (count): Total number of admissions of patients with laboratory-confirmed COVID-19 in the previous week (including both adult and pediatric admissions) in the entire jurisdiction
• New Hospital Admissions Rate Value (Admissions per 100k): Total number of new admissions of patients with laboratory-confirmed COVID-19 in the past week (including both adult and pediatric admissions) for the entire jurisdiction divided by 2019 intercensal population estimate for that jurisdiction multiplied by 100,000. (Note: This metric is used to determine each county’s COVID-19 Hospital Admissions Level for a given week).
• New COVID-19 Hospital Admissions Rate Level: qualitative value of new COVID-19 hospital admissions rate level [Low, Medium, High, Insufficient Data]
• New hospital admissions percent change from prior week: Percent change in the current weekly total new admissions of patients with laboratory-confirmed COVID-19 per 100,000 population compared with the prior week.
• New hospital admissions percent change from prior week level: Qualitative value of percent change in hospital admissions rate from prior week [Substantial decrease, Moderate decrease, Stable, Moderate increase, Substantial increase, Insufficient data]
• COVID-19 Inpatient Bed Occupancy Value: Percentage of all staffed inpatient beds occupied by patients with laboratory-confirmed COVID-19 (including both adult and pediatric patients) within the in the entire jurisdiction is calculated as an average of valid daily values within the past week (e.g., if only three valid values, the average of those three is taken). Averages are separately calculated for the daily numerators (patients hospitalized with confirmed COVID-19) and denominators (staffed inpatient beds). The average percentage can then be taken as the ratio of these two values for the entire jurisdiction.
• COVID-19 Inpatient Bed Occupancy Level: Qualitative value of inpatient beds occupied by COVID-19 patients level [Minimal, Low, Moderate, Substantial, High, Insufficient data]
• COVID-19 Inpatient Bed Occupancy percent change from prior week: The absolute change in the percent of staffed inpatient beds occupied by patients with laboratory-confirmed COVID-19 represents the week-over-week absolute difference between the average occupancy of patients with confirmed COVID-19 in staffed inpatient beds in the past week, compared with the prior week, in the entire jurisdiction.
• COVID-19 ICU Bed Occupancy Value: Percentage of all staffed inpatient beds occupied by adult patients with confirmed COVID-19 within the entire jurisdiction is calculated as an average of valid daily values within the past week (e.g., if only three valid values, the average of those three is taken). Averages are separately calculated for the daily numerators (adult patients hospitalized with confirmed COVID-19) and denominators (staffed adult ICU beds). The average percentage can then be taken as the ratio of these two values for the entire jurisdiction.
• COVID-19 ICU Bed Occupancy Level: Qualitative value of ICU beds occupied by COVID-19 patients level [Minimal, Low, Moderate, Substantial, High, Insufficient data]
• COVID-19 ICU Bed Occupancy percent change from prior week: The absolute change in the percent of staffed ICU beds occupied by patients with laboratory-confirmed COVID-19 represents the week-over-week absolute difference between the average occupancy of patients with confirmed COVID-19 in staffed adult ICU beds for the past week, compared with the prior week, in the in the entire jurisdiction.
• For all metrics, if there are no data in the specified locality for a given week, the metric value is displayed as “insufficient data”.

Notes: June 15, 2023: Due to incomplete or missing hospital data received for the June 4, 2023, through June 10, 2023, reporting period, the COVID-19 Hospital Admissions Level could not be calculated for CNMI and AS and will be reported as “NA” or “Not Available” in the COVID-19 Hospital Admissions Level data released on June 15, 2023.

July 10, 2023: Due to incomplete or missing hospital data received for the June 25, 2023, through July 1, 2023, reporting period, the COVID-19 Hospital Admissions Level could not be calculated for CNMI and AS and will be reported as “NA” or “Not Available” in the COVID-19 Hospital Admissions Level data released on July 10, 2023.

July 17, 2023: Due to incomplete or missing hospital data received for the July 2, 2023, through July 8, 2023, reporting

The North America Patient Generated Health Data market report offers a thorough competitive analysis, mapping key players’ strategies, market share, and business models. It provides insights into competitor dynamics, helping companies align their strategies with the current market landscape and future trends.

Graph and download economic data for Consumer Price Index for All Urban Consumers: Hospital and Related Services in U.S. City Average (CUSR0000SEMD) from Jan 1978 to Jun 2025 about hospitals, urban, consumer, services, CPI, inflation, price index, indexes, price, and USA.

The high-risk surgical patient revisited

The Statewide Planning and Research Cooperative System (SPARCS) Inpatient De-identified dataset contains discharge level detail on patient characteristics, diagnoses, treatments, services, and charges. This data contains basic record level detail regarding the discharge; however the data does not contain protected health information (PHI) under Health Insurance Portability and Accountability Act (HIPAA). The health information is not individually identifiable; all data elements considered identifiable have been redacted. For example, the direct identifiers regarding a date have the day and month portion of the date removed. A downloadable file with this data is available for ease of download at: https://health.data.ny.gov/Health/Hospital-Inpatient-Discharges-SPARCS-De-Identified/3m9u-ws8e. For more information check out: http://www.health.ny.gov/statistics/sparcs/ or go to the “About” tab.

Provisional count of deaths involving coronavirus disease 2019 (COVID-19) in the United States by week of death and by hospital referral region (HRR). HRR is determined by county of occurrence. Weekly weighted counts of deaths from all causes and due to COVID-19 are provided by HRR overall and for decedents 65 years and older. The weighted counts by HRRs are based on published methods for aggregating county-level data to HRRs. More detail about aggregating to HRRs from counties can be found in the following: https://github.com/Dartmouth-DAC/covid-19-hrr-mapping https://dartmouthatlas.org/covid-19/hrr-mapping/

This table provides statistics on the Distribution of Payments, Number of Services and Discrete Patients for Out-of-Province Basic Health Services under the Alberta Health Care Insurance Plan (AHCIP). This table is an Excel version of a table in the “Alberta Health Care Insurance Plan Statistical Supplement” report published annually by Alberta Health.

The publication relates to activity at consultant led outpatient services in Health and Social Care hospitals in Northern Ireland. Data includes the number of new and review attendances, missed appointments (DNAs), appointments cancelled by patients (CNAs) and appointments cancelled by hospitals, split by HSC Trust, hospital and specialty.

Source agency: Health, Social Service and Public Safety (Northern Ireland)

Designation: National Statistics

Language: English

Alternative title: Northern Ireland Outpatient Statistics

According to a report carried out by the Consumer Choice Center in 2023, among chronic patients telemedicine had the greatest potential for time savings in healthcare in Italy. It was estimated in the most optimistic scenario those suffering from chronic conditions in Italy could save up to *** minutes, which is almost *** hours per year. Even in a conservative estimate, Italians with a chronic condition could save over two hours with an uptake in telemedicine use.

UPDATE 22 November 2016: The Key Facts have been corrected for this publication. The main report document "Hospital Maternity Activity, 2015-16: Summary Report" has been updated and the operational note amended to reference this correction. We apologise for any inconvenience caused. This is a report on deliveries in English NHS hospitals. This annual publication covers the financial year ending March 2016. The data are taken from the Hospital Episodes Statistics (HES) data warehouse. HES contains records of all admissions, appointments and attendances for patients admitted to NHS hospitals in England. The HES data used in this publication are called 'delivery episodes'. This publication shows the number of delivery episodes during the period, with a number of breakdowns including by the woman's age, delivery method and place of delivery. The purpose of this publication is to inform and support strategic and policy-led processes for the benefit of patient care. This document will also be of interest to researchers, journalists and members of the public interested in NHS hospital activity in England.

DHA71 - In-Patient Hospital Discharge Data for Principal Procedures. Published by Department of Health. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).In-Patient Hospital Discharge Data for Principal Procedures...