This data package contains claims-based data about beneficiaries of Medicare program services including Inpatient, Outpatient, related to Chronic Conditions, Skilled Nursing Facility, Home Health Agency, Hospice, Carrier, Durable Medical Equipment (DME) and data related to Prescription Drug Events. It is necessary to mention that the values are estimated and counted, by using a random sample of fee-for-service Medicare claims.

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

Dataset

This dataset was created by Bunty Shah

Contents

2016-2019. This dataset is a de-identified summary table of prevalence rates for vision and eye health data indicators from the Medicaid Analytic eXtract (MAX) data. Medicaid MAX are a set of de-identified person-level data files with information on Medicaid eligibility, service utilization, diagnoses, and payments. The MAX data contain a convenience sample of claims processed by Medicaid and Children’s Health Insurance Program (CHIP) fee for service and managed care plans. Not all states are included in MAX in all years, and as of November 2019, 2014 data is the latest available. Prevalence estimates are stratified by all available combinations of age group, gender, and state. Detailed information on VEHSS Medicare analyses can be found on the VEHSS Medicaid MAX webpage (cdc.gov/visionhealth/vehss/data/claims/medicaid.html). Information on available Medicare claims data can be found on the ResDac website (www.resdac.org). The VEHSS Medicaid MAX dataset was last updated May 2023.

This data package contains Medicare spending statistics for beneficiaries grouped according to their age, gender, race/ethnicity and geographical location. At the same time, it provides data about spendings taking into consideration provider specific coordinates like the Hospital Referral Region (HRR) or Hospital Service Area (HSA). The data package contains as well as spending statistics based on the payment system, like the Outpatient Prospective Payment System.

The Agency for Healthcare Research and Quality (AHRQ) created SyH-DR from eligibility and claims files for Medicare, Medicaid, and commercial insurance plans in calendar year 2016. SyH-DR contains data from a nationally representative sample of insured individuals for the 2016 calendar year. SyH-DR uses synthetic data elements at the claim level to resemble the marginal distribution of the original data elements. SyH-DR person-level data elements are not synthetic, but identifying information is aggregated or masked.

This UMB dataset consists of a 10% random sample of the IQVIA Health Plans Claims Database which is comprised of adjudicated claims from health plan and self-employed groups. It is a mix of commercial PPO and commercial Medicare and Medicaid data for millions of unique patients. In addition to standard fields such as inpatient and outpatient diagnoses and procedures, retail and mail order prescription records, the database has detailed information on the pharmacy and medical benefit (copay/coinsurance amount, deductible), the inpatient stay (admission type and source, discharge status) and provider details (specialty, zip code, attending, referring, rendering, prescribing, primary care provider). The dataset is maintained by the Pharmaceutical Research Computing Center within the Department of Pharmaceutical Health Services Research at the University of Maryland School of Pharmacy. The Center provides computer programming, data management, pharmaceutical classification, and analytical support for health services research and evaluation.

Abstract

The page contains materials from the PHS Seminar on Weighting Techniques for Large Private Claims Data that was held on On October 24, 2024, as well as some additional documentation and the weights themselves.

Methodology

On October 24, 2024, PHS hosted a Seminar on Weighting Techniques for Large Private Claims Data. Using the MarketScan Commercial Database as an example case, Social Scientist Sarah Hirsch discussed three schemes for weighting private claims data using US census-based surveys, and the associated methods and techniques. She provided researchers with the tools to implement these methodologies, or to formulate their own for other datasets.

We invite you to view the Recording of the Seminar to learn more about this topic! The slide deck and transcript are also available for reference.

We have also added some code scripts, a written description of the weighting process, and the final MarketScan weights. Some additional have also been made related to the following:

• The region imputation.
• Patients from Puerto Rico (who are under a different survey from those employed here), who are being removed.
• Imputation based on non-null values in other years that were available for some people.

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Data provided by insurers, on the premiums written and claims incurred for the 2013 fiscal year. Based on reporting on the consolidated pages of the P&C-1 or Life-1 Annual returns. This data is also reported in the Superintendent of Insurance’s Annual Report.

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Claims Processing Software Market Size 2024-2028

The claims processing software market size is forecast to increase by USD 22.8 billion at a CAGR of 8.2% between 2023 and 2028. The market is witnessing significant growth due to increasing government regulations mandating insurance coverage in developing countries and the rapid expansion of the cyber insurance market. The importance of streamlining and securing the claims process is paramount in today's healthcare landscape. Patient scheduling, filing, updating, and processing medical claims are essential functions of claims processing software. Diagnosis, treatment, and medication management are integral components of the claims process that require accurate and efficient handling. Claims preparation software must be strong and secure to protect sensitive patient information and ensure business continuity. As the market evolves, providers must prioritize cybersecurity measures to mitigate risks and maintain compliance with regulatory requirements.

What will be the Size of the Market During the Forecast Period?

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The market in the healthcare sector is witnessing significant growth due to the increasing need for automation and efficiency in managing medical claims. The healthcare industry deals with a large volume of claims daily, involving various stages such as filing, updating, and processing medical claims. These stages require precise diagnosis, treatment information, medication records, and prior authorization. The healthcare claims lifecycle involves several intricacies, including the involvement of healthcare professionals, insurers, and manufacturing enterprises. Automation in healthcare is a key trend driving the market's growth, as it helps streamline processes, reduce errors, and enhance customer experience. Artificial intelligence (AI) plays a crucial role in claims processing software. It helps analyze complex data, including chronic diseases and patient history, to ensure accurate claims processing. Moreover, AI-powered software can adapt to changing business rules and automated processes, making it a cost-effective solution for insurers. Security concerns, such as cyberattacks like the Blackcat ransomware, pose a significant challenge to the market.

However, digital payments and electronic payment platforms integrated with claims processing software offer a secure solution, ensuring seamless transactions and data protection. Claims preparation software is another segment of the market that is gaining traction. It automates the process of claim submission, reducing manual effort and errors. Machinify, an intelligent claims management software, is an example of a solution that offers automated claims operations, prior authorization, and customer-focused services. Agile methodologies are being adopted to develop claims processing software, ensuring adaptability and flexibility to meet the evolving needs of the industry. The market is expected to continue growing, providing numerous opportunities for companies to offer cost-effective, intelligent, and customer-focused solutions. In conclusion, the market in the healthcare sector is witnessing growth due to the increasing need for automation, efficiency, and security. AI, electronic payments platforms, claims preparation software, and agile methodologies are key trends driving the market's growth.

Market Segmentation

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Component

  Software
  Services


Geography

  North America

    US


  Europe

    Germany
    UK


  APAC

    China
    Japan


  South America



  Middle East and Africa

By Component Insights

The software segment is estimated to witness significant growth during the forecast period. In the dynamic and evolving HealthTech industry, claims processing software plays a pivotal role in streamlining the healthcare claims lifecycle. Machinify and CoverSelf are leading electronic payment platforms that leverage artificial intelligence (AI) to automate the claims processing workflow. These solutions enable prior authorization, final payments, and payment integrity, ensuring a seamless experience for healthcare providers and insurers. The adoption of AI in claims processing software is a game-changer, enabling automated decision-making, real-time fraud detection, and enhanced operational efficiency. The initial funding and investment in HealthTech startups developing these solutions have been significant, indicating a bright future for the market.

By automating the claims processing workflow, businesses can save time and resources while maintaining high levels of accuracy and compliance. The use of AI in claims processing software is expected t

Validity indices for the definitions of the cause of death based on claims data.

Identifying the cause of death is important for the study of end-of-life patients using claims data in Japan. However, the validity of how cause of death is identified using claims data remains unknown. Therefore, this study aimed to verify the validity of the method used to identify the cause of death based on Japanese claims data. Our study population included patients who died at two institutions between January 1, 2018 and December 31, 2019. Claims data consisted of medical data and Diagnosis Procedure Combination (DPC) data, and five definitions developed from disease classification in each dataset were compared with death certificates. Nine causes of death, including cancer, were included in the study. The definition with the highest positive predictive values (PPVs) and sensitivities in this study was the combination of “main disease” in both medical and DPC data. For cancer, these definitions had PPVs and sensitivities of > 90%. For heart disease, these definitions had PPVs of > 50% and sensitivities of > 70%. For cerebrovascular disease, these definitions had PPVs of > 80% and sensitivities of> 70%. For other causes of death, PPVs and sensitivities were < 50% for most definitions. Based on these results, we recommend definitions with a combination of “main disease” in both medical and DPC data for cancer and cerebrovascular disease. However, a clear argument cannot be made for other causes of death because of the small sample size. Therefore, the results of this study can be used with confidence for cancer and cerebrovascular disease but should be used with caution for other causes of death.

Graph and download economic data for Initial Claims (ICSA) from 1967-01-07 to 2025-08-02 about initial claims, headline figure, and USA.

BackgroundSepsis is a major cause of preventable deaths in hospitals. Feasible and valid methods for comparing quality of sepsis care between hospitals are needed. The aim of this study was to develop a risk-adjustment model suitable for comparing sepsis-related mortality between German hospitals.MethodsWe developed a risk-model using national German claims data. Since these data are available with a time-lag of 1.5 years only, the stability of the model across time was investigated. The model was derived from inpatient cases with severe sepsis or septic shock treated in 2013 using logistic regression with backward selection and generalized estimating equations to correct for clustering. It was validated among cases treated in 2015. Finally, the model development was repeated in 2015. To investigate secular changes, the risk-adjusted trajectory of mortality across the years 2010–2015 was analyzed.ResultsThe 2013 deviation sample consisted of 113,750 cases; the 2015 validation sample consisted of 134,851 cases. The model developed in 2013 showed good validity regarding discrimination (AUC = 0.74), calibration (observed mortality in 1st and 10th risk-decile: 11%-78%), and fit (R2 = 0.16). Validity remained stable when the model was applied to 2015 (AUC = 0.74, 1st and 10th risk-decile: 10%-77%, R2 = 0.17). There was no indication of overfitting of the model. The final model developed in year 2015 contained 40 risk-factors. Between 2010 and 2015 hospital mortality in sepsis decreased from 48% to 42%. Adjusted for risk-factors the trajectory of decrease was still significant.ConclusionsThe risk-model shows good predictive validity and stability across time. The model is suitable to be used as an external algorithm for comparing risk-adjusted sepsis mortality among German hospitals or regions based on administrative claims data, but secular changes need to be taken into account when interpreting risk-adjusted mortality.

Note:- Only publicly available data can be worked upon

APISCRAPY leads the way in delivering hassle-free AI-driven healthcare data scraping, offering a seamless and ethical approach to accessing vital information. The platform excels in extracting Hospital Data, Healthcare Provider (HCP) Data, Pharma Data, telemedicine Data, and crucial COVID-19 Data, all without any associated costs.

Ensuring accuracy and real-time updates, APISCRAPY's advanced technology navigates the intricacies of healthcare systems, providing users with valuable insights at no expense. Hospital Data, encompassing bed capacities and specialized services, is retrieved effortlessly to empower stakeholders in making informed decisions. The extraction of HCP Data supports collaboration and advancements in medical science without financial barriers.

Pharma Data, Medical Imagery Data, and Medical Claims Data are sourced by APISCRAPY for market research, diagnostic breakthroughs, and streamlined financial workflows, respectively, all without incurring costs. Patient Data and Electronic Health Record (EHR) Data are handled with utmost privacy and compliance, enabling healthcare practitioners to access personalized information at no charge.

In the realm of Telemedicine, APISCRAPY facilitates virtual healthcare services without imposing financial burdens. As a socially responsible entity, APISCRAPY offers free access to critical COVID-19 Data, contributing to global efforts in research and strategy development to combat the ongoing pandemic.

APISCRAPY's commitment to providing cost-free, AI-driven healthcare data scraping underscores its dedication to making valuable information accessible to all, fostering a more inclusive and collaborative healthcare landscape. Through its innovative approach, APISCRAPY ensures that stakeholders can harness the power of data without financial constraints.

[Related tags: Hospital Data, Healthcare Provider (HCP) Data,Pharma Data, Medical Imagery Data, Medical Claims Data, Patient Data, Electronic Health, Record (EHR) Data, Telemedicine Data, COVID-19 Data, Wearables Data , Donor Data, Healthcare Professionals Database , Healthcare data, Medical Data Extraction, Data Extraction, Web Scraping Medical Data]

This dataset is a de-identified summary table of prevalence rates for vision and eye health data indicators from the 2016 MarketScan® Commercial Claims and Encounters Data (CCAE) is produced by Truven Health Analytics, a division of IBM Watson Health. The CCEA data contain a convenience sample of insurance claims information from person with employer-sponsored insurance and their dependents, including 43.6 million person years of data. Prevalence estimates are stratified by all available combinations of age group, gender, and state. Detailed information on VEHSS MarketScan analyses can be found on the VEHSS MarketScan webpage (cdc.gov/visionhealth/vehss/data/claims/marketscan.html). Information on available Medicare claims data can be found on the IBM MarketScan website (https://marketscan.truvenhealth.com). The VEHSS MarketScan summary dataset was last updated November 2019.

Analysis of ‘Initial Claims By County (All Programs)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/bc31fe5c-49c7-4a1b-9190-78fa17248dca on 26 January 2022.

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

Initial Claims by County (all programs) - The number of claims submitted for all UI programs. Initial claims totals are not representative of the number of individuals filing as a claimant can have multiple initial claims. ∙ Initial Claims by County - The data provided is the number of Unemployment Insurance (UI) initial claim counts, which includes new claims, additional claims, and transitional claims.
• A "new claim" is the first claim for a benefit year period (e.g., for the regular UI program it is 52 weeks). An individual would only have one new claim during a benefit year period.
• An "additional claim" is when another claim is filed during the same benefit year and there is intervening work between the current claim and the previous claim. For example, an individual files a new claim, goes back to work, gets laid off and files another claim before the benefit year period of the first claim expires. An individual can have multiple additional claims during the same benefit year if the individual meets the eligibility requirements.
• A "transitional claim" is when a claimant is still collecting benefits at the end of their benefit year period and had sufficient wage earnings during that year to start up a new claim once the first benefit year period ends.
∙ The data by county represents the mailing address given by the claimant at the time of filing for UI. It is possible that an individual can reside in a different county than their mailing address. Also, this information does not represent the county where the individual worked. It is also possible that a claimant could have moved or changed their mailing address after filing for UI which would not be reflected here. Data for claimants residing outside of California but collecting benefits are not included in these figures nor are invalid addresses in California where a county cannot be determined.
"∙ Initial claims does not represent total individuals as an individual can have multiple claims. For example, someone may begin collecting UI benefits, then go off UI to return to work, then get laid off and go back on UI. In this example, the individual would have two claim counts. "
∙ Data includes the regular UI program and the federal extended benefit programs. The Federal extended benefit programs are:
∙ Emergency Unemployment Compensation (EUC) Tier 1 - California began paying benefits in July 2008.
∙ Emergency Unemployment Compensation (EUC) Tier 2 - California began paying benefits in January 2009, payments retroactive to November 2008.
∙ Emergency Unemployment Compensation (EUC) Tier 3 - California began paying benefits in December 2009, payments retroactive to November 2009.
∙ Emergency Unemployment Compensation (EUC) Tier 4 - California began paying benefits in January 2010, payments retroactive to December 2009.
∙ FED-ED - California began paying benefits May 2009, payments retroactive to February 2009.

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

Analysis of ‘Exhausted Claims By County (All Programs)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/5fbb6ae7-3159-4acc-bbc5-202af8d684f1 on 26 January 2022.

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

"∙ The data provided is the number of Unemployment Insurance (UI) claims that have exhausted, regardless of the program. The data includes exhaustion counts for the regular UI and the federal extended programs. The data counts the number of individuals who ran out of benefits in a specific program who may or may not qualify for additional benefits. For example, individuals who have exhausted a regular UI claim may qualify for a federal extension. Individuals who have exhausted all available benefits are also included in the data. The data is representative of those claims that were processed during the month and does not necessarily represent the month the final payment was made in. For example, if a claimant is entitled to benefits for the week-ending January 30, 2010, the claim might not get processed until early February and that count would display in the February data. There are a small percentage of claimants that could go back onto a training extension even after exhausting their FED-ED claim. "
∙ The data by county represents the mailing address given by the claimant at the time of filing for UI. It is possible that an individual can reside in a different county than their mailing address. Also, this information does not represent the county where the individual worked. It is also possible that a claimant could have moved or changed their mailing address after filing for UI which would not be reflected here. Data for claimants residing outside of California but collecting benefits are not included in these figures nor are invalid addresses in California where a county cannot be determined.
∙ Data includes the regular UI program and the federal extended benefit programs. The Federal extended benefit programs are:
∙ Emergency Unemployment Compensation (EUC) Tier 1 - California began paying benefits in July 2008.
∙ Emergency Unemployment Compensation (EUC) Tier 2 - California began paying benefits in January 2009, payments retroactive to November 2008.
∙ Emergency Unemployment Compensation (EUC) Tier 3 - California began paying benefits in December 2009, payments retroactive to November 2009.
∙ Emergency Unemployment Compensation (EUC) Tier 4 - California began paying benefits in January 2010, payments retroactive to December 2009.
∙ FED-ED - California began paying benefits May 2009, payments retroactive to February 2009.
∙ Data may include multiple counts for the same individual. For example, a claimant could have exhausted their Regular UI claim in January and then exhausted their EUC Tier I claim in June.

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

Claims Clearinghouse Platform Market Outlook

As per our latest research, the global Claims Clearinghouse Platform market size reached USD 7.8 billion in 2024, driven by increasing digitization in healthcare and the growing need for efficient claims processing. The market is expected to expand at a robust CAGR of 9.2% from 2025 to 2033, reaching a projected value of USD 17.4 billion by 2033. This significant growth is primarily fueled by the escalating volume of healthcare claims, increasing adoption of electronic health records (EHRs), and the need for reducing administrative costs and errors in claims management. The market’s upward trajectory is further supported by regulatory mandates for electronic claims submission and the integration of advanced technologies such as artificial intelligence and machine learning.

The surge in healthcare data, coupled with the complexity of insurance claims processes, has underscored the importance of efficient claims clearinghouse platforms. These platforms streamline the submission, validation, and adjudication of claims, reducing manual intervention and minimizing errors. The rising pressure on healthcare providers and payers to improve operational efficiency and ensure faster reimbursements is a major growth driver. Additionally, the increasing focus on patient-centric care and the need for seamless interoperability among healthcare systems have accelerated the adoption of claims clearinghouse solutions. The integration of advanced analytics and automation within these platforms not only enhances accuracy but also enables real-time monitoring and reporting, further propelling market growth.

Another critical growth factor is the expanding regulatory landscape across various regions. Governments and healthcare authorities are implementing stringent regulations to ensure transparency, accuracy, and compliance in claims processing. The Health Insurance Portability and Accountability Act (HIPAA) in the United States, for instance, mandates the electronic submission of healthcare claims, thereby boosting the demand for claims clearinghouse platforms. Moreover, the increasing prevalence of value-based care models and the shift towards outcome-driven reimbursement structures are compelling healthcare organizations to adopt advanced claims management solutions. These trends are expected to continue shaping the market dynamics over the forecast period, with vendors focusing on enhancing platform capabilities to meet evolving regulatory requirements.

The proliferation of cloud-based solutions is also transforming the claims clearinghouse platform market. Cloud deployment offers scalability, flexibility, and cost-effectiveness, making it an attractive option for both large enterprises and small and medium-sized healthcare organizations. The ability to access claims data remotely and collaborate seamlessly across multiple locations has become crucial, especially in the wake of the COVID-19 pandemic. Cloud-based platforms also facilitate faster implementation, easier integration with existing systems, and improved data security, all of which are pivotal for healthcare providers and payers. As digital transformation accelerates across the healthcare industry, the adoption of cloud-based claims clearinghouse solutions is expected to witness significant growth, further augmenting the overall market expansion.

From a regional perspective, North America continues to dominate the global claims clearinghouse platform market, accounting for the largest share in 2024. The region’s advanced healthcare infrastructure, high adoption of digital health technologies, and supportive regulatory environment contribute to its leadership position. However, emerging markets in Asia Pacific and Latin America are exhibiting rapid growth, driven by increasing healthcare investments, rising insurance penetration, and government initiatives to digitize health systems. Europe also holds a substantial market share, supported by robust healthcare systems and ongoing efforts to improve administrative efficiency. As healthcare ecosystems worldwide embrace digital transformation, the claims clearinghouse platform market is poised for sustained growth across all major regions.

The Medicare Fee-for-Service (FFS) Comprehensive Error Rate Testing (CERT) dataset provides information on a random sample of FFS claims to determine if they were paid properly under Medicare coverage, coding, and payment rules. The dataset contains information on type of FFS claim, Diagnosis Related Group (DRG) and Healthcare Common Procedure Coding System (HCPCS) codes, provider type, type of bill, review decision, and error code. Please note, each reporting year (RY) contains claims submitted July 1 two years before the report through June 30 one year before the report. For example, the 2024 data contains claims submitted July 1, 2022 through June 30, 2023.