NOTE: Please Read Text File named "ERD Relationship Text" for Detailed Information.

This dataset represents a complete healthcare management system modeled as a relational database containing over 20 interlinked tables. It captures the entire lifecycle of healthcare operations from patient registration to diagnosis, treatment, billing, inventory, and vendor management. The data structure is designed to simulate a real-world hospital information system (HIS), enabling advanced analytics, data modeling, and visualization. You can easily visualize and explore the schema using tools like dbdiagram.io by pasting the provided table definitions.

The dataset covers multiple operational areas of a hospital including patient information, clinical operations, financial transactions, human resources, and logistics.

Patient Information includes personal, contact, and emergency details, along with identification and insurance. Clinical Operations include visits, appointments, diagnoses, treatments, and medications. Financial Transactions cover bills, payments, and vendor settlements. Human Resources include staff details, departments, and medical teams. Logistics and Inventory include equipment, medicines, supplies, and vendor relationships.

• Patients (STG_EHP_PATN) are linked to Appointments, Visits, Diagnoses, Treatments, Bills, and Insurance Policies.
• Medical Teams (STG_EHP_MEDT) connect Staff with Visits and Treatments.
• Allergies and Patient Allergies tables track patient-specific allergy information.
• Financial tables (Bills, Payments, Vendor Payments) are interconnected through reference numbers for consistent transaction tracing.
• Inventory tables record medicine and equipment stock movements, supply receipts, and vendor sourcing.

This dataset can be used for data modeling and SQL practice for complex joins and normalization, healthcare analytics projects involving cost analysis, treatment efficiency, and patient demographics, visualization projects in Power BI, Tableau, or Domo for operational insights, building ETL pipelines and data warehouse models for healthcare systems, and machine learning applications such as predicting patient readmission, billing anomalies, or treatment outcomes.

To explore the data relationships visually, go to dbdiagram.io, paste the entire provided schema code, and press 2 then 1 (or 2 and Enter) to auto-align the diagram. You’ll see an interactive Entity Relationship Diagram (ERD) representing the entire healthcare ecosystem.

Total Tables: 20+ Total Columns: 200+ Primary Focus: Patient Management, Clinical Operations, Billing, and Supply Chain

Evaluating Health Home Care Quality

CMS Core Set and Health Home SPA Measures

By Health Data New York [source]

About this dataset

This dataset provides comprehensive measures to evaluate the quality of medical services provided to Medicaid beneficiaries by Health Homes, including the Centers for Medicare & Medicaid Services (CMS) Core Set and Health Home State Plan Amendment (SPA). This allows us to gain insight into how well these health homes are performing in terms of delivering high-quality care. Our data sources include the Medicaid Data Mart, QARR Member Level Files, and New York State Delivery System Inform Incentive Program (DSRIP) Data Warehouse. With this data set you can explore essential indicators such as rates for indicators within scope of Core Set Measures, sub domains, domains and measure descriptions; age categories used; denominators of each measure; level of significance for each indicator; and more! By understanding more about Health Home Quality Measures from this resource you can help make informed decisions about evidence based health practices while also promoting better patient outcomes

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How to use the dataset

This dataset contains measures that evaluate the quality of care delivered by Health Homes for the Centers for Medicare & Medicaid Services (CMS). With this dataset, you can get an overview of how a health home is performing in terms of quality. You can use this data to compare different health homes and their respective service offerings.

The data used to create this dataset was collected from Medicaid Data Mart, QARR Member Level Files, and New York State Delivery System Incentive Program (DSRIP) Data Warehouse sources.

In order to use this dataset effectively, you should start by looking at the columns provided. These include: Measurement Year; Health Home Name; Domain; Sub Domain; Measure Description; Age Category; Denominator; Rate; Level of Significance; Indicator. Each column provides valuable insight into how a particular health home is performing in various measurements of healthcare quality.

When examining this data, it is important to remember that many variables are included in any given measure and that changes may have occurred over time due to varying factors such as population or financial resources available for healthcare delivery. Furthermore, changes in policy may also affect performance over time so it is important to take these things into account when evaluating the performance of any given health home from one year to the next or when comparing different health homes on a specific measure or set of indicators over time

Research Ideas

• Using this dataset, state governments can evaluate the effectiveness of their health home programs by comparing the performance across different domains and subdomains.
• Healthcare providers and organizations can use this data to identify areas for improvement in quality of care provided by health homes and strategies to reduce disparities between individuals receiving care from health homes.
• Researchers can use this dataset to analyze how variations in cultural context, geography, demographics or other factors impact delivery of quality health home services across different locations

Acknowledgements

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

License

See the dataset description for more information.

Columns

File: health-home-quality-measures-beginning-2013-1.csv | Column name | Description | |:--------------------------|:----------------------------------------------------| | Measurement Year | The year in which the data was collected. (Integer) | | Health Home Name | The name of the health home. (String) | | Domain | The domain of the measure. (String) | | Sub Domain | The sub domain of the measure. (String) | | Measure Description | A description of the measure. (String) | | Age Category | The age category of the patient. (String) | | Denominator | The denominator of the measure. (Integer) | | Rate | The rate of the measure. (Float) | | Level of Significance | The level of significance of the measure. (String) | | Indicator | The indicator of the measure. (String) |

Acknowledgements

...

Overview

New York, NY – Oct 01, 2025 – The Global Healthcare and Medical System Integrators Market size is expected to be worth around USD 9.6 Billion by 2033 from USD 3.8 Billion in 2024, growing at a CAGR of 9.7% during the forecast period from 2025 to 2033.

The healthcare industry is undergoing rapid transformation driven by the demand for improved patient care, digital health adoption, and cost optimization. Healthcare and medical system integrators are playing a crucial role in enabling hospitals, clinics, and research institutions to seamlessly adopt advanced technologies.

System integration solutions are being deployed to connect disparate healthcare IT systems, including electronic health records (EHR), medical imaging platforms, laboratory information systems, and telehealth solutions. This interoperability is enhancing data sharing, streamlining workflows, and improving clinical decision-making. Furthermore, the integration of artificial intelligence (AI), cloud computing, and Internet of Things (IoT) technologies is supporting real-time monitoring, predictive analytics, and personalized treatment planning.

The growth of this sector is being driven by rising healthcare digitization initiatives, regulatory requirements for data security and compliance, and the need to reduce operational inefficiencies. Healthcare providers are increasingly relying on integrators to ensure smooth implementation, scalability, and security of interconnected systems.

As global healthcare spending continues to rise, the role of system integrators is becoming more significant in building smart hospitals and connected care ecosystems. Strategic collaborations between technology vendors, healthcare providers, and integrators are expected to accelerate innovation in digital health and ensure improved patient outcomes.

At The Dartmouth Institute for Health Policy and Clinical Practice, we have created a set of instructions for researchers to recreate a set of selected patient outcomes measured among health systems’ adult Medicare fee-for-service beneficiaries. The foundation of these measures is health care utilization submitted under the Centers for Medicare and Medicaid Services’ (CMS’) Medicare program. We have rolled these up to the health system level, profiling those systems included in the Agency for Healthcare Research and Quality’s (AHRQ’s) Compendium of U.S. Health Systems: https://www.ahrq.gov/chsp/data-resources/compendium.html. Using a combination of CMS Medicare claims data, publicly available health measures (e.g., the Prevention Quality Index [PQI]) and commercially available health system organizational data from IQVIA, these SAS programs build these broad cohorts, assign them to health systems per year and calculate measures (e.g., 30-day hospital readmission). The result is a dataset of health system measures that can be linked back to the annual Compendium files available from AHRQ.

Digital health technologies (DHTs) expand healthcare access, improve care coordination, and reduce costs. However, integrating these tools into care faces complex barriers. Understanding the perspectives of health system leaders is essential for developing sustainable DHTs. The objective of this project is to explore the experiences and priorities of health system stakeholders regarding the implementation of DHTs. The study team conducted semi-structured interviews with 12 stakeholders from diverse U.S. health systems, including clinical, operational, and executive leadership. Interviewees were selected using purposeful and snowball sampling. Interviews were transcribed and analyzed thematically using the Consolidated Framework for Implementation Research (CFIR). A constant comparative coding process was used to identify and organize key themes. Participants viewed DHTs as a way to enhance healthcare access and efficiency and improve public health operations, especially in rural or underserved settings. However, several major adoption challenges emerged: (1) integrating DHTs into existing workflows and electronic health records is operationally burdensome; (2) digital care can introduce risks to quality, continuity, and equity; and (3) external factors (reimbursement policy, regulatory constraints, infrastructure investment) are critical to long-term adoption. Digital health is seen as essential to the future of healthcare delivery, but meaningful integration requires alignment across clinical, operational, and policy domains. Coordinated investment, regulatory reform, and robust data infrastructure are needed to ensure DHTs are scalable and sustainable.

Overview

New York, NY – Aug 13, 2026 – The Global Real-Time E-Healthcare System Market Size is expected to be worth around US$ 63.6 billion by 2034 from US$ 35.2 billion in 2024, growing at a CAGR of 6.1% during the forecast period 2025 to 2034. North America held a dominant market position, capturing more than a 43.7% share and holding a US$ 15.4 billion market value for the year.

The real-time e-healthcare system market is growing significantly due to the rising prevalence of chronic diseases, increasing adoption of digital health solutions, and demand for continuous patient monitoring. According to the Centers for Disease Control and Prevention (CDC), 6 in 10 U.S. adults live with at least one chronic disease, while 4 in 10 adults have two or more chronic conditions, including diabetes, hypertension, and heart disease. These conditions require regular monitoring and timely interventions.

Real-time e-healthcare systems use remote patient monitoring (RPM), wearable devices, artificial intelligence (AI), and cloud-based platforms to collect and analyze health data continuously. The CDC states that chronic diseases account for nearly 90% of the US$4.1 trillion annual healthcare expenditure in the United States, increasing demand for cost-efficient digital care solutions that reduce hospital visits and improve outcomes.

Telehealth adoption has accelerated market growth. The American Medical Association (AMA) reported that physician telemedicine use increased from 14% in 2016 to nearly 80% in 2022, highlighting the shift toward virtual and connected healthcare models. AI-powered analytics further enhance these systems by analyzing real-time patient data, predicting health risks, and supporting personalized treatment decisions.

Integration of electronic health records (EHRs), IoMT devices, and communication platforms is creating connected healthcare ecosystems. In September 2023, Abbott acquired Bigfoot Biomedical to strengthen smart insulin management and personalized diabetes care solutions. With rising chronic disease burden, healthcare digitalization, and demand for proactive monitoring, real-time e-healthcare systems are becoming a key technology in modern healthcare delivery.
https://market.us/wp-content/uploads/2025/09/Real-Time-E-Healthcare-System-Market-Size.jpg">

OVERVIEW

Dmand AI's Healthcare Professionals dataset covers 7M+ US physicians, nurse practitioners, physician assistants, nurses and allied health professionals. Every record is anchored to a public identifier (NPI) and mapped to the organizations where the professional actually practices today: site of care, physician group, hospital, health system and IDN. Contact details are added through waterfall enrichment across multiple healthcare-specific sources and verified before delivery.

The dataset is built for teams selling, recruiting or marketing to US providers: Health IT and digital health vendors, healthcare staffing firms, MedTech and life sciences commercial teams, and agencies and consultancies serving them.

WHAT IS INCLUDED

Identity: full name, NPI, credentials, gender, taxonomy code and primary specialty, sub-specialties, license state(s). Practice: primary and secondary practice locations with full address, practice phone, practice type. Affiliations: current site of care, physician group, hospital, health system and IDN, with the parent-child hierarchy attached so you can roll up or drill down. Contact: verified work email, direct dial, LinkedIn URL. Profile depth: education and training, work history, board certifications, associations and board roles, publications, clinical trial involvement, procedure and patient volume, Open Payments history. Organization context: EHR vendor, service lines, bed count, ownership and workforce data for the affiliated site and system.

COVERAGE

United States, all 50 states and DC. Physicians, nurses and allied health across hospitals and health systems, physician groups, ASCs, FQHCs, urgent care, dialysis, imaging, skilled nursing, assisted living, home health and hospice.

HOW THE DATA IS BUILT

Provider and facility records start from public federal and state sources: NPPES, PECOS, CMS Provider of Services, HCRIS cost reports, Care Compare, HRSA and state licensing boards. Records are then linked into org hierarchies using ownership filings, health system directories and press coverage. Emails, direct dials and LinkedIn URLs are added through a waterfall across multiple healthcare-specific vendors rather than a single contact source, and validated before delivery. Affiliations are refreshed continuously, since stale affiliation is the most common failure in healthcare contact data.

WHAT MAKES IT DIFFERENT

Horizontal B2B databases assign people to companies by email domain, which breaks in healthcare: a physician can practice at three sites under two systems, and a health system can hold dozens of hospitals and hundreds of sites. Dmand maps professionals to the real org tree, so you reach the right person at the right entity. Coverage of facility-level roles that generic databases miss (post-acute clinical leadership, practice administrators) comes from starting with healthcare records and enriching them, not the other way round.

DELIVERY

Custom extracts (CSV or Excel) filtered by specialty, geography, org type, affiliation or any attribute, one-time or with scheduled refresh.

Access inside the Dmand AI platform, with plain-English AI search, list building, enrichment, sequencing and Salesforce and HubSpot sync, if you want to activate the data rather than ingest it.

USE CASES

Sales prospecting and target list building; CRM enrichment and affiliation clean-up; physician and nurse recruiting; KOL, investigator and referral network identification; territory planning and TAM sizing; account-based marketing.

COMPLIANCE

Business contact and professional data only. No patient data, no PHI. Not subject to HIPAA. Individuals can access, correct or remove their profile through the Dmand AI Privacy Center. Customers agree to use the data in compliance with CAN-SPAM, TCPA and applicable state privacy laws. Not provided for FCRA-regulated purposes.

SAMPLES AND PRICING

Sample extracts are available on request against your specialty, geography and org-type criteria. Pricing is quoted by record count, fields and refresh frequency.

This dataset provides a list of Health Resources and Services Administration (HRSA)-supported health centers. These health centers provide comprehensive, affordable, accessible, high-quality primary health care services to medically underserved communities and populations. Health centers are community-based and patient-directed organizations.

PolicyMap downloads Hospitals, Federally Qualified Health Center (FQHC), Home Health Agencies, Nursing facilities, Psychiatric & Community Mental Health Services, and other health facilities points from the HRSA Geospatial Database. These geocoded locations from the HRSA Geospatial Data Warehouse are from a “Provider of Service” extract from the Online Survey and Certification Reporting System database maintained by Centers for Medicare and Medicaid Services. They are included in the HRSA Warehouse because they are the most readily-obtainable data on various classes of health care facility such as hospitals, hospices, rural health clinics, etc.

The Nursing Facility locations provided by HRSA are those facilities participating in Medicare and Medicaid for individuals requiring nursing care and assistance with daily life activities. The Hospitals are those facilities participating in Medicare and Medicaid Services for individuals requiring temporary or long-term medical treatment. The Critical Access Hospitals are those institutions participating in Medicare and Medicaid and meeting the following requirements: being located in rural areas and being located more than 35 miles from any other Hospital or Critical Access Hospital, having no more than 25 inpatient beds and maintaining an average length of stay of 96 hours per patient for acute inpatient care, and providing 24 hour emergency care services.

“Federally Qualified Health Centers (FQHCs)” (often referred to as “Community Health Centers”) receive funding under the Health Center Cluster federal grant program to provide care for underserved populations. The types of providers eligible include Community Health Centers, Migrant Health Centers, Health Care for the Homeless Programs, Public Housing Primary Care Programs, and care providers for some tribal organizations.

HRSA facilities PolicyMap Last Update: 2026-02.

On PolicyMap there is also a dataset called “Community Health Centers and Look-Alikes”, which PolicyMap downloads from the Health Resources and Services Administration (HRSA) website. This includes those receiving grants and community health centers that are eligible but not currently receiving grant funding. Although they are not receiving grants, these providers – or “look-alikes” – are eligible for some benefits including enhanced reimbursement from Medicare and Medicaid. Mapping both FQHCs and “look-alikes” might provide a fuller picture of the health-care safety net in a community.

PolicyMap joins individual health center performance data to the Community Health Centers and Look-Alikes point dataset. HRSA tracks this performance data via the Uniform Data System (UDS), which is a reporting requirement for grantees of the following HRSA primary care programs, as defined in the Public Health Service Act: Community Health Center, Migrant Health Center, Health Care for the Homeless, Public Housing Primary Care. Because the UDS data is self-reported, some performance data did not match up with the Community Health Centers and Look-Alikes dataset. Please refer to the Uniform Data System for the latest 2024 data. Note that the UDS data is released on a different schedule than the HRSA FQHC and look-alikes data. The data source suppressed health center confidential data and patient counts between 1-15 to protect patient privacy. In addition, where Health Center Location Setting is categorized as Domestic Violence, the location was geocoded to the Health Center Address. The source does not publicly disclose exact site addresses to protect the patients at the location. For the Community Health Centers and Look-Alikes dataset, the list and locations of sites were obtained from HRSA in March 2026. This is separate from the HRSA Facilities dataset that includes FQHCs mentioned above.

HRSA CHC PolicyMap Last Update: 2026-04.

Medically Underserved Areas (MUAs) are census tracts designated by the Health Resources and Services Administration as having too few primary care providers, high infant mortality, high poverty, and/or high elderly population. See: http://muafind.hrsa.gov/. Medically Underserved Populations (MUP) are areas where a specific population group is underserved, including groups with economic, cultural, or linguistic barriers to primary medical care. If a population group does not meet the criteria for an MUP, but exceptional conditions exist which are a barrier to health services, they can be designated with a recommendation from the state’s Governor.

Due to what HRSA terms a “source data error”, some areas have multiple designations. In these instances, if any designation is MUA, MUA is shown on the map. If MUP and Governor are designated for a single area, MUP is shown. If multiple IMU scores are provided, the lowest score is shown on the map. Although MUA and MUP data is shown on PolicyMap at the tract level, it is provided by HRSA at the tract, county, and minor civil division (MCD). County and MCD level data is shown at the tract level. In cases where a tract was only partially covered by an applicable MCD, it was labeled as not being an MUA or MUP.

HRSA MUA PolicyMap Last Update:2026-06.

The data layers on PolicyMap related to health care professions, health facilities, and hospital utilization are all from HRSA’s Area Health Resource File (AHRF). This data is compiled by HRSA from multiple original data sources, which means that different indicators are available for different years. PolicyMap calculated all the rates in this dataset using the Census’s population estimates for the appropriate year. For more information about the AHRF, see: https://data.hrsa.gov/data/about.

HRSA AHRF PolicyMap Last Update:2026-08.

Health Professional Shortage Areas (HPSAs) are defined by the Health Resources and Services Administration (HRSA) as areas that need more health providers in primary care, dental health, or mental health. All HPSAs are defined on the basis of three basic criteria: the ratio of population to health providers, percent of population below the federal poverty level, and travel time to the nearest source of care outside the HPSA area. Dental HPSAs also consider an area’s water flouridation status. Mental HPSAs also consider substance and alcohol abuse prevalence, and percentage of the population over the age of 65 or under the age of 18. Primary Care HPSAs also consider infant mortality rate and low birth weight rate. HPSAs may be designated as “geographic”, “population”, or “point” shortage areas. A geographic HPSA is an area where all residents may experience a shortage of providers. A population HPSA is an area where a specific group of people may experience a shortage of providers. A point HPSA is a specific facility, clinic, or health center that may experience a shortage of providers. PolicyMap excludes point HPSAs as they may not be reflective of the larger population or geographic area, and excludes data with HPSA status of "Withdrawn". If a census tract is designated as a HPSA more than once, the status and scores for the most recently updated HPSA are shown. HRSA updates HPSA status frequently, so there may be a lag between the data shown on PolicyMap and the latest designation from HRSA. Go to https://data.hrsa.gov/tools/shortage-area to check whether a site of interest lies within a HPSA.

Maternity Care Health Professional Target Areas (MCTAs) are areas within an existing Primary Care Health Professional Shortage Areas (HPSA) that are experiencing a shortage of maternity health care professionals. Maternity Care Target Areas can receive a score between 0-25. What goes into the MCTA score: Population-to-Full-Time-Equivalent Maternity Care Health Professional Ratio [5 points max], Percentage of Population With Income at or Below 200 Percent of the Federal Poverty Level (FPL) [5 points max], Travel Distance/Time to Nearest Source of Accessible Care Outside of the MCTA [5 points max], Fertility Rate [2 points max], Social Vulnerability [2 points max], Maternal Health Indicators, Pre-Pregnancy Obesity [1 point max], Pre-Pregnancy Diabetes [1 point max], Pre-Pregnancy Hypertension [1 point max], Cigarette Smoking [1 point max], Prenatal Care Initiation in the 1st Trimester [1 point max], Behavioral Health Factor [1 point max]

HRSA HPSA PolicyMap Last Update:2026-09.

PolicyMap also displays pharmacies enrolled in the 340B Drug Pricing Program, downloaded from HRSA's Office of Pharmacy Affairs OPAIS system. The 340B Drug Pricing Program allows eligible health care organizations (covered entities) to purchase outpatient drugs from manufacturers at significantly reduced prices. These covered entities then contract with retail pharmacy partners to dispense the discounted medications to patients. PolicyMap has displayed the locations of these contracted pharmacies and provided information on the covered entities they are contracted with. While pharmacies can have multiple contracts with different covered entities, only one pharmacy location is displayed. Further, PolicyMap has chosen to only include pharmacies that are currently active in the 340B program. For more information please visit https://340bopais.hrsa.gov/

HRSA 340B PolicyMap Last Update:2026-03.

Hospital Care Quality Measures

Timely & Effective Care Across the U.S

By Health [source]

About this dataset

This dataset includes provider-level data revealing the quality of timely and effective care from hospitals across the United States. It allows us to analyze heart attack, heart failure, pneumonia, surgical, emergency department, preventive care for children's asthma and stroke prevention and treatment data for pregnancy and delivery care courtesy of the Centers for Medicare & Medicaid Services. With this dataset you can analyze hospital's performance on all these areas using Hospital Name, Addresss , City , State , ZIP Code , County Name , Phone Number as well as scores creditable to Measure Name , Sample size from which it was derived a Footnote explanation based on location. Dig deep into each provider's level of care with this dataset to understand their performance on providing timely effective care

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How to use the dataset

To get the most out of this dataset, it is important to understand each column in the dataset: Hospital Name identifies the health care facility; Address provides the address of the hospital; City identifies the city where it is located; State specifies which state it belongs to; ZIP Code denotes its specific zip code; County Name mentions what county it belongs to; Phone Number connects you with an immediate contact at the facility if needed; Condition categorizes types of tests/treatments being monitored in that case study; Measure Name outlines all related measures under said condition umbrella or metric(s) studied as part of that investigative research project/condition category (i.e., infection prevention); Score grades out how well that measure was doing compared against expectations or goals for quality & safe patient protections (higher scores are indicative of better performance on those surveyed & tracked items); Sample details how many patients were involved in this particular study topic component and involved participant sample size selection & unit evaluation criteria definition considerations during research recruitment and retention efforts associated with a particular area of specialty treatment/testing cluster system activity factors reviewed directionally by researchers via cohort based review activities over time [note: matching non-patients or control subject population reference points also sometimes may be used depending on written scope descriptions outlined by investigators]; Footnotes can amplify additional evaluations/CAVEATS sometimes noted regarding high-lighted findings(-such as improvement yet still not meeting standards), etc.; Measure Start Date defines when all test students were allowed entry into their respective study groups associated with one another for convergence analysis purposes within a defined subject patient group prospectively selected category designation feature component selection batch cases (new patients added mid-project have crossed design frontiers at random intervals sometimes necessary). Lastly, Measure End Date reflects terminal endpoint lead review periods cut off times when no new data entries can be accepted post-data collection stopped official time period specifications if designated by protocol order via institutional clinical trial board IRB approved advanced notification statements issued throughout any official project undertaking design process stages at its multiplex points).

Understanding each column's features will assist you in selecting relevant variables from this dataset according to your research needs. Additionally, using Location can help narrow down search results geographically. With this information researchers can gain valuable insight into overall trends regarding timely and effective care in different hospitals across different states

Research Ideas

• Create an interactive heatmap to visualize provider-level data across different states. This can allow researchers, consumers and policy makers to identify areas of excellence as well as opportunities for improvement in timely and effective care measures.
• Develop a web app that allows users to locate hospitals in their area based on any given health condition, measure name, score or timeframe data provided by this dataset. This could give patients access to quality care options and help them make informed decisions while seeking medical attention.
• Utilizing the geographic coordinates data included in the Location column, create a virtual tour function that lets people virtually explore the interior of hospital facilities associated with this dataset...

This dataset contains data for the Healthcare Payments Data (HPD) Services report. The term "Services" refers to individual procedures reported on the service lines of healthcare claims in California, categorized using the Restructured Berenson-Eggers Type of Services (BETOS) Classification System (RBCS) from the Centers for Medicare & Medicaid Services (CMS). The data in the report includes three main metrics: Total services, the total member count, and the service rate per 1,000 members. Total services represents the total number of services received by members during the reporting year. The member count reports the total number of unique individuals who received at least one service during the reporting year. The service rate per 1,000 members is calculated by dividing the total number of services during the reporting year by the total sum of monthly member enrollments (provided in the data) and multiplying the result by 12,000. The metrics can be grouped by year, age, sex (assigned at birth), county of residence (including an option for Los Angeles Service Planning Areas, or SPAs), Covered California Region, and payer. Users can choose to view the data at two different levels. The most aggregate level groups the data by the eight main RBCS categories: Anesthesia, Durable Medical Equipment (DME), Evaluation and Management (E&M), Imaging, Procedure, Test, Treatment and Other. The second level breaks the eight aggregate RBCS categories into more specific subcategories. Data files are provided for each choice.

The Real-Time E-Healthcare System Market was valued at USD 26.9 Billion in 2025 and is projected to grow to USD 55.6 Billion by 2035, at a CAGR of 7.5%. Real Time E Healthcare System Market Overview: The Real-Time E-Healthcare System Market Size was valued at 25 USD Billion in 2024. The Real-Time E-Healthcare System Market is expected to grow from 26.9 USD Billion in 2025 to 55.6 USD Billion by 2035. The Real-Time E-Healthcare System Market CAGR (growth rate) is expected to be around 7.5% during the forecast period (2025 - 2035). Key Real Time E Healthcare System Market Trends Highlighted The Global Real-Time E-Healthcare System Market is witnessing significant growth, driven by the increasing demand for remote patient monitoring and the growing need for secure and efficient healthcare services. Key market drivers include the rising prevalence of chronic diseases and the need for cost-effective healthcare solutions, which have led to an increased interest in telehealth services. Moreover, the adoption of wearable devices and mobile health applications is gaining traction, facilitating real-time health data exchange and monitoring. As many governments emphasize digital health initiatives, there are opportunities to be explored in creating innovative healthcare solutions that leverage artificial intelligence and big data analytics to enhance patient care.Trends in recent times highlight a shift towards more patient-centric care models facilitated by the real-time collection and analysis of health data. This trend supports proactive healthcare, allowing providers to make timely decisions based on up-to-date patient information. Additionally, the integration of cloud computing in healthcare systems is promoting flexibility and scalability, enabling healthcare providers in various regions to adopt real-time e-healthcare solutions. There is also increasing collaboration between technology companies and healthcare providers, to bridge the gap between technology and daily healthcare practices. This collaborative environment fosters innovation and helps streamline processes in delivering care.As the global healthcare landscape evolves, the emphasis will likely continue to be on transparency and data security, further pushing the demand for robust and trustworthy real-time e-healthcare systems. Source: Primary Research, Secondary Research, WGR Database and Analyst Review Real Time E Healthcare System Market Segment Insights: Real Time E Healthcare System Market Regional Insights The Global Real-Time E-Healthcare System Market exhibits significant regional diversity, with North America holding the majority share, valued at 10 USD Billion in 2024 and projected to reach 22 USD Billion by 2035. This region's dominance is driven by advanced healthcare infrastructure and high adoption rates of digital health technologies. Europe follows with noteworthy growth potential, benefiting from increasing investments in healthcare innovation and a rising demand for efficient e-health solutions. The Asia-Pacific (APAC) region is experiencing strong growth as emerging economies adopt advanced e-health systems to enhance their healthcare services, while South America shows steady expansion, supported by increasing internet penetration and mobile device usage for healthcare access.The Middle East and Africa (MEA) are also on a gradual path of growth, with a focus on developing healthcare systems and improving health outcomes through technology integration. Overall, the trends across these regions highlight an opportunity for the Real-Time E-Healthcare System Market to evolve with advancements in technology and changing patient needs, thereby influencing the market dynamics significantly. Source: Primary Research, Secondary Research, WGR Database and Analyst Review North America : Rapid adoption of AIoT technologies in healthcare is reshaping patient monitoring and diagnostic capabilities. The recent U.S. Federal Telehealth Expansion Act supports telehealth initiatives, driving investments in e-health systems. The market is bolstered by a substantial increase in the usage of wearable health devices, projec

Health Equity Analytics AI Market Size and Growth Outlook

Our analysis shows that the global Health Equity Analytics AI market size reached USD 1.47 billion in 2024, reflecting robust adoption across healthcare ecosystems. The market is experiencing a dynamic expansion, with a CAGR of 22.6% projected from 2025 to 2033. By the end of 2033, the Health Equity Analytics AI market is forecasted to attain a value of USD 11.98 billion, driven by the urgent need to address disparities in healthcare access and outcomes through advanced data analytics and artificial intelligence solutions. This growth is underpinned by the increasing focus on social determinants of health, governmental policy initiatives, and the integration of AI into population health management strategies.

A primary growth factor for the Health Equity Analytics AI market is the heightened awareness of health inequities, especially in the wake of the COVID-19 pandemic, which exposed and exacerbated existing disparities in care delivery. Healthcare organizations and public health agencies are leveraging AI-powered analytics to identify, monitor, and address gaps in care among different demographic groups. These solutions enable stakeholders to move beyond traditional data analysis, offering actionable insights into the root causes of disparities, such as socioeconomic status, race, ethnicity, and geographic location. The increasing volume and diversity of healthcare data, coupled with advances in machine learning algorithms, have empowered organizations to implement targeted interventions and measure their impact more accurately, further fueling market growth.

Another significant driver is the proliferation of value-based care models and regulatory mandates that emphasize health equity as a core performance metric. Governments and payers are incentivizing providers to demonstrate measurable improvements in health outcomes across populations, particularly among underserved communities. This shift is compelling healthcare systems to invest in Health Equity Analytics AI solutions that can stratify risk, allocate resources more effectively, and ensure compliance with evolving reporting requirements. Moreover, the integration of AI with electronic health records (EHRs) and other clinical systems allows for real-time analysis and decision support, enhancing the ability of providers to deliver personalized, equitable care.

Technological advancements and the growing adoption of cloud-based analytics platforms are also propelling the Health Equity Analytics AI market. Cloud deployment offers scalability, interoperability, and cost-efficiency, making advanced analytics accessible to organizations of all sizes, including smaller healthcare providers and community health centers. The convergence of AI with other emerging technologies, such as natural language processing and predictive analytics, is enabling more nuanced analysis of unstructured data sources, including clinical notes and social media feeds. This expanded analytical capability is critical for capturing the full spectrum of factors influencing health equity, driving continuous innovation and market expansion.

From a regional perspective, North America currently dominates the Health Equity Analytics AI market, accounting for the largest share due to its advanced healthcare infrastructure, supportive regulatory environment, and significant investments in health IT. However, the Asia Pacific region is expected to exhibit the fastest growth over the forecast period, driven by rising healthcare digitization, increasing government initiatives to address health disparities, and a burgeoning population with diverse health needs. Europe is also witnessing steady adoption, particularly in countries with strong public health systems and a focus on reducing health inequalities. Meanwhile, Latin America and the Middle East & Africa are gradually increasing their investments in health equity analytics, albeit from a smaller base, as awareness and digital infrastructure continue to improve.

Health Equity Analytics AI Market

This dataset provides a list of federally-funded health centers that provide health services. For more than 40 years, Health Resources and Services Administration (HRSA)-supported health centers have provided comprehensive, culturally competent, quality primary health care services to medically underserved communities and vulnerable populations. Health centers are community-based and consumer-run organizations that serve populations with limited access to health care. These include low-income populations, the uninsured, those with limited English proficiency, migratory and seasonal agricultural workers, individuals and families experiencing homelessness, and those living in public housing.

In 2023, Singapore dominated the ranking of the world's health and health systems, followed by Japan and South Korea. The health index score is calculated by evaluating various indicators that assess the health of the population, and access to the services required to sustain good health, including health outcomes, health systems, sickness and risk factors, and mortality rates. The health and health system index score of the top ten countries with the best healthcare system in the world ranged between 82 and 86.9, measured on a scale of zero to 100. Global Health Security Index Numerous health and health system indexes have been developed to assess various attributes and aspects of a nation's healthcare system. One such measure is the Global Health Security (GHS) index. This index evaluates the ability of 195 nations to identify, assess, and mitigate biological hazards in addition to political and socioeconomic concerns, the quality of their healthcare systems, and their compliance with international finance and standards. In 2021, the United States was ranked at the top of the GHS index, but due to multiple reasons, the U.S. government failed to effectively manage the COVID-19 pandemic. The GHS Index evaluates capability and identifies preparation gaps; nevertheless, it cannot predict a nation's resource allocation in case of a public health emergency. Universal Health Coverage Index Another health index that is used globally by the members of the United Nations (UN) is the universal health care (UHC) service coverage index. The UHC index monitors the country's progress related to the sustainable developmental goal (SDG) number three. The UHC service coverage index tracks 14 indicators related to reproductive, maternal, newborn, and child health, infectious diseases, non-communicable diseases, service capacity, and access to care. The main target of universal health coverage is to ensure that no one is denied access to essential medical services due to financial hardships. In 2021, the UHC index scores ranged from as low as 21 to a high score of 91 across 194 countries.

WellSpan Health is a nonprofit integrated healthcare system serving 12 counties in south-central Pennsylvania and northern Maryland, a region marked by wide disparities in the demographic, social, and economic conditions of its people. Consequently, it is also marked by wide disparities in health status and health outcomes, including life expectancy. Beginning in the mid-1990s, WellSpan’s community health needs assessments identified numerous potential drivers of health disparities, and in recent years the hospital system has conducted increasingly sophisticated community health improvement plans to address disparities in ways compatible with the hospital system’s resources, capabilities, mission, and priorities. Because there is limited published information about how nonprofit health systems are implementing and evaluating community health improvement plans, we have documented key aspects of WellSpan’s progress including guiding principles and strategies, how pilot projects were identified and conducted, how community partnerships were developed and leveraged, and how data sources were used to guide decisions. WellSpan’s efforts have culminated in the recent adoption of a 30-year plan to improve overall life expectancy and quality of life and reduce disparities in these outcomes in the region served. The purpose of this paper is to share experiences and lessons learned during the multi-year effort leading to the development of this 30-year plan.

About this dataset

Looking for a dataset on hospitals in the United States? Look no further! This dataset contains information on all of the hospitals registered with Medicare in the US, including their addresses, phone numbers, hospital type, and more. With such a large amount of data, this dataset is perfect for anyone interested in studying the US healthcare system.

This dataset can also be used to study hospital ownership, emergency services

How to use the dataset

If you want to study the US healthcare system, this dataset is perfect for you. It contains information on all of the hospitals registered with Medicare, including their addresses, phone numbers, hospital type, and more. With such a large amount of data, this dataset is perfect for anyone interested in studying the US healthcare system.

This dataset can also be used to study hospital ownership, emergency services, and EHR usage. In addition, the hospital overall rating and various comparisons are included for safety of care, readmission rates

Research Ideas

1. Predicting readmission rates for different hospital conditions
2. Analyzing relationships between hospital ownership and quality of care
3. Studying the relationship between hospital type and patient experience

Acknowledgements

This dataset was originally published by Centers for Medicare and Medicaid Services and has been modified for this project

Columns

File: Hospital_General_Information.csv | Column name | Description | |:-------------------------------------------------------|:----------------------------------------------------------------------------------------------------------| | Hospital Name | The name of the hospital. (String) | | Hospital Name | The name of the hospital. (String) | | Address | The address of the hospital. (String) | | Address | The address of the hospital. (String) | | City | The city in which the hospital is located. (String) | | City | The city in which the hospital is located. (String) | | State | The state in which the hospital is located. (String) | | State | The state in which the hospital is located. (String) | | ZIP Code | The ZIP code of the hospital. (Integer) | | ZIP Code | The ZIP code of the hospital. (Integer) | | County Name | The county in which the hospital is located. (String) | | County Name | The county in which the hospital is located. (String) | | Phone Number | The phone number of the hospital. (String) | | Phone Number | The phone number of the hospital. (String) | | Hospital Type | The type of hospital. (String) | | Hospital Type | The type of hospital. (String) | | Hospital Ownership | The ownership of the hospital. (String) | | Hospital Ownership | The ownership of the hospital. (String) | | Emergency Services | Whether or not the...

NUMBER OF MEASURES FOR WHICH GROUP FARED WORSE, THE SAME, OR BETTER COMPARED TO WHITE PEOPLE:

IntroductionSpatially segregated, socio-economically deprived communities in Europe are at risk of being neglected in terms of health care. In Hungary, poor monitoring systems and poor knowledge on the health status of people in these segregated areas prevent the development of well-informed effective interventions for these vulnerable communities.AimsWe used data available from National Health Insurance Fund Management to better describe health care performance in segregated communities and to develop more robust monitoring systems.MethodsA cross-sectional study using 2020 health care data was conducted on each general medical practice (GMP) in Hungary providing care to both segregated and nonsegregated (complementary) adult patients. Segregated areas were mapped and ascertained by a governmental decree that defines them as within settlement clusters of adults with low level of education and income. Age, sex, and eligibility for exemption certificate standardized indicators for health care delivery, reimbursement, and premature mortality were computed for segregated and nonsegregated groups of adults and aggregated at the country level. The ratio of segregation and nonsegregation specific indicators (relative risk, RR) was computed with the corresponding 95% confidence intervals (95% CI).ResultsBroad variations between GMPs were detected for each indicator. Segregated groups had a significantly higher rate of health care service use than complementary groups (RR = 1.22, 95% CI: 1.219;1.223) while suffering from significantly reduced health care reimbursement (RR = 0.940, 95% CI: 0.929;0.951). The risk of premature mortality was significantly higher among segregated patients (RR = 1.184, 95% CI: 1.087;1.289). Altogether, living in a segregated area led to an increase in visits to health care services by 18.1% with 6.6% less health spending.ConclusionAdults living in segregated areas use health care services more frequently than those living in nonsegregated areas; however, the amount of health care reimbursement they receive is significantly lower, suggesting lower quality of care. The health status of segregated adults is remarkably lower, as evidenced by their higher premature mortality rate. These findings demonstrate the need for intervention in this vulnerable group. Because our study reveals serious variation across GMPs, segregation-specific monitoring is necessary to support programs sensitive to local issues and establish necessary benchmarks.

Health Information Management Systems Market Outlook

According to our latest research, the global Health Information Management Systems market size stands at USD 34.5 billion in 2025, reflecting robust and accelerating adoption across healthcare providers worldwide. The market is expected to expand at a compound annual growth rate (CAGR) of 10.6% through the forecast period of 2026 to 2034, reaching an estimated USD 86.2 billion by 2034. This impressive growth trajectory is driven by increasing digitalization in healthcare, the urgent need for efficient and interoperable data management, and a growing global focus on regulatory compliance and patient-centric care models. Historical data from 2019 through 2024 confirms a consistent upward trend, establishing a firm foundation for the forecast outlook. Advanced health information management solutions are becoming integral to healthcare delivery and administration across every major geography and provider segment.

The primary growth factor propelling the market is the accelerated adoption of electronic health records and digital patient information platforms. Healthcare providers are increasingly recognizing the value of centralized, real-time access to patient data, which enhances clinical decision-making and enables seamless care coordination. Regulatory mandates, including the Health Information Technology for Economic and Clinical Health (HITECH) Act in the United States and analogous national eHealth frameworks across Europe, Asia Pacific, and Latin America, have incentivized the transition from paper-based to digital systems. This has created a highly receptive environment for health information technology solutions, as organizations strive to improve operational efficiency, reduce errors, and ensure data accuracy across complex care networks. The expanding volume of healthcare data, combined with the imperative for interoperability and secure data exchange, continues to drive investment in sophisticated management platforms.

Another significant driver is the increasing emphasis on healthcare analytics and data-driven decision-making. Health Information Management Systems are evolving far beyond basic record-keeping to incorporate advanced analytics, artificial intelligence, and machine learning capabilities. These technologies enable healthcare organizations to extract actionable insights from vast datasets, optimize resource allocation, and improve patient outcomes. The integration of predictive analytics is facilitating proactive care management, early disease detection, and personalized treatment protocols. As healthcare providers commit to delivering value-based care, the demand for comprehensive information management solutions that support population health management and performance measurement is surging. This trend is amplified by the rapid expansion of telemedicine and remote patient monitoring, both of which generate substantial additional data streams requiring effective, structured management.

The proliferation of cloud computing and mobile technologies is also transforming the landscape. Cloud-based solutions offer scalability, flexibility, and cost-efficiency, making them attractive to healthcare organizations of all sizes. These platforms enable secure, remote access to patient information, supporting collaborative care and care continuity across different settings. Mobile health applications are empowering patients to participate actively in their care journeys, fostering engagement and transparency. The convergence of cloud, mobile, and health information management is enabling real-time data sharing, streamlined administrative workflows, and enhanced patient experiences. As data privacy and cybersecurity concerns intensify, vendors are investing aggressively in robust security frameworks to safeguard sensitive health information, further bolstering market confidence. The role of Healthcare IT in sup