Patient Demographics & Records: This dataset includes fictional names, ages, genders, blood types, admission and discharge dates, diagnoses, and treatment plans for patients.

Hospital Resource Usage: This dataset captures bed occupancy rates, ICU availability, equipment usage, and doctor-to-patient ratios in the hospital.

Medical Records (Prescriptions & Tests): This dataset details the dates, prescribed medications, ordered tests, and test results linked to each patient.

Financial Data (Billing & Insurance): This dataset contains billing amounts, insurance coverage percentages, and out-of-pocket expenses for healthcare services.

This dataset contains three healthcare datasets in Hindi and Punjabi, translated from English. The datasets cover medical diagnoses, disease names, and related healthcare information. The data has been carefully cleaned and formatted to ensure accuracy and usability for various applications, including machine learning, NLP, and healthcare analysis.

Diagnosis: Description of the medical condition or disease. Symptoms: List of symptoms associated with the diagnosis. Treatment: Common treatments or recommended procedures. Severity: Severity level of the disease (e.g., mild, moderate, severe). Risk Factors: Known risk factors associated with the condition. Language: Specifies the language of the dataset (Hindi, Punjabi, or English). The purpose of these datasets is to facilitate research and development in regional language processing, especially in the healthcare sector.

Column Descriptions: Original Data Columns: patient_id – Unique identifier for each patient. age – Age of the patient. gender – Gender of the patient (e.g., Male/Female/Other). Diagnosis – The diagnosed medical condition or disease. Remarks – Additional notes or comments from the doctor. doctor_id – Unique identifier for the doctor treating the patient. Patient History – Medical history of the patient, including previous conditions. age_group – Categorized age group (e.g., Child, Adult, Senior). gender_numeric – Numeric encoding for gender (e.g., 0 = Female, 1 = Male). symptoms – List of symptoms reported by the patient. treatment – Recommended treatment or medication. timespan – Duration of the illness or treatment period. Diagnosis Category – General category of the diagnosis (e.g., Cardiovascular, Neurological). Pseudonymized Data Columns: These columns replace personally identifiable information with anonymized versions for privacy compliance:

Pseudonymized_patient_id – An anonymized patient identifier. Pseudonymized_age – Anonymized age value. Pseudonymized_gender – Anonymized gender field. Pseudonymized_Diagnosis – Diagnosis field with anonymized identifiers. Pseudonymized_Remarks – Anonymized doctor notes. Pseudonymized_doctor_id – Anonymized doctor identifier. Pseudonymized_Patient History – Anonymized version of patient history. Pseudonymized_age_group – Anonymized version of age groups. Pseudonymized_gender_numeric – Anonymized numeric encoding of gender. Pseudonymized_symptoms – Anonymized symptom descriptions. Pseudonymized_treatment – Anonymized treatment descriptions. Pseudonymized_timespan – Anonymized illness/treatment duration. Pseudonymized_Diagnosis Category – Anonymized category of diagnosis.

Generally, do the health care apps, websites, or online patient portals you use make managing your health care easier, more difficult, or does it not make a difference?. Notes: Asked of those who ever used health care apps, reported among total adults. See topline for full question wording.

Graph and download economic data for Per Capita Personal Consumption Expenditures: Services: Health Care for South Dakota (SDPCEPCHLTHCARE) from 1997 to 2024 about healthcare, SD, health, PCE, consumption expenditures, consumption, per capita, personal, services, and USA.

Find data on health care facilities in Massachusetts that are licensed or certified by the Department of Public Health.

Graph and download economic data for Economic Policy Uncertainty Index: Categorical Index: Health care (EPUHEALTHCARE) from Jan 1985 to Feb 2026 about healthcare, uncertainty, health, World, and indexes.

The Healthcare Analytics Market Report is Segmented by Analytics Type (Descriptive, Diagnostic, and More), Component (Hardware, Software, and Services), Delivery Mode (On-Premise, Cloud-Based, and Hybrid), Application (Clinical, Financial/Revenue-Cycle, Operational/Administrative, and More), End User (Providers, Payers, and More), and Geography (North America, and More). Market Forecasts are Provided in Terms of Value (USD).

A 2025 survey found that over half of U.S. individuals indicated the cost of accessing treatment was the biggest problem facing the national healthcare system. This is much higher than the global average of 33 percent and is in line with the high cost of health care in the U.S. compared to other high-income countries. Bureaucracy along with a lack of staff were also considered to be pressing issues.

Graph and download economic data for Gross Domestic Product: Health Care and Social Assistance (62) in Nevada (NVHLTHSOCASSNGSP) from 1997 to 2024 about healthcare, social assistance, NV, health, GSP, education, private industries, services, private, industry, GDP, and USA.

The Big Data Healthcare Market Report is Segmented by Component (Software, Services), Deployment (On-Premise, Cloud), Analytics Type (Descriptive Analytics, Predictive Analytics, Prescriptive Analytics), Application (Financial Analytics, and More), End User (Healthcare Providers, and More), and Geography (North America, Europe, Asia-Pacific, and More). The Market Forecasts are Provided in Terms of Value (USD).

Metro E sites maintained by City of New Orleans Information Technology Department at various locations across the city. Details on internet technology, etc. Updated yearly

Point geometry with attributes displaying all health care related businesses in East Baton Rouge Parish, Louisiana.

Share of adults who reported the following financial conditions, 2021. Notes: *All differences between "No medical debt" and "Has medical debt" significant at p < .05.

A 2024 survey found that over half of individuals in Great Britain indicated that access to treatment and long waiting times were the biggest problem facing the national healthcare system. Access to treatment and/or long waiting times were also considered to be pressing issues. This statistic reveals the share of individuals who said select problems were the biggest facing the health care system in Great Britain in 2024.

In the next few years, do you expect health care costs for you and your family to become more affordable, less affordable, or stay about the same?. Notes: See topline for full question wording.

Graph and download economic data for Hires: Health Care and Social Assistance (JTS6200HIL) from Dec 2000 to Dec 2025 about hires, social assistance, health, and USA.

Percent of immigrant adults who say they skipped or postponed getting health care in the past 12 months for each of the following reasons:. Notes: Asked of those who skipped or postponed health care in the past 12 months. Third item asked of those who skipped or postponed health care in the past 12 months and completed the survey in a non-English language. Insufficient sample size to report findings for uninsured immigrant adults who skipped or postponed health care in the past 12 months and completed the survey in a non-English language. See topline for full question wording.

Big Data Analytics In Healthcare Market size was valued at USD 37.22 Billion in 2024 and is projected to reach USD 74.82 Billion by 2032, growing at a CAGR of 9.12% from 2026 to 2032.Rising adoption of electronic health records (EHRs) and digital healthcare solutions: The widespread adoption of electronic health records (EHRs) and other digital healthcare solutions is the foundational driver for the big data analytics market. With over 96% of U.S. hospitals now using EHRs, a vast, standardized, and machine-readable data source has become available.Growing need to reduce healthcare costs through efficient data-driven decision-making: Healthcare costs are a global concern, and the pressure to reduce expenditures without compromising care quality is a powerful driver for the adoption of big data analytics. Analytics provides a data-driven approach to identifying and eliminating waste, fraud, and abuse.

According to a 2025 survey, 68 percent of individuals indicated a lack of staff was the biggest problem facing the Swedish healthcare system. Access to treatment or long waiting times were also considered to be pressing issues.

Graph and download economic data for Labor Productivity for Health Care and Social Assistance: Medical and Diagnostic Laboratories (NAICS 62151) in the United States (IPURN62151L000000000) from 1994 to 2024 about diagnostic labs, medical, healthcare, social assistance, productivity, health, NAICS, IP, labor, and USA.