The Master Facility Inventory (MFI) data collection provides a comprehensive list of hospital facilities in the United States in 1976. The criteria for inclusion were that a facility provided medical, nursing, personal, or custodial care to groups of unrelated persons on an inpatient basis and was licensed or operated by federal or state agencies. The American Hospital Association conducted the survey, supplying the resulting data to the National Center for Health Statistics in order to update its Master Facility Inventory on the number and kinds of hosptals in the United States and the changes in the list since the last MFI survey. Information gathered is for the previous calendar year and includes facility identification information, ownership, number of full- and part-time staff, number of beds per unit, number of adult and pediatric inpatients, numbers in newborn nursery, outpatient utlilization (e.g., emergency care and clinics), major and minor surgical operations, hospital classification (e.g., government, non-government, investor-owned), and finances (e.g., total revenue, expenses, and assets) for 7,271 institutions.

This Dataset contains indicators-wise data related to Family Planning, Maternal Health, and Immunization under HMIS for for each state. It also contains age-group and rural and urban facility wise data.

Graph and download economic data for Expenditures: Medical Services by Highest Education: College Graduate: Master's, Professional, Doctoral Degree (CXUMEDSERVSLB1409M) from 2012 to 2023 about doctoral degree, medical, professional, tertiary schooling, expenditures, education, services, and USA.

Please note: This is a Synthetic data file, also known as a Dummy file - it is not real data. This synthetic file should not be used for purposes other than to develop an test computer programs that are to be submitted by remote access. Each record in the synthetic file matches the format and content parameters of the real Statistics Canada Master File with which it is associated, but the data themselves have been 'made up'. They do NOT represent responses from real individuals and should NOT be used for actual analysis. These data are provided solely for the purpose of testing statistical package 'code' (e.g. SPSS syntax, SAS programs, etc.) in preperation for analysis using the associated Master File in a Research Data Centre, by Remote Job Submission, or by some other means of secure access. If statistical analysis 'code' works with the synthetic data, researchers can have some confidence that the same code will run successfully against the Master File data in the Resource Data Centres. In the fall of 1991, the National Health Information Council recommended that an ongoing national survey of population health be conducted. This recommendation was based on consideration of the economic and fiscal pressures on the health care systems and the requirement for information with which to improve the health status of the population in Canada. Commencing in April 1992, Statistics Canada received funding for development of a National Population Health Survey (NPHS). The NPHS collects information related to the health of the Canadian population and related socio-demographic information to: aid in the development of public policy by providing measures of the level, trend and distribution of the health status of the population, provide data for analytic studies that will assist in understanding the determinants of health, and collect data on the economic, social, demographic, occupational and environmental correlates of health. In addition the NPHS seeks to increase the understanding of the relationship between health status and health care utilization, including alternative as well as traditional services, and also to allow the possibility of linking survey data to routinely collected administrative data such as vital statistics, environmental measures, community variables, and health services utilization. The NPHS collects information related to the health of the Canadian population and related socio-demographic information. It is composed of three components: the Households, the Health Institutions, and the North components. The Household component started in 1994/1995 and is conducted every two years. The first three cycles (1994/1995, 1996/1997, 1997/1998) were both cross-sectional and longitudinal. The NPHS longitudinal sample includes 17,276 persons from all ages in 1994/1995 and these same persons are to be interviewed every two years. Beginning in Cycle 4 (2000/2001) the survey became strictly longitudinal (collecting health information from the same individuals each cycle). The cross-sectional and longitudinal documentation of the Household component is presented separately as well as the documentation for the Health Institutions and North components. The cross-sectional component of the Population Health Survey Program has been taken over by the Canadian Community Health Survey (CCHS). With the introduction of the Canadian Community Health Survey (CCHS), there were many changes to the 2000-2001 National Population Health Survey - Household questionnaire. Since NPHS is strictly a longitudinal survey, some content was migrated to the CCHS (such as the two-week disability section and certain questions on place where health care was provided) or was dropped (e.g. certain chronic conditions), while the order of the questionnaire changed. As only the longitudinal respondent is now surveyed, it was no longer necessary to distinguish between the General questionnaire and the Health component. Health Canada, Public Health Agency of Canada and provincial ministries of health use NPHS longitudinal data to plan, implement and evaluate programs and health policies to improve health and the efficiency of health services. Non-profit health organizations and researchers in the academic fields use the information to move research ahead and to improve health.

ABSTRACT The health care model based on the Family Health Strategy, created in the early 1990s, encouraged changes in health education, highlighting the need to create lato and stricto sensu postgraduate courses aimed at empowering professionals that foster comprehensive health care. Periodic evaluations are carried out and encouraged by Capes/MEC in order to maintain the quality of postgraduate courses, but evaluations of recently-introduced professional master’s degree courses in family health remain scarce. Objectives To describe the academic profile, contribution, motivations and expectations of graduates of a Professional Master’s in Family Health. Method Cross-sectional and quantitative study to analyze the results of 102 questionnaires answered by graduates of the Professional Master’s Degree in Family Health of the Estácio de Sá University (RJ), who had concluded the course between 2007 and 2012. The instrument consisted of open-ended and closed-ended questions, sent by e-mail and made available online through the electronic platform Survey Monkey. The study evaluated age, gender, regional origin, academic background, as well as the contributions, expectations and motivations related to the course. Results The survey sample was formed predominantly by female graduates, aged over 30, from 13 Brazilian states and, mainly from Medicine and Nursing courses. The contribution of the master’s degree to the graduate’s professional life was evaluated as excellent by 77% of the interviewees. The expectations regarding the course were positively evaluated and the main reasons for seeking the qualification were scientific-technical improvement and personal satisfaction, rather than better salaries or job stability. Conclusion The course was evaluated positively by the graduates, having exceeded their expectations and satisfied the interests that led them to it, thus producing changes to their personal and professional life. A longitudinal analysis of the impact of the professional master’s degree in the career of graduates will require a sequence of similar studies, as has been stimulated by Capes/MEC in recent years.

Abstract: Introduction: COVID-19 has brought numerous challenges for the Health System in Brazil. In Primary Health Care, these challenges add to those that already exist. Objective: To analyze the experience of facing the COVID-19 pandemic among doctors of the Mais Médicos Brasil Program and master’s degree students in Family Health and those working in Primary Health Care. Methods: Qualitative study involving eight doctors from Primary Health Care in Alagoas who are also studying the professional master’s degree in Family Health (PROFSAÚDE). Five questions were developed, the answers to which were analyzed based on Content Analysis theory. Results: Three categories and four subcategories were observed: Category 1 - Study and work process (Subcategory 1.1- Characteristics of PROFSAÚDE; Subcategory 1.2 - Personal strategies developed); Category 2 - Challenges in Life Management (Subcategory 2.1 - Changes in daily life; Subcategory 2.2 - Impact on emotions) and Category 3 - Personal and Professional Growth. Conclusion: During the pandemic, medical professionals experience complex and dynamic situations due to a dual and cumulative process - working in PHC and studying for their master’s degree. Despite all the difficulties faced, the master’s degree allowed them to improve skills in dealing with critical situations.

World-readable Google Sheet with geographical master data for Guinea, including ADM1/2/3 p-codes and links to GeoJSON shape files. HXL tagged. Also includes list of known Guinea health facilities, a list of IPC partner organisations, and code lists for sector (public/private) and infection-control training type. Used by the Guinea health facility training activities dataset.

This pie chart illustrates the distribution of degrees—Bachelor’s, Master’s, and Doctoral—among PERM graduates from Epidemiology Medical Statistics. It shows the educational composition of students who have pursued and successfully obtained permanent residency through their qualifications in Epidemiology Medical Statistics. This visualization helps to understand the diversity of educational backgrounds that contribute to successful PERM applications, reflecting the major’s role in fostering students’ career paths towards permanent residency in the U.S.

As of June 28, 2010, the Master Veteran Index (MVI) database based on the enhanced Master Patient Index (MPI) is the authoritative identity service within the VA, establishing, maintaining and synchronizing identities for VA clients, Veterans and beneficiaries. The MVI includes authoritative sources for health identity data and contains over 17 million patient entries populated from all VHA facilities nationwide. The MVI provides the access point mechanism for linking patient's information to enable an enterprise-wide view of patient information, uniquely identifies all active patients who have been admitted, treated, or registered in any VHA facility, and assigns a unique identifier to the patient. The MVI correlates a patient's identity across the enterprise, including all VistA systems and external systems, such as Department of Defense (DoD) and the Nationwide Health Information Network (NwHIN). The MVI facilitates the sharing of health information, resulting in coordinated and integrated health care for Veterans. New Information Technology systems must be interoperable with the MVI and legacy systems will establish integration by October 1, 2012. The Healthcare Identity Management (HC IdM) Team within VHA's Data Quality Program is the steward of patient identity data, performing maintenance and support activities.

The Master Facility Inventory data collection provides a comprehensive list of nursing, personal care, and domiciliary care facilities in the United States in 1976. The criteria for inclusion were that a facility provided medical, nursing, personal, or custodial care to groups of unrelated persons on an inpatient basis. The survey was conducted by the National Center for Health Statistics in order to update its Master Facility Inventory on the number and kinds of such facilities in the United States and the changes to the list since the last MFI survey. Information gathered is for the previous calendar year and includes facility identification information, ownership and type of facility, capacity and type of beds (i.e., total beds set up and staffed for use and number of beds certified by Medicare or Medicaid as skilled and intermediate), acceptance criteria, and total number of male and female residents (or patients) for 26,748 institutions.

For current version see: https://www.sandiegocounty.gov/content/sdc/hhsa/programs/phs/maternal_child_family_health_services/MCFHSstatistics.html

Infant Mortality - Cohort Dataset Note: The Infant Mortality Rate is infant deaths (under one year of age) per 1,000 live births, by geography. Numerator represents infant's race/ethnicity. Denominator represents mother's race/ethnicity.

***API: Asian/Pacific Islander. ***AIAN: American Indian/Alaska Native. Blank Cells: Rates not calculated for fewer than 5 events. Rates not calculated in cases where infant's zip code of residence is unknown.

Sources: State of California, Department of Public Health, Death Statistical Master Files (before 2014), California Comprehensive Death Files (2014 and later), and Birth Statistical Master Files. Prepared by: County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics Unit, 2019.

Interpretation: "There were 5 infant deaths per 1,000 live births in Geography X".

Data Guide, Dictionary, and Codebook: https://www.sandiegocounty.gov/content/dam/sdc/hhsa/programs/phs/CHS/Community%20Profiles/Public%20Health%20Services%20Codebook_Data%20Guide_Metadata_10.2.19.xlsx

The PATH Study was launched in 2011 to inform the Food and Drug Administration's regulatory activities under the Family Smoking Prevention and Tobacco Control Act (TCA). The PATH Study is a collaboration between the National Institute on Drug Abuse (NIDA), National Institutes of Health (NIH), and the Center for Tobacco Products (CTP), Food and Drug Administration (FDA). The study sampled over 150,000 mailing addresses across the United States to create a national sample of people who do and do not use tobacco. 45,971 adults and youth constitute the first (baseline) wave, Wave 1, of data collected by this longitudinal cohort study. These 45,971 adults and youth along with 7,207 "shadow youth" (youth ages 9 to 11 sampled at Wave 1) make up the 53,178 participants that constitute the Wave 1 Cohort. Respondents are asked to complete an interview at each follow-up wave. Youth who turn 18 by the current wave of data collection are considered "aged-up adults" and are invited to complete the Adult Interview. Additionally, "shadow youth" are considered "aged-up youth" upon turning 12 years old, when they are asked to complete the Youth Interview after parental consent. At Wave 4, a probability sample of 14,098 adults, youth, and shadow youth ages 10 to 11 was selected from the civilian, noninstitutionalized population at the time of Wave 4. This sample was recruited from residential addresses not selected for Wave 1 in the same sampled Primary Sampling Units (PSUs) and segments using similar within-household sampling procedures. This "replenishment sample" was combined for estimation and analysis purposes with Wave 4 adult and youth respondents from the Wave 1 Cohort who were in the civilian, noninstitutionalized population at the time of Wave 4. This combined set of Wave 4 participants, 52,731 participants in total, forms the Wave 4 Cohort. At Wave 7, a probability sample of 14,863 adults, youth, and shadow youth ages 9 to 11 was selected from the civilian, noninstitutionalized population at the time of Wave 7. This sample was recruited from residential addresses not selected for Wave 1 or Wave 4 in the same sampled PSUs and segments using similar within-household sampling procedures. This second replenishment sample was combined for estimation and analysis purposes with Wave 7 adult and youth respondents from the Wave 4 Cohort who were at least age 15 and in the civilian, noninstitutionalized population at the time of Wave 7. This combined set of Wave 7 participants, 46,169 participants in total, forms the Wave 7 Cohort. Please refer to the Restricted-Use Files User Guide that provides further details about children designated as "shadow youth" and the formation of the Wave 1, Wave 4, and Wave 7 Cohorts. Dataset 0001 (DS0001) contains the data from the Public-Use File Master Linkage File (PUF-MLF). This file contains 93 variables and 82,139 cases. The file provides a master list of every person's unique identification number and what type of respondent they were in each wave for data that are available in the Public-Use Files and Special Collection Public-Use Files. Dataset 0002 (DS0002) contains the data from the Restricted-Use File Master Linkage File (RUF-MLF). This file contains 198 variables and 82,139 cases. The file provides a master list of every person's unique identification number and what type of respondent they were in each wave for data that are available in the Restricted-Use Files, Special Collection Restricted-Use Files, and Biomarker Restricted-Use Files.

Financial overview and grant giving statistics of Association Health And Welfare Veba Master Tr

Graph and download economic data for Expenditures: Medical Supplies by Highest Education: College Graduate: Master's, Professional, Doctoral Degree (CXUMEDSUPPLLB1409M) from 2012 to 2023 about doctoral degree, medical, professional, supplies, tertiary schooling, education, expenditures, and USA.

The dataset provides basic information of the FFS providers enrolled in the Medi-Cal program as of July 1, 2025. The data was retrieved from the Provider Master File (PMF), which has been used in the claims payment process and maintained by the Provider Enrollment Division (PED). The Variables in the dataset include provider number, name, type, specialty, geographic information, etc. This dataset does not include the Managed Care providers.

PLEASE NOTE: This is a Synthetic data file, also known as a Dummy File - it is NOT real data. This synthetic data file should not be used for purposes other than to develop and test computer programs that are to be submitted by remote access. Each record in the synthetic file matches the format and content parameters of the real Statistics Canada Master File with which it is associated, but the data themselves have been 'made up'. They do NOT represent responses from real individuals and should NOT be used for actual analysis. These data are provided solely for the purpose of testing statistical packing 'code' (e.g. SPSS syntax, SAS programs, etc.) in preparation for analysis using the associated Master File in a Research Data Centre, by Remote Job Submission, or by some other means of secure access. If statistical analysis 'code' works with the synthetic data, researchers can have some confidence that the same code will run successfully against the Master File data in the Research Data Centres. The Canadian Community Health Survey (CCHS) is a cross-sectional survey that collects information related to health status, health care utilization and health determinants for the Canadian population. It surveys a large sample of respondents and is designed to provide reliable estimates at the health region level. In 2007, major changes were made to the CCHS design. Data is now collected on an ongoing basis with annual releases, rather than every two years as was the case prior to 2007. The survey's objectives were also revised and are as follows: • support health surveillance programs by providing health data at the national, provincial and intra-provincial levels; • provide a single data source for health research on small populations and rare characteristics; • timely release of information easily accessible to a diverse community of users; and • create a flexible survey instrument that includes a rapid response option to address emerging issues related to the health of the population. The CCHS data is always collected from persons aged 12 and over living in private dwellings in the 115 health regions covering all provinces and territories. Excluded from the sampling frame are individuals living on Indian Reserves and on Crown Lands, institutional residents, full-time members of the Canadian Forces, and residents of certain remote regions. The CCHS covers approximately 98% of the Canadian population aged 12 and over. The CCHS produces three types of microdata files: master files; share files; and public use microdata files (PUMF). The characteristics of each of these files are presented in the User Guide. The PUMF is released every two years and contains two years of data.

A collection of data of an epidemiological study of chronic disease in the oldest old based on information collected from Kaiser Permanente facilities in Northern California (KPNC). The initial sample was drawn from the Kaiser''s active membership lists for the years 1971 and 1980. The sample was restricted to members that had a Multiphasic Health Checkup examination (MHC) within 7 years of the baseline date. The sample was stratified to attain equal numbers of observations (1,000 in each) in three sex-age cells for each cohort: 65-69, 70-79, and 80+. Each cohort was followed for 9 years through existing medical records and computerized hospitalization tapes. Mortality data was collected by matching the sampled data with state Vital Statistics data for an additional 3 years for a total follow-up time of 12 years. Part 1 of the data collections consists of Master Records, which includes information from the morbidity review, in which over 35 chronic conditions or diagnoses were abstracted from the member charts, as well as detailed diagnostic criteria for the major conditions. A prevalence review was done, which included the 4 years prior to the baseline date for these same conditions. Recurrent disease is included for the following conditions: cancers, myocardial infarction, and various forms of strokes. A detailed account of outpatient health services use, and data from the multiphasic health checkup, which was administered to each participant during the nine yearly follow-ups, are also included in the Master Records file. The labs and procedures included: chemistry, hematology, urinalysis, bacteriology, chest x-ray, GI x-ray, ultrasound, CT/MRI, mammogram, resting ECG, treadmill ECG, echocardiograms, nuclear scans, outpatient breast biopsy, cystoscopy, and cataract surgery. Inpatient utilization includes all hospitalizations, procedures done during a hospital stay, length of stay, admitting/discharge diagnosis. Part 2, Hospitalization, contains records of causes and dates of hospitalizations and discharges and nursing home admissions. There is also a section on incomplete reviews and the reasons for them. Demographic information and some lifestyle information from the multiphasic health checkup (e.g., smoking, alcohol, and Body Mass Index) are also in this file. Data Availability: These datasets have been documented extensively and are available from the ICPSR (Study No. 4219). * Dates of Study: 1971-1992 * Study Features: Longitudinal, Anthropometric Measures * Sample Size: ** 1971 cohort: 2,877 (baseline) ** 1980 cohort: 3,113 (baseline) ** 1971 & 1980: 5,990 ** Hospitalization: 14,730 Links: * ICPSR: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/04219 * HSRR: http://wwwcf.nlm.nih.gov/hsrr_search/view_hsrr_record_table.cfm?TITLE_ID=381&PROGRAM_CAME=toc_with_source2.cfm

The Master Patient Index (MPI) software market is experiencing robust growth, driven by the increasing need for accurate and unified patient data across healthcare systems. The market's expansion is fueled by several key factors: the rising adoption of electronic health records (EHRs), the increasing prevalence of chronic diseases requiring comprehensive patient data management, and the growing emphasis on interoperability and data exchange between healthcare providers. Consolidation within the healthcare industry and the demand for improved patient care further contribute to the market's upward trajectory. While precise market sizing data is unavailable, considering a global market for healthcare IT solutions with a similar trajectory, we can estimate the 2025 market size at approximately $2 billion USD, and a Compound Annual Growth Rate (CAGR) of 10% based on conservative projections for the foreseeable future. This growth will be driven primarily by the implementation of advanced features such as data cleansing, deduplication, and real-time patient identification within the MPI systems. Furthermore, increased integration with other healthcare IT systems, including EHRs and patient portals, will drive further market expansion. The competitive landscape includes both established players like McKesson, Oracle, and Epic (inferred based on market presence) and smaller, specialized vendors. These companies are investing heavily in research and development to enhance the functionality and scalability of their MPI solutions. Challenges to growth include high implementation costs, data security concerns, and the complexity of integrating MPI systems with diverse legacy systems across different healthcare settings. Despite these challenges, the long-term outlook remains positive, with continued growth driven by technological advancements, increasing regulatory requirements for data interoperability, and the overarching goal of improving patient safety and care quality. The forecast period of 2025-2033 suggests a significant expansion of this market, with opportunities for both established companies and emerging players alike.

Market Size and Drivers: The global Master Data Management (MDM) market in healthcare is projected to grow from $XXX million in 2025 to $XXX million by 2033 at a CAGR of 5%. Key drivers include the rising demand for accurate and consistent patient data, increased healthcare regulations, the need for improved patient outcomes, and the adoption of interoperable healthcare systems. Competitive Landscape and Future Trends: Major companies in the healthcare MDM market include SAP, IBM, Reltio, Amitech, and MEDfx. The market is expected to witness further consolidation as vendors focus on integrating advanced technologies such as artificial intelligence (AI) and machine learning (ML) into their solutions. Additionally, cloud-based MDM platforms are gaining popularity due to their scalability, cost-effectiveness, and accessibility. Healthcare organizations are also increasingly adopting data governance frameworks to ensure the quality and security of their master data, resulting in increased demand for MDM solutions with robust governance capabilities.

For current version see: https://www.sandiegocounty.gov/content/sdc/hhsa/programs/phs/maternal_child_family_health_services/MCFHSstatistics.html

Basic Metadata Note: Fetal Mortality Rate is rate of fetal deaths (at least 20 complete weeks of gestation) per 1,000 live births and fetal deaths, by geography. Reporting of fetal deaths is known to be incomplete.

Blank Cells: Rates not calculated for fewer than 5 events. Rates not calculated in cases where mother's zip code of residence is unknown.

Sample Interpretation: "There were 5 fetal deaths per 1,000 live births and fetal deaths in Geography X".

Prepared by: County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics Unit, 2019.

Sources: VRBIS- California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Vital Records Business Intelligence System, 2017. Cohort file: State of California, Department of Public Health, Death Statistical Master Files (before 2014), California Comprehensive Death Files (2014 and later), and Birth Statistical Master Files.