Abstract

The Colorado All Payer Claims Database (CO APCD) is a state-legislated, secure health care claims database compliant with all federal privacy laws. It contains nearly 920 million claims for approximately 65 percent of insured lives in Colorado, with information from 42 commercial health insurance plans. Health insurance payers submit data monthly and the entire database is refreshed every other month, so the CO ACPD is continually evolving and being enhanced.

Usage

The dataset was extracted by the Center for Improving Value in Health Care (CIVHC) to support Stanford University COVID Long Haul Analysis. It includes medical, pharmacy, and dental claims files with coverage dates from 01/01/2012 to 08/31/2021.

For more information of CO APCD please refer to https://www.civhc.org/get-data/whats-in-the-co-apcd/

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Data Documentation

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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).

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.

This dataset is designed to analyze prescription drug use and spending among New York State residents at the active ingredient level. The dataset includes the number of prescriptions filled by unique members by payer type, rankings by volume, nonproprietary name, amount insurer paid, and more.

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

Ever wonder how many claims or people are in the Colorado All Payer Claims Database (CO APCD), and how it differs by type of claim (medical, dental, pharmacy), by year and by payer type? Use this interactive dashboard to understand what the CO APCD includes and what percentage of the population is represented in each county. Additional information is also available on race and ethnicity data, behavioral health services, dental code volume, and vision claims that are in the data warehouse under the Resources link.

This data set includes comparative cost and quality information for clinics with five or more physicians for medical claims in 2016. Only clinics with eligible Total Cost of Care indices and three quality measures, Breast Cancer Screening, A1c testing, and Medical Attention for Nephropathy are included.

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

This dataset contains data for the Healthcare Payments Data (HPD) Snapshot visualization. The Enrollment data file contains counts of claims and encounter data collected for California's statewide HPD Program. It includes counts of enrollment records, service records from medical and pharmacy claims, and the number of individuals represented across these records. Aggregate counts are grouped by payer type (Commercial, Medi-Cal, or Medicare), product type, and year. The Medical data file contains counts of medical procedures from medical claims and encounter data in HPD. Procedures are categorized using claim line procedure codes and grouped by year, type of setting (e.g., outpatient, laboratory, ambulance), and payer type. The Pharmacy data file contains counts of drug prescriptions from pharmacy claims and encounter data in HPD. Prescriptions are categorized by name and drug class using the reported National Drug Code (NDC) and grouped by year, payer type, and whether the drug dispensed is branded or a generic.

Analysis of ‘WA-APCD Quality and Cost Summary Report: Practice Quality’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/10d4ddee-0987-4f16-a780-430181a47bf2 on 28 January 2022.

--- 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 ---

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 ---

Analysis of ‘WA-APCD Quality and Cost Summary Report: County Cost’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/d69107cb-99d1-40ec-93f3-b94800f5e61e on 26 January 2022.

--- 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 ---

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.

Yearly citation counts for the publication titled "Feasibility of Capturing Cancer Treatment Data in the Utah All-Payer Claims Database".

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".

Organizations can license synthetic, Medicare claims data generated by Syntegra.

Example fields in the claims data include: Patient demographics (gender, race, etc.), Diagnosis & procedure information (ICD-10 code, description, physician NPI, etc.), Encounter and discharge information, Payer coverage and payer type, and Claim information (claim ID, bill code, encounter ID, etc.).

The claims data is available in the following formats: Cleaned, analytics-ready (a layer of clean and normalized concepts in Tuva Health’s standard relational data model format FHIR CARIN for Blue Button Medicare Standard CCLF

Our synthetic data engine is trained on a broadly representative dataset made up of diverse, realistic healthcare information of approximately 7 million unique patient records. Notably, synthetic data generation allows for the creation of any number of records needed to power your project.

The synthetic data maintains full statistical accuracy, yet does not contain any actual patients, thus removing any patient privacy liability risk. Privacy is preserved in a way that goes beyond HIPAA or GDPR compliance. Our industry-leading metrics prove that both privacy and fidelity are fully maintained.

— Generate the data needed for product development, testing, demo, or other needs — Access data at a scalable price point — Build your desired population, both in size and demographics — Scale up and down to fit specific needs, increasing efficiency and affordability

Syntegra's synthetic data engine also has the ability to augment the original data: — Expand population sizes, rare cohorts, or outcomes of interest — Address algorithmic fairness by correcting bias or introducing intentional bias — Conditionally generate data to inform scenario planning

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

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.

Variables and data resources in the study.

IntroductionThis study aimed to estimate the direct medical costs and out-of-pocket (OOP) expenses associated with inpatient and outpatient care for IHD, based on types of health insurance. Additionally, we sought to identify time trends and factors associated with these costs using an all-payer health claims database among urban patients with IHD in Guangzhou City, Southern China.MethodsData were collected from the Urban Employee-based Basic Medical Insurance (UEBMI) and the Urban Resident-based Basic Medical Insurance (URBMI) administrative claims databases in Guangzhou City from 2008 to 2012. Direct medical costs were estimated in the entire sample and by types of insurance separately. Extended Estimating Equations models were employed to identify the potential factors associated with the direct medical costs including inpatient and outpatient care and OOP expenses.ResultsThe total sample included 58,357 patients with IHD. The average direct medical costs per patient were Chinese Yuan (CNY) 27,136.4 [US dollar (USD) 4,298.8] in 2012. The treatment and surgery fees were the largest contributor to direct medical costs (52.0%). The average direct medical costs of IHD patients insured by UEBMI were significantly higher than those insured by the URBMI [CNY 27,749.0 (USD 4,395.9) vs. CNY 21,057.7(USD 3,335.9), P < 0.05]. The direct medical costs and OOP expenses for all patients increased from 2008 to 2009, and then decreased during the period of 2009–2012. The time trends of direct medical costs between the UEBMI and URBMI patients were different during the period of 2008-2012. The regression analysis indicated that the UEBMI enrollees had higher direct medical costs (P < 0.001) but had lower OOP expenses (P < 0.001) than the URBMI enrollees. Male patients, patients having percutaneous coronary intervention operation and intensive care unit admission, patients treated in secondary hospitals and tertiary hospitals, patients with the LOS of 15–30 days, 30 days and longer had significantly higher direct medical costs and OOP expenses (all P < 0.001).ConclusionsThe direct medical costs and OOP expenses for patients with IHD in China were found to be high and varied between two medical insurance schemes. The type of insurance was significantly associated with direct medical costs and OOP expenses of IHD.

This data set includes comparative information for clinics in Utah for medical claims for the years 2022 - 2023.

This data set was calculated by the Utah Department of Health and Human, Health Care Statistics Program using Utah’s All Payer Claims Database (APCD).

Prior years (2019-2021) may be viewed at: https://opendata.utah.gov/Health/2019-2021-Utah-Clinic-Quality-Comparisons/42ks-dsb5

Prior years (2017-2018) may be viewed at: https://opendata.utah.gov/Health/2017-2018-Utah-Clinic-Quality-Comparisons/9nhy-jp5r

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