These data are quality measures for each Utah small area calculated by the Utah Department of Health, Office of Healthcare Statistics (OHCS) using Utah’s All Payer Claims Database (APCD).

This dataset is designed to analyze prescription drug use and spending among New York State residents at the drug product level (pharmacy claims data that have been aggregated by labeler code and product code segments of the National Drug Code). The dataset includes the number of prescriptions filled by unique members by payer type, nonproprietary name, labeler name, dosage characteristics, amount insurer paid, and more.

This dataset tracks the updates made on the dataset "All-Payer Claims Data (APD De-Identified): Prescription Drug Detail 2021" as a repository for previous versions of the data and metadata.

WA-APCD - Washington All-Payer Claims Database The WA-APCD is the state’s most complete source of health care eligibility, medical claims, pharmacy claims, and dental claims insurance data. It contains claims from more than 50 data suppliers, spanning commercial, Medicaid, and Medicare managed care. The WA-APCD has historical claims data for five years (2013-2017), with ongoing refreshes scheduled quarterly. Workers' compensation data from the Washington Department of Labor & Industries will be added in fall 2018. Download the attachment for the data dictionary and more information about WA-APCD and the data.

Analysis of ‘WA-APCD Quality and Cost Summary Report: Hospital Quality’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/13e6499e-0f20-42f7-b51c-0dc0174855a9 on 12 November 2021.

--- Dataset description provided by original source is as follows ---

WA-APCD - Washington All-Payer Claims Database

The WA-APCD is the state’s most complete source of health care eligibility, medical claims, pharmacy claims, and dental claims insurance data. It contains claims from more than 50 data suppliers, spanning commercial, Medicaid, and Medicare managed care. The WA-APCD has historical claims data for five years (2013-2017), with ongoing refreshes scheduled quarterly. Workers' compensation data from the Washington Department of Labor & Industries will be added in fall 2018.

Download the attachment for the data dictionary and more information about WA-APCD and the data.

--- Original source retains full ownership of the source dataset ---

Yearly citation counts for the publication titled "Consistency Between State's Cancer Registry and All-Payer Claims Database in Documented Radiation Therapy Among Patients Who Received Breast Conservative Surgery".

A national dataset of de-identified all-payer claims detailing outpatient and inpatient visit volumes, stratified by provider type, location, and service line. Used to benchmark market share and care utilization trends.

The Nationwide Readmissions Database (NRD) is database under the Healthcare Cost and Utilization Project (HCUP) which contains nationally representative information on hospital readmissions for all ages, including all payers and the uninsured. The NRD contains data from approximately 18 million discharges per year (35 million weighted discharges) across most of the United States.

Data elements include:

• Discharge month, quarter, and year
• Verified patient linkage number
• Timing between admissions for a patient
• Length of inpatient stay (days)
• Transfers, same-day stays, and combined transfer records
• Identification of patient residency in the state in which he or she received hospital care
• International Classification of Diseases (ICD-9-CM) diagnosis, procedure, and external cause of injury codes (prior to October 1, 2015)
• ICD-10-CM/PCS diagnosis, procedures, and external cause of morbidity codes (beginning October 1, 2015)
• Patient demographics (e.g., sex, age, income quartile, rural/urban residency)
• Expected payment source (e.g., Medicare, Medicaid, private insurance, self-pay, those billed as 'no charge', and other insurance types)
• Total charges and hospital cost (calculated using the "Cost-to-Charge Ratio" file)

The NRD consists of four data files:

• Core File: Available for all years of the NRD and contains commonly used data elements (e.g., age, expected primary payer, discharge status, ICD-10-CM/PCS codes, total charges)
• Severity File: Available for all years of the NRD and contains additional data elements related to identifying health conditions at discharge.
• Diagnosis and Procedure Groups File: Contains additional information on ICD-10-CM/PCS; available beginning in 2018.
• Hospital File: Available for all years of the NRD and contains additional information on participating hospital characteristics.

The Healthcare Cost and Utilization Project (HCUP) Nationwide Emergency Department Sample (NEDS) is the largest all-payer emergency department (ED) database in the United States. yielding national estimates of hospital-owned ED visits. Unweighted, it contains data from over 30 million ED visits each year. Weighted, it estimates roughly 145 million ED visits nationally. Developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality, HCUP data inform decision making at the national, State, and community levels.

Sampled from the HCUP State Inpatient Databases (SID) and State Emergency Department Databases (SEDD), the HCUP NEDS can be used to create national and regional estimates of ED care. The SID contain information on patients initially seen in the ED and subsequently admitted to the same hospital. The SEDD capture information on ED visits that do not result in an admission (i.e., treat-and-release visits and transfers to another hospital). Developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality, HCUP data inform decision making at the national, State, and community levels.

The NEDS contain information about geographic characteristics, hospital characteristics, patient characteristics, and the nature of visits (e.g., common reasons for ED visits, including injuries). The NEDS contains clinical and resource use information included in a typical discharge abstract, with safeguards to protect the privacy of individual patients, physicians, and hospitals (as required by data sources). It includes ED charge information for over 85% of patients, regardless of expected payer, including but not limited to Medicare, Medicaid, private insurance, self-pay, or those billed as ‘no charge’. The NEDS excludes data elements that could directly or indirectly identify individuals, hospitals, or states.Restricted access data files are available with a data use agreement and brief online security training.

The Healthcare Cost and Utilization Project (HCUP) Nationwide Readmissions Database (NRD) is a unique and powerful database designed to support various types of analyses of national readmission rates for all payers and the uninsured. The NRD includes discharges for patients with and without repeat hospital visits in a year and those who have died in the hospital. Repeat stays may or may not be related. The criteria to determine the relationship between hospital admissions is left to the analyst using the NRD. This database addresses a large gap in health care data - the lack of nationally representative information on hospital readmissions for all ages. Outcomes of interest include national readmission rates, reasons for returning to the hospital for care, and the hospital costs for discharges with and without readmissions. Unweighted, the NRD contains data from approximately 18 million discharges each year. Weighted, it estimates roughly 35 million discharges. Developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality, HCUP data inform decision making at the national, State, and community levels. The NRD is drawn from HCUP State Inpatient Databases (SID) containing verified patient linkage numbers that can be used to track a person across hospitals within a State, while adhering to strict privacy guidelines. The NRD is not designed to support regional, State-, or hospital-specific readmission analyses. The NRD contains more than 100 clinical and non-clinical data elements provided in a hospital discharge abstract. Data elements include but are not limited to: diagnoses, procedures, patient demographics (e.g., sex, age), expected source of payer, regardless of expected payer, including but not limited to Medicare, Medicaid, private insurance, self-pay, or those billed as ‘no charge, discharge month, quarter, and year, total charges, length of stay, and data elements essential to readmission analyses. The NIS excludes data elements that could directly or indirectly identify individuals. Restricted access data files are available with a data use agreement and brief online security training.

This data set includes comparative information for clinics in Utah for medical claims for the years 2017 and 2018.

This data set was calculated by the Utah Department of Health, Office of Healthcare Statistics (OHCS) using Utah’s All Payer Claims Database (APCD).

Prior years (2015-2016) may be viewed at: https://opendata.utah.gov/Health/2016-2015-Clinic-Quality-Comparisons-for-Clinics-w/35s3-nmpm

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Usage

The National (Nationwide) Inpatient Sample (NIS) is part of a family of databases and software tools developed for the Healthcare Cost and Utilization Project (HCUP). The NIS is the largest publicly available all-payer inpatient healthcare database designed to produce U.S. regional and national estimates of inpatient utilization, access, cost, quality, and outcomes. Unweighted, it contains data from around 7 million hospital stays each year. Weighted, it estimates around 35 million hospitalizations nationally. Developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality (AHRQ), HCUP data inform decision making at the national, State, and community levels.

The Nationwide Emergency Department Sample (NEDS) was created to enable analyses of emergency department (ED) utilization patterns and support public health professionals, administrators, policymakers, and clinicians in their decision-making regarding this critical source of care. The NEDS can be weighted to produce national estimates. The NEDS is the largest all-payer ED database in the United States. It was constructed using records from both the HCUP State Emergency Department Databases (SEDD) and the State Inpatient Databases (SID), both also described in healthdata.gov. The SEDD capture information on ED visits that do not result in an admission (i.e., treat-and-release visits and transfers to another hospital). The SID contain information on patients initially seen in the emergency room and then admitted to the same hospital. The NEDS contains 25-30 million (unweighted) records for ED visits for over 950 hospitals and approximates a 20-percent stratified sample of U.S. hospital-based EDs. The NEDS contains information about geographic characteristics, hospital characteristics, patient characteristics, and the nature of visits (e.g., common reasons for ED visits, including injuries). The NEDS contains clinical and resource use information included in a typical discharge abstract, with safeguards to protect the privacy of individual patients, physicians, and hospitals (as required by data sources). It includes ED charge information for over 75% of patients, regardless of payer, including patients covered by Medicaid, private insurance, and the uninsured. The NEDS excludes data elements that could directly or indirectly identify individuals, hospitals, or states.

This dataset is designed to analyze prescription drug use and spending among New York State residents at the drug product level (pharmacy claims data that have been aggregated by labeler code and product code segments of the National Drug Code). The dataset includes the number of prescriptions filled by unique members by payer type, nonproprietary name, labeler name, dosage characteristics, amount insurer paid, and more.

The datasets contain hospital discharges counts (numerators, denominators, volume counts), observed, expected and risk-adjusted rates with corresponding 95% confidence intervals for Patient Safety Indicators generated using methodology developed by Agency for Healthcare Research and Quality (AHRQ).

The PSIs are a set of indicators providing information on potential in hospital complications and adverse events following surgeries, procedures, and childbirth. The PSIs were developed by AHRQ after a comprehensive literature review, analysis of ICD-9-CM codes, review by a clinician panel, implementation of risk adjustment, and empirical analyses.

All PSI measures were calculated using Statewide Planning and Research Cooperative System (SPARCS) inpatient data beginning 2009. US Census data files provided by AHRQ were used to derive denominators for county level (area level) PSI measures.

The mortality, volume and utilization measures PSIs are presented by hospital as rates or counts. Area-level measures are presented by county as rates.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

This chart displays the total number of inpatient discharges per hospital by type of insurance for the hospitals with the largest number of discharges.

The chart is based on data collected on patients and hospital discharges in the Statewide Planning and Research Cooperative System (SPARCS).

Not all hospitals are shown in the initial visualization display. To expand the display, explore the different filter options.

The SPARCS data has been divided into two distinct datasets, Hospital Discharges by Patient County of Residence and Hospital Discharges by Facility to preserve the confidentiality of identifiable individual information.

This dataset includes the facility names.

For more information check out http://www.health.ny.gov/statistics/sparcs/. The "About" tab contains additional details concerning this dataset.

ContextThe Affordable Care Act resulted in unprecedented reductions in the uninsured population through subsidized private insurance and an expansion of Medicaid. Early estimates from the beginning of 2014 showed that the Medicaid expansion decreased uninsured discharges and increased Medicaid discharges with no change in total discharges.ObjectiveTo provide new estimates of the effect of the ACA on discharges for specific conditions.Design, setting, and participantsWe compared outcomes between states that did and did not expand Medicaid using state-level all-capture discharge data from 2009–2014 for 42 states from the Healthcare Costs and Utilization Project’s FastStats database; for a subset of states we used data through 2015. We stratified the analysis by baseline uninsured rates and used difference-in-differences and synthetic control methods to select comparison states with similar baseline characteristics that did not expand Medicaid.Main outcomeOur main outcomes were total and condition-specific hospital discharges per 1,000 population and the share of total discharges by payer. Conditions reported separately in FastStats included maternal, surgical, mental health, injury, and diabetes.ResultsThe share of uninsured discharges fell in Medicaid expansion states with below (-4.39 percentage points (p.p.), -6.04 –-2.73) or above (-7.66 p.p., -9.07 –-6.24) median baseline uninsured rates. The share of Medicaid discharges increased in both small (6.42 p.p. 4.22–6.62) and large (10.5 p.p., 8.48–12.5) expansion states. Total and most condition-specific discharges per 1,000 residents did not change in Medicaid expansion states with high or low baseline uninsured rates relative to non-expansion states (0.418, p = 0.225), with one exception: diabetes. Discharges for that condition per 1,000 fell in states with high baseline uninsured rates relative to non-expansion states (-0.038 95% p = 0.027).ConclusionsEarly changes in payer mix identified in the first two quarters of 2014 continued through the Medicaid expansion’s first year and are distributed across all condition types studied. We found no change in total discharges between Medicaid expansion and non-expansion states, however residents of states that should have been most affected by the Medicaid expansion were less likely to be hospitalized for diabetes.

Comparative information from the All Payer Claims Database on two quality measures, Avoidance of Antibiotic Treatment in Adults with Acute Bronchitis and Comprehensive Diabetes Care: Hemoglobin A1c (HbA1c) Testing for clinics with five or more physicians.

The datasets contain hospital discharges counts (numerators, denominators, volume counts), observed, expected and risk-adjusted rates with corresponding 95% confidence intervals for Patient Safety Indicators generated using methodology developed by Agency for Healthcare Research and Quality (AHRQ).

The PSIs are a set of indicators providing information on potential in hospital complications and adverse events following surgeries, procedures, and childbirth. The PSIs were developed by AHRQ after a comprehensive literature review, analysis of ICD-9-CM codes, review by a clinician panel, implementation of risk adjustment, and empirical analyses.

All PSI measures were calculated using Statewide Planning and Research Cooperative System (SPARCS) inpatient data beginning 2009. US Census data files provided by AHRQ were used to derive denominators for county level (area level) PSI measures.

The mortality, volume and utilization measures PSIs are presented by hospital as rates or counts. Area-level measures are presented by county as rates.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

This dataset tracks the updates made on the dataset "All-Payer Claims Data (APD De-Identified): Prescription Drug Summary 2020" as a repository for previous versions of the data and metadata.