Abstract

Optum DOD (Date of Death) v8.0 database in the OMOP data model (https://www.ohdsi.org/data-standardization/the-common-data-model/)

Section 10

A Condition Era is defined as a span of time when the Person is assumed to have a given condition. Similar to Drug Eras, Condition Eras are chronological periods of Condition Occurrence. Combining individual Condition Occurrences into a single Condition Era serves two purposes:

• It allows aggregation of chronic conditions that require frequent ongoing care, instead of treating each Condition Occurrence as an independent event.
• It allows aggregation of multiple, closely timed doctor visits for the same Condition to avoid double-counting the Condition Occurrences.

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For example, consider a Person who visits her Primary Care Physician (PCP) and who is referred to a specialist. At a later time, the Person visits the specialist, who confirms the PCP's original diagnosis and provides the appropriate treatment to resolve the condition. These two independent doctor visits should be aggregated into one Condition Era.

Conventions

• Condition Era records will be derived from the records in the CONDITION_OCCURRENCE table using a standardized algorithm.
• Each Condition Era corresponds to one or many Condition Occurrence records that form a continuous interval.
• Condition Eras are built with a Persistence Window of 30 days, meaning, if no occurrence of the same condition_concept_id happens within 30 days of any one occurrence, it will be considered the condition_era_end_date.

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The text above is taken from the OMOP CDM v5.3 Specification document.

Section 5

The CONCEPT_ANCESTOR table is designed to simplify observational analysis by providing the complete hierarchical relationships between Concepts. Only direct parent-child relationships between Concepts are stored in the CONCEPT_RELATIONSHIP table. To determine higher level ancestry connections, all individual direct relationships would have to be navigated at analysis time. The CONCEPT_ANCESTOR table includes records for all parent-child relationships, as well as grandparent-grandchild relationships and those of any other level of lineage.

Using the CONCEPT_ANCESTOR table allows for querying for all descendants of a hierarchical concept. For example, drug ingredients and drug products are all descendants of a drug class ancestor.

Conventions

• The concept_name field contains a valid Synonym of a concept, including the description in the concept_name itself. I.e. each Concept has at least one Synonym in the CONCEPT_SYNONYM table. As an example, for a SNOMED-CT Concept, if the fully specified name is stored as the concept_name of the CONCEPT table, then the Preferred Term and Synonyms associated with the Concept are stored in the CONCEPT_SYNONYM table.
• Only Synonyms that are active and current are stored in the CONCEPT_SYNONYM table. Tracking synonym/description history and mapping of obsolete synonyms to current Concepts/Synonyms is out of scope for the Standard Vocabularies.
• Currently, only English Synonyms are included.

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The text above is taken from the OMOP CDM v5.3 Specification document.

Section 4

The COST table captures records containing the cost of any medical entity recorded in one of the DRUG_EXPOSURE, PROCEDURE_OCCURRENCE, VISIT_OCCURRENCE or DEVICE_OCCURRENCE tables.

The information about the cost is defined by the amount of money paid by the Person and Payer, or as the charged cost by the healthcare provider. So, the COST table can be used to represent both cost and revenue perspectives. The cost_type_concept_id field will use concepts in the Standardized Vocabularies to designate the source of the cost data. A reference to the health plan information in the PAYER_PLAN_PERIOD table is stored in the record that is responsible for the determination of the cost as well as some of the payments.

Convention

The COST table will store information reporting money or currency amounts. There are three types of cost data, defined in the cost_type_concept_id: 1) paid or reimbursed amounts, 2) charges or list prices (such as Average Wholesale Prices), and 3) costs or expenses incurred by the provider. The defined fields are variables found in almost all U.S.-based claims data sources, which is the most common data source for researchers. Non-U.S.-based data holders are encouraged to engage with OHDSI to adjust these tables to their needs.

One cost record is generated for each response by a payer. In a claims databases, the payment and payment terms reported by the payer for the goods or services billed will generate one cost record. If the source data has payment information f

Final model coefficients and hazard ratios in the derivation cohort, MarketScan, 2007–2014.

[Keywords] Market include IBM, Definitive Healthcare, Optum, SAS, Tableau

The global market for IT spending on clinical analytics is experiencing robust growth, driven by the increasing adoption of electronic health records (EHRs), the rising prevalence of chronic diseases, and the growing need for data-driven decision-making in healthcare. The market's expansion is fueled by the ability of clinical analytics to improve patient outcomes, reduce healthcare costs, and enhance operational efficiency. Advancements in big data analytics, artificial intelligence (AI), and machine learning (ML) are further accelerating market growth. While precise figures weren't provided, let's assume, based on industry reports and the stated study period of 2019-2033, a conservative market size of $25 billion in 2025 and a Compound Annual Growth Rate (CAGR) of 12%. This suggests a significant expansion to approximately $60 billion by 2033. The market segmentation reveals strong demand across both stand-alone and integrated solutions, with payer and provider applications equally vital. Major players like Allscripts, Cerner, and Optum are driving innovation, developing sophisticated platforms that integrate diverse data sources and provide actionable insights. However, challenges remain. High implementation costs, data security concerns, and the need for skilled professionals to interpret complex analytics can hinder broader adoption. Furthermore, interoperability issues between different healthcare systems continue to pose a significant obstacle. Despite these hurdles, the long-term outlook for IT spending on clinical analytics remains positive, driven by increasing government initiatives promoting digital health and the inherent value proposition of data-driven healthcare improvements.

Optum Inc Business Operations, Opportunities, Challenges and Risk (SWOT, PESTLE and Porters Five Forces Analysis); Corporate and ESG Strategies; Competitive Intelligence; Financial KPI’s; Operational KPI’s; Recent Trends: “ Read More

Optum validation sensitivity analysis: Algorithm chosen pregnancy start categorized by difference in either direction from infertility procedure derived start.

Countries detailed in Fig 4.

The top health IT companies in the U.S. as of 2018 includes Optum, Cerner Corp. and Cognizant Technology Solutions. The top company, Optum, generated about 8.1 billion dollars in revenue during that year. Optum generated significantly more revenue than the second health IT company, Cerner Corp., which generated 5.1 billion dollars in revenue during that year.

Health IT

Health IT in the U.S. is a multifaceted industry with many different parts and specializations. Health IT includes a variety of sectors including electronic health records (EHR), personal health records (PHR), electronic prescribing and privacy and security, to name a few. The percentage of U.S. physicians that had EHR systems in their practices has increased dramatically in recent years. Likewise, the number of e-prescriptions made in the U.S. is on the rise in recent years.

Health data breaches

Health care data breaches are one of the primary concerns among health IT companies. Recent data suggests that the number of data breaches in the U.S. health and medical sector have increased over the last decade. Compared to other industries, data breaches in the healthcare segment are significantly more expensive. Despite the number of health data breaches increasing, the number of U.S. residents that have been affected seems to vary considerably from year to year.

Get the sample copy of Healthcare Data Analytics Market Report 2025 (Global Edition) which includes data such as Market Size, Share, Growth, CAGR, Forecast, Revenue, list of Healthcare Data Analytics Companies (Allscripts (US), Cerner (US), Health Catalyst (US), IBM (US), Inovalon (US), McKesson (US), MedeAnalytics (US), Optum (US), Oracle (US), SAS (US), Wipro (India), Verscend (US), CitusTech (US), VitreosHealth (US), SCIO Health (US)), Market Segmented by Type (Descriptive, Predictive, Prescriptive), by Application (Clinical, Hospital, Goverment, Others)

Selected patient characteristics for all adults with prevalent and incident atrial fibrillation annually, 2017–2020.

The clinical data analytics market revenue totaled around US$ 15,100.1 million in 2022 and is expected to reach US$ 18,769.4 million in 2023. Furthermore, with rising adoption in the healthcare industry, the overall demand for clinical data analytics is projected to record a staggering CAGR of 25.9% between 2023 and 2033, totaling a valuation of US$ 1,88,305 million by 2033.

Scope of Report

The global Healthcare Enterprise Content Management (ECM) market size was valued at USD 674.52 million in 2025 and is projected to grow from USD 991.71 million in 2023 to USD 2,743.04 million by 2033, exhibiting a CAGR of 10.29% during the forecast period. The market growth is attributed to the increasing adoption of cloud-based ECM solutions, rising demand for efficient document management and workflow automation, and increasing compliance regulations in the healthcare industry. Key drivers of the market include the need for improved patient care coordination, the rising adoption of electronic health records (EHRs), and the increasing demand for data privacy and security. Furthermore, the growing adoption of mobile devices in healthcare settings and the increasing use of artificial intelligence (AI) and machine learning (ML) in healthcare applications are also contributing to the market growth. The market is segmented into software, services, and infrastructure, with the software segment holding the largest share. The major players in the market include IBM, Siemens Healthineers, Oracle, Meditech, Veeva Systems, Allscripts Healthcare Solutions, McKesson, Epic Systems, Nuance Communications, Optum, Philips Healthcare, Lexmark, Cerner, Athenahealth, and InterSystems. The Healthcare Enterprise Content Management (ECM) market exhibits a moderately concentrated landscape, with leading players holding significant market share. These established vendors have well-established solutions and a strong customer base. However, the market also features a number of niche players specializing in specific areas of ECM within healthcare. The industry's characteristics include a focus on innovation, regulatory compliance, end-user concentration, and M&A activity. Recent developments include: , Recent developments in the Healthcare Enterprise Content Management Market have seen significant activity, particularly regarding mergers and acquisitions among key players. IBM and Oracle have been focused on enhancing their content management capabilities, leading to strategic partnerships aimed at better data integration. Siemens Healthineers announced advancements in digital health platforms, emphasizing improved content management in healthcare documentation. Meditech and Veeva Systems are also expanding their market reach through collaborative efforts to streamline healthcare workflows, while Allscripts Healthcare Solutions and McKesson are investing in innovative solutions to improve patient care and operational efficiency., Epic Systems has seen growth driven by the rising demand for tailored healthcare content management solutions, impacting its market valuation positively. Additionally, companies like Nuance Communications and Optum are leveraging artificial intelligence to enhance their healthcare content management systems, contributing to overall market growth. The heightened emphasis on data security and compliance with regulations continues to be a driving force in this sector, prompting companies like Philips Healthcare and Cerner to invest heavily in secure content management systems. The increasing consolidation in the market is reshaping the competitive landscape and reflecting a strong focus on improving healthcare delivery through efficient content management., Healthcare Enterprise Content Management Market Segmentation Insights. Key drivers for this market are: Cloud-based solutions adoption, Integration with AI technologies; Regulatory compliance enhancement; Improved data security measures; Personalized patient experiences. Potential restraints include: Digital transformation in healthcare, Compliance and regulatory requirements; Increasing data volume and complexity; Enhanced patient engagement solutions; and Need for operational efficiency..

All numbers and percentages in this table are unadjusted.

Descriptive Analytics: Focuses on analyzing historical data to provide insights into past performance and identify trends.Predictive Analytics: Leverages advanced algorithms to forecast future events and outcomes, enabling proactive decision-making. Recent developments include: In February 2022, Optum launched the Optum Speciality Fusion which is a first-of-a-kind medical management solution that offers simplified care to patients suffering from complex conditions, with a lower cost for expensive drugs , In March 2022, Microsoft announced the launch of the Azure Health Data Services platform that is designed to support Protected Health Information (PHI) in the cloud. It is a fresh way of working with unified data that supports both transactional and analytical workloads and enables cloud computing to deliver AI across the healthcare system .

The healthcare natural language processing (NLP) market is projected to reach a value of $2.86 billion by 2033, expanding at a compound annual growth rate (CAGR) of 17.5% from 2025 to 2033. The increasing adoption of electronic health records (EHRs) and the need for efficient patient data management are driving the growth of the market. The growing number of healthcare applications, such as clinical documentation, patient interaction, and data mining, is also contributing to the market's growth. Cloud-based deployment is gaining traction, as it offers cost-effective and scalable solutions for healthcare providers. Key players in the healthcare NLP market include Microsoft, Google, Optum, HealthAPIx, Amazon, IBM, 3M, Computable General Equilibrium, Philips, Mayo Clinic, Siemens Healthineers, Cerner, Cognizant, Nuance Communications, and Verint Systems. Recent developments include: , The Healthcare Natural Language Processing Market has recently seen significant advancements, driven largely by the rising demand for better healthcare services and efficient data management. Companies like Microsoft and Google are investing heavily in AI-driven language processing solutions to enhance patient care and streamline administrative tasks. Optum has launched new applications that leverage natural language understanding to improve clinical documentation and patient interactions. Moreover, Amazon has been expanding its healthcare offerings by integrating NLP capabilities into its services to optimize patient engagement., Mergers and acquisitions have also reshaped the landscape; IBM acquired a technology firm specializing in natural language processing to bolster its health solutions, while Cerner announced a strategic partnership that focuses on refining data interpretation systems. The continued growth in the valuation of firms such as Nuance Communications and 3M is indicative of a robust market, emphasizing the increasing reliance on advanced NLP technologies in health informatics. Siemens Healthineers and Philips are also ramping up their technological capabilities, further highlighting the competitive environment in the global healthcare NLP space, which is poised for substantial growth and innovation., Healthcare Natural Language Processing Market Segmentation Insights. Key drivers for this market are: Patient data analysis enhancement, Clinical decision support automation; Improved patient engagement tools; Revenue cycle management optimization; and Real-time transcription services.. Potential restraints include: Rising healthcare data volume, Increasing adoption of AI; Enhanced patient engagement tools; Need for cost-effective solutions; Growing regulatory compliance requirements.

Prevalence ratio estimated from the period prevalence of atrial fibrillation among all adults vs. adults with a recent ischemic stroke in 2017–2020.

[Keywords] Market include GE Healthcare, Optum Health, MISUMI Europa GmbH, Cerner Corporation, Cleo

The global healthcare medical system integrator market is projected to reach a value of $674.52 million by 2033, expanding at a CAGR of 4.95% from 2025 to 2033. Key drivers fueling market growth include the rising adoption of electronic health records, the need for interoperability between different healthcare systems, and the increasing demand for data integration and analytics. The trend towards cloud-based systems and the growing focus on value-based healthcare are also expected to contribute to market growth. Key market segments include application, end-user, service type, and technology. Based on application, the clinical workflow management segment holds the largest market share, while the telemedicine solutions segment is expected to grow at the fastest rate during the forecast period. Hospitals and healthcare providers are the primary end-users of healthcare medical system integrators, with pharmaceutical companies and diagnostic laboratories also contributing to market growth. Consulting services and integration services are the most common service types, while cloud-based systems are gaining popularity due to their scalability, cost-effectiveness, and ease of use. Major companies operating in the market include Oracle Corporation, Siemens Healthineers, Allscripts Healthcare Solutions, Roper Technologies, General Electric, Epic Systems, Optum, and Philips. Rapidly evolving technological advancements are revolutionizing the healthcare industry, driving the demand for integrated solutions that enhance operational efficiency, improve patient care, and reduce costs. The healthcare medical system integrator market is poised for significant growth, fueled by the increasing adoption of digital health technologies, government initiatives, and a shift towards value-based care models. Recent developments include: Recent developments in the Global Healthcare and Medical System Integrator Market show significant activity among major players. Oracle Corporation continues to expand its cloud healthcare solutions, focusing on data management and interoperability. Siemens Healthineers recently announced advancements in AI-enabled imaging solutions, enhancing diagnostic accuracy. Allscripts Healthcare Solutions is strengthening its electronic health record systems to improve patient engagement and outcomes. Roper Technologies has been acquiring companies to bolster its healthcare analytics capabilities, while General Electric emphasizes innovations in imaging technology to streamline healthcare operations. Epic Systems and Optum are enhancing population health management tools, reflecting the ongoing shift toward value-based care. Philips has launched new monitoring solutions aimed at improving patient care in various settings, and Medtronic is focusing on integrating its devices with software platforms for better clinical insights. Meanwhile, IBM Watson Health is investing substantially in predictive analytics for personalized medicine, and Cerner Corporation continues to drive advancements in health data integration. Notably, there has been an increase in interest in partnerships and collaborations as these companies seek to adapt to changing market dynamics and improve patient outcomes through technology integration.. Key drivers for this market are: Telehealth integration expansion, AI-driven analytics solutions; Interoperability enhancement solutions; Cloud-based healthcare systems; IoT integration for patient monitoring. Potential restraints include: Technological advancements, Increasing healthcare IT investments; Growing demand for interoperability; Rise in patient-centric solutions; Regulatory compliance challenges.