Introduction

AI in Healthcare Statistics: Artificial intelligence (AI) is swiftly reshaping the healthcare sector, transforming areas such as diagnostics, treatment planning, patient management, and drug development. By analyzing large volumes of data and delivering precise insights, AI is boosting clinical decision-making, enhancing patient outcomes, and optimizing healthcare operations.

Key advancements in machine learning, natural language processing, and other AI technologies are propelling this shift, with healthcare systems worldwide increasingly adopting these innovations to improve efficiency and offer more personalized care. The ongoing potential of AI to refine healthcare delivery is reshaping the industry's future.

Health Services Research Information Central (HSRIC) is a web portal and current awareness service of information on health services research. Alerts the communities to meetings, webinars, new web-born reports (analyses, statistics), datasets, and general news. Currently contains over 3,000 items. This resource was retired on September 14, 2021 and is no longer updated.

FQHC Location Data

Detailed Address Data on Federally Qualified Health Centers in the US

By US Department of Health and Human Services [source]

About this dataset

This dataset provides comprehensive address-level information on Federally Qualified Health Centers (FQHCs) in the United States. FQHCs are community-driven and consumer run organizations that serve populations with limited access to health care, including those who are low-income, uninsured, have a limited grasp of English, migrating and seasonal farm workers, individuals experiencing homelessness, and those living in public housing. In addition to detailed location addressing data such as postal code and city name for each center in the scope of this dataset; users can find optional information about an individual center such as its operator description or the type of population it serves, along with rich backroom management data which includes grant number, grantee name and uniform resource locator (URL). Get familiarized with this essential dataset to help provide quality medical care access to under served communities across the US

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How to use the dataset

This dataset is an address-level dataset on the locations of Federally Qualified Health Centers (FQHCs). This dataset includes information on the FQHCs such as name, address, contact information, operating hours per week and grant number. It can be used to locate FQHCs in a particular area and to gain insights into the services they provide.

In order to use this data set, it is important to understand what attributes are included. These are broken down into categories including basic site information (name, telephone number etc.), service description (what population is served etc.), region info (HHS region code etc.) and supplemental info including records for operator and grantee organization.

Once you have identified what fields you are interested in, you can then use this data set for further analysis such as counting how many FQHCs exist within a certain area or determining which states have higher numbers of FQHCs than others. You can also filter by features such as services offered or population served to gain further insights into a particular segment of the FQHC market.

It should also be noted that there may be discrepancies between different sources regarding different fields due to variations in data collection methods; however this dataset is sourced from reliable government datasets making it more accurate than other options. Additionally it contains multiple years of data which provides invaluable insight over time trends that would otherwise not be available through other sources

Research Ideas

• Monitoring health outcomes in a given region and comparing changes over time in terms of FQHC locations, services available, and populations served.
• Analyzing the regional distribution of FQHCs and determining whether there are underserved areas based on population density and access to healthcare services.
• Creating a geographic information system (GIS) map to visualize the FQHC locations across the United States, highlighting rural or underserved areas in need of additional support for healthcare access

Acknowledgements

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

License

Unknown License - Please check the dataset description for more information.

Columns

File: SITE_HCC_FCT_DET.csv | Column name | Description | |:-----------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------| | Site Name | Name of the FQHC. (String) | | UDS Number | Unique identifier assigned by the US Department of Human Services for each FQHC. (Integer) | | Site Telephone Number | Telephone number of the FQHC. (String) | | Site Facsimile Telephone Number | Facsimile telephone number of the FQHC. (String) | | **Administrati...

The amount of global healthcare data is expected to increase dramatically by the year 2020. Despite the growing amount of data, there is not enough storage space to accommodate the data being generated. It is projected that by 2020 there will be 985 exabytes of storage available for healthcare data but there will be 2,314 exabytes of healthcare data generated.

In the first half of 2024, the highest number of data breaches impacted network servers data. These entities, that usually gather the most sensitive patient data, saw *** data breach incidents. Meanwhile, e-mail services ranked second.

According to a survey conducted in a selection of countries in Latin America in 2024, Argentina was the nation with the highest share of respondents that believed there was readily available information on healthcare services in the country, with ** percent of interviewees agreeing with that statement. Meanwhile, only ** percent of respondents in Peru claimed the same about their local health care system. In 2020, Argentina was one of the Latin American countries with the highest share of GDP allocated to health care. By 2024, health expenditure in the country is expected to reach around ***** percent of the Argentinian gross domestic product (GDP).

The size of the US Health Information Exchange Industry market was valued at USD 0.66 Million in 2023 and is projected to reach USD 1.47 Million by 2032, with an expected CAGR of 12.12% during the forecast period. Recent developments include: In October 2022, Mpowered Health launched its xChange, the United States consumer-mediated healthcare data exchange. The exchange enables health plans, health systems, and other healthcare organizations to request and obtain medical records from consumers with their consent., In March 2022, mpro5 Inc announced its launch into the United States market with a strategy of enabling the collection and leverage of real-time data to simplify the most complex operational challenges in healthcare and hospitals.. Key drivers for this market are: Increasing Demand for Electronic Health Records Resulting in the Expansion of the Market, Government Support via Various Programs and Incentives; Reduction in Healthcare Cost and Improved Efficacy. Potential restraints include: Huge Initial Infrastructural Investment and Slow Return on Investment, Data Privacy and Security Concerns. Notable trends are: The Decentralized/Federated Model is Expected to Hold a Notable Market Share Over the Forecast Period.

This publication provides the most timely statistics available relating to NHS funded secondary mental health, learning disabilities and autism services in England. This information will be of use to people needing access to information quickly for operational decision making and other purposes. These statistics are derived from submissions made using version 2.0 of the Mental Health Services Dataset (MHSDS). NHS Digital review the quality and completeness of the submissions used to create these statistics on an ongoing basis. More information about this work can be found in the Accuracy and reliability section of this report. Fully detailed information on the quality and completeness of particular statistics in this release is not available due to the timescales involved in reviewing submissions and engaging with data providers. The information that has been obtained at the time of publication is made available in the Provider Feedback sections of the Data Quality Reports which accompany this release. Information gathered after publication is released in future editions of this publication series. More detailed information on the quality and completeness of these statistics and a summary of how these statistics may be interpreted is made available later in our Mental Health Bulletin: Annual Report publication series. All elements of this publication, other editions of this publication series, and related annual publication series' can be found in the Related Links below. The Mental Health Data Hub was launched In February 2018; the hub brings together information on mental health data into a single place and contains visualisations and time series of select data from within this publication. The hub is available here: https://digital.nhs.uk/data-tools-and-services/services/mental-health-data-hub. Included in this months publication is a further exploratory perinatal report. This exploratory analysis is an analysis of women in contact with mental health services who were new or expectant mothers between January 2017 and December 2017. Please note, the Quarter 4 Children and Young People Receiving Second Contact With Services measure will not be included in the June 2018 publication. A validation of this data is currently underway; we expect statistics for the full 2017/18 financial year to be published in the July 2018 publication. MHSDS Monthly: Final January to March 2018 Mental Health Services Selected NHS England Measures Reference Tables has been updated with an additional note, no values have changed. A revised version of Bed days on adult wards for people aged 0-17 and Number of people aged 0-17 on adult wards is available on our supplementary information pages; this file adjusts the measures for known data quality issues to produce the most accurate information possible. A correction has been made to this publication on 10 September 2018. This amendment relates to statistics in the monthly CSV data file; the specific measures effected are listed in the “Corrected Measures” CSV. All listed measures have now been corrected. NHS Digital apologises for any inconvenience caused.

The Washington State Department of Health presents this information as a service to the public. True and correct copies of legal disciplinary actions taken after July 1998 are available on our Provider Credential Search site. These records are considered certified by the Department of Health. This includes information on health care providers. Please contact our Customer Service Center at 360-236-4700 for information about actions before July 1998. The information on this site comes directly from our database and is updated daily at 10:00 a.m.. This data is a primary source for verification of credentials and is extracted from the primary database at 2:00 a.m. daily. News releases about disciplinary actions taken against Washington State healthcare providers, agencies or facilities are on the agency's Newsroom webpage. Disclaimer The absence of information in the Provider Credential Search system doesn't imply any recommendation, endorsement or guarantee of competence of any healthcare professional. The presence of information in this system doesn't imply a provider isn't competent or qualified to practice. The reader is encouraged to carefully evaluate any information found in this data set.

This is a monthly report on publicly funded community services for people of all ages using data from the Community Services Data Set (CSDS) reported in England for September 2024. It has been developed to help achieve better outcomes and provide data that will be used to commission services in a way that improves health, reduces inequalities, and supports service improvement and clinical quality. These statistics are classified as experimental and should be used with caution. Experimental statistics are new official statistics undergoing evaluation. More information about experimental statistics can be found on the UK Statistics Authority website (linked at the bottom of this page). A provisional data file for October 2024 is now included in this publication. Please note this is intended as an early view until providers submit a refresh of their data, which will be published next month.

Comprehensive Medical Q&A Dataset

Unlocking Healthcare Data with Natural Language Processing

By Huggingface Hub [source]

About this dataset

The MedQuad dataset provides a comprehensive source of medical questions and answers for natural language processing. With over 43,000 patient inquiries from real-life situations categorized into 31 distinct types of questions, the dataset offers an invaluable opportunity to research correlations between treatments, chronic diseases, medical protocols and more. Answers provided in this database come not only from doctors but also other healthcare professionals such as nurses and pharmacists, providing a more complete array of responses to help researchers unlock deeper insights within the realm of healthcare. This incredible trove of knowledge is just waiting to be mined - so grab your data mining equipment and get exploring!

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How to use the dataset

In order to make the most out of this dataset, start by having a look at the column names and understanding what information they offer: qtype (the type of medical question), Question (the question in itself), and Answer (the expert response). The qtype column will help you categorize the dataset according to your desired question topics. Once you have filtered down your criteria as much as possible using qtype, it is time to analyze the data. Start by asking yourself questions such as “What treatments do most patients search for?” or “Are there any correlations between chronic conditions and protocols?” Then use simple queries such as SELECT Answer FROM MedQuad WHERE qtype='Treatment' AND Question LIKE '%pain%' to get closer to answering those questions.

Once you have obtained new insights about healthcare based on the answers provided in this dynmaic data set - now it’s time for action! Use all that newfound understanding about patient needs in order develop educational materials and implement any suggested changes necessary. If more criteria are needed for querying this data set see if MedQuad offers additional columns; sometimes extra columns may be added periodically that could further enhance analysis capabilities; look out for notifications if these happen.

Finally once making an impact with the use case(s) - don't forget proper citation etiquette; give credit where credit is due!

Research Ideas

• Developing medical diagnostic tools that use natural language processing (NLP) to better identify and diagnose health conditions in patients.
• Creating predictive models to anticipate treatment options for different medical conditions using machine learning techniques.
• Leveraging the dataset to build chatbots and virtual assistants that are able to answer a broad range of questions about healthcare with expert-level accuracy

Acknowledgements

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

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: train.csv | Column name | Description | |:--------------|:------------------------------------------------------| | qtype | The type of medical question. (String) | | Question | The medical question posed by the patient. (String) | | Answer | The expert response to the medical question. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Huggingface Hub.

The Presidents Information Technology Advisory Committee PITAC is appointed by the President to provide independent expert advice on maintaining Americas preeminence in advanced information technology IT. PITAC members are IT leaders in industry and academia with expertise relevant to critical elements of the national information infrastructure such as high-performance computing, large-scale networking, and high-assurance software and systems design. The Committees studies help guide the Administrations efforts to accelerate the development and adoption of information technologies vital for American prosperity in the 21st century.

This is a monthly report on publicly funded community services for children, young people and adults using data from the Community Services Data Set (CSDS) reported in England for August 2019. The CSDS is a patient-level dataset and has been developed to help achieve better outcomes for children, young people and adults. It provides data that will be used to commission services in a way that improves health, reduces inequalities, and supports service improvement and clinical quality. These services can include NHS Trusts, health centres, schools, mental health trusts, and local authorities. The data collected in CSDS includes personal and demographic information, diagnoses including long-term conditions and disabilities and care events plus screening activities. These statistics are classified as experimental and should be used with caution. Experimental statistics are new official statistics undergoing evaluation. They are published in order to involve users and stakeholders in their development and as a means to build in quality at an early stage. More information about experimental statistics can be found on the UK Statistics Authority website. We hope this information is helpful and would be grateful if you could spare a couple of minutes to complete a short customer satisfaction survey. Please use the survey in the related links to provide us with any feedback or suggestions for improving the report.

Introduction

Diabetes Care Statistics: Diabetes continues to pose a significant global health concern, impacting millions and exerting considerable pressure on healthcare systems worldwide. Having accurate and current diabetes care statistics is crucial for comprehending the disease's prevalence, pinpointing management shortcomings, and guiding effective healthcare policies and interventions.

These statistics cover prevalence rates, patient demographics, treatment compliance, complications, and health outcomes, offering a detailed overview of diabetes care today. Through thorough data analysis, healthcare professionals and policymakers can optimize resource allocation, enhance patient education, and design focused strategies to improve care quality and minimize diabetes's long-term effects...

This database is part of the National Medical Information System (NMIS). The National Health Care Practitioner Database (NHCPD) supports Veterans Health Administration Privacy Act requirements by segregating personal information about health care practitioners such as name and social security number from patient information recorded in the National Patient Care Database for Ambulatory Care Reporting and Primary Care Management Module.

Snapshot img { margin: 10px !important; } Healthcare Information Systems Market Size 2025-2029

The healthcare information systems market size is forecast to increase by USD 142.3 billion, at a CAGR of 9.8% between 2024 and 2029.

The global healthcare information systems market is primarily shaped by regulatory mandates requiring advanced digital solutions to break down data silos and improve care coordination. This drives the adoption of compliant electronic health records and healthcare interoperability solution market technologies. The strategic shift toward cloud-based deployment and SaaS models further redefines healthcare it, offering a more scalable and cost-effective operational paradigm. This trend emphasizes the need for systems that support decentralized care delivery and remote patient monitoring tools, transforming how healthcare services are accessed and managed across different settings. The evolution of these systems is critical for enhancing both operational efficiency and patient outcomes.The migration to cloud architectures, while offering significant benefits, introduces the formidable challenge of sophisticated cybersecurity threats. This constant operational and financial drain necessitates immense ongoing investment in defensive measures and incident response planning to protect sensitive medical information. The interconnected nature of modern healthcare services market ecosystems, from the hospital information system to pharmacy information systems, creates a large and attractive attack surface for malicious actors. This makes robust cybersecurity in healthcare a primary consideration for providers as they invest in new healthcare analytics platforms and other digital tools to support patient care.

What will be the Size of the Healthcare Information Systems Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019 - 2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe hospital information system and pharmacy information systems are evolving through healthcare it initiatives that prioritize data aggregation and integration. The move toward value-based care models necessitates robust healthcare analytics and clinical workflow optimization. The healthcare cloud computing market is enabling this shift by providing scalable infrastructure for managing patient-generated health data and supporting ehealth software and services market platforms, ensuring data is accessible and actionable across the care continuum.The integration of generative AI and predictive analytics is transforming clinical decision support systems within the broader healthcare information systems market. However, effective data migration and overcoming interoperability hurdles remain critical for success. Ensuring robust cybersecurity in healthcare is essential for protecting patient data access across telemedicine platforms and mobile health applications. The efficacy of population health management systems ultimately hinges on seamless health information exchange and the universal adoption of standardized data formats like FHIR.

How is this Healthcare Information Systems Industry 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 AmericaUSCanadaMexicoEuropeGermanyUKFranceThe NetherlandsItalySpainAsiaRest of World (ROW)

By Application Insights

The revenue cycle management segment is estimated to witness significant growth during the forecast period.Revenue cycle management systems represent a significant application segment, focused on managing financial workflows from patient registration to final payment collection. These platforms integrate clinical and administrative data to streamline claims processing automation, manage denials, and optimize coding accuracy optimization. The increasing complexity of modern reimbursement models and the fundamental shift toward value-based care are primary drivers for the adoption of these advanced financial visibility tools across healthcare organizations.Rising patient financial responsibility also necessitates integrated features such as payment estimation tools and flexible payment portals. The criticality of resilient RCM systems was recently highlighted by a major cybersecurity incident that disrupted operations for thousands of providers. This event has accelerated investments in secure, cloud-based solutions with embedded AI for p

A service that provides disease information managed by the Health Insurance Review & Assessment Service according to criteria such as disease code, medical (Western)/Oriental medicine classification, etc. - Disease name/code search, disease gender and age-based statistics, disease inpatient outpatient statistics, disease medical institution type statistics, disease medical institution region statistics available ※ The Health Insurance Review & Assessment Service builds and manages a disease master file that reflects disease symbols and various additional information related to diseases required for claiming medical care benefits based on the 'Korean Standard Classification of Diseases and Causes of Death (KCD)' of Statistics Korea. The master file can be viewed on the Health Insurance Review & Assessment Service website (www.hira.or.kr) > System/Policy > Insurance Approval Criteria > Data Room.

Introduction

EHR Industry Statistics: Electronic Health Records (EHRs) are digital versions of patient paper charts, revolutionizing healthcare by providing instant, secure access to comprehensive medical information.

They include details like medical history, diagnoses, medications, and test results, consolidating data from various sources into one accessible record.

EHRs enhance patient care by supporting better coordination among healthcare providers, improving efficiency through reduced paperwork, and enabling patient engagement via access to their records.

Challenges include high implementation costs, interoperability issues between different systems, and concerns about data privacy.

Looking ahead, advancements aim to improve interoperability, enhance data analytics, and integrate with telemedicine for more efficient and personalized healthcare delivery.

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

This is the current Medical Service Study Area. California Medical Service Study Areas are created by the California Department of Health Care Access and Information (HCAI).Check the Data Dictionary for field descriptions.Search for the Medical Service Study Area data on the CHHS Open Data Portal.Checkout the California Healthcare Atlas for more Medical Service Study Area information.This is an update to the MSSA geometries and demographics to reflect the new 2020 Census tract data. The Medical Service Study Area (MSSA) polygon layer represents the best fit mapping of all new 2020 California census tract boundaries to the original 2010 census tract boundaries used in the construction of the original 2010 MSSA file. Each of the state's new 9,129 census tracts was assigned to one of the previously established medical service study areas (excluding tracts with no land area), as identified in this data layer. The MSSA Census tract data is aggregated by HCAI, to create this MSSA data layer. This represents the final re-mapping of 2020 Census tracts to the original 2010 MSSA geometries. The 2010 MSSA were based on U.S. Census 2010 data and public meetings held throughout California.Source of update: American Community Survey 5-year 2006-2010 data for poverty. For source tables refer to InfoUSA update procedural documentation. The 2010 MSSA Detail layer was developed to update fields affected by population change. The American Community Survey 5-year 2006-2010 population data pertaining to total, in households, race, ethnicity, age, and poverty was used in the update. The 2010 MSSA Census Tract Detail map layer was developed to support geographic information systems (GIS) applications, representing 2010 census tract geography that is the foundation of 2010 medical service study area (MSSA) boundaries. ***This version is the finalized MSSA reconfiguration boundaries based on the US Census Bureau 2010 Census. In 1976 Garamendi Rural Health Services Act, required the development of a geographic framework for determining which parts of the state were rural and which were urban, and for determining which parts of counties and cities had adequate health care resources and which were "medically underserved". Thus, sub-city and sub-county geographic units called "medical service study areas [MSSAs]" were developed, using combinations of census-defined geographic units, established following General Rules promulgated by a statutory commission. After each subsequent census the MSSAs were revised. In the scheduled revisions that followed the 1990 census, community meetings of stakeholders (including county officials, and representatives of hospitals and community health centers) were held in larger metropolitan areas. The meetings were designed to develop consensus as how to draw the sub-city units so as to best display health care disparities. The importance of involving stakeholders was heightened in 1992 when the United States Department of Health and Human Services' Health and Resources Administration entered a formal agreement to recognize the state-determined MSSAs as "rational service areas" for federal recognition of "health professional shortage areas" and "medically underserved areas". After the 2000 census, two innovations transformed the process, and set the stage for GIS to emerge as a major factor in health care resource planning in California. First, the Office of Statewide Health Planning and Development [OSHPD], which organizes the community stakeholder meetings and provides the staff to administer the MSSAs, entered into an Enterprise GIS contract. Second, OSHPD authorized at least one community meeting to be held in each of the 58 counties, a significant number of which were wholly rural or frontier counties. For populous Los Angeles County, 11 community meetings were held. As a result, health resource data in California are collected and organized by 541 geographic units. The boundaries of these units were established by community healthcare experts, with the objective of maximizing their usefulness for needs assessment purposes. The most dramatic consequence was introducing a data simultaneously displayed in a GIS format. A two-person team, incorporating healthcare policy and GIS expertise, conducted the series of meetings, and supervised the development of the 2000-census configuration of the MSSAs.MSSA Configuration Guidelines (General Rules):- Each MSSA is composed of one or more complete census tracts.- As a general rule, MSSAs are deemed to be "rational service areas [RSAs]" for purposes of designating health professional shortage areas [HPSAs], medically underserved areas [MUAs] or medically underserved populations [MUPs].- MSSAs will not cross county lines.- To the extent practicable, all census-defined places within the MSSA are within 30 minutes travel time to the largest population center within the MSSA, except in those circumstances where meeting this criterion would require splitting a census tract.- To the extent practicable, areas that, standing alone, would meet both the definition of an MSSA and a Rural MSSA, should not be a part of an Urban MSSA.- Any Urban MSSA whose population exceeds 200,000 shall be divided into two or more Urban MSSA Subdivisions.- Urban MSSA Subdivisions should be within a population range of 75,000 to 125,000, but may not be smaller than five square miles in area. If removing any census tract on the perimeter of the Urban MSSA Subdivision would cause the area to fall below five square miles in area, then the population of the Urban MSSA may exceed 125,000. - To the extent practicable, Urban MSSA Subdivisions should reflect recognized community and neighborhood boundaries and take into account such demographic information as income level and ethnicity. Rural Definitions: A rural MSSA is an MSSA adopted by the Commission, which has a population density of less than 250 persons per square mile, and which has no census defined place within the area with a population in excess of 50,000. Only the population that is located within the MSSA is counted in determining the population of the census defined place. A frontier MSSA is a rural MSSA adopted by the Commission which has a population density of less than 11 persons per square mile. Any MSSA which is not a rural or frontier MSSA is an urban MSSA. Last updated December 6th 2024.