The Analysis of Longitudinal Claims Databases (R1 Part B): Effect of Variation in Health Coverage, Employment, and Community Resources on Adverse Events and Healthcare Costs and Utilization, United States is the second of a three-part project that examined claims data from Medicare, Medicaid, and/or Optum databases to explore aging trajectories, use of preventative services, and healthcare outcomes for individuals with several types of physical disabilities. This study made use of existing national databases to examine various health outcomes among individuals with disability. Using 2007-2016 Medicaid and Medicare Data, the researchers conducted three separate types of analyses: At the state level, examine the effect of variation in health coverage and related health policies on adverse health events and health outcomes among youth and adults with disability. At the county level, examine the variation in employment and community participatory living on adverse health and health outcomes among youth and adult with disability. At the state level, examine the effect of variation in Medicaid long-term care and community centers on health outcomes among youth and adult with disability.

2003 forward. CMS compiles claims data for Medicare and Medicaid patients across a variety of categories and years. This includes Inpatient and Outpatient claims, Master Beneficiary Summary Files, and many other files. Indicators from this data source have been computed by personnel in CDC's Division for Heart Disease and Stroke Prevention (DHDSP). This is one of the datasets provided by the National Cardiovascular Disease Surveillance System. The system is designed to integrate multiple indicators from many data sources to provide a comprehensive picture of the public health burden of CVDs and associated risk factors in the United States. The data are organized by location (national and state) and indicator. The data can be plotted as trends and stratified by sex and race/ethnicity.

Claims & EHR Data Linkage Services Market Outlook

According to our latest research, the global Claims & EHR Data Linkage Services market size reached USD 3.29 billion in 2024, reflecting robust momentum driven by the digital transformation of healthcare data management. The market is projected to expand at a CAGR of 13.8% from 2025 to 2033, reaching a forecasted value of USD 9.17 billion by 2033. This growth is primarily attributed to the increasing emphasis on interoperability, regulatory mandates for data integration, and the rising adoption of value-based care models across healthcare ecosystems worldwide.

The exponential growth of the Claims & EHR Data Linkage Services market is fueled by the surging need for seamless integration of disparate healthcare data sources. Healthcare organizations are increasingly recognizing the value of linking claims data with electronic health records (EHRs) to obtain a holistic view of patient journeys. This integration is pivotal for enhancing clinical decision-making, minimizing medical errors, and supporting precision medicine initiatives. Additionally, the proliferation of digital health solutions and the growing volume of healthcare data generated from various sources have created an urgent demand for advanced data linkage services, enabling organizations to unlock actionable insights and improve patient outcomes.

Another key driver accelerating market expansion is the evolving regulatory landscape. Governments and regulatory bodies worldwide are intensifying efforts to promote data interoperability and standardization. Initiatives such as the United States’ 21st Century Cures Act and the European Union’s General Data Protection Regulation (GDPR) are compelling healthcare stakeholders to invest in robust data integration and validation services. Compliance with these regulations not only ensures the secure and ethical use of health data but also fosters trust among patients and providers, further propelling the adoption of claims and EHR data linkage solutions.

Furthermore, the shift towards value-based care and population health management is catalyzing the need for comprehensive data analytics and enrichment services. Payers, providers, and pharmaceutical companies are leveraging linked claims and EHR data to identify care gaps, manage chronic diseases, and optimize resource allocation. The ability to aggregate and analyze longitudinal patient data supports risk adjustment, quality reporting, and clinical research, thereby driving operational efficiencies and improving health outcomes on a systemic level.

Regionally, North America continues to dominate the Claims & EHR Data Linkage Services market, accounting for over 45% of the global revenue in 2024. This leadership is underpinned by a mature healthcare IT infrastructure, strong regulatory frameworks, and significant investments in health information exchange (HIE) initiatives. However, the Asia Pacific region is anticipated to witness the highest CAGR during the forecast period, fueled by expanding healthcare digitization, increasing government support, and the rapid adoption of cloud-based data integration solutions in emerging economies.

Service Type Analysis

The Service Type segment of the Claims & EHR Data Linkage Services market encompasses Data Integration, Data Validation, Data Enrichment, Data Analytics, and other ancillary services. Among these, Data Integration services hold the largest market share, as they form the foundational layer enabling disparate health data systems to communicate effectively. The increasing complexity of healthcare data, generated from various sources such as electronic medical records, insurance claims, and laboratory results, necessitates advanced integration capabilities. These services enable healthcare organizations to unify patient information, streamline workflows, and facilitate the seamless exchange of data

Real World Evidence Solutions Market size was valued at USD 1.30 Billion in 2024 and is projected to reach USD 3.71 Billion by 2032, growing at a CAGR of 13.92% during the forecast period 2026-2032.Global Real World Evidence Solutions Market DriversThe market drivers for the Real World Evidence Solutions Market can be influenced by various factors. These may include:Growing Need for Evidence-Based Healthcare: Real-world evidence (RWE) is becoming more and more important in healthcare decision-making, according to stakeholders such as payers, providers, and regulators. In addition to traditional clinical trial data, RWE solutions offer important insights into the efficacy, safety, and value of healthcare interventions in real-world situations.Growing Use of RWE by Pharmaceutical Companies: RWE solutions are being used by pharmaceutical companies to assist with market entry, post-marketing surveillance, and drug development initiatives. Pharmaceutical businesses can find new indications for their current medications, improve clinical trial designs, and convince payers and providers of the worth of their products with the use of RWE.Increasing Priority for Value-Based Healthcare: The emphasis on proving the cost- and benefit-effectiveness of healthcare interventions in real-world settings is growing as value-based healthcare models gain traction. To assist value-based decision-making, RWE solutions are essential in evaluating the economic effect and real-world consequences of healthcare interventions.Technological and Data Analytics Advancements: RWE solutions are becoming more capable due to advances in machine learning, artificial intelligence, and big data analytics. With the use of these technologies, healthcare stakeholders can obtain actionable insights from the analysis of vast and varied datasets, including patient-generated data, claims data, and electronic health records.Regulatory Support for RWE Integration: RWE is being progressively integrated into regulatory decision-making processes by regulatory organisations including the European Medicines Agency (EMA) and the U.S. Food and Drug Administration (FDA). The FDA's Real-World Evidence Programme and the EMA's Adaptive Pathways and PRIority MEdicines (PRIME) programme are two examples of initiatives that are making it easier to incorporate RWE into regulatory submissions and drug development.Increasing Emphasis on Patient-Centric Healthcare: The value of patient-reported outcomes and real-world experiences in healthcare decision-making is becoming more widely acknowledged. RWE technologies facilitate the collection and examination of patient-centered data, offering valuable insights into treatment efficacy, patient inclinations, and quality of life consequences.Extension of RWE Use Cases: RWE solutions are being used in medication development, post-market surveillance, health economics and outcomes research (HEOR), comparative effectiveness research, and market access, among other healthcare fields. The necessity for a variety of RWE solutions catered to the needs of different stakeholders is being driven by the expansion of RWE use cases.

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.

Summary of model objectives.

Lifetime prevalence of disease attributes among individuals diagnosed with SCD in Medicaid, Medicare, and dual-eligibility cohorts.

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 ---

Here, you will find resources to use the Bynum-Standard 1-Year Algorithm including a README file that accompanies SAS and Stata scripts for the 1-Year Standard Method for identifying Alzheimer’s Disease and Related Dementias (ADRD) in Medicare Claims data. There are seven script files (plus a parameters file for SAS [parm.sas]) for both SAS and Stata. The files are numbered in the order in which they should be run; the five “1” files may be run in any order.The full algorithm requires access to a single year of Medicare Claims data for (1) MedPAR, (2) Home Health Agency (HHA) Claims File, (3) Hospice Claims File, (4) Carrier Claims and Line Files, and (5) Hospital Outpatient File (HOF) Claims and Revenue Files. All Medicare Claims files are expected to be in SAS format (.sas7bdat).For each data source, the script will output three files*:Diagnosis-level file: Lists individual ADRD diagnoses for each beneficiary for a given visit. This file allows researchers to identify which ICD-9-CM or ICD-10-CM codes are used in the claims data.Service Date-level file: Aggregated from the Diagnosis-level file, this file includes all beneficiaries with an ADRD diagnosis by Service Date (date of a claim with at least one ADRD diagnosis).Beneficiary-level file: Aggregated from the Service Date-level file, this file includes all beneficiaries with at least one* ADRD diagnosis at any point in the year within a specific file* The algorithm combines the Carrier and HOF files at the Service Date-level. The final combined Carrier and HOF Beneficiary-level file includes those with at least two (2) claims that are seven (7) or more days apart.​A final combined file is created by merging all Beneficiary-level files. This file is used to identify beneficiaries with ADRD and can be merged onto other files by the Beneficiary ID (BENE_ID).​With appreciation & acknowledgement to colleagues at the NIA IMPACT Collaboratory for their involvement in development & validation of the Bynum-Standard 1-Year Algorithm:

Background: In Brazil, studies that map electronic healthcare databases in order to assess their suitability for use in pharmacoepidemiologic research are lacking. We aimed to identify, catalogue, and characterize Brazilian data sources for Drug Utilization Research (DUR).Methods: The present study is part of the project entitled, “Publicly Available Data Sources for Drug Utilization Research in Latin American (LatAm) Countries.” A network of Brazilian health experts was assembled to map secondary administrative data from healthcare organizations that might provide information related to medication use. A multi-phase approach including internet search of institutional government websites, traditional bibliographic databases, and experts’ input was used for mapping the data sources. The reviewers searched, screened and selected the data sources independently; disagreements were resolved by consensus. Data sources were grouped into the following categories: 1) automated databases; 2) Electronic Medical Records (EMR); 3) national surveys or datasets; 4) adverse event reporting systems; and 5) others. Each data source was characterized by accessibility, geographic granularity, setting, type of data (aggregate or individual-level), and years of coverage. We also searched for publications related to each data source.Results: A total of 62 data sources were identified and screened; 38 met the eligibility criteria for inclusion and were fully characterized. We grouped 23 (60%) as automated databases, four (11%) as adverse event reporting systems, four (11%) as EMRs, three (8%) as national surveys or datasets, and four (11%) as other types. Eighteen (47%) were classified as publicly and conveniently accessible online; providing information at national level. Most of them offered more than 5 years of comprehensive data coverage, and presented data at both the individual and aggregated levels. No information about population coverage was found. Drug coding is not uniform; each data source has its own coding system, depending on the purpose of the data. At least one scientific publication was found for each publicly available data source.Conclusions: There are several types of data sources for DUR in Brazil, but a uniform system for drug classification and data quality evaluation does not exist. The extent of population covered by year is unknown. Our comprehensive and structured inventory reveals a need for full characterization of these data sources.

Prescription Data for Insurance Market Outlook

According to our latest research, the global Prescription Data for Insurance market size reached USD 3.4 billion in 2024, with a robust compound annual growth rate (CAGR) of 13.2% projected through 2033. By the end of 2033, the market is forecasted to attain a valuation of USD 10.2 billion, driven by the increasing integration of advanced data analytics and artificial intelligence in insurance operations. The surge in demand for real-time prescription insights and the rising adoption of electronic health records are key factors propelling the market's growth trajectory, as per our latest research findings.

One of the most significant growth factors fueling the Prescription Data for Insurance market is the increasing need for accurate and timely risk assessment within the insurance industry. As healthcare costs continue to escalate globally, insurers are under immense pressure to price their products competitively while minimizing losses due to adverse selection or fraud. Prescription data, when analyzed effectively, enables insurers to gain a granular understanding of an individual’s medication history, adherence patterns, and potential comorbidities. This data-driven approach improves underwriting precision, allowing insurers to offer personalized premiums, reduce claim denials, and enhance customer satisfaction. Moreover, the integration of prescription data with other health data sources such as electronic health records and claims data further augments the predictive capabilities of insurers, supporting a more comprehensive risk evaluation process.

Another pivotal driver behind the expansion of the Prescription Data for Insurance market is the growing regulatory emphasis on transparency and fraud prevention. Regulatory bodies worldwide are mandating stricter compliance measures to ensure fair pricing and minimize fraudulent claims in the insurance sector. Prescription data plays a crucial role in this context by providing verifiable evidence of medication dispensation, which can be cross-referenced against claims submitted by policyholders. The deployment of advanced analytics and machine learning algorithms further empowers insurers to detect anomalies, flag suspicious activities, and prevent fraudulent claims before they escalate into significant financial losses. This regulatory alignment not only safeguards insurers’ interests but also fosters greater trust among policyholders, thereby driving broader adoption of prescription data solutions.

Technological advancements are also a cornerstone of growth in the Prescription Data for Insurance market. The proliferation of cloud computing, big data analytics, and interoperable health information systems has revolutionized the way prescription data is collected, stored, and analyzed. Cloud-based platforms facilitate seamless integration and sharing of prescription data across multiple stakeholders, including insurance companies, pharmacy benefit managers, and healthcare providers. This interoperability accelerates decision-making, enhances operational efficiency, and enables the development of innovative insurance products tailored to diverse demographic segments. Furthermore, the advent of real-time prescription benefit data allows insurers to access up-to-the-minute information, supporting dynamic pricing models and more responsive claims management strategies.

From a regional perspective, North America continues to lead the Prescription Data for Insurance market, accounting for the largest share in 2024. This dominance is attributed to the high adoption rate of electronic health records, strong regulatory frameworks, and the presence of major market players in the United States and Canada. Europe follows closely, driven by increasing digitalization of healthcare infrastructure and supportive government initiatives. The Asia Pacific region is emerging as a high-growth market, fueled by rapid urbanization, expanding insurance penetration, and rising investments in health IT infrastructure. Latin America and the Middle East & Africa are also witnessing steady growth, albeit at a comparatively slower pace, as they gradually embrace digital health solutions and enhance their regulatory environments.

Data Type Analysis

The Data Type segment of the Prescription Data for Insurance market encompasses a diverse range of sources, including claims data, electronic health records (EHRs),

The peer-reviewed publication for this dataset has been presented in the 2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), and can be accessed here: https://arxiv.org/abs/2205.02596. Please cite this when using the dataset.

This dataset contains a heterogeneous set of True and False COVID claims and online sources of information for each claim.

The claims have been obtained from online fact-checking sources, existing datasets and research challenges. It combines different data sources with different foci, thus enabling a comprehensive approach that combines different media (Twitter, Facebook, general websites, academia), information domains (health, scholar, media), information types (news, claims) and applications (information retrieval, veracity evaluation).

The processing of the claims included an extensive de-duplication process eliminating repeated or very similar claims. The dataset is presented in a LARGE and a SMALL version, accounting for different degrees of similarity between the remaining claims (excluding respectively claims with a 90% and 99% probability of being similar, as obtained through the MonoT5 model). The similarity of claims was analysed using BM25 (Robertson et al., 1995; Crestani et al., 1998; Robertson and Zaragoza, 2009) with MonoT5 re-ranking (Nogueira et al., 2020), and BERTScore (Zhang et al., 2019).

The processing of the content also involved removing claims making only a direct reference to existing content in other media (audio, video, photos); automatically obtained content not representing claims; and entries with claims or fact-checking sources in languages other than English.

The claims were analysed to identify types of claims that may be of particular interest, either for inclusion or exclusion depending on the type of analysis. The following types were identified: (1) Multimodal; (2) Social media references; (3) Claims including questions; (4) Claims including numerical content; (5) Named entities, including: PERSON − People, including fictional; ORGANIZATION − Companies, agencies, institutions, etc.; GPE − Countries, cities, states; FACILITY − Buildings, highways, etc. These entities have been detected using a RoBERTa base English model (Liu et al., 2019) trained on the OntoNotes Release 5.0 dataset (Weischedel et al., 2013) using Spacy.

The original labels for the claims have been reviewed and homogenised from the different criteria used by each original fact-checker into the final True and False labels.

The data sources used are:

• The CoronaVirusFacts/DatosCoronaVirus Alliance Database. https://www.poynter.org/ifcn-covid-19-misinformation/

• CoAID dataset (Cui and Lee, 2020) https://github.com/cuilimeng/CoAID

• MM-COVID (Li et al., 2020) https://github.com/bigheiniu/MM-COVID

• CovidLies (Hossain et al., 2020) https://github.com/ucinlp/covid19-data

• TREC Health Misinformation track https://trec-health-misinfo.github.io/

• TREC COVID challenge (Voorhees et al., 2021; Roberts et al., 2020) https://ir.nist.gov/covidSubmit/data.html

The LARGE dataset contains 5,143 claims (1,810 False and 3,333 True), and the SMALL version 1,709 claims (477 False and 1,232 True).

The entries in the dataset contain the following information:

• Claim. Text of the claim.

• Claim label. The labels are: False, and True.

• Claim source. The sources include mostly fact-checking websites, health information websites, health clinics, public institutions sites, and peer-reviewed scientific journals.

• Original information source. Information about which general information source was used to obtain the claim.

• Claim type. The different types, previously explained, are: Multimodal, Social Media, Questions, Numerical, and Named Entities.

Funding. This work was supported by the UK Engineering and Physical Sciences Research Council (grant no. EP/V048597/1, EP/T017112/1). ML and YH are supported by Turing AI Fellowships funded by the UK Research and Innovation (grant no. EP/V030302/1, EP/V020579/1).

References

• Arana-Catania M., Kochkina E., Zubiaga A., Liakata M., Procter R., He Y.. Natural Language Inference with Self-Attention for Veracity Assessment of Pandemic Claims. NAACL 2022 https://arxiv.org/abs/2205.02596

• Stephen E Robertson, Steve Walker, Susan Jones, Micheline M Hancock-Beaulieu, Mike Gatford, et al. 1995. Okapi at trec-3. Nist Special Publication Sp,109:109.

• Fabio Crestani, Mounia Lalmas, Cornelis J Van Rijsbergen, and Iain Campbell. 1998. “is this document relevant?. . . probably” a survey of probabilistic models in information retrieval. ACM Computing Surveys (CSUR), 30(4):528–552.

• Stephen Robertson and Hugo Zaragoza. 2009. The probabilistic relevance framework: BM25 and beyond. Now Publishers Inc.

• Rodrigo Nogueira, Zhiying Jiang, Ronak Pradeep, and Jimmy Lin. 2020. Document ranking with a pre-trained sequence-to-sequence model. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings, pages 708–718.

• Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi. 2019. Bertscore: Evaluating text generation with bert. In International Conference on Learning Representations.

• Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692.

• Ralph Weischedel, Martha Palmer, Mitchell Marcus, Eduard Hovy, Sameer Pradhan, Lance Ramshaw, Nianwen Xue, Ann Taylor, Jeff Kaufman, Michelle Franchini, et al. 2013. Ontonotes release 5.0 ldc2013t19. Linguistic Data Consortium, Philadelphia, PA, 23.

• Limeng Cui and Dongwon Lee. 2020. Coaid: Covid-19 healthcare misinformation dataset. arXiv preprint arXiv:2006.00885.

• Yichuan Li, Bohan Jiang, Kai Shu, and Huan Liu. 2020. Mm-covid: A multilingual and multimodal data repository for combating covid-19 disinformation.

• Tamanna Hossain, Robert L. Logan IV, Arjuna Ugarte, Yoshitomo Matsubara, Sean Young, and Sameer Singh. 2020. COVIDLies: Detecting COVID-19 misinformation on social media. In Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020, Online. Association for Computational Linguistics.

• Ellen Voorhees, Tasmeer Alam, Steven Bedrick, Dina Demner-Fushman, William R Hersh, Kyle Lo, Kirk Roberts, Ian Soboroff, and Lucy Lu Wang. 2021. Trec-covid: constructing a pandemic information retrieval test collection. In ACM SIGIR Forum, volume 54, pages 1–12. ACM New York, NY, USA.

Claims Analytics Market Outlook

According to our latest research, the global claims analytics market size reached USD 3.4 billion in 2024, and is expected to grow at a robust CAGR of 18.2% from 2025 to 2033, reaching a projected value of USD 16.5 billion by the end of the forecast period. The marketÂ’s rapid expansion is primarily driven by the increasing need for advanced data analytics in the insurance and healthcare sectors, where organizations seek to maximize operational efficiency, reduce fraud, and enhance customer experience.

A primary growth factor for the claims analytics market is the surge in digital transformation initiatives across the insurance and healthcare industries. As organizations digitize their workflows, the volume and complexity of claims data have increased exponentially. This trend necessitates sophisticated analytics solutions capable of extracting actionable insights from vast and disparate data sources. Claims analytics tools enable companies to automate claims processing, streamline operations, and reduce manual errors, leading to cost savings and improved accuracy. Furthermore, the integration of artificial intelligence (AI) and machine learning (ML) technologies into claims analytics platforms is revolutionizing the way organizations detect patterns, predict outcomes, and make data-driven decisions, thereby fueling market growth.

Another significant driver is the rising incidence of fraudulent claims, which poses substantial financial risks to insurers and healthcare providers. The claims analytics market is witnessing heightened demand for advanced fraud detection and risk assessment solutions that can proactively identify suspicious patterns and anomalies. By leveraging predictive analytics, machine learning algorithms, and real-time data processing, organizations can minimize losses associated with fraudulent activities and improve their overall risk management frameworks. Additionally, regulatory pressures and compliance requirements are compelling companies to adopt robust analytics solutions that ensure transparency, auditability, and adherence to evolving industry standards.

The growing emphasis on customer-centricity and personalized experiences is also propelling the adoption of claims analytics. Modern consumers expect faster, more transparent, and seamless claims processes. Claims analytics platforms empower organizations to segment customers, analyze behavioral data, and tailor their offerings to individual preferences. This not only enhances customer satisfaction and loyalty but also enables insurers and healthcare providers to identify cross-selling and up-selling opportunities. Moreover, the proliferation of cloud-based deployment models and the increasing availability of scalable analytics solutions are lowering the barriers to entry for small and medium enterprises (SMEs), further expanding the marketÂ’s addressable base.

In the realm of claims management, Claims Fraud Analytics has emerged as a pivotal tool for insurers and healthcare providers striving to combat fraudulent activities. By employing sophisticated algorithms and machine learning techniques, these analytics solutions can scrutinize vast datasets to uncover hidden patterns indicative of fraudulent behavior. This proactive approach not only helps in identifying and mitigating potential fraud but also enhances the overall integrity of the claims process. As the sophistication of fraud schemes continues to evolve, the role of Claims Fraud Analytics becomes increasingly critical, providing organizations with the necessary insights to safeguard their financial interests and maintain regulatory compliance.

From a regional perspective, North America continues to dominate the claims analytics market, accounting for the largest revenue share in 2024, followed by Europe and Asia Pacific. The regionÂ’s leadership is attributed to the presence of major insurance and healthcare providers, advanced IT infrastructure, and early adoption of digital technologies. However, Asia Pacific is anticipated to exhibit the highest growth rate over the forecast period, driven by rapid urbanization, increasing insurance penetration, and rising investments in healthcare IT. Latin America and the Middle East & Africa are also witnessing steady growth, supported by regulatory reforms and the gradual modernization of legacy systems.

Transportation Security AdministrationClaims Management SystemThe Transportation Security Administration is responsible for securing of all modes of transportation. Pursuant to this authority TSA screens all aviation passengers and their baggage before granting access to the airport’s sterile area. When a passenger experiences a loss of or damage to his or her personal property or suffers a personal injury and feels this occurred as a result of TSA negligence, the passenger or property owner may file a claim with TSA seeking compensation under the Federal Tort Claims Act (FTCA). TSA employees may file a claim with TSA for loss of or damage to personal property incident to service under the Military Personnel and Civilian Employees’ Claims Act(MPCECA).TSA receives approximately 10,000 claims a year from the traveling public and TSA personnel, typically for issues arising out of airport screening activities. In order to facilitate efficient processing of claims, TSA developed an automated case management database called CMS. CMS is used to intake, process, analyze, and track claims.This data has no metadata or descriptions available at the source link; however, the privacy review file (privacy_pia_tsacms009a-may2019.pdf) appears to describe the data upstream from this process. Not all data described in the privacy anaysis (e.g. PII) are present in these datasets. The description above is from that file, which is included in the documentation folder.

BackgroundPeople are frequently confronted with untrustworthy claims about the effects of treatments. Uncritical acceptance of these claims can lead to poor, and sometimes dangerous, treatment decisions, and wasted time and money. Resources to help people learn to think critically about treatment claims are scarce, and they are widely scattered. Furthermore, very few learning-resources have been assessed to see if they improve knowledge and behavior.ObjectivesOur objectives were to develop the Critical thinking and Appraisal Resource Library (CARL). This library was to be in the form of a database containing learning resources for those who are responsible for encouraging critical thinking about treatment claims, and was to be made available online. We wished to include resources for groups we identified as ‘intermediaries’ of knowledge, i.e. teachers of schoolchildren, undergraduates and graduates, for example those teaching evidence-based medicine, or those communicating treatment claims to the public. In selecting resources, we wished to draw particular attention to those resources that had been formally evaluated, for example, by the creators of the resource or independent research groups.MethodsCARL was populated with learning-resources identified from a variety of sources—two previously developed but unmaintained inventories; systematic reviews of learning-interventions; online and database searches; and recommendations by members of the project group and its advisors. The learning-resources in CARL were organised by ‘Key Concepts’ needed to judge the trustworthiness of treatment claims, and were made available online by the James Lind Initiative in Testing Treatments interactive (TTi) English (www.testingtreatments.org/category/learning-resources).TTi English also incorporated the database of Key Concepts and the Claim Evaluation Tools developed through the Informed Healthcare Choices (IHC) project (informedhealthchoices.org).ResultsWe have created a database of resources called CARL, which currently contains over 500 open-access learning-resources in a variety of formats: text, audio, video, webpages, cartoons, and lesson materials. These are aimed primarily at ‘Intermediaries’, that is, ‘teachers’, ‘communicators’, ‘advisors’, ‘researchers’, as well as for independent ‘learners’. The resources included in CARL are currently accessible at www.testingtreatments.org/category/learning-resourcesConclusionsWe hope that ready access to CARL will help to promote the critical thinking about treatment claims, needed to help improve healthcare choices.

Health Data Exchange for Insurers Market Outlook

According to our latest research, the global Health Data Exchange for Insurers market size reached USD 4.9 billion in 2024, reflecting a robust surge in adoption across the insurance sector. The market is projected to grow at a CAGR of 15.2% from 2025 to 2033, reaching an estimated USD 16.4 billion by 2033. This impressive growth trajectory is primarily driven by the increasing need for seamless information flow, regulatory mandates for interoperability, and the rising demand for data-driven insurance operations.

A significant growth factor for the Health Data Exchange for Insurers market is the escalating complexity of healthcare data and the urgent need for insurers to access, process, and analyze vast volumes of health information in real time. As insurers strive to offer personalized policies and optimize risk management, the integration of advanced health data exchange platforms becomes indispensable. These platforms enable insurers to aggregate data from multiple sources, including electronic health records (EHRs), claims databases, and wearable devices, facilitating more accurate underwriting and streamlined claims processing. The adoption of interoperability standards such as HL7 FHIR and the growing collaboration between payers and providers further drive the market forward, enabling efficient data sharing and reducing administrative overhead.

Another pivotal driver is the global shift towards value-based care and digital transformation within the insurance ecosystem. Insurers are increasingly leveraging health data exchange solutions to enhance fraud detection, improve risk assessment, and deliver superior customer engagement. The rising prevalence of chronic diseases, coupled with the increasing use of telemedicine and remote monitoring, has led to a surge in health data generation. Insurers now require robust data exchange frameworks to harness this information, derive actionable insights, and remain competitive in a rapidly evolving landscape. Moreover, the growing emphasis on regulatory compliance, particularly in regions with stringent data privacy laws, is compelling insurers to invest in secure and interoperable data exchange solutions.

Technological advancements, including the integration of artificial intelligence (AI), blockchain, and advanced analytics into health data exchange platforms, are further fueling market expansion. These innovations enable insurers to automate routine processes, enhance data security, and deliver real-time insights to both underwriters and policyholders. The proliferation of cloud-based solutions has also played a crucial role, offering scalability, cost efficiency, and improved accessibility. As insurers increasingly adopt cloud-based data exchange models, they are better positioned to respond to market dynamics, regulatory changes, and evolving customer expectations.

Regionally, North America continues to dominate the Health Data Exchange for Insurers market in 2024, accounting for nearly 38% of global revenue. This leadership is attributed to the region’s advanced healthcare infrastructure, widespread adoption of digital health technologies, and favorable regulatory environment. Europe follows closely, driven by the increasing digitization of health insurance processes and government initiatives promoting data interoperability. The Asia Pacific region is poised for the fastest growth, with a projected CAGR of 18.5% through 2033, fueled by expanding insurance coverage, rapid urbanization, and the proliferation of mobile health technologies. Latin America and the Middle East & Africa are also witnessing steady adoption, albeit at a comparatively moderate pace, as insurers in these regions gradually embrace digital transformation.

Component Analysis

The Component segment of the Health Data Exchange for Insurers market is categorized into Software, Services, and Hardware. Software solutions form the backbone of health data exchange, encompassing platforms for interoperability, data aggregation, analytics, and workflow automation. These software platforms are increasingly being designed to support regulatory compliance, real-time data sharing, and integration with legacy insurance systems. As insurers seek to harness the full potential of health data, demand for advanced analytics, AI-driven insights, and blockchain-enabled security within software offerings is surging. The ongoing shift from on-premises to cloud-ba

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Insurance Claims Services Market Size 2025-2029

The insurance claims services market size is forecast to increase by USD 155.1 billion at a CAGR of 12.7% between 2024 and 2029.

The market is experiencing significant growth, driven by the ongoing urbanization and economic development worldwide. This trend is increasing the demand for efficient and effective claims processing services to cater to the expanding population and growing number of insured individuals. Furthermore, the digital transformation in the insurance industry is revolutionizing claims services, enabling faster and more accurate processing through advanced technologies such as artificial intelligence and machine learning. However, the market faces challenges that must be addressed to fully capitalize on its growth potential. Stringent regulatory compliance, particularly in the areas of data privacy and security, poses a significant hurdle for market participants. Additionally, inconsistencies in the supply chain, including the availability and reliability of data from various sources, can hinder the efficiency and accuracy of claims processing. To succeed in this dynamic market, companies must navigate these challenges by investing in robust compliance frameworks and implementing advanced technologies to streamline their operations and ensure data accuracy. By doing so, they will be well-positioned to meet the evolving needs of their customers and capture new opportunities in the rapidly expanding the market.

What will be the Size of the Insurance Claims Services Market during the forecast period?

Request Free SampleThe market is characterized by a growing emphasis on efficiency and accuracy in claims processing. Claims documentation and best practices are crucial for effective claims settlement, while claims insights derived from data analysis help insurers identify trends and improve performance. Fraud detection algorithms and claims benchmarking are essential tools for mitigating fraud and ensuring fair pricing. Claims management solutions, including digital claims processing and mobile reporting, streamline the claims journey for policyholders. Remote claims assessment and customer self-service portals enable faster resolution of disputes through claims negotiation and litigation. Insurance companies leverage claims analytics platforms to measure performance, forecast costs, and assess risks. Policy validation and claims cost management are integral components of these solutions. Claims triage and audit help insurers prioritize claims and maintain compliance with industry regulations. Insurtech innovations, such as claims dashboards and automation tools, facilitate real-time claims processing and analysis. These advancements enable insurers to respond promptly to claims and deliver a superior customer experience. Overall, the market is evolving to meet the demands of a digital age, prioritizing transparency, accuracy, and efficiency.

How is this Insurance Claims Services Industry segmented?

The insurance claims services industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. TypeProperty and casualty claimsHealth insurance claimsMotor insurance claimsLife insurance claimsEnd-userIndividual policyholderCommercial policyholderGovernment and public sectorGeographyNorth AmericaUSCanadaEuropeFranceGermanyUKAPACAustraliaChinaIndiaJapanSouth AmericaBrazilRest of World (ROW)

By Type Insights

The property and casualty claims segment is estimated to witness significant growth during the forecast period.The market, notably in the property and casualty sector, is marked by its handling of losses involving property damage and accidents or injuries' liability. This segment addresses incidents such as fires, theft, natural disasters, and various forms of liability. The intricacy of property and casualty insurance claims necessitates comprehensive assessment, rigorous investigation, and collaboration with external adjusters to guarantee fair and precise settlements. A key factor fueling growth in the property and casualty claims segment is the rising frequency and intensity of natural disasters. Events like hurricanes, floods, and wildfires are increasingly common, resulting in a surge in claims. Machine learning, claims data analytics, and artificial intelligence are increasingly being integrated into claims platforms to streamline the claims process, improve operational efficiency, and reduce costs. Personal lines insurance and commercial insurance each present unique challenges in claims handling. Personal lines insurance involves managing claims for individual policies, while commercial insurance encompasses claims for businesses. Industry best practices and insurance regulation play a crucial role in ensuring a consistent

Workers' Compensation Claims (NYS)

Injury Types, Claim Processing, and Benefits Recipients

By State of New York [source]

About this dataset

The dataset includes more than twenty years of Workers’ Compensation Claims records starting from the year 2000, giving a comprehensive overview of this important segment of the labor market. From information on claimants' age, gender and zip code to details on claim type, injury type, injury source and event exposure they each hold invaluable insights into the health of workers' compensation. Stay up-to-date with WIBC's constantly growing database featuring essential data that can help you in making informed decisions on how to manage claims and look after your workforce. Learn what types of injuries lead to successful claims; understand which carrier types are most often involved in claims; research claim assembly process times; gain an understanding of slow or disputed claims; find out about wage averages for claimants; and numerous other aspects related to workers’ compensation. With such insight available at your fingertips make sure you capitalize on its potential as you work towards better management and protection of your workforce - Now with complete data from 2000 all the way up till today!

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How to use the dataset

Welcome to the Workers’ Compensation Claims in New York State: 2000-Present dataset! This dataset contains information about workers’ compensation claims in New York State between the years 2000 and present.

This guide will provide you with an overview of the data included, as well as how to use this information for your own research and exploration.

Firstly, you should familiarize yourself with the data columns. This dataset includes a variety of fields related to workers’ compensation claims such as claim type, injury type, district name and current claim status, age at injury, assembly date and ANCR date (claim acceptance or denial). Additionally it contains details about medical costs covered by WCB (Average Weekly Wage) , dispute resolution mechanisms (Alternative Dispute Resolution), legal representatives handling the case(Attorney/Representative), insurance carrier involved(Carrier Name)and other useful details pertinent for understanding workers' compenstation cases such as Hearing count & Closed count.

Once you understand all available fields/columns and their respective values/labels in this dataset you can start exploring them one by one & create custom queries based on specific parameters within each field. For example some common analysis could include:

- Analyzing worker benefits based on salary ranges or specific professions; 

- Comparing survival rates of injured employees across different regions; 

- Seeing how injuries vary across gender lines;

- Studying dispute resolution patterns over time;

- Examining attorney or representative impact on settlement outcomes; And much more!  

With this guide hopefully you have been equipped with a basic understanding of how to use the Workers’ Compensation Claims in New York State: 2000-Present Kaggle database so that your explorations become more fruitful! Enjoy!

Research Ideas

• Identifying potential problem areas in the workers’ compensation system and illustrating how to best resolve those issues.
• Demonstrating potential correlations between types of injuries, claim types, and outcomes in order to inform better decision-making with regards to workplace safety.
• Estimating the financial impact of future claims based on current trends in workers' compensation data

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: Assembled_Workers_Compensation_Claims_Beginning_2000.csv | Column ...

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Insurance Analytics Market Size 2025-2029

The insurance analytics market size is valued to increase by USD 16.12 billion, at a CAGR of 16.7% from 2024 to 2029. Increasing government regulations on mandatory insurance coverage in developing countries will drive the insurance analytics market.

Market Insights

North America dominated the market and accounted for a 36% growth during the 2025-2029.
By Deployment - Cloud segment was valued at USD 4.41 billion in 2023
By Component - Tools segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 328.64 million 
Market Future Opportunities 2024: USD 16123.20 million
CAGR from 2024 to 2029 : 16.7%

Market Summary

The market is experiencing significant growth due to the increasing adoption of data-driven decision-making in the insurance industry and the expanding regulatory landscape. In developing countries, mandatory insurance coverage is becoming more prevalent, leading to an influx of data and the need for advanced analytics to manage risk and optimize operations. Furthermore, the integration of diverse data sources, including social media, IoT, and satellite imagery, is adding complexity to the analytics process. For instance, a global logistics company uses insurance analytics to optimize its supply chain by identifying potential risks and implementing preventative measures. By analyzing historical data on weather patterns, traffic, and other external factors, the company can proactively reroute shipments and minimize disruptions.
Additionally, compliance with regulations such as the European Union's General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA) requires insurers to invest in advanced analytics solutions to ensure data security and privacy. Despite these opportunities, challenges remain. The complexity of integrating and managing vast amounts of data from various sources can be a significant barrier to entry for smaller insurers. Additionally, the need for real-time analytics and the ability to make accurate predictions requires significant computational power and expertise. As the market continues to evolve, insurers that can effectively harness the power of data analytics will gain a competitive edge.

What will be the size of the Insurance Analytics Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free Sample

The market is a dynamic and ever-evolving landscape, driven by advancements in technology and the growing demand for data-driven insights. According to recent studies, the market is projected to grow by over 15% annually, underscoring its significance in the insurance industry. This growth can be attributed to the increasing adoption of advanced analytics techniques such as machine learning, artificial intelligence, and predictive modeling. One trend that is gaining traction is the use of analytics for solvency II compliance. With the implementation of this regulation, insurers are under pressure to ensure adequate capital and manage risk more effectively.
Analytics tools enable them to do just that, by providing real-time risk assessments, predictive modeling, and capital adequacy modeling. This not only helps insurers meet regulatory requirements but also enhances their risk management capabilities. Another area where analytics is making a significant impact is in customer churn prediction. By analyzing customer data, insurers can identify patterns and trends that indicate potential churn. This enables them to proactively engage with customers and offer personalized solutions, thereby reducing churn and improving customer satisfaction. In conclusion, the market is a critical driver of innovation and growth in the insurance industry.
Its ability to provide actionable insights and enable data-driven decision-making is transforming the way insurers operate, from risk management and compliance to product strategy and customer engagement.

Unpacking the Insurance Analytics Market Landscape

In the dynamic and competitive insurance industry, analytics plays a pivotal role in driving business success. Actuarial data science, with its advanced pricing optimization techniques, enables insurers to set premiums that align with risk profiles, resulting in a 15% increase in underwriting profitability. Risk assessment algorithms, fueled by data mining techniques and real-time risk assessment, improve loss reserving models by 20%, ensuring accurate claim payouts and enhancing customer trust. Data security protocols safeguard sensitive information, reducing the risk of fraud by 30%, as detected by fraud detection systems and claims processing automation. Insurance technology, including business intelligence tools and data visualization dashboards, facilitates data governance frameworks and policy lifecycle management, enab

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