The Medicare Clinical Laboratory Fee Schedule (CLFS) dataset provides raw data reported by any applicable laboratories that reported a volume greater than 10 tests for the data collection period. As described by the Protecting Access to Medicare Act, Applicable Laboratories must report to CMS private payor rates and associated volumes for laboratory tests on the Clinical Laboratory Fee Schedule.

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 dataset tracks the updates made on the dataset "Medicare Clinical Laboratory Fee Schedule Private Payer Rates and Volumes" 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.

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).

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 first Social Drivers of Health (SDoH) dataset contains percentages of preventable hospitalizations (i.e., discharges) by Race/Ethnicity, preferred language spoken, expected payer, percent of employment, percent of home ownership, percent of park access and percent of access to basic kitchen facilities by the stated year. Preventable hospitalizations rates were created by dividing the number of patients who are 18 years and older and were admitted to a hospital for at least one of the preventable hospitalization diagnoses (see list below) by the total number of hospitalizations. List of preventable hospitalization diagnoses: diabetes with short-term complications, diabetes with long-term complications, uncontrolled diabetes without complications, diabetes with lower-extremity amputation, chronic obstructive pulmonary disease, asthma, hypertension, heart failure, angina without a cardiac procedure, dehydration, bacterial pneumonia, or urinary tract infection were counted as a preventable hospitalization. These conditions correspond with the conditions used in the Agency for Healthcare Research and Quality’s (AHRQ), Prevention Quality Indicator - Overall Composite Measure (PQI #90). The SDoH "overtime" dataset contains percentages of preventable hospitalizations (i.e., discharges) by Race/Ethnicity, preferred language spoken and expected payer overtime in the stated year range.

Introduction

The Global Healthcare Payer Services Market is projected to reach a value of US$ 133.3 billion by 2033, up from US$ 66.5 billion in 2023, with a compound annual growth rate (CAGR) of 7.2% during the forecast period. North America holds a dominant market share, accounting for more than 41.7%, with a market value of US$ 27.7 billion in 2023. The increasing complexity of healthcare reimbursement systems and the growing demand for cost-effective solutions are key factors driving this market. Healthcare payers are increasingly outsourcing non-core functions like claims processing and customer support to enhance efficiency and reduce operational costs.

One of the key trends influencing the growth of healthcare payer services is the integration of digital health technologies. The widespread use of telemedicine and virtual care platforms has prompted payers to adopt digital solutions, such as artificial intelligence (AI) and blockchain, to streamline operations and enhance data security. The COVID-19 pandemic accelerated the adoption of these technologies, highlighting their importance in maintaining healthcare accessibility. Digital solutions are essential for improving the delivery of care and ensuring that payers can keep up with the evolving needs of the healthcare system.

Data analytics is playing an increasingly critical role in the healthcare payer services market. With the growing volume of healthcare data, payers are turning to data analytics to optimize resource allocation and improve patient outcomes. Additionally, fraud detection and prevention have become a major focus. Advanced fraud management systems powered by AI and machine learning are being implemented to identify and prevent fraudulent activities. This emphasis on data analytics and fraud management helps ensure that payers can navigate the complexities of the healthcare system while maintaining high levels of service quality.

Regulatory mandates and compliance requirements are another driving force behind the healthcare payer services market. Government regulations designed to control healthcare costs and modernize healthcare systems require payers to adopt specialized outsourcing services. These services help payers ensure compliance with evolving regulations and adapt to changing healthcare policies. By outsourcing complex processes, payers can meet compliance standards efficiently and focus on core business activities, reducing administrative burdens.

Finally, emerging markets, particularly in the Asia Pacific region, are presenting significant growth opportunities for healthcare payer services. Rising medical expenses, higher insurance penetration, and a growing focus on healthcare infrastructure improvements are driving demand for efficient payer solutions. Investments in these regions are expected to drive market expansion and introduce innovative service models tailored to local populations. As these markets continue to develop, healthcare payer services are positioned for further growth and innovation.

This dataset contains data for the Healthcare Payments Data (HPD) Healthcare Measures report. The data cover three measurement categories: Health conditions, Utilization, and Demographics. The health condition measurements quantify the prevalence of long-term illnesses and major medical events prominent in California’s communities like diabetes and heart failure. Utilization measures convey rates of healthcare system use through visits to the emergency department and different categories of inpatient stays, such as maternity or surgical stays. The demographic measures describe the health coverage and other characteristics (e.g., age) of the Californians included in the data and represented in the other measures. The data include both a count or sum of each measure and a count of the base population so that data users can calculate the percentages, rates, and averages in the visualization. Measures are grouped by year, age band, sex (assigned sex at birth), payer type, Covered California Region, and county.

The column chart illustrates the distribution of inpatient discharges by service category for Medicaid, Commercial HMO, and Commercial PPO Payers. The chart uses statewide average rates of all insurance plans. For more information please visit http://www.health.ny.gov/health_care/managed_care/reports/quality_performance_improvement.htm#link5. The "About" tab contains additional details concerning this dataset.

This line chart compares the median cost vs. median charge for chest pain with a minor severity of illness by hospital. The dataset contains information submitted by New York State Article 28 Hospitals as part of the New York Statewide Planning and Research Cooperative (SPARCS) and Institutional Cost Report (ICR) data submissions. The dataset contains information on the volume of discharges, All Payer Refined Diagnosis Related Group (APR-DRG), the severity of illness level (SOI), medical or surgical classification the median charge, median cost, average charge and average cost per discharge. When interpreting New York’s data, it is important to keep in mind that variations in cost may be attributed to many factors. Some of these include overall volume, teaching hospital status, facility specific attributes, geographic region and quality of care provided.For more information, check out: http://www.health.ny.gov/statistics/sparcs/. The "About" tab contains additional details concerning this dataset.

The Healthcare Cost and Utilization Project (HCUP) Kids' Inpatient Database (KID) is the largest publicly available all-payer pediatric inpatient care database in the United States, containing data from two to three million hospital stays each year. Its large sample size is ideal for developing national and regional estimates and enables analyses of rare conditions, such as congenital anomalies, as well as uncommon treatments, such as organ transplantation. 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 KID is a sample of pediatric discharges from 4,000 U.S. hospitals in the HCUP State Inpatient Databases yielding approximately two to three million unweighted hospital discharges for newborns, children, and adolescents per year. About 10 percent of normal newborns and 80 percent of other neonatal and pediatric stays are selected from each hospital that is sampled for patients younger than 21 years of age. The KID 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 discharge status, diagnoses, procedures, patient demographics (e.g., sex, age), expected source of primary payment (e.g., Medicare, Medicaid, private insurance, self-pay, and other insurance types), and hospital charges and cost. Restricted access data files are available with a data use agreement and brief online security training.

The column chart shows rates of satisfaction for managed care plans by year. The chart can be filtered by measurement year or measure by changing these options under the Filter tab. The chart uses statewide average rates of all insurance plans. Removing the statewide average filter is not recommended. For more information, check out http://www.health.ny.gov/health_care/managed_care/reports/quality_performance_improvement.htm. The "About" tab contains additional details concerning this dataset.

The column chart shows performance measurement rates for the managed care provider network by payer. The chart uses the statewide average rates of all insurance plans. For more information, check out http://www.health.ny.gov/health_care/managed_care/reports/quality_performance_improvement.htm. The "About" tab contains additional details concerning this dataset.

On an annual basis (individual hospital fiscal year), individual hospitals and hospital systems report detailed facility-level data on services capacity, inpatient/outpatient utilization, patients, revenues and expenses by type and payer, balance sheet and income statement.

Due to the large size of the complete dataset, a selected set of data representing a wide range of commonly used data items, has been created that can be easily managed and downloaded. The selected data file includes general hospital information, utilization data by payer, revenue data by payer, expense data by natural expense category, financial ratios, and labor information.

There are two groups of data contained in this dataset: 1) Selected Data - Calendar Year: To make it easier to compare hospitals by year, hospital reports with report periods ending within a given calendar year are grouped together. The Pivot Tables for a specific calendar year are also found here. 2) Selected Data - Fiscal Year: Hospital reports with report periods ending within a given fiscal year (July-June) are grouped together.

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.

This dataset contains the statewide number and (unadjusted) rate for all-cause, unplanned, 30-day inpatient readmissions in California hospitals. Data are categorized by age, sex, race/ethnicity, expected payer and county.

This dataset contains data for the Healthcare Payments Data (HPD): Medical Out-of-Pocket Costs and Chronic Conditions report. The data covers three measurement categories: annual member count, annual median out-of-pocket count, annual median claim count. The annual member count quantify the number of unique individuals who received at least one medical service in the reporting year. Annual median out-of-pocket measurements quantifies the sum of copay, coinsurance, and deductible incurred by members. Annual median claim count measurements quantifies the number of distinct claims or encounters associated with members. Both 25th and 75th percentiles for out-of-pocket cost and claim count are also included. Measures are grouped by payer types, chronic conditions flag, chronic condition types, and chronic condition numbers.

This dataset contains information submitted by New York State Article 28 Hospitals as part of the New York Statewide Planning and Research Cooperative (SPARCS) and Institutional Cost Report (ICR) data submissions. The file contains information on the volume of discharges, All Payer Refined Diagnosis Related Group (APR-DRG), the severity of illness level (SOI), medical or surgical classification the median charge, median cost, average charge and average cost per discharge.

This line chart compares the median cost vs. median charge for other pneumonia with a minor severity of illness by hospital. The dataset contains information submitted by New York State Article 28 Hospitals as part of the New York Statewide Planning and Research Cooperative (SPARCS) and Institutional Cost Report (ICR) data submissions. The dataset contains information on the volume of discharges, All Payer Refined Diagnosis Related Group (APR-DRG), the severity of illness level (SOI), medical or surgical classification the median charge, median cost, average charge and average cost per discharge. When interpreting New York’s data, it is important to keep in mind that variations in cost may be attributed to many factors. Some of these include overall volume, teaching hospital status, facility specific attributes, geographic region and quality of care provided.For more information, check out: http://www.health.ny.gov/statistics/sparcs/. The "About" tab contains additional details concerning this dataset.