Department of State Hospitals Patient Population Demographic (Fiscal Effective Dates: 2010-2020)

The distribution (according to length-of-stay and diagnosis group) and the most important demographic indicators of the patients. Data are shown in an , age, percentage of females format, with age statistics arranged in Mean (Median) SD (IQR) manner. Columns indicate minimum (and not exact) length-of stay, so the same patient may appear in several cells.

Patient demographics and clinical data.

The dataset contains counts of inpatient visits leading to a discharge to hospice care. Inpatient visits included in the counts consist of individuals aged 18 or over with a discharge disposition leading to home or facility hospice care. The total counts per each individual year can be viewed based on different patient characteristics, including patient age groups, individual counties of residence, primary payer type, diagnosis category, and patient sex/race/ethnicity. The disease categories include circulatory conditions, diabetes, malignant/benign neoplasms, malnutrition, neurodegenerative disease, renal failure or other kidney diagnoses, respiratory conditions and circulatory conditions. The categories represent common groupings of diagnoses seen in other studies related to hospice care and were created by grouping together relevant medical MSDRG codes in the HCAI inpatient data.

Demographic data and patients’ characteristics.

In 2022, children and teens are over-represented as health center patients compared to their proportion in the population. This statistic depicts the age distribution of health center patients compared to overall U.S. population as of 2022.

The UK Cystic Fibrosis Registry Demographic is made up of data items relating key demographic information about CF patients, relating to their diagnosis and genotype.

Demographic and clinical data of patients and controls

Patient demographic data. Clinical Knowledge Manager (CKM)

This dataset encompasses responses from a survey designed to explore the relationships between perceived service quality, patient satisfaction, treatment effectiveness, and patients' behavioral intentions in government-owned public healthcare facilities. The dataset provides insights into how these factors influence patient loyalty and intent to return to the healthcare facilities. Nature of the Data: - Survey Responses: The dataset includes quantitative ratings provided by patients on various aspects of healthcare services. Each item is rated on a scale from 1 to 5, with 1 indicating strong disagreement or dissatisfaction and 5 indicating strong agreement or satisfaction. - Demographic Information: It contains demographic details of respondents, including gender, age group, marital status, occupation, and level of education. - Hospital Information: Respondents have indicated the name of the hospital they visited. Scope of the Data: - Survey Items: The data evaluates multiple dimensions of hospital services, including cleanliness, staff professionalism, equipment quality, signboards readability, outpatient services, billing accuracy, emergency response, and overall patient satisfaction. -Respondent Details: The dataset includes responses from a diverse demographic group, offering a comprehensive view of patient experiences and satisfaction across different ages, marital statuses, and education levels. Potential Uses: - Service Improvement: The data can be used by healthcare administrators to identify strengths and weaknesses in their service delivery and implement strategies to enhance patient care and satisfaction. - Benchmarking: It provides a basis for comparing patient satisfaction and service quality across different public healthcare facilities. - Academic Research: Researchers can use the dataset to analyze the impact of perceived service quality and patient satisfaction on behavioral intentions and to further investigate the factors influencing patient loyalty and intent to return.

Patient demographic data for the 165 samples in the patient cohort.

These datasets are for a cohort of n=1540 anonymised hospitalised COVID-19 patients, and the data provide information on outcomes (i.e. patient death or discharge), demographics and biomarker measurements for two New York hospitals: State University of New York (SUNY) Downstate Health Sciences University and Maimonides Medical Center.

The file "demographics_both_hospitals.csv" contains the ultimate outcomes of hospitalisation (whether a patient was discharged or died), demographic information and known comorbidities for each of the patients.

The file "dynamics_clean_both_hospitals.csv" contains cleaned dynamic biomarker measurements for the n=1233 patients where this information was available and the data passed our various checks (see https://doi.org/10.1101/2021.11.12.21266248 for information of these checks and the cleaning process). Patients can be matched to demographic data via the "id" column.

Study approval and data collection

Study approval was obtained from the State University of New York (SUNY) Downstate Health Sciences University Institutional Review Board (IRB#1595271-1) and Maimonides Medical Center Institutional Review Board/Research Committee (IRB#2020-05-07). A retrospective query was performed among the patients who were admitted to SUNY Downstate Medical Center and Maimonides Medical Center with COVID-19-related symptoms, which was subsequently confirmed by RT PCR, from the beginning of February 2020 until the end of May 2020. Stratified randomization was used to select at least 500 patients who were discharged and 500 patients who died due to the complications of COVID-19. Patient outcome was recorded as a binary choice of “discharged” versus “COVID-19 related mortality”. Patients whose outcome was unknown were excluded. Demographic, clinical history and laboratory data was extracted from the hospital’s electronic health records.

The population share of the Latino/Hispanic ethnic group in the United States was 19 percent, whereas they accounted for 32 percent of health center patients. Health center had a disproportionally high amount of patients of ethnic minorities. This statistic depicts the share of ethnic minorities in health centers compared to the share in the overall U.S. population as of 2022.

Notes: UPDRS = Unified Parkinson’s Disease Rating Scale.

*Mean ankle brachial indices were all greater than 1 despite one patient having arterial disease; this was due to this same patient also having diabetes mellitus.

This dataset contains electronic health records used to study associations between PFAS occurrence and multimorbidity in a random sample of UNC Healthcare system patients. The dataset contains the medical record number to uniquely identify each individual as well as information on PFAS occurrence at the zip code level, the zip code of residence for each individual, chronic disease diagnoses, patient demographics, and neighborhood socioeconomic information from the 2010 US Census. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Because this data has PII from electronic health records the data can only be accessed with an approved IRB application. Project analytic code is available at L:/PRIV/EPHD_CRB/Cavin/CARES/Project Analytic Code/Cavin Ward/PFAS Chronic Disease and Multimorbidity. Format: This data is formatted as a R dataframe and associated comma-delimited flat text file. The data has the medical record number to uniquely identify each individual (which also serves as the primary key for the dataset), as well as information on the occurrence of PFAS contamination at the zip code level, socioeconomic data at the census tract level from the 2010 US Census, demographics, and the presence of chronic disease as well as multimorbidity (the presence of two or more chronic diseases). This dataset is associated with the following publication: Ward-Caviness, C., J. Moyer, A. Weaver, R. Devlin, and D. Diazsanchez. Associations between PFAS occurrence and multimorbidity as observed in an electronic health record cohort. Environmental Epidemiology. Wolters Kluwer, Alphen aan den Rijn, NETHERLANDS, 6(4): p e217, (2022).

Demographic data collected from a cohort of patients who are HIV positive and not on treatment at the time.

The All CMS Data Feeds dataset is an expansive resource offering access to 119 unique report feeds, providing in-depth insights into various aspects of the U.S. healthcare system including nursing facility owners and accountable care organization participants contact data. With over 25.8 billion rows of data meticulously collected since 2007, this dataset is invaluable for healthcare professionals, analysts, researchers, and businesses seeking to understand and analyze healthcare trends, performance metrics, and demographic shifts over time. The dataset is updated monthly, ensuring that users always have access to the most current and relevant data available.

Dataset Overview:

118 Report Feeds: - The dataset includes a wide array of report feeds, each providing unique insights into different dimensions of healthcare. These topics range from Medicare and Medicaid service metrics, patient demographics, provider information, financial data, and much more. The breadth of information ensures that users can find relevant data for nearly any healthcare-related analysis. - As CMS releases new report feeds, they are automatically added to this dataset, keeping it current and expanding its utility for users.

25.8 Billion Rows of Data:

• With over 25.8 billion rows of data, this dataset provides a comprehensive view of the U.S. healthcare system. This extensive volume of data allows for granular analysis, enabling users to uncover insights that might be missed in smaller datasets. The data is also meticulously cleaned and aligned, ensuring accuracy and ease of use.

Historical Data Since 2007: - The dataset spans from 2007 to the present, offering a rich historical perspective that is essential for tracking long-term trends and changes in healthcare delivery, policy impacts, and patient outcomes. This historical data is particularly valuable for conducting longitudinal studies and evaluating the effects of various healthcare interventions over time.

Monthly Updates:

• To ensure that users have access to the most current information, the dataset is updated monthly. These updates include new reports as well as revisions to existing data, making the dataset a continuously evolving resource that stays relevant and accurate.

Data Sourced from CMS:

• The data in this dataset is sourced directly from the Centers for Medicare & Medicaid Services (CMS). After collection, the data is meticulously cleaned and its attributes are aligned, ensuring consistency, accuracy, and ease of use for any application. Furthermore, any new updates or releases from CMS are automatically integrated into the dataset, keeping it comprehensive and current.

Use Cases:

Market Analysis:

• The dataset is ideal for market analysts who need to understand the dynamics of the healthcare industry. The extensive historical data allows for detailed segmentation and analysis, helping users identify trends, market shifts, and growth opportunities. The comprehensive nature of the data enables users to perform in-depth analyses of specific market segments, making it a valuable tool for strategic decision-making.

Healthcare Research:

• Researchers will find the All CMS Data Feeds dataset to be a robust foundation for academic and commercial research. The historical data, combined with the breadth of coverage across various healthcare metrics, supports rigorous, in-depth analysis. Researchers can explore the effects of healthcare policies, study patient outcomes, analyze provider performance, and more, all within a single, comprehensive dataset.

Performance Tracking:

• Healthcare providers and organizations can use the dataset to track performance metrics over time. By comparing data across different periods, organizations can identify areas for improvement, monitor the effectiveness of initiatives, and ensure compliance with regulatory standards. The dataset provides the detailed, reliable data needed to track and analyze key performance indicators.

Compliance and Regulatory Reporting:

• The dataset is also an essential tool for compliance officers and those involved in regulatory reporting. With detailed data on provider performance, patient outcomes, and healthcare utilization, the dataset helps organizations meet regulatory requirements, prepare for audits, and ensure adherence to best practices. The accuracy and comprehensiveness of the data make it a trusted resource for regulatory compliance.

Data Quality and Reliability:

The All CMS Data Feeds dataset is designed with a strong emphasis on data quality and reliability. Each row of data is meticulously cleaned and aligned, ensuring that it is both accurate and consistent. This attention to detail makes the dataset a trusted resource for high-stakes applications, where data quality is critical.

Integration and Usability:

Ease of Integration:

• The dataset is provided in a CSV format, which is widely compatible with most data analysis too...

1Based on the age at onset of < or ≥13 yr, patients were classified to the early-onset group.

This dataset tracks the updates made on the dataset "Patient Demographics" as a repository for previous versions of the data and metadata.