A 2024 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 32 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. This statistic reveals the share of individuals who said select problems were the biggest facing the health care system in the United States in 2024.

AI in Healthcare - Quick Overview Statistics

Artificial Intelligence in healthcare refers to the use of advanced computer algorithms and machine learning techniques to analyze data in the healthcare sector to provide better healthcare services.

AI helps healthcare providers make more accurate and real-time diagnoses, personalize treatment plans, and improve patient safety by identifying health risks earlier.

Types of AI Applications in Healthcare Statistics

• Medical imaging analysis
• Natural language processing (NLP)
• Disease prediction and risk assessment
• Virtual Assistants and Chabot’s
• Drug discovery and development
• Robot-assisted surgery
• Patient engagement
• Diagnosis and treatment
• Machine learning

This page leads to several types of statistics relating to health care and indemnity insurance, grouped by theme. Select the theme that interests you by clicking on it:

• Health care: Statistics on health care providers, cost for health care insurance, patient co-payment, prescriptions, etc.

• Allowances: Statistics on primary work incapacity, invalidity, maternity, birth leave, etc.

• Medical assessment and control: Statistics on the monitoring of health care providers and evaluation of medical practice.

• Administrative control: Statistics on the control of mutual funds and the fight against social fraud.

• Medications: Statistics and specific analyzes on drugs dispensed in public pharmacies: prescription, volume, cost for insurance, etc.

• Frontier workers: Statistics on frontier workers entering and leaving Belgium.

• People affiliated with a health insurance fund: Statistics on the number of people affiliated to a health insurance fund to benefit from healthcare and indemnity insurance in Belgium. Customize your search with our web program.

As of 2023, 46 percent of U.S. adults rated the quality of healthcare as excellent or good in the United States. In comparison, nearly a third rated the country's health care coverage as excellent or good. This statistic presents the ratings for U.S. healthcare quality and coverage among adults in the United States from 2001 to 2023.

Between January and September 2024, healthcare organizations in the United States saw 491 large-scale data breaches, resulting in the loss of over 500 records. This figure has increased significantly in the last decade. To date, the highest number of large-scale data breaches in the U.S. healthcare sector was recorded in 2023, with a reported 745 cases.

AI in Healthcare Statistics: AI in healthcare has been a hot topic for the past few years, and the report says that the industry is expected to reach $187.95 billion by the end of 2030. The fact of this platform in 2023 suggests a huge boom in the market size worldwide, with a compound annual increase rate (CAGR) of 40.1% from 2023 to 2030. The worldwide Artificial intelligence in the healthcare marketplace length changed into worth $20.65 billion in 2023 which has increased from last year. These AI in Healthcare Statistics include insights from various aspects and sources that will provide effective light on the importance of AI in the healthcare industry around the world in recent times. In 2023, the Market share records the gradual adoption of AI which is advancing the sector, and has been observed that 85% of organizations have already implemented AI. Additionally, 1/2 of the executives claimed that AI is indicating a tremendous shift inside and outside the industry. Aid of AI-based healthcare companies used solutions like telemedicine and remote tools and sensors backed by means of large information that can reduce healthcare charges improve access, and promote better outcomes, and performance. Key Takeaways According to AI in Healthcare Statistics, the platform when implemented Artificial Intelligence has experienced a huge increase, with a CAGR of 40.1% from 2023 to 2030 and a global market size expected to attain $187.95 billion by 2030. Around the world, approximately 40% of healthcare industries are regularly using AI and Machine Language in the sector. In 2023, Healthcare executives are increasingly adopting AI in their techniques, and nearly 1/2 of the executives surveyed are already using it. This is being adopted globally, with answers like telemedicine and faraway tools and sensors backed through huge information that could lessen healthcare charges and equitably improve admission to, results, and performance.

In a sunlit hospital room in San Diego, a pediatrician glances at a screen not to read a chart but to receive a real-time, AI-generated diagnosis that considers thousands of similar cases. This is not fiction, it’s today’s reality. AI has become a silent partner in healthcare, revolutionizing diagnosis, treatment,...

This statistic, biennative and censal in nature, allows, from the Free Insurance Entities operating in the C.A. of the Basque Country, to give information on the main magnitudes of insurance in health care (insured persons, premiums and type of insurance)

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.

This statistic shows the size of the global big data market related to healthcare in 2016 and a forecast for 2025. It is estimated that over this period, the market will increase from around 11.5 billion to nearly 70 billion U.S. dollars.

Graph and download economic data for All Employees, Health Care (CES6562000101) from Jan 1990 to Sep 2025 about health, establishment survey, education, services, employment, and USA.

In 2024, roughly***** of individuals worldwide stated the quality of the healthcare they had access to in their country was good. The highest quality rating were given by people from Malaysia, Switzerland, and the Netherlands, while individuals in Hungary, Poland, and Peru rated their country's healthcare quality most poorly. This statistic presents the percentage of adults in select countries worldwide who agreed that the quality of the healthcare they had access to in their country was good or poor as of 2024.

In 2024, ** percent of adults worldwide agreed that many people in their country could not afford good healthcare. Individuals in Brazil were most likely to agree with this statement "Many people in my country cannot afford good healthcare.", while the least share of individuals agreed in Sweden. The results generally reflect the wealth of a nation, with people from wealthier countries tending to agree that good healthcare is affordable. The biggest exception being the U.S. where over ********* of U.S. respondents agreed that good health care is unaffordable to many despite being one of the richest country in the world. This statistic shows the percentage of adults in select countries worldwide who agreed that many people in their country could not afford good healthcare as of 2024.

Healthcare Cost and Utilization Project (HCUP) Fast Stats provides easy access to the latest HCUP-based statistics for health care information topics. HCUP Fast Stats uses visual statistical displays in stand-alone graphs, trend figures, or simple tables to convey complex information at a glance. Fast Stats is updated regularly for timely, topic-specific national and State-level statistics. Fast Stats topics and graphics on hospital stays and emergency department visits, including information at the national, and state levels, trends over time, and selected priority topics such as: State Trends in Hospital User by Payer National Hospital Utilization and Costs Hurricane Impact on Hospital Use Opioids & Neonatal Abstinence Syndrome Severe Maternal Morbidity

Verified dataset of healthcare marketing in 2025: U.S. digital ad spend, patient search behavior, online reviews, SEO & social trends, and email benchmarks.

Graph and download economic data for Expenditures: Healthcare by Age: from Age 25 to 34 (CXUHEALTHLB0403M) from 1984 to 2023 about healthcare, age, 25 years +, health, expenditures, and USA.

A national summary of results for those parts of the Healthcare workforce that are currently collected within the workforce Minimum Dataset (wMDS) as at 31 March 2015, giving national level statistics for each area covering Hospital and Community Services (Medical and Dental staff; and Non-medical staff); General and Personal Medical Services; and Independent Sector Healthcare Providers. The publication focusses on more detailed statistics covering General and Personal Medical Services. Accompanying this publication you will find Excel tables, CSV files and a Practice Level Indicator Tool to enable users to complete their own analysis.

WHO Health Indicators for Algeria

Healthcare Delivery, Socioeconomic and Demographic Statistics

By Humanitarian Data Exchange [source]

About this dataset

This dataset from the World Health Organization (WHO) contains comprehensive data on various health indicators for Algeria. It covers various topics such as mortality, sustainable development, global health estimates, health systems, malaria and tuberculosis, child and infection diseases, public health and environment, substance use and mental health tobacco injuries and violence HIV/AIDS nutrition urban Health noncommunicable diseases financial protection medical equipment demographic socioeconomic statistics essential health technologies medical equipment insecticide resistance oral Health universal healthcare global observatory for eHealth human resources information systems youth AMR glass noncommunicable diseases mental healthcare workforce neglected tropical diseases AMR GASP ICD sexual reproductive care and many more.

It provides resource descriptions that allow users to access individual indicator metadata as well as detailed coverage on different countries in the world. The dataset also includes methods related to registry interlinked with last updated information from WHO’s data portal and license terms provided under a variety of other sources.

The analysis of this dataset allows us to know more about the state of public healthcare in Algeria which can eventually lead us not only to an improved understanding but also better initiatives that are designed to benefit the wellbeing of citizens across this nation

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How to use the dataset

This dataset contains a wide variety of health-related indicators for Algeria from the World Health Organization’s (WHO) global health portal. This data can be used to gain insights into various socio-economic conditions, health systems, and public policy strategies in Algeria.

Getting Started

Firstly, you should download the dataset from Kaggle and unzip it into a folder of your choice. Then open the .csv file with your favorite spreadsheet or text editor application. After you have opened the dataset file, you will be able to see all of the available categories and indicator variables included in this dataset.

Understanding The Dataset

The columns in this dataset are divided into two categories; GHO (Global Health Observatory) metadata fields and variable fields describing each indicator value. The GHO metadata fields provide contextual information on where each individual healthcare indicator was sourced from including its reference year(s), geographic region/country, data source code/url and publication state code/url among others. These types of fields can be helpful when interpreting more specific results related to an entire given region or country for example. The second category includes variable fields that contain individual healthcare indicators such as mortality rates or access to clean water for example related to a specific region or population group within Algeria as well as their corresponding statistical values such as low & high values collected over a period of time etc.. Additionally it is important to note that columns with ** after them indicate labels which are relevant only if applicable e..g Low**

Best Practices For Analysing Data

When analysing this type of data consider which comparison type(s) would work best given your end goal: absolute comparison between 2+ geographies over same timeframe? Two periods compared comparatively within same geography? Or different measurements all using same base geography (ie one country)? Once you decide what type of analysis makes sense then use applicable filters/areas such as regions , provinces etc & start slicing up datasets according to whatever measure works best until desired outcomes are found e..g filter out by age groups / sex / marital status / ethnicity etc rather than downloading entire table with all stats together thereby simplifying efforts & narrowing down scope greatly improving accuracy along way whilst identifying potential must know trends quickly through visualisations generated Charts / tables when combined often highlight underlying relationships quickly which is key before analysing further Deep diving combined datasets by cross referencing various indices allowing viewers gain even better insights specially when combined structured narrative explanations composed backed up by facts

Research Ideas

• Creating visualizations that show changes in health indicators over tim...

AI in healthcare is accelerating transformation, from diagnosing illnesses faster to streamlining hospital workflows. Impact spans advanced diagnostics to drug development and patient documentation. For example, AI is cutting drug development timelines by over 50% in some cases, and ambient‑AI scribes are already easing burnout for clinicians. Explore the stats...

~ This dataset provides comprehensive health and healthcare statistics for 30+ countries across various regions, covering the year 2024. It includes demographic, mortality, disease burden, healthcare resources, and health risk indicators, making it suitable for global health research, policy analysis, and data-driven decision-making.

~ The dataset combines population data, life expectancy metrics, disease-specific mortality rates, and healthcare system indicators, allowing for comparative studies and predictive modeling across nations.

💡 Possible Use Cases:

Comparative health analysis between countries and regions.

Correlation studies between healthcare spending and health outcomes.

Predictive modeling for mortality rates and life expectancy.

Data visualization of global health trends.

Policy analysis to identify areas needing intervention.

📌 Source & Notes:

This dataset has been compiled from publicly available global health statistics for the year 2024. While values are realistic, they may be aggregated from multiple official reports (WHO, World Bank, national health agencies).