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. 

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.

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...

snapshot-tab-pane Healthcare Information Systems Market Size 2025-2029The healthcare information systems market size is valued to increase by USD 142.30 billion, at a CAGR of 9.8% from 2024 to 2029. Regulatory mandates and government initiatives promoting interoperability will drive the healthcare information systems market.Major Market Trends & InsightsNorth America dominated the market and accounted for a 46.1% growth during the forecast period.By Application - Revenue cycle management segment was valued at USD 101.50 billion in 2023By Technology - EHRs segment accounted for the largest market revenue share in 2023Market Size & ForecastMarket Opportunities: USD 191.80 billionMarket Future Opportunities: USD 142.30 billionCAGR from 2024 to 2029 : 9.8%Market SummaryThe healthcare information systems market is foundational to modern healthcare, facilitating the digital management of clinical and administrative data. Growth is driven by the imperative to improve operational efficiency and patient outcomes through technologies like electronic health records (EHR) and telemedicine platforms. A key trend is the integration of AI-driven analytics to enable predictive insights, moving beyond simple data storage.For instance, a health system can leverage a population health management platform to analyze patient-generated health data (PGHD) and identify individuals at high risk for chronic disease complications. By using care coordination tools triggered by this analysis, the provider can proactively intervene, schedule virtual check-ins via integrated telehealth modules, and adjust care plans, ultimately reducing costly hospital readmissions.This scenario highlights the shift toward proactive, data-informed care delivery. However, the market faces persistent challenges related to data interoperability standards and ensuring robust cybersecurity resilience frameworks to protect sensitive information across an increasingly connected and decentralized care model.What will be the Size of the Healthcare Information Systems Market during the forecast period? Get Key Insights on Market Forecast (PDF) Get Free SampleHow is the Healthcare Information Systems Market Segmented?The healthcare information systems industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in "USD million" for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.ApplicationRevenue cycle managementHospital information systemMedical imaging information systemPharmacy information systemsLaboratory information systemsTechnologyEHRsEMRsMobile healthTelemedicine platformsPopulation health management systemsComponentSoftwareServicesHardwareGeographyNorth AmericaUSCanadaMexicoEuropeGermanyUKFranceAsiaRest of World (ROW)By Application InsightsThe revenue cycle management segment is estimated to witness significant growth during the forecast period.The revenue cycle management segment is integral to provider financial health, automating and managing the entire lifecycle of patient financial interactions. These systems, utilizing claims processing automation and denial management solutions, are crucial for navigating complex value-based care reimbursement models.A primary focus is on improving patient financial responsibility tools to handle growing out-of-pocket expenses. The integration of a data aggregation platform and AI-driven analytics enhances forecasting and operational alignment.Effective RCM software streamlines workflows from patient registration to final payment, directly impacting financial stability.Advanced systems have shown to improve clean claim submission rates by over 15%, reducing administrative waste and accelerating cash flow through better automated coding accuracy and predictive denial management, which are key for successful financial outcomes. Get Free SampleThe Revenue cycle management segment was valued at USD 101.50 billion in 2023 and showed a gradual increase during the forecast period. Get Free SampleRegional AnalysisNorth America is estimated to contribute 46.1% to the growth of the global market during the forecast period.Technavio’s analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period. See How Healthcare Information Systems Market Demand is Rising in North America Get Free SampleThe global market exhibits a distinct geographic divide.In North America, where EHR adoption is nearly universal, the focus is on optimizing existing systems through AI-powered diagnostics and achieving compliance with stringent data interoperability standards, which has improved data access times by 30%.European markets are harmonizing under new data sharing regulations, driving investment in p

Social Determinants of Health Data Platforms Market Outlook

According to our latest research, the global Social Determinants of Health (SDOH) Data Platforms market size reached USD 3.2 billion in 2024. The market is expected to grow at a robust CAGR of 18.7% during the forecast period, reaching a projected value of USD 15.1 billion by 2033. This significant growth is primarily driven by the increasing recognition of how non-clinical factors—such as economic stability, education, neighborhood, and social context—profoundly impact health outcomes and healthcare costs worldwide.

One of the most compelling growth factors for the Social Determinants of Health Data Platforms market is the intensifying focus on value-based care and population health management among healthcare stakeholders. As healthcare systems globally transition from traditional fee-for-service models to value-based care, there is a growing need to incorporate SDOH data into clinical workflows, risk stratification, and care coordination. Payers, providers, and government agencies are investing in platforms that aggregate, analyze, and operationalize diverse data sources, including demographic, socioeconomic, and behavioral factors. This integration enables healthcare organizations to identify at-risk populations, personalize interventions, and ultimately reduce costly health disparities, fueling substantial market demand.

Another pivotal driver is the expanding regulatory and policy support for addressing social determinants in healthcare delivery. Government agencies, especially in North America and Europe, are enacting mandates and incentives to encourage the collection and utilization of SDOH data. For instance, the Centers for Medicare & Medicaid Services (CMS) in the United States has introduced new requirements and payment models that reward the integration of social risk factors into patient assessments and care planning. Similarly, the World Health Organization (WHO) and other international bodies are emphasizing the importance of SDOH in achieving equitable health outcomes. These regulatory tailwinds are prompting healthcare organizations to adopt advanced SDOH data platforms, further accelerating market growth.

Technological advancements in data analytics, artificial intelligence, and interoperability are also propelling the Social Determinants of Health Data Platforms market forward. Modern SDOH data platforms leverage machine learning algorithms and predictive analytics to derive actionable insights from vast, complex datasets. Enhanced interoperability standards, such as FHIR (Fast Healthcare Interoperability Resources), are making it easier to integrate SDOH data with electronic health records (EHRs) and other health IT systems. These innovations are not only improving the accuracy and timeliness of SDOH data capture but also enabling real-time decision support for clinicians and care managers. As a result, healthcare organizations are increasingly deploying sophisticated SDOH data platforms to gain a competitive edge and improve patient outcomes.

From a regional perspective, North America currently dominates the Social Determinants of Health Data Platforms market, accounting for the largest share in 2024, followed by Europe and the Asia Pacific. The United States, in particular, is at the forefront due to its advanced healthcare IT infrastructure, proactive regulatory environment, and substantial investments in population health initiatives. However, the Asia Pacific region is expected to register the fastest CAGR during the forecast period, driven by rising healthcare digitization, growing awareness of health disparities, and supportive government policies. Europe is also witnessing steady growth, bolstered by cross-border health data initiatives and strong public health systems. Latin America and the Middle East & Africa are gradually emerging as promising markets as healthcare modernization efforts gain momentum.

The integration of Social Determinants of Health Analytics AI is becoming increasingly vital in the healthcare industry. By leveraging artificial intelligence, healthcare providers can analyze vast amounts of SDOH data to uncover patterns and insights that were previously unattainable. AI-driven analytics enable the identification of at-risk populations more accurately and efficiently

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

Healthcare Data Lakes Market Outlook

According to our latest research, the global Healthcare Data Lakes market size reached USD 6.4 billion in 2024, reflecting robust expansion driven by the digital transformation of healthcare systems worldwide. The market is projected to maintain a strong growth trajectory, registering a CAGR of 21.7% from 2025 to 2033. By the end of 2033, the Healthcare Data Lakes market is forecasted to reach USD 45.1 billion. This remarkable growth is primarily attributed to the increasing adoption of advanced analytics, artificial intelligence, and the proliferation of electronic health records (EHRs) across healthcare organizations globally.

One of the primary growth drivers for the Healthcare Data Lakes market is the exponential rise in healthcare data volume. With the widespread implementation of EHRs, connected medical devices, and healthcare IoT, organizations are generating massive amounts of structured and unstructured data daily. Traditional data management solutions often struggle to handle this scale and diversity, resulting in inefficiencies and missed opportunities. Healthcare data lakes provide a scalable and flexible architecture that enables organizations to store, manage, and analyze vast datasets from diverse sources. This capability is crucial for supporting clinical research, population health management, and personalized medicine initiatives, all of which hinge on the ability to extract actionable insights from complex, multi-source data.

Another significant factor fueling market growth is the increasing emphasis on value-based care and outcome-driven healthcare delivery models. Healthcare providers and payers are under mounting pressure to improve patient outcomes while controlling costs. Data lakes empower stakeholders to integrate and analyze clinical, financial, and operational data, facilitating more informed decision-making and enabling predictive analytics for risk stratification, resource allocation, and disease management. The ability to derive real-time insights from aggregated data not only enhances patient care but also supports regulatory compliance and reporting requirements, further incentivizing the adoption of healthcare data lake solutions.

The rapid advancements in artificial intelligence, machine learning, and big data analytics are also catalyzing the adoption of healthcare data lakes. These technologies require access to large, high-quality datasets to train algorithms and derive meaningful insights. Data lakes, with their capacity to ingest and harmonize disparate data types, provide a fertile ground for next-generation analytics applications, including genomic research, precision medicine, and early disease detection. As healthcare organizations increasingly recognize the strategic value of data-driven innovation, investments in data lake infrastructure are expected to accelerate, further propelling market expansion.

From a regional perspective, North America continues to dominate the Healthcare Data Lakes market, accounting for the largest revenue share in 2024. This leadership position is underpinned by the presence of advanced healthcare infrastructure, high adoption rates of digital health technologies, and significant investments in research and development. Europe follows closely, driven by government initiatives to promote interoperability and data sharing across healthcare ecosystems. The Asia Pacific region is emerging as a high-growth market, fueled by expanding healthcare IT investments, increasing awareness of data-driven healthcare, and the rising prevalence of chronic diseases. Latin America and the Middle East & Africa are also witnessing steady growth, supported by digital health transformation efforts and the modernization of healthcare delivery systems.

Component Analysis

The Healthcare Data Lakes market is segmented by component into Solutions and Services, each playing a pivotal role in the overall market landscape. Solutions encompass the core data lake platforms and asso

The global Healthcare Information System (HCIS) market is booming, projected to reach $92.5 billion by 2033, driven by EHR adoption, telehealth, and AI. Explore market trends, key players (Cerner, Epic, Allscripts), and regional insights in this comprehensive analysis.

The global Healthcare Information System (HCIS) market is booming, projected to reach $500 billion by 2033, driven by EHR adoption, telehealth, and AI. Discover key trends, market segmentation, leading companies, and regional growth in this comprehensive analysis.

Abstract

This survey covering 252 primary health facilities and 30 local governments was carried out in the states of Kogi and Lagos in Nigeria in the latter part of 2002. Nigeria is one of the few countries in the developing world to systematically decentralize the delivery of basic health and education services to locally elected governments. Its health policy has also been guided by the Bamako Initiative to encourage and sustain community participation in primary health care services. The survey data provide systematic evidence on how these institutions of decentralization are functioning at the level local—governments and community based organizations—to deliver primary health service.

The evidence shows that locally elected governments indeed do assume responsibility for services provided in primary health care facilities. However, the service delivery environments between the two states are strikingly different. In largely urban Lagos, public delivery by local governments is influenced by the availability of private facilities and proximity to referral centers in the state. In largely rural Kogi, primary health services are predominantly provided in public facilities, but with extensive community participation in the maintenance of service delivery. The survey identified an issue which is highly relevant for decentralization policies—the non-payment of health staff salaries in Kogi—which is suggestive of problems with local accountability when local governments are heavily dependent on fiscal transfers from higher tiers of government.

Geographic coverage

Data were collected in 30 local governments, 252 health facilities, and from over 700 health workers, in Lagos and Kogi states.

Kind of data

Sample survey data [ssd]

Sampling procedure

A multi-stage sampling process was employed where first 15 local governments were randomly selected from each state; second, 100 facilities from Lagos and 152 facilities from Kogi were selected using a combination of random and purposive sampling from the list of all public primary health care facilities in the 30 selected LGAs that was provided by the state governments; third, the field data collectors were instructed to interview all staff present at the health facility at the time of the visit, if the total number of staff in a facility were less than or equal to 10. In cases where the total number of staff were greater than 10, the field staff were instructed to randomly select 10 staff, but making sure that one staff in each of the major ten categories of primary health care workers was included in the sample.

Health facilities were selected through a combination of random and purposive sampling. First, all facilities were randomly selected from the available list for 30 LGAs. This process resulted in no facility being selected from a few LGAs. Between 1-3 facilities were then randomly selected from these LGAs, and an equal number of facilities were randomly dropped from overrepresented LGAs, defined as those where the proportion of selected facility per LGA is higher than the average proportion of selected facilities for all sampled LGAs.

A list of replacement facilities was also randomly selected in the event of closure or non-functioning of any facility in the original sample. An inordinate amount of facilities were replaced in Kogi (27 in total), some due to inaccessibility given remote locations and hostile terrain, and some due to non-availability of any health staff. The local community volunteered in these cases that the reason there was no staff available was because of non-payment of salaries by the LGA. This characteristic of the functioning of health facilities in Kogi is a striking result that will be discussed in this report.

Mode of data collection

Face-to-face [f2f]

Research instrument

The approach adopted to addressing these issues revolves around extensive and rigorous survey work, at the level of the primary health care facilities and the local governments. Two basic survey instruments of primary data collection were agreed upon, based on collecting information from government officials and public service delivery facilities: 1. Survey of primary health care facilities—including interviews of facility managers and workers, as well as direct collection of data on inputs and outputs from facility records. 2. Survey of local governments (under whose jurisdiction the health facilities reside)—including interviewers of local government treasurers for information on budgeted resources and investment activity, and interviews of primary health care coordinators for roles, responsibilities, and outcomes at the local government level.

Survey instruments at the health facility level

The facility level survey instruments were designed to collect data along the following lines: 1. Basic characteristics of the health facility: who built it; when was it built; what other facilities exist in the neighborhood; access to the facility; hours of service etc. 2. Type of services provided: focusing on ante-natal care; deliveries; outpatient services, with special emphasis on malaria and routine immunization 3. Availability of essential equipment to provide the above services 4. Availability of essential drugs to provide the above services 5. Utilization of the above services, referral practices 6. Tracking and use of epidemiological and public health data 7. Characteristics of health facility staff: professional qualifications; training; salary structure, and whether payments are received in a timely fashion; informal payments received; fringe benefits received; do they have their own private practice; time allocation across different services; residence; place of origin 8. Sources of financing-who finances the building infrastructure and its maintenance; who finances the purchase of basic equipment; who finances the purchase of drugs; what is the user fee policy; revenues from user fees; retention rate of these revenues; financing available from the community 9. Management structure and institutions of accountability: activities of and interaction with the local government and with the community development committees

Survey instrument at the local government level

The local government survey instruments were designed to collect data along the following lines: 1. Basic characteristics: when was the local government created, population, proportion urban and rural, presence of an urban center, presence of NGOs and international donors 2. Number of primary health care facilities by type (types 1 and 2) and ownership (public-local government, state, and federal government; private-for-profit; private-not-for-profit) 3. Supervisory responsibilities over the general functioning of the primary health care centers 4. Health staff: number of staff by type of professional training and civil service cadre; salary; 5. Monitoring the performance of health staff: how is staff performance monitored and by whom; are staff rewarded for good performance or sanctioned for poor performance, and how; instances when local government has received complaints; what disciplinary action was taken 6. Budget and financing: data on actual LGA revenues and expenditure from available budget documents; 7. Management structures: functioning of the Primary Health Care Management Committee (PHCMC), the Primary Health Care Technical Committee (PHCTC), and the community based organizations-the Village Development Committee (VDC) and the District Development Committee (DDC) 8. Health services outputs at the local government level: records of immunization, and environmental health activities

The focus of the study is thus public service delivery outcomes as measured at the level of frontline delivery agencies—the public primary health care facilities. We also originally planned to include interviews of patients present at the health facilities, to get the user’s perspective on public service delivery, but found that difficult to follow-through given local capacity constraints in implementing a survey of this kind.

The survey instruments were developed through an iterative process of discussions between the World Bank team, NPHCDA, and local consultants at the University of Ibadan, over the months of March-May 2002. During May 2002, four questionnaires were finalized through repeated field-testing—1) Health Facility Questionnaire: to be administered to the health facility manager, and to collect recorded data on inputs and outputs at the facility level; 2) Staff Questionnaire: to be administered to individual health workers; 3) Local Government Treasurer Questionnaire: to collect local government budgetary information; and 4) Primary Health Care Coordinator Questionnaire: to collect information on local government activities and policies in primary health care service service delivery.

Cleaning operations

Random Data Checking Procedure

Following the dual data entry of all records by Nigerian consultants and the merging and cleaning of the data files(as outlined below) by World Bank staff, the hard copies of the questionnaires were randomly checked against the entries in the data files (*) for errors by World Bank staff. Five LGAs were selected at random in both the Kogi and Lagos states. In each of these ten LGAs, the hard copy of the PHC Coordinator Questionnaire, the hard copy of the LGA Treasurer Questionnaire, and up to five hard copies of both the Staff Questionnaires and the Health Facility Questionnaires were randomly selected and checked against the entries in the data files. While in several instances parts of the alphanumeric entries were abbreviated or omitted, no substantive differences between the hard copies of the

Number of health systems that control a given share of a Metropolitan Statistical Area (MSA) market

The Electronic Health Tracking System (EHTS) market is booming, projected to reach $37.58 billion by 2033, driven by EHR adoption and digital health initiatives. Learn about market trends, key players (Epic, Cerner, Athenahealth), and challenges in this insightful analysis.

Hospital_ID :- Unique identifier assigned to each hospital in the dataset. It allows tracking and differentiating records across different healthcare institutions.

Department :- The medical department within the hospital where the patient interaction or treatment occurred (e.g., Cardiology, Neurology, Emergency).

EHR_Implemented :- Indicates whether an Electronic Health Record (EHR) system is implemented in the hospital or department. Typical values: Yes – EHR system is in use No – EHR system is not implemented

Patient_Wait_Time_Min :- Average time (in minutes) that a patient waits before receiving medical attention.

Documentation_Time_Min :- Average time (in minutes) healthcare staff spend documenting patient information and medical records.

Order_to_Fulfillment_Min :- The time (in minutes) taken from when a medical order (test, medication, procedure) is placed until it is fulfilled.

Medical_Errors_Reported :- Number of medical errors reported during the observation period. These may include medication errors, diagnostic errors, or procedural mistakes.

Decision_Support_Used :- Indicates whether clinical decision support tools (such as alerts, recommendations, or automated guidelines within EHR systems) were used during patient care.

Patient_Safety_Incidents :- Total number of patient safety incidents recorded, such as falls, incorrect medication administration, or procedural complications.

Care_Coordination_Score :- A numerical score representing the effectiveness of coordination among healthcare providers in delivering patient care. Higher values generally indicate better coordination.

Data_Accuracy_Percent :- Percentage value indicating the accuracy of patient records and medical data stored in the system.

Reporting_Accuracy_Score :- Score measuring the reliability and correctness of medical reports, documentation, and system-generated outputs.

Year :- The year in which the healthcare data was recorded or analyzed.

Diagnosis Phase :- List or description of diagnostic procedures or activities performed to identify the patient's medical condition (e.g., imaging, lab tests, clinical evaluation).

Treatment Phase :- List of treatment procedures or interventions provided to the patient (e.g., medication, surgery, rehabilitation).

The National Hospital Care Survey (NHCS) is designed to provide accurate and reliable health care statistics that answer key questions of interest to health care and public health professionals, researchers, and health care policy makers. This includes tracking the latest trends affecting hospitals and health care organizations and factors that influence the use of health care resources, the quality of health care, and disparities in health care services provided to population subgroups in the United States. NHCS collects data on patient care in hospital-based settings to describe patterns of health care delivery and utilization in the United States. Settings include inpatient, emergency (EDs), and outpatient departments (OPDs). The survey will provide hospital utilization statistics for the Nation. In addition, NHCS will also be able to monitor national trends in substance use-related ED visits including opioid visits.

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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For more datasets, click here.

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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

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The datasets provide medical encounter counts across three hospital settings (Inpatient Discharges, Emergency Department visits, and Ambulatory Surgery). The data is categorized by healthcare system and facility, and further grouped by common health conditions (including anxiety, asthma, behavioral syndromes, cancer, cardiac arrest, chronic obstructive pulmonary disease (COPD), COVID-19, depression, diabetes, homelessness, hypertension, mood disorders excluding depression, non-mood psychotic disorders, nonpsychotic disorders excluding anxiety, obesity, pneumonia, respiratory arrest/failure, sepsis, stroke, substance use disorders, and unspecified mental disorders), as well as demographic characteristics such as age group, race/ethnicity, assigned sex at birth, and expected payer.

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:2024-04.

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. 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:2025-09.

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.

ABSTRACT This study aims to analyze the possible transformations in health care management and production processes, in the context of practice, based on the Emergency and Urgent Health Care Network (RUE) policy. The research has a qualitative character and is characterized as a case study. Interviews were conducted with 16 health service managers in four municipalities of different population sizes. The material was analyzed with reference to the context of the Public Policy Cycle Approach practice. It is observed that the RUE is not recognized as a public policy, although some of its elements are identified, such as the implementation of Emergency Care Units, protocols, risk classification, new care technologies, regulatory arrangements, and lines of care. Looking at the ‘RUE on scene’ mainly points to three issues: the relationship between primary care and emergency doors in meeting spontaneous demand; the medical regulation of Mobile Emergency Care Service (SAMU), as a promoter of access and quality; and the (dis)continuity in health care. There is evidence of live movements and productions induced by the policy that qualify health care in urgent and emergency situations. However, inequities are maintained or produced, and the need for articulation between network components, although evoked, translates into fragile and non-regular connections.

Andalusian Public Healthcare System (SSPA)

In Spain, the competence for healthcare is the responsibility of the regions. The Andalusian Public Healthcare System (SSPA) is an ecosystem of public and universal healthcare provision, which is made up of a series of public agencies, managed by the Government of Andalusia.

The SSPA has a total of 32 hospitals in the region of Andalusia.

Within this ecosystem, the main healthcare provider agency is the Andalusian Health Service.

Andalusian Health Service (SAS)

The Andalusian Health Service, created in 1986 by Law 8/1986, of May 6, 1986, on the Andalusian Health Service, is attached to the Regional Ministry of Health and Consumer Affairs and performs the functions attributed to it under the supervision and control of the same.

Its mission is to provide healthcare to the citizens of Andalusia, offering quality public health services, ensuring accessibility, equity and user satisfaction, seeking efficiency and optimal use of resources.

The SAS guarantees free public health care to more than 8 million inhabitants, which represents about 17% of the Spanish population. The Andalusian Health Service has 28 hospitals, distributed throughout Andalusia. It is also functionally responsible for the centers belonging to the Public Health Business Agencies and the Aljarafe Public Health Consortium. In addition, there are 14 Health Management Areas.

The Andalusian Health Service is an administrative agency of those provided for in Article 65 of Law 9/2007, of October 22, is attached to the Ministry of Health and Consumer Affairs, depending specifically on the Vice-Ministry, according to Decree 156/2022, of August 9, which establishes the organisational structure of the Ministry of Health and Consumer Affairs. The Andalusian Health Service exercises the functions specified in this Decree, subject to the guidelines and general criteria of health policy in Andalusia and, in particular, the following:

- The management of the set of health services in the field of health promotion and protection, disease prevention, healthcare and rehabilitation that corresponds to it in the territory of the Autonomous Community of Andalusia.

- The administration and management of the health institutions, centers and services that act under its organic and functional dependence.

- The management of the human, material and financial resources assigned to it for the development of its functions.

Diraya: system used in the Andalusian Health Service to support the Electronic Health Record in Andalusia

Diraya is the system used in the Andalusian Health Service to support electronic health records. It integrates all the health information of each person treated in the healthcare centers in Andalusia, so that it is available wherever and whenever it is necessary to treat them, and it is also used for the management of the healthcare system.

Diraya's conceptual model and technological architecture have aroused significant interest in other healthcare administrations thanks to, among others, cutting-edge services such as the electronic prescription or the centralised appointment system.

A description of the Diraya system (objectives, basic components, modules, functional and technological architecture, impact assessment of its implementation, etc.) can be found in the following document: Health Care Information and Management Integrated System.

Health Equity Analytics AI Market Outlook

According to our latest research, 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.

Component Analysis