Software suite of proprietary databases that contain one of longest running and largest collection of privately and publicly insured, de identified patient data in USA. Family of data sets that fully integrate many types of data for healthcare research.

The IBM MarketScan® Research Databases contain real-world data for healthcare research and analytics to examine health economics and treatment outcomes.

Abstract

The MarketScan Dental Database is a standalone product that corresponds with and is linkable to a given year and version of the IBM MarketScan Commercial Claims and Encounters Database and the MarketScan Medicare Supplemental and Coordination of Benefits Database. Currently, data is available for the years: 2005 - 2023. In order to view the MarketScan Dental user guide or data dictionary, you must have data access to this dataset.

Usage

In addition to what's on this page, we also have:

• The MarketScan Commercial Database
• The MarketScan Medicare Supplemental Database
• The MarketScan Benefit Plan Design data

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• MarketScan Redbook (The MarketScan Code Reference Book)

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Before Manuscript Submission

All manuscripts (and other items you'd like to publish) must be submitted to

support@stanfordphs.freshdesk.com for approval prior to journal submission.

We will check your cell sizes and citations.

For more information about how to cite PHS and PHS datasets, please visit:

https:/phsdocs.developerhub.io/need-help/citing-phs-data-core

Data Documentation

Data access is required to view this section.

Section 3

Metadata access is required to view this section.

Section 4

Metadata access is required to view this section.

Section 5

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Section 6

Metadata access is required to view this section.

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.

Abstract

MarketScan databases in the OMOP data model (https://www.ohdsi.org/data-standardization/the-common-data-model/)

Abstract

The MarketScan Medicare Supplemental Database provides detailed cost, use and outcomes data for healthcare services performed in both inpatient and outpatient settings.

It Include Medicare Supplemental records for all years, and Medicare Advantage records starting in 2020.

This page also contains the MarketScan Medicare Lab Database starting in 2018.

Methodology

MarketScan Research Databases are a family of data sets that fully integrate many types of data for healthcare research, including:

• De-identified records of more than 250 million patients (medical, drug and dental)

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• Laboratory results

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• Hospital discharges

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The MarketScan Databases track millions of patients throughout the healthcare system. The data are contributed by large employers, managed care organizations, hospitals, EMR providers and Medicare.

Before Manuscript Submission

All manuscripts (and other items you'd like to publish) must be submitted to

support@stanfordphs.freshdesk.com for approval prior to journal submission.

We will check your cell sizes and citations.

For more information about how to cite PHS and PHS datasets, please visit:

https:/phsdocs.developerhub.io/need-help/citing-phs-data-core

Data Documentation

Data access is required to view this section.

Section 2

Metadata access is required to view this section.

Section 3

Metadata access is required to view this section.

Abstract

This is an empty dataset for the purposes of managing permissions. This dataset will be decommissioned in January of 2021. Please add it to any study where you are using IBM MarketScan. This will ensure you do not lose data access.

The potential therapeutic benefit of fluoxetine with standard of care treatment was evaluated in GBM patients cohort using electronic medical records from the IBM MarketScan Dataset (2003-2017). GBM Patients with two other SSRIs, citalopram and escitalopram, were used as controls. The dataset includes six figures: data S1 Figures 1-6 which provide more details of the data overview, data extraction pipeline, exclusion criteria, enrichment for GBM patients, statistical analyses, and results.

Demographics of unique persons in IBM MarketScan database with at least one health insurance claim with diagnosis of bipolar disorder, schizophrenia, Parkinson disease, personality disorder, epilepsy, or major depression during 2003 to 2013.

Baseline characteristics of HF patients stratified by ejection fraction class (HFrEF, < 0.45; or HFpEF, ≥ 0.45).

The AI Radiology Tool market is experiencing robust growth, driven by the increasing volume of medical images, the need for improved diagnostic accuracy, and the rising adoption of AI-powered solutions in healthcare. The market's expansion is fueled by several key trends, including advancements in deep learning algorithms, the development of cloud-based radiology platforms, and the growing demand for efficient and cost-effective diagnostic tools. While regulatory hurdles and data privacy concerns pose challenges, the overall market outlook remains positive. Considering a typical CAGR of 15-20% for emerging medical technologies and a base year of 2025, we can project a market size of approximately $2 billion in 2025, growing steadily to reach $5-7 billion by 2033. This growth will be significantly influenced by the expansion of telehealth and remote diagnostics, as AI-powered tools can readily facilitate accurate image analysis across geographical distances. Furthermore, the market segmentation, encompassing tools for various imaging modalities (X-ray, CT, MRI, etc.) and specific applications (cancer detection, cardiovascular analysis, etc.), shows robust growth across all segments. The major players in the market, including IBM, Bay Labs, Resonance Health, Zebra Medical Vision, Samsung Electronics, Arterys, Koninklijke Philips, Nuance Communications, Siemens Healthineers AG, and OrCam, are constantly innovating to improve their offerings and expand their market share. Competitive pressures are driving advancements in both the accuracy and speed of AI-powered diagnostics, leading to a race to develop more effective and efficient tools. The geographic distribution is expected to show a strong concentration in North America and Europe initially, with a gradual increase in adoption in the Asia-Pacific and other regions fueled by improving healthcare infrastructure and increased digitalization in these areas. The integration of AI radiology tools within existing hospital information systems (HIS) and picture archiving and communication systems (PACS) is a key factor driving market penetration. Overall, the AI Radiology Tool market is poised for substantial growth over the next decade, fueled by technological advancements, increasing healthcare needs, and expanding adoption across various geographic regions.

Steps to identify care episodes for three study populations from the linked claims-EHR databases.

The most common first-line PD medication for US patients in the IBM marketscan databasea.

The medical imaging solutions market is experiencing robust growth, driven by technological advancements, an aging global population necessitating increased diagnostic procedures, and rising prevalence of chronic diseases. The market, encompassing software and hardware solutions across modalities like CT, MRI, X-ray, and ultrasound, is projected to maintain a healthy Compound Annual Growth Rate (CAGR). Software-based solutions are gaining significant traction due to their cost-effectiveness, ease of integration, and ability to enhance diagnostic accuracy through AI-powered image analysis. Hardware-based solutions, while representing a larger segment currently, are witnessing innovation in areas like portable devices and improved image resolution, fueling continued market demand. Key players like Intel, IBM, NVIDIA, and established medical device manufacturers are actively shaping the market landscape through strategic partnerships, acquisitions, and continuous product development. North America and Europe currently hold the largest market share, owing to advanced healthcare infrastructure and higher adoption rates. However, rapidly developing economies in Asia-Pacific are expected to witness significant growth in the coming years, driven by increasing healthcare spending and rising disposable incomes. The market faces challenges such as high initial investment costs for advanced imaging equipment, stringent regulatory approvals, and data security concerns related to patient information. Despite these challenges, the long-term outlook for medical imaging solutions remains positive. The integration of artificial intelligence (AI) and machine learning (ML) is revolutionizing diagnostic accuracy and workflow efficiency, enabling faster and more precise diagnoses. This is further augmented by the increasing adoption of cloud-based solutions, enabling better data management and collaboration among healthcare providers. The market segmentation by application (CT, MRI, X-ray, Ultrasound) and solution type (software, hardware) provides crucial insights for strategic planning and investment decisions. Further regional analysis unveils significant market opportunities in emerging markets, which will play a significant role in shaping the overall market trajectory over the next decade. Continuous innovation in image processing techniques and the integration of advanced analytics are set to propel the growth of this dynamic and crucial segment of the healthcare technology sector.

Primary analysis and subgroup- specific performance.

The global medical imaging solutions market is experiencing robust growth, driven by technological advancements, an aging population, increasing prevalence of chronic diseases, and rising demand for improved diagnostic accuracy. The market, currently estimated at $25 billion in 2025, is projected to exhibit a compound annual growth rate (CAGR) of 5%, reaching approximately $33 billion by 2033. This growth is fueled by the adoption of advanced imaging modalities such as AI-powered image analysis, cloud-based solutions for improved accessibility and collaboration, and the integration of minimally invasive procedures. The increasing focus on preventative healthcare and early diagnosis, coupled with government initiatives to improve healthcare infrastructure, particularly in emerging economies, are also key contributing factors. Software-based solutions, with their flexibility and scalability, are witnessing significant adoption, while hardware-based solutions remain crucial for capturing high-quality images. Among applications, CT, MRI, and X-ray segments dominate the market, though ultrasound is experiencing a surge in demand due to its portability and non-invasive nature. The market landscape is highly competitive, with major players such as Intel, IBM, NVIDIA, and several specialized medical imaging companies constantly innovating and expanding their product portfolios. Strategic partnerships, mergers, and acquisitions are common strategies employed to gain market share and access new technologies. However, challenges remain, including high initial investment costs associated with advanced imaging equipment, data privacy concerns surrounding the use of patient data, and the need for skilled professionals to operate and interpret the increasingly complex imaging systems. Regional variations in market growth are expected, with North America and Europe maintaining a significant share due to advanced healthcare infrastructure and high adoption rates, while the Asia Pacific region is poised for substantial growth driven by expanding healthcare expenditure and increasing awareness. The continued focus on enhancing image quality, reducing radiation exposure, and integrating artificial intelligence will further shape the evolution of this dynamic market.

Background: Trajectories of comorbidities among individuals at risk of Alzheimer’s disease (AD) may differ from those aging without AD clinical syndrome. Therefore, characterizing the comorbidity burden and pattern associated with AD risk may facilitate earlier detection, enable timely intervention, and help slow the rate of cognitive and functional decline in AD. This case-control study was performed to compare the prevalence of comorbidities between AD cases and controls during the 5 years prior to diagnosis (or index date for controls); and to identify comorbidities with a differential time-dependent prevalence trajectory during the 5 years prior to AD diagnosis.Methods: Incident AD cases and individually matched controls were identified in a United States claims database between January 1, 2000 and December 31, 2016. AD status and comorbidities were defined based on the presence of diagnosis codes in administrative claims records. Generalized estimating equations were used to assess evidence of changes over time and between AD and controls. A principal component analysis and hierarchical clustering was performed to identify groups of AD-related comorbidities with respect to prevalence changes over time (or trajectory), and differences between AD and controls.Results: Data from 186,064 individuals in the IBM MarketScan Commercial Claims and Medicare Supplementary databases were analyzed (93,032 AD cases and 93,032 non-AD controls). In total, there were 177 comorbidities with a ≥ 5% prevalence. Five main clusters of comorbidities were identified. Clusters differed between AD cases and controls in the overall magnitude of association with AD, in their diverging time trajectories, and in comorbidity prevalence. Three clusters contained comorbidities that notably increased in frequency over time in AD cases but not in controls during the 5-year period before AD diagnosis. Comorbidities in these clusters were related to the early signs and/or symptoms of AD, psychiatric and mood disorders, cerebrovascular disease, history of hazard and injuries, and metabolic, cardiovascular, and respiratory complaints.Conclusion: We demonstrated a greater comorbidity burden among those who later developed AD vs. controls, and identified comorbidity clusters that could distinguish these two groups. Further investigation of comorbidity burden is warranted to facilitate early detection of individuals at risk of developing AD.

The AI in Medical Imaging market is experiencing explosive growth, projected to reach a value of $5.86 billion in 2025 and exhibiting a remarkable Compound Annual Growth Rate (CAGR) of 28.32% from 2025 to 2033. This surge is driven by several key factors. Firstly, the increasing availability of large, high-quality medical image datasets fuels the development and refinement of sophisticated AI algorithms capable of detecting subtle anomalies often missed by the human eye. This leads to earlier and more accurate diagnoses, improving patient outcomes and reducing healthcare costs. Secondly, advancements in computing power and the decreasing cost of high-performance computing are making AI-powered image analysis more accessible and cost-effective for healthcare providers. Thirdly, regulatory approvals and increasing industry collaborations are streamlining the adoption of AI solutions in clinical practice. The market is segmented by offering (software tools/platforms and services), image acquisition technology (X-ray, CT, MRI, Ultrasound, Molecular Imaging), and end-user (hospitals, clinics, research labs, diagnostic centers). The leading players, including Siemens Healthineers, GE Healthcare, and IBM Watson Health, are investing heavily in research and development, driving innovation and competition within the sector. This competitive landscape fosters rapid technological advancements and ensures a diverse range of solutions catering to various healthcare needs. The substantial growth in the AI in medical imaging market is further amplified by evolving trends such as the growing adoption of cloud-based solutions for image storage and analysis, enabling seamless data sharing and collaborative diagnostics. The integration of AI with other medical technologies, such as wearable sensors and telehealth platforms, promises to further enhance diagnostic capabilities and improve patient monitoring. However, challenges remain, including the need for robust data security and privacy measures, the establishment of clear regulatory guidelines for AI-driven diagnostics, and the need for ongoing education and training for healthcare professionals to effectively utilize these advanced technologies. Addressing these challenges will be crucial in fully realizing the transformative potential of AI in revolutionizing medical imaging and patient care. Recent developments include: November 2022 - The annual conference of the Radiological Society of North America (RSNA) presented a portfolio of smart diagnostic equipment and disruptive workflow solutions from Royal Philips, a leading global provider of health technology. The firm will deliver its most current systems and informatics solutions powered by AI that enable providers to offer high-quality imaging services that are patient-centric quickly., July 2022 - Exo, the health information and medical devices company, announced the acquisition of Medo, a developer of artificial intelligence (AI) technology based in Canada to enhance ultrasound imaging by making it faster and easier by integrating Medo's proprietary Sweep AI technology into its ultrasound platform, and make ultrasound imaging widely accessible to a broader range of healthcare providers.. Key drivers for this market are: Increasing Imaging Volumes. Potential restraints include: Increasing Imaging Volumes. Notable trends are: Computed Tomography is Expected to Drive the Market Growth.

The global radiology AI market is experiencing rapid growth, driven by the increasing adoption of artificial intelligence in healthcare. The market is projected to reach a substantial size, with a Compound Annual Growth Rate (CAGR) reflecting significant year-on-year expansion. While precise figures for market size and CAGR are not provided, based on industry reports and the involvement of major players like Arterys, Aidoc, GE, IBM, Medtronic, Qure.ai, and Siemens, a reasonable estimate would place the 2025 market size in the range of $1.5 Billion to $2 Billion USD, with a CAGR exceeding 20% for the forecast period (2025-2033). This robust growth is fueled by several key factors. The increasing volume of medical images requiring analysis, coupled with a shortage of radiologists, creates a strong demand for AI-powered solutions that can improve diagnostic accuracy and efficiency. Furthermore, advancements in deep learning algorithms and the decreasing cost of computing power are making AI-based radiology tools more accessible and affordable. The market is segmented by modality (e.g., CT, MRI, X-ray), application (e.g., disease detection, image analysis), and end-user (e.g., hospitals, clinics). However, market penetration faces certain restraints. Concerns regarding data privacy, regulatory hurdles for AI medical devices, and the need for robust validation and clinical trials before widespread adoption pose significant challenges. Despite these challenges, the long-term outlook for the radiology AI market remains positive. Continuous technological innovation, increasing investment in AI research, and the growing acceptance of AI in clinical workflows will further propel market expansion in the coming years. The integration of AI in radiology is transforming how medical images are analyzed, leading to faster diagnoses, improved patient outcomes, and enhanced healthcare efficiency. The market's future success will hinge on addressing the existing challenges while leveraging the potential of AI to revolutionize radiology practices.

Recent studies have suggested that dipeptidyl peptidase 4 (DPP4) inhibitors increase the risk of development of bullous pemphigoid (BP), which is the most common autoimmune blistering skin disease; however, the associated mechanisms remain unclear, and thus far, no therapeutic targets responsible for drug-induced BP have been identified. Therefore, we used clinical data mining to identify candidate drugs that can suppress DPP4 inhibitor-associated BP, and we experimentally examined the underlying molecular mechanisms using human peripheral blood mononuclear cells (hPBMCs). A search of the US Food and Drug Administration Adverse Event Reporting System and the IBM® MarketScan® Research databases indicated that DPP4 inhibitors increased the risk of BP, and that the concomitant use of lisinopril, an angiotensin-converting enzyme inhibitor, significantly decreased the incidence of BP in patients receiving DPP4 inhibitors. Additionally, in vitro experiments with hPBMCs showed that DPP4 inhibitors upregulated mRNA expression of MMP9 and ACE2, which are responsible for the pathophysiology of BP in monocytes/macrophages. Furthermore, lisinopril and Mas receptor (MasR) inhibitors suppressed DPP4 inhibitor-induced upregulation of MMP9. These findings suggest that the modulation of the renin-angiotensin system, especially the angiotensin1-7/MasR axis, is a therapeutic target in DPP4 inhibitor-associated BP.