The Agency for Healthcare Research and Quality (AHRQ) created SyH-DR from eligibility and claims files for Medicare, Medicaid, and commercial insurance plans in calendar year 2016. SyH-DR contains data from a nationally representative sample of insured individuals for the 2016 calendar year. SyH-DR uses synthetic data elements at the claim level to resemble the marginal distribution of the original data elements. SyH-DR person-level data elements are not synthetic, but identifying information is aggregated or masked.

The largest all-payer ambulatory surgery database in the United States, the Healthcare Cost and Utilization Project (HCUP) Nationwide Ambulatory Surgery Sample (NASS) produces national estimates of major ambulatory surgery encounters in hospital-owned facilities. Major ambulatory surgeries are defined as selected major therapeutic procedures that require the use of an operating room, penetrate or break the skin, and involve regional anesthesia, general anesthesia, or sedation to control pain (i.e., surgeries flagged as "narrow" in the HCUP Surgery Flag Software). Unweighted, the NASS contains approximately 9.0 million ambulatory surgery encounters each year and approximately 11.8 million ambulatory surgery procedures. Weighted, it estimates approximately 11.9 million ambulatory surgery encounters and 15.7 million ambulatory surgery procedures. Sampled from the HCUP State Ambulatory Surgery and Services Databases (SASD) and State Emergency Department Databases (SEDD) in order to capture both planned and emergent major ambulatory surgeries, the NASS can be used to examine selected ambulatory surgery utilization patterns. Developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality, HCUP data inform decision making at the national, State, and community levels. The NASS contains clinical and resource-use information that is included in a typical hospital-owned facility record, including patient characteristics, clinical diagnostic and surgical procedure codes, disposition of patients, total charges, facility characteristics, and expected source of payment, regardless of payer, including patients covered by Medicaid, private insurance, and the uninsured. The NASS excludes data elements that could directly or indirectly identify individuals, hospitals, or states. The NASS is limited to encounters with at least one in-scope major ambulatory surgery on the record, performed at hospital-owned facilities. Procedures intended primarily for diagnostic purposes are not considered in-scope. Restricted access data files are available with a data use agreement and brief online security training.

Usage

The National (Nationwide) Inpatient Sample (NIS) is part of a family of databases and software tools developed for the Healthcare Cost and Utilization Project (HCUP). The NIS is the largest publicly available all-payer inpatient healthcare database designed to produce U.S. regional and national estimates of inpatient utilization, access, cost, quality, and outcomes. Unweighted, it contains data from around 7 million hospital stays each year. Weighted, it estimates around 35 million hospitalizations nationally. Developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality (AHRQ), HCUP data inform decision making at the national, State, and community levels.

The information flow of the Hospital Discharge database (SDO flow) is the tool for collecting information relating to all hospitalization episodes provided in public and private hospitals throughout the national territory.

Born for purely administrative purposes of the hospital setting, the SDO, thanks to the wealth of information contained, not only of an administrative but also of a clinical nature, has become an indispensable tool for a wide range of analyzes and elaborations, ranging from areas to support of health planning activities for monitoring the provision of hospital assistance and the Essential Levels of Assistance, for use for proxy analyzes of other levels of assistance as well as for more strictly clinical-epidemiological and outcome analyzes. In this regard, the SDO database is a fundamental element of the National Outcomes Program (PNE).

The information collected includes the patient's personal characteristics (including age, sex, residence, level of education), characteristics of the hospitalization (for example institution and discharge discipline, hospitalization regime, method of discharge, booking date, priority class of hospitalization) and clinical features (e.g. main diagnosis, concomitant diagnoses, diagnostic or therapeutic procedures)

Information relating to drugs administered during hospitalization or adverse reactions to them (subject to other specific information flows) is excluded from the discharge form.

The National Health Data System (SNDS) will make it possible to link:

• health insurance data (SNIIRAM database);
• hospital data (PMSI database);
• the medical causes of death (base of the CépiDC of Inserm);
• disability-related data (from MDPH - CNSA data);
• a sample of data from complementary health insurance organisations.

The first two categories of data are already available and constitute the first version of the SNDS. The medical causes of death should feed the SNDS from the second half of 2017. The first data from the CNSA will arrive from 2018 and the sample of complementary organizations in 2019.

The purpose of the SNDS is to make these data available in order to promote studies, research or evaluations of a nature in the public interest and contributing to one of the following purposes:

• health information;
• the implementation of health policies;
• knowledge of health expenditure;
• informing professionals and establishments about their activities;
• innovation in the fields of health and medico-social care;
• monitoring, surveillance and health security.

The Nationwide Emergency Department Sample (NEDS) is part of a family of databases and software tools developed for the Healthcare Cost and Utilization Project (HCUP). NEDS is the largest all-payer emergency department (ED) database in the United States, yielding national estimates of hospital-based ED visits. One of the most distinctive features of the NEDS is its large sample size, which allows for analysis across hospital types and the study of relatively uncommon disorders and procedures.

In a historical and developmental sense, the former one-year reporting on employees employed in healthcare grew during 1990/91. in the continuous collection and monitoring of data through the state Register of Health Professionals. The department maintains data on all healthcare workers and healthcare associates, and on administrative and technical staff for now only numerically, according to the number of permanent employees at the end of the year. In the future, it is intended to register employees who are not health-oriented and work in healthcare, and healthcare professionals who work outside the healthcare system can also be registered.

Data on health workers and health care associates are required to be submitted not only by state and county-owned health institutions, but also by all private institutions, health workers who independently perform private practice, as well as trading companies for the performance of health activities, regardless of whether they have a contract with the Croatian Institute for health insurance.

All employees are assigned a registration number (code) upon entry into the Registry's database on the day of employment. The connection with the Croatian Health Insurance Institute exists through the use of the registration number when registering, recognizing within the CEZIH system, as well as when registering prescriptions, referrals and other documents of the HZZO. that is, in monitoring and building the health information system.

As an integral part of the same, relational databases also include data on health organizational units, representing the Register of Health Institutions. Namely, in addition to data on employees, the Registry, based on the decision of the Ministry of Health on work authorization, also records basic data on health institutions, surgeries and all other types of independent health units, regardless of the contract with the Croatian Health Insurance Institute or the type of ownership. As for employees, received data on the opening, closing, change of name, address, type and activity of the health organizational unit is also updated daily.

Thus, the organizational structure of healthcare is monitored through the database, according to levels of healthcare, types of healthcare institutions, healthcare activities performed by institutions, divisions with regard to the type of ownership as well as territorial distribution.

In addition to the importance of data on human potential and space, that is, the units where health care is provided, medical equipment is also an important factor in management and planning. One part of the department's work is related to the collection of data on this material resource. In the near future, it is planned to form a Register of Medically Expensive Equipment, which would be technologically and functionally connected with the existing two registers into a whole register of resources in healthcare.

Also, the statistical research aims to include those entities that are not part of the health system, and in which health workers work, i.e. health activities are performed, such as long-term care homes, which means expanding the existing data of the Register of Health Institutions.

In the last decade, a new IT application of the Registry of Health Care Professionals was created and an even better connection with the Croatian Institute for Health Insurance, for example through the use of the so-called population register or the register of insured persons. The register continues to be the source of data and the authorized institution for the delivery of data to international bodies such as the WHO and the joint WHO/Eurostat/OECD database. Within the scope of the Department's activities are also activities in international initiatives and programs, and with regard to the problems of statistical monitoring, shortages and planning of health workers. Since 2012, we have been involved in the implementation of the "Global Code of Practice on International Recruitment of Health Personnel", a recommendation that is also an instrument in the regulation, improvement and establishment of standards in the migration process.

In the same year, the Department was involved in the work in the part of the program platform on the topic of Joint Action on European Health Workforce Planning and Forecasting.

Also, during the past years, there has been cooperation on the topic of health workers within the framework of the South-eastern Europe Health Network (SEEHN).

The Healthcare Cost and Utilization Project (HCUP) Nationwide Emergency Department Sample (NEDS) is the largest all-payer emergency department (ED) database in the United States. yielding national estimates of hospital-owned ED visits. Unweighted, it contains data from over 30 million ED visits each year. Weighted, it estimates roughly 145 million ED visits nationally. Developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality, HCUP data inform decision making at the national, State, and community levels.

Sampled from the HCUP State Inpatient Databases (SID) and State Emergency Department Databases (SEDD), the HCUP NEDS can be used to create national and regional estimates of ED care. The SID contain information on patients initially seen in the ED and subsequently admitted to the same hospital. The SEDD capture information on ED visits that do not result in an admission (i.e., treat-and-release visits and transfers to another hospital). Developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality, HCUP data inform decision making at the national, State, and community levels.

The NEDS contain information about geographic characteristics, hospital characteristics, patient characteristics, and the nature of visits (e.g., common reasons for ED visits, including injuries). The NEDS contains clinical and resource use information included in a typical discharge abstract, with safeguards to protect the privacy of individual patients, physicians, and hospitals (as required by data sources). It includes ED charge information for over 85% of patients, regardless of expected payer, including but not limited to Medicare, Medicaid, private insurance, self-pay, or those billed as ‘no charge’. The NEDS excludes data elements that could directly or indirectly identify individuals, hospitals, or states.Restricted access data files are available with a data use agreement and brief online security training.

The Nuclear Medicine National HQ System database is a series of MS Excel spreadsheets and Access Database Tables by fiscal year. They consist of information from all Veterans Affairs Medical Centers (VAMCs) performing or contracting nuclear medicine services in Veterans Affairs medical facilities. The medical centers are required to complete questionnaires annually (RCS 10-0010-Nuclear Medicine Service Annual Report). The information is then manually entered into the Access Tables, which includes: * Distribution and cost of in-house VA - Contract Physician Services, whether contracted services are made via sharing agreement (with another VA medical facility or other government medical providers) or with private providers. * Workload data for the performance and/or purchase of PET/CT studies. * Organizational structure of services. * Updated changes in key imaging service personnel (chiefs, chief technicians, radiation safety officers). * Workload data on the number and type of studies (scans) performed, including Medicare Relative Value Units (RVUs), also referred to as Weighted Work Units (WWUs). WWUs are a workload measure calculated as the product of a study's Current Procedural Terminology (CPT) code, which consists of total work costs (the cost of physician medical expertise and time), and total practice costs (the costs of running a practice, such as equipment, supplies, salaries, utilities etc). Medicare combines WWUs together with one other parameter to derive RVUs, a workload measure widely used in the health care industry. WWUs allow Nuclear Medicine to account for the complexity of each study in assessing workload, that some studies are more time consuming and require higher levels of expertise. This gives a more accurate picture of workload; productivity etc than using just 'total studies' would yield. * A detailed Full-Time Equivalent Employee (FTEE) grid, and staffing distributions of FTEEs across nuclear medicine services. * Information on Radiation Safety Committees and Radiation Safety Officers (RSOs). Beginning in 2011 this will include data collection on part-time and non VA (contract) RSOs; other affiliations they may have and if so to whom they report (supervision) at their VA medical center.Collection of data on nuclear medicine services' progress in meeting the special needs of our female veterans. Revolving documentation of all major VA-owned gamma cameras (by type) and computer systems, their specifications and ages. * Revolving data collection for PET/CT cameras owned or leased by VA; and the numbers and types of PET/CT studies performed on VA patients whether produced on-site, via mobile PET/CT contract or from non-VA providers in the community. Types of educational training/certification programs available at VA sites * Ongoing funded research projects by Nuclear Medicine (NM) staff, identified by source of funding and research purpose. * Data on physician-specific quality indicators at each nuclear medicine service. Academic achievements by NM staff, including published books/chapters, journals and abstracts. * Information from polling field sites re: relevant issues and programs Headquarters needs to address. * Results of a Congressionally mandated contracted quality assessment exercise, also known as a Proficiency study. Study results are analyzed for comparison within VA facilities (for example by mission or size), and against participating private sector health care groups. * Information collected on current issues in nuclear medicine as they arise. Radiation Safety Committee structures and membership, Radiation Safety Officer information and information on how nuclear medicine services provided for female Veterans are examples of current issues.The database is now stored completely within MS Access Database Tables with output still presented in the form of Excel graphs and tables.

A 100-patient database that contains in total 100 virtual patients, 372 admissions, and 111,483 lab observations.

Summary Trend TablesThe HCUP Summary Trend Tables include information on hospital utilization derived from the HCUP State Inpatient Databases (SID), State Emergency Department Databases (SEDD), National Inpatient Sample (NIS), and Nationwide Emergency Department Sample (NEDS). State statistics are displayed by discharge month and national and regional statistics are displayed by discharge quarter. Information on emergency department (ED) utilization is dependent on availability of HCUP data; not all HCUP Partners participate in the SEDD.The HCUP Summary Trend Tables include downloadable Microsoft® Excel tables with information on the following topics:Overview of trends in inpatient and emergency department utilizationAll inpatient encounter typesInpatient encounter typeNormal newbornsDeliveriesNon-elective inpatient stays, admitted through the EDNon-elective inpatient stays, not admitted through the EDElective inpatient staysInpatient service lineMaternal and neonatal conditionsMental health and substance use disordersInjuriesSurgeriesOther medical conditionsED treat-and-release visitsDescription of the data source, methodology, and clinical criteria (Excel file, 43 KB)Change log (Excel file, 65 KB)For each type of inpatient stay, there is an Excel file for the number of discharges, the percent of discharges, the average length of stay, the in-hospital mortality rate per 100 discharges,1 and the population-based rate per 100,000 population.2 Each Excel file contains State-specific, region-specific, and national statistics. For most files, trends begin in January 2017. Also included in each Excel file is a description of the HCUP databases and methodology.

Usage

The National (Nationwide) Inpatient Sample (NIS) is part of a family of databases and software tools developed for the Healthcare Cost and Utilization Project (HCUP). The NIS is the largest publicly available all-payer inpatient healthcare database designed to produce U.S. regional and national estimates of inpatient utilization, access, cost, quality, and outcomes. Unweighted, it contains data from around 7 million hospital stays each year. Weighted, it estimates around 35 million hospitalizations nationally. Developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality (AHRQ), HCUP data inform decision making at the national, State, and community levels.

The Healthcare Cost and Utilization Project (HCUP) National Inpatient Sample (NIS) is the largest publicly available all-payer inpatient care database in the United States. The NIS is designed to produce U.S. regional and national estimates of inpatient utilization, access, cost, quality, and outcomes. Unweighted, it contains data from more than 7 million hospital stays each year. Weighted, it estimates more than 35 million hospitalizations nationally. Developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality (AHRQ), HCUP data inform decision making at the national, State, and community levels. Starting with the 2012 data year, the NIS is a sample of discharges from all hospitals participating in HCUP, covering more than 97 percent of the U.S. population. For prior years, the NIS was a sample of hospitals. The NIS allows for weighted national estimates to identify, track, and analyze national trends in health care utilization, access, charges, quality, and outcomes. The NIS's large sample size enables analyses of rare conditions, such as congenital anomalies; uncommon treatments, such as organ transplantation; and special patient populations, such as the uninsured. NIS data are available since 1988, allowing analysis of trends over time. The NIS inpatient data include clinical and resource use information typically available from discharge abstracts with safeguards to protect the privacy of individual patients, physicians, and hospitals (as required by data sources). Data elements include but are not limited to: diagnoses, procedures, discharge status, patient demographics (e.g., sex, age), total charges, length of stay, and expected payment source, including but not limited to Medicare, Medicaid, private insurance, self-pay, or those billed as ‘no charge’. The NIS excludes data elements that could directly or indirectly identify individuals. Restricted access data files are available with a data use agreement and brief online security training.

Predicting health outcomes from longitudinal health histories is of central importance to healthcare. Observational healthcare databases such as patient diary databases provide a rich resource for patient-level predictive modeling. In this paper, we propose a Bayesian hierarchical vector autoregressive (VAR) model to predict medical and psychological conditions using multivariate time series data. Compared to the existing patient-specific predictive VAR models, our model demonstrated higher accuracy in predicting future observations in terms of both point and interval estimates due to the pooling effect of the hierarchical model specification. In addition, by adopting an elastic-net prior, our model offers greater interpretability about the associations between variables of interest on both the population level and the patient level, as well as between-patient heterogeneity. We apply the model to two examples: 1) predicting substance use craving, negative affect and tobacco use among college students, and 2) predicting functional somatic symptoms and psychological discomforts.

The Nationwide Emergency Department Sample (NEDS) was created to enable analyses of emergency department (ED) utilization patterns and support public health professionals, administrators, policymakers, and clinicians in their decision-making regarding this critical source of care. The NEDS can be weighted to produce national estimates. The NEDS is the largest all-payer ED database in the United States. It was constructed using records from both the HCUP State Emergency Department Databases (SEDD) and the State Inpatient Databases (SID), both also described in healthdata.gov. The SEDD capture information on ED visits that do not result in an admission (i.e., treat-and-release visits and transfers to another hospital). The SID contain information on patients initially seen in the emergency room and then admitted to the same hospital. The NEDS contains 25-30 million (unweighted) records for ED visits for over 950 hospitals and approximates a 20-percent stratified sample of U.S. hospital-based EDs. The NEDS contains information about geographic characteristics, hospital characteristics, patient characteristics, and the nature of visits (e.g., common reasons for ED visits, including injuries). The NEDS contains clinical and resource use information included in a typical discharge abstract, with safeguards to protect the privacy of individual patients, physicians, and hospitals (as required by data sources). It includes ED charge information for over 75% of patients, regardless of payer, including patients covered by Medicaid, private insurance, and the uninsured. The NEDS excludes data elements that could directly or indirectly identify individuals, hospitals, or states.

IMA-AIM can provide you with detailed data on the health care system in Belgium. Their data collection includes information on the reimbursed care and medicines of the 11 million citizens insured in our country. The data is collected by the 7 health insurance funds and processed, analysed and made available for research by IMA-AIM.

The seven health insurance funds in Belgium collect a lot of data about their members in order to be able to carry out their tasks. IMA-AIM brings these data together in databases for the purpose of analysis and research. The databases contain three types of data: population data (demographic and socio-economic characteristics), information about reimbursed health care and information about reimbursed medicines.

The Permanent Sample (EPS) is a longitudinal dataset containing data from the Population, Health Care and Pharmanet databases, as well as data on hospitalisations. The data are available in separate datasets per calendar year. The aim of EPS is to make the administrative data of the health insurance funds permanently available to a number of federal and regional partners. More information about the EPS: https://metadata.ima-aim.be/nl/app/bdds/Ps

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The degree to which artificial intelligence healthcare research is informed by data and stakeholders from community settings has not been previously described. As communities are the principal location of healthcare delivery, engaging them could represent an important opportunity to improve scientific quality. This scoping review systematically maps what is known and unknown about community-engaged artificial intelligence research and identifies opportunities to optimize the generalizability of these applications through involvement of community stakeholders and data throughout model development, validation, and implementation. Embase, PubMed, and MEDLINE databases were searched for articles describing artificial intelligence or machine learning healthcare applications with community involvement in model development, validation, or implementation. Model architecture and performance, the nature of community engagement, and barriers or facilitators to community engagement were reported according to PRISMA extension for Scoping Reviews guidelines. Of approximately 10,880 articles describing artificial intelligence healthcare applications, 21 (0.2%) described community involvement. All articles derived data from community settings, most commonly by leveraging existing datasets and sources that included community subjects, and often bolstered by internet-based data acquisition and subject recruitment. Only one article described inclusion of community stakeholders in designing an application–a natural language processing model that detected cases of likely child abuse with 90% accuracy using harmonized electronic health record notes from both hospital and community practice settings. The primary barrier to including community-derived data was small sample sizes, which may have affected 11 of the 21 studies (53%), introducing substantial risk for overfitting that threatens generalizability. Community engagement in artificial intelligence healthcare application development, validation, or implementation is rare. As healthcare delivery occurs primarily in community settings, investigators should consider engaging community stakeholders in user-centered design, usability, and clinical implementation studies to optimize generalizability.

ObjectiveMenopause and the changes it brings to a woman’s life necessitate a comprehensive approach to face and experience the transition. This paper aims at synthesizing results from qualitative studies of menopausal experiences among Chinese and other women of similar ethnicity and culture.Design and methodA comprehensive search strategy of multiple databases along with bibliographic hand searches was employed to identify qualitative studies published in English peer-reviewed journals between 2008 and 2022 focused on the menopausal experiences (peri and post-menopause) of Chinese and other women of similar ethnic backgrounds. Twelve studies met the inclusion criteria. The final sample consisted of 238 women aged between 40 to 60 years who had experienced menopausal symptoms. This qualitative systematic literature review adopted Noblit and Hare’s seven-stage theoretical meta-ethnographic approach to construct an inductive and interpretive form of synthesis and subsequent analysis.Syntheses of findingsThe synthesis of primary data identified four key concepts that entitle women’s menopausal experiences: being menopausal, ramifications on well-being, family and social support around menopause, and healthcare throughout menopause. The subsequent second-order interpretation revealed that women accepted the inevitability of the natural aging process in the decline of sexual drive, reinvented relationships with partners, and expressed the significance of a supportive environment in order to successfully navigate the transition. Third-order interpretations sought to establish a link between physiological complications, loss of femininity, patriarchal-dominated societal norms, and a support system that spans the entire menopause experience. Healthcare’s contribution has also been deemed to be insufficient due to a lack of information and empathy from health experts. Negligence or reluctance to seek healthcare and skepticism toward hormone replacement therapy (HRT) had also been a source of concern, as they have had the potential to exacerbate medical difficulties and emotional turmoil.Conclusions and implications for practiceA comprehensive approach that considers women’s physiological and psychological well-being and major attempts to change cultural beliefs and norms about women’s sexual health may be effective in aiding menopausal women during their transition. Additionally, appropriate guidelines and management should be in place to enable women to address menopause difficulties effectively with the assistance of healthcare experts and the support of their families and community.

An example of ER visit event logs.