The Medical Expenditure Panel Survey (MEPS) is a set of large-scale surveys of families and individuals, their medical providers (doctors, hospitals, pharmacies, etc.), and employers across the United States. MEPS collects data on the specific health services that Americans use, how frequently they use them, the cost of these services, and how they are paid for, as well as data on the cost, scope, and breadth of health insurance held by and available to U.S. workers. Data is publicly-available for two of the four MEPS components: the Household Component and the Insurance Component. Access to Medical Provider Component and Nursing Home Component data requires an application to the Agency for Health Care Research and Quality (AHRQ).

The Consumer Expenditure Estimates dataset was created by SimplyAnalytics using small area estimation techniques. The Consumer Expenditure (CE) Public Use Microdata (PUMD) samples thousands of respondents (referred to as consumer units, or "CUs") across Texas. Each CU is assigned a weight that reflects the relative proportion of all American CUs that they represent. To estimate expenditures at the Census block group and ZCTA5 levels, we use data from the American Community Survey 5-Year Estimates as a proxy for how CUs are distributed over small areas, and use this information to derive expenditure estimates for all CE spending categories. Due to limitations on the PUMD sample size, and to account for national-level weighting of all CUs, the estimates are further adjusted to account for regional fluctuations in cost of living.

The Consumer Expenditure Estimates dataset was created by SimplyAnalytics using small area estimation techniques. The Consumer Expenditure (CE) Public Use Microdata (PUMD) samples thousands of respondents (referred to as consumer units, or "CUs") across Texas. Each CU is assigned a weight that reflects the relative proportion of all American CUs that they represent. To estimate expenditures at the Census block group and ZCTA5 levels, we use data from the American Community Survey 5-Year Estimates as a proxy for how CUs are distributed over small areas, and use this information to derive expenditure estimates for all CE spending categories. Due to limitations on the PUMD sample size, and to account for national-level weighting of all CUs, the estimates are further adjusted to account for regional fluctuations in cost of living.

Introduction

Introducing the US English Scripted Monologue Speech Dataset for the Healthcare Domain, a voice dataset built to accelerate the development and deployment of English language automatic speech recognition (ASR) systems, with a sharp focus on real-world healthcare interactions.

Speech Data

This dataset includes over 6,000 high-quality scripted audio prompts recorded in US English, representing typical voice interactions found in the healthcare industry. The data is tailored for use in voice technology systems that power virtual assistants, patient-facing AI tools, and intelligent customer service platforms.

Topic Coverage

The prompts span a broad range of healthcare-specific interactions, such as:

Contextual Depth

To maximize authenticity, the prompts integrate linguistic elements and healthcare-specific terms such as:

These elements make the dataset exceptionally suited for training AI systems to understand and respond to natural healthcare-related speech patterns.

Transcription

Every audio recording is accompanied by a verbatim, manually verified transcription.

The Drug Product Database (DPD) system captures information on Canadian human, veterinary and disinfectant products approved for use by Health Canada. To facilitate the use of the drug product data, multiple Drug Product files are available. Users can access the complete data set through the “Drug Product” file. Subsets of the data can be accessed in the “Drug Product By …” files. The data in these files are filtered based on the current drug product status. For example, only drug product data for Approved products will be found in the “Drug Product By Approved Status” file.

The National Social Life, Health and Aging Project (NSHAP) is the first population-based study of health and social factors on a national scale, aiming to understand the well-being of older, community-dwelling Americans by examining the interactions among physical health, illness, medication use, cognitive function, emotional health, sensory function, health behaviors, and social connectedness. It is designed to provide health providers, policy makers, and individuals with useful information and insights into these factors, particularly on social and intimate relationships. The National Opinion Research Center (NORC), along with Principal Investigators at the University of Chicago, conducted more than 3,000 interviews during 2005 and 2006 with a nationally representative sample of adults aged 57 to 85. Face-to-face interviews and biomeasure collection took place in respondents' homes. Round 2 interviews were conducted from August 2010 through May 2011, during which Round 1 Respondents were re-interviewed. An attempt was also made to interview individuals who were sampled in Round 1 but declined to participate. In addition, spouses or co-resident partners were also interviewed using the same instruments as the main respondents. This process resulted in 3,377 total respondents. The following files constitute Round 2: Core Data, Disposition of Round 1 Partner Data, Social Networks Data, Social Networks Update Data, Partner History Data, Partner History Update Data, Medications Data, Proxy Data, and Sleep Statistics Data. Included in the Core files (Datasets 1 and 2) are demographic characteristics, such as gender, age, education, race, and ethnicity. Other topics covered respondents' social networks, social and cultural activity, physical and mental health including cognition, well-being, illness, history of sexual and intimate partnerships, and patient-physician communication, in addition to bereavement items. Data were also collected from respondents on the following items and modules: social activity items, physical contact module, sexual interest module, get up and go assessment of physical function, and a panel of biomeasures, including weight, waist circumference, height, blood pressure, smell, saliva collection, and taste. The Disposition of Round 1 Partner files (Datasets 3 and 4) detail information derived from Section 6A items regarding the partner from Round 1 within the questionnaire. This provides a complete history for respondent partners across both rounds. The Social Networks files (Datasets 5 and 6) contain one record for each person identified on the network roster. Respondents who refused to participate in the roster or who did not identify anyone are not represented in this file. The Social Networks Update files (Datasets 7 and 8) detail respondents' current relationship status with each person identified on the network roster. The Partner History file (Dataset 9) contains one record for each marriage, cohabitation, or romantic relationship identified in Section 6A of the questionnaire, including a current partner in Round 2 but excluding the partner from Round 1. The Partner History Update file (Dataset 10) details respondents' current sexual partner information, as well as marital and cohabiting status. The Medications Data file (Dataset 11) contains records for items listed in the medications log. The Proxy Data files (Datasets 12 and 13) contain information from proxy interviews administered for Round 1 Respondents who were either deceased or whose health was too poor to participate in Round 2. The Sleep Statistics Data files (Dataset 14 and 15) provide information on actigraphy sleep variables. NACDA also maintains a Colectica portal with the NSHAP Core data across rounds 1-3, which allows users to interact with variables across rounds and create customized subsets. Registration is required.

This feature layer provides access to OpenStreetMap (OSM) point data of medical facilities for North America, which is updated every 15 minutes with the latest edits. This hosted feature layer view is referencing a hosted feature layer of OSM point (node) data in ArcGIS Online that is updated with minutely diffs from the OSM planet file. This feature layer view includes amenity features defined as a query against the hosted feature layer where the amenity value is any of 'hospital', 'clinic', 'doctors', or 'pharmacy'.In OSM, amenities are useful and important facilities for visitors and residents, such as hospitals and clinics. These features are identified with an amenity tag. There are thousands of different tag values used in the OSM database. In this feature layer, unique symbols are used for the most common amenity tags used for medical facilities.Zoom in to large scales (e.g. Neighborhood level or 1:20k scale) to see the amenity features display. You can click on a feature to get the name of the amenity. The name of the amenity will display by default at very large scales (e.g. Building level of 1:2k scale). Labels can be turned off in your map if you prefer.Create New LayerIf you would like to create a more focused version of this medical facilities layer displaying just one or two amenity types, you can do that easily! Just add the layer to a map, copy the layer in the content window, add a filter to the new layer (e.g. amenity is hospital), rename the layer as appropriate, and save layer. You can also change the layer symbols or popup if you like. Esri will publish a few such layers (e.g. Places of Worship, Schools, and Parking) that are ready to use, but not for every type of amenity.Important Note: if you do create a new layer, it should be provided under the same Terms of Use and include the same Credits as this layer. You can copy and paste the Terms of Use and Credits info below in the new Item page as needed.

This dataset is about countries per year in South America. It has 12 rows and is filtered where the date is 2021. It features 4 columns: country, currency, and health expenditure.

This dataset is about countries per year in South America. It has 12 rows and is filtered where the date is 2021. It features 4 columns: country, demonym, and health expenditure.

Success.ai’s Healthcare Industry Leads Data for the North American Healthcare Sector provides businesses with a comprehensive dataset designed to connect with healthcare organizations, decision-makers, and key stakeholders across the United States, Canada, and Mexico. Covering hospitals, pharmaceutical firms, biotechnology companies, and medical equipment providers, this dataset delivers verified contact information, firmographic details, and actionable business insights.

With access to over 170 million verified professional profiles and 30 million company profiles, Success.ai ensures your outreach, market research, and strategic initiatives are powered by accurate, continuously updated, and AI-validated data. Backed by our Best Price Guarantee, this solution is your key to success in the North American healthcare market.

Why Choose Success.ai’s Healthcare Industry Leads Data?

1. Verified Contact Data for Precision Targeting

   • Access verified work emails, phone numbers, and LinkedIn profiles of healthcare executives, clinical managers, procurement officers, and compliance leaders.
   • AI-driven validation ensures 99% accuracy, reducing bounce rates and improving engagement efficiency.

2. Comprehensive Coverage of North America’s Healthcare Sector

   • Includes profiles of organizations such as hospitals, private clinics, research facilities, biotech firms, and medical supply distributors.
   • Gain visibility into the unique healthcare dynamics of the United States, Canada, and Mexico, including regional trends, regulatory differences, and market opportunities.

3. Continuously Updated Datasets

   • Real-time updates reflect changes in leadership, organizational structures, service offerings, and market activities.
   • Ensure your outreach and strategy stay relevant and aligned with the rapidly evolving healthcare industry.

4. Ethical and Compliant

   • Adheres to GDPR, CCPA, and other global privacy regulations, ensuring responsible and compliant use of data for your campaigns.

Data Highlights:

• 170M+ Verified Professional Profiles: Engage with decision-makers and influencers across North America’s healthcare sector.
• 30M Company Profiles: Access detailed firmographic data, including organization sizes, revenue ranges, and geographic footprints.
• Decision-Maker Contacts: Connect with CEOs, CMOs, clinical directors, R&D leaders, and procurement managers shaping healthcare strategies.
• Operational Insights: Understand supply chains, service lines, and product pipelines within the healthcare ecosystem.

Key Features of the Dataset:

1. Healthcare Decision-Maker Profiles

   • Identify and connect with healthcare leaders driving innovation, procurement decisions, and patient care delivery.
   • Engage with professionals responsible for technology adoption, regulatory compliance, and resource management.

2. Advanced Filters for Precision Targeting

   • Filter companies by sector (hospitals, biotech, pharma, medical devices), geographic location, revenue size, or workforce composition.
   • Tailor your outreach to align with the unique needs and priorities of North American healthcare organizations.

3. Market and Operational Insights

   • Analyze trends such as telemedicine adoption, value-based care initiatives, and investments in AI and automation.
   • Leverage these insights to position your solutions effectively within a rapidly transforming industry.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data allow for personalized messaging, highlight your value propositions, and improve engagement outcomes with healthcare stakeholders.

Strategic Use Cases:

1. Sales and Lead Generation

   • Offer technology solutions, medical devices, or consulting services to healthcare organizations seeking operational improvements.
   • Build relationships with procurement managers, clinical directors, and decision-makers responsible for resource allocation.

2. Marketing and Demand Generation

   • Target marketing teams and outreach coordinators within healthcare organizations to promote software solutions, diagnostic tools, or patient engagement platforms.
   • Leverage verified contact data to launch impactful email and multi-channel marketing campaigns.

3. Regulatory Compliance and Risk Mitigation

   • Connect with compliance officers and legal teams responsible for adhering to healthcare regulations and standards.
   • Present solutions for streamlined reporting, risk management, and quality assurance processes.

4. Recruitment and Workforce Optimization

   • Engage HR professionals and hiring managers in recruiting healthcare talent, from clinical staff to administrative roles.
   • Provide staffing solutions, training platforms, or workforce management tools tailored to healthcare environments.

Why Choose Success.ai?

1. Best Price Guarantee
   • Access premium-q...

Artificial Intelligence in Precision Medicine Market Outlook

The Artificial Intelligence in Precision Medicine Market is projected to grow exponentially, achieving a valuation of USD XX billion by 2032, driven by the increasing demand for personalized healthcare solutions and technological advancements in AI. The market is poised for a significant CAGR of X% during the forecast period from 2024 to 2032.

One of the primary growth factors of the Artificial Intelligence (AI) in Precision Medicine Market is the increasing prevalence of chronic diseases such as cancer, diabetes, and cardiovascular disorders. These conditions require highly individualized treatment plans, which AI can help develop with a high degree of accuracy. AI's ability to analyze large datasets quickly and provide insights into patient-specific factors facilitates more effective and targeted treatments, thus driving the market's growth. Additionally, AI technologies enable the identification of novel biomarkers and therapeutic targets, further enhancing the precision of medical interventions.

Another significant driver is the advancement in AI technologies, particularly in machine learning, deep learning, and natural language processing. These technologies are revolutionizing the healthcare industry by providing tools that can predict disease progression, recommend personalized treatment options, and even discover new drugs. For example, AI algorithms can process vast amounts of genomic data to identify genetic mutations associated with specific diseases. This capability not only accelerates the drug discovery process but also improves the design of personalized treatment plans, thereby enhancing patient outcomes and reducing healthcare costs.

The growing investment in healthcare infrastructure and increasing adoption of electronic health records (EHRs) also contribute to the market's expansion. EHRs store extensive patient data, which AI systems can analyze to glean valuable insights into patient health trends and treatment responses. Governments and private enterprises are investing heavily in healthcare digitization, which is expected to provide a significant boost to the AI in Precision Medicine Market. Moreover, the COVID-19 pandemic has underscored the need for advanced healthcare solutions, further accelerating the adoption of AI in precision medicine.

Regionally, North America is expected to dominate the market due to its advanced healthcare infrastructure, significant healthcare expenditure, and strong presence of key market players. However, the Asia Pacific region is anticipated to witness the highest growth rate, driven by increasing healthcare investments, a growing patient population, and rising awareness of personalized medicine. Europe, Latin America, and the Middle East & Africa are also expected to contribute to the market's growth, albeit at varying rates depending on their respective healthcare landscapes and adoption of AI technologies.

Component Analysis

The AI in Precision Medicine Market by component is segmented into software, hardware, and services. The software segment is expected to hold the largest share due to the critical role AI algorithms and platforms play in analyzing complex healthcare data. Software solutions are essential for interpreting genomic data, predicting disease outcomes, and recommending personalized treatment plans. Companies are continually developing advanced AI software that can integrate seamlessly with existing healthcare systems, enhancing their utility and adoption.

The hardware segment, although smaller compared to software, is also crucial. This segment includes advanced computing systems, data storage solutions, and specialized devices required to run complex AI algorithms. With the increasing complexity of AI models and the growing volume of healthcare data, there is a rising demand for high-performance computing hardware. Innovations in chip technology and the development of AI-specific processors are expected to drive growth in this segment.

The services segment encompasses various support and consultancy services that facilitate the implementation and maintenance of AI systems in precision medicine. This includes services such as data management, system integration, training, and technical support. As healthcare providers and pharmaceutical companies adopt AI solutions, the need for expert services to ensure the smooth operation and optimization of these systems is growing. Service providers play a vital role in helping organizations navigate the complexities of AI techn

Urgent Care Facilities Urgent care is defined as the delivery of ambulatory medical care outside of a hospital emergency department on a walk-in basis without a scheduled appointment. (Source: Urgent Care Association of America) The Urgent Care dataset consists of any location that is capable of providing emergency medical care and must provide emergency medical treatment beyond what can normally be provided by an EMS unit, must be able to perform surgery, or must be able to provide recuperative care beyond what is normally provided by a doctor's office. In times of emergency, the facility must be able to accept patients from the general population or patients from a significant subset of the general population (e.g., children). Florida and Arizona license Urgent Care facilities within their state. However, the criteria for licensing and the criteria for inclusion in this dataset do not appear to be the same. For these two states, this dataset contains entities that fit TGS' criteria for an Urgent Care facility but may not be licensed as Urgent Care by the state. During processing, TGS found that this is a rapidly changing industry. Although TGS intended for all Urgent Care facilities to be included in this dataset, the newest facilities may not be included. Entities that are excluded from this dataset are administrative offices, physician offices, workman compensation facilities, free standing emergency rooms, and hospitals. Urgent Care facilities that are operated by and co-located with a hospital are also excluded because the locations are included in the hospital dataset. ID# 10194253 is a "mobile" urgent care center that provides urgent care to private residences. This entity is plotted at its administrative building. Records with "-DOD" appended to the end of the [NAME] value are located on a military base, as defined by the Defense Installation Spatial Data Infrastructure (DISDI) military installations and military range boundaries. At the request of NGA, text fields in this dataset have been set to all upper case to facilitate consistent database engine search results. At the request of NGA, all diacritics (e.g., the German umlaut or the Spanish tilde) have been replaced with their closest equivalent English character to facilitate use with database systems that may not support diacritics. This dataset does not contain any Urgent Care facilities in American Samoa, Guam, the Virgin Islands, or the Commonwealth of the Northern Mariana Islands. The currentness of this dataset is indicated by the [CONTDATE] field. Based upon this field, the oldest record is dated 11/22/2004 and the newest record is dated 07/17/2009.

Key Indicators of Heart Disease

2022 annual CDC survey data of 400k+ adults related to their health status

What subject does the dataset cover?

According to the CDC, heart disease is a leading cause of death for people of most races in the U.S. (African Americans, American Indians and Alaska Natives, and whites). About half of all Americans (47%) have at least 1 of 3 major risk factors for heart disease: high blood pressure, high cholesterol, and smoking. Other key indicators include diabetes status, obesity (high BMI), not getting enough physical activity, or drinking too much alcohol. Identifying and preventing the factors that have the greatest impact on heart disease is very important in healthcare. In turn, developments in computing allow the application of machine learning methods to detect "patterns" in the data that can predict a patient's condition.

Where did the data set come from and what treatments has it undergone?

The dataset originally comes from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to collect data on the health status of U.S. residents. As described by the CDC: "Established in 1984 with 15 states, BRFSS now collects data in all 50 states, the District of Columbia, and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world. The most recent dataset includes data from 2023. In this dataset, I noticed many factors (questions) that directly or indirectly influence heart disease, so I decided to select the most relevant variables from it. I also decided to share with you two versions of the most recent dataset: with NaNs and without it.

What can you do with this data set?

As described above, the original dataset of nearly 300 variables was reduced to 40variables. In addition to classical EDA, this dataset can be used to apply a number of machine learning methods, especially classifier models (logistic regression, SVM, random forest, etc.). You should treat the variable "HadHeartAttack" as binary ("Yes" - respondent had heart disease; "No" - respondent did not have heart disease). Note, however, that the classes are unbalanced, so the classic approach of applying a model is not advisable. Fixing the weights/undersampling should yield much better results. Based on the data set, I built a logistic regression model and embedded it in an application that might inspire you: https://share.streamlit.io/kamilpytlak/heart-condition-checker/main/app.py. Can you indicate which variables have a significant effect on the likelihood of heart disease?

What steps did you use to convert the dataset?

Check out this notebook in my GitHub repository: https://github.com/kamilpytlak/data-science-projects/blob/main/heart-disease-prediction/2022/notebooks/data_processing.ipynb

BackgroundThe usefulness of aspirin to defend against cardiovascular disease in both primary and secondary settings is well recognized by the medical profession. Multiple studies also have found that daily aspirin significantly reduces cancer incidence and mortality. Despite these proven health benefits, aspirin use remains low among populations targeted by cardiovascular prevention guidelines. This article seeks to determine the long-term economic and population-health impact of broader use of aspirin by older Americans at higher risk for cardiovascular disease.Methods and FindingsWe employ the Future Elderly Model, a dynamic microsimulation that follows Americans aged 50 and older, to project their lifetime health and spending under the status quo and in various scenarios of expanded aspirin use. The model is based primarily on data from the Health and Retirement Study, a large, representative, national survey that has been ongoing for more than two decades. Outcomes are chosen to provide a broad perspective of the individual and societal impacts of the interventions and include: heart disease, stroke, cancer, life expectancy, quality-adjusted life expectancy, disability-free life expectancy, and medical costs. Eligibility for increased aspirin use in simulations is based on the 2011–2012 questionnaire on preventive aspirin use of the National Health and Nutrition Examination Survey. These data reveal a large unmet need for daily aspirin, with over 40% of men and 10% of women aged 50 to 79 presenting high cardiovascular risk but not taking aspirin. We estimate that increased use by high-risk older Americans would improve national life expectancy at age 50 by 0.28 years (95% CI 0.08–0.50) and would add 900,000 people (95% CI 300,000–1,400,000) to the American population by 2036. After valuing the quality-adjusted life-years appropriately, Americans could expect $692 billion (95% CI 345–975) in net health benefits over that period.ConclusionsExpanded use of aspirin by older Americans with elevated risk of cardiovascular disease could generate substantial population health benefits over the next twenty years and do so very cost-effectively.

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Big Data Spending In Healthcare Sector Market Size 2025-2029

The big data spending in healthcare sector market size is valued to increase by USD 7.78 billion, at a CAGR of 10.2% from 2024 to 2029. Need to improve business efficiency will drive the big data spending in healthcare sector market.

Market Insights

APAC dominated the market and accounted for a 31% growth during the 2025-2029.
By Service - Services segment was valued at USD 5.9 billion in 2023
By Type - Descriptive analytics segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 108.28 million 
Market Future Opportunities 2024: USD 7783.80 million
CAGR from 2024 to 2029 : 10.2%

Market Summary

The healthcare sector's adoption of big data analytics is a global trend that continues to gain momentum, driven by the need to improve business efficiency, enhance patient care, and ensure regulatory compliance. Big data in healthcare refers to the large and complex data sets generated from various sources, including Electronic Health Records, medical devices, and patient-generated data. This data holds immense potential for identifying patterns, predicting outcomes, and driving evidence-based decision-making. One real-world scenario illustrating this is supply chain optimization. Hospitals and healthcare providers can leverage big data analytics to optimize their inventory management, reduce wastage, and ensure timely availability of essential medical supplies.
For instance, predictive analytics can help anticipate demand for specific medical equipment or supplies, enabling healthcare providers to maintain optimal stock levels and minimize the risk of stockouts or overstocking. However, the adoption of big data analytics in healthcare is not without challenges. Data privacy and security concerns related to patients' medical data are a significant concern, with potential risks ranging from data breaches to unauthorized access. Ensuring robust Data security measures and adhering to regulatory guidelines, such as the Health Insurance Portability and Accountability Act (HIPAA) in the US, is essential for maintaining trust and protecting sensitive patient information.
In conclusion, the use of big data analytics in healthcare is a transformative trend that offers numerous benefits, from improved operational efficiency to enhanced patient care and regulatory compliance. However, it also presents challenges related to data privacy and security, which must be addressed to fully realize the potential of this technology.

What will be the size of the Big Data Spending In Healthcare Sector Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free Sample

The market continues to evolve, with recent research indicating a significant increase in investments. This growth is driven by the need for improved patient care, regulatory compliance, and cost savings. One trend shaping the market is the adoption of advanced analytics techniques to gain insights from large datasets. For instance, predictive analytics is being used to identify potential health risks and improve patient outcomes.
Additionally, data visualization software and data analytics platforms are essential tools for healthcare organizations to make data-driven decisions. Compliance is another critical area where big data is making a significant impact. With the increasing amount of patient data being generated, there is a growing need for data security and privacy. Data encryption methods and data anonymization techniques are being used to protect sensitive patient information. Budgeting is also a significant consideration for healthcare organizations investing in big data. Cost benefit analysis and statistical modeling are essential tools for evaluating the return on investment of big data initiatives.
As healthcare organizations continue to invest in big data, they must balance the benefits against the costs to ensure they are making informed decisions. In conclusion, the market is experiencing significant growth, driven by the need for improved patient care, regulatory compliance, and cost savings. The adoption of advanced analytics techniques, data visualization software, and data analytics platforms is essential for healthcare organizations to gain insights from large datasets and make data-driven decisions. Additionally, data security and privacy are critical considerations, with data encryption methods and data anonymization techniques being used to protect sensitive patient information.
Budgeting is also a significant consideration, with cost benefit analysis and statistical modeling essential tools for evaluating the return on investment of big data initiatives.

Unpacking the Big Data Spending In Healthcare Sector Market Landscape

In the dynamic healthcare sector, the adoption of big data technologies has become a st

The global healthcare quantum computing market is poised for significant growth, projected to reach $61 million in 2025 and exhibiting a Compound Annual Growth Rate (CAGR) of 3.7% from 2025 to 2033. This expansion is driven by several key factors. Firstly, the increasing need for faster and more accurate diagnostic assistance is fueling demand for quantum computing's superior processing power. Quantum algorithms can analyze complex medical datasets far more efficiently than classical computers, enabling earlier and more precise disease diagnosis. Secondly, the rise of precision medicine, with its focus on personalized treatments, necessitates advanced computational capabilities to analyze individual patient genomes and tailor therapies accordingly. This intricate analysis is perfectly suited to quantum computing's unique strengths. Finally, advancements in quantum hardware and software are making the technology more accessible and cost-effective, further accelerating market adoption. Hospital systems and research institutions are leading adopters, leveraging quantum computing for drug discovery, clinical trial optimization, and improved patient care. While the market is still in its nascent stages, the potential applications and ongoing technological advancements indicate a promising future for healthcare quantum computing. The market segmentation reveals strong growth prospects across various applications. Diagnostic assistance, leveraging quantum machine learning for image analysis and disease prediction, is expected to dominate the market. Precision medicine, enabling personalized treatments based on genomic data, is another key driver of growth. Geographically, North America, particularly the United States, is expected to hold a significant market share due to substantial investments in research and development and the presence of major players. However, Europe and Asia-Pacific regions are also showing rapid growth potential, driven by increasing healthcare expenditure and a growing focus on technological advancements in healthcare delivery. The competitive landscape includes a mix of established technology giants like IBM and Microsoft, alongside specialized quantum computing companies like D-Wave and Rigetti. This dynamic environment fosters innovation and competition, further accelerating the overall market development.

In January 2018, 798 Hispanic/Latino adults living in the United States were recruited through Qualtrics Panels to complete a survey in English or Spanish. Respondents were diverse in their nativity (e.g., 52% Mexican or Mexican American; 17% Puerto Rican; 8.5% Cuban). The survey included the following measures: -Demographic and Health Information – Demographic and Health Data Questionnaire (DHDQ). This researcher-constructed questionnaire is designed to obtain participant information such as: (a) race/ethnicity, (b) age, (c) gender, (d) sexual orientation, (e) relationship status, (f) household income, (g) generational status, (h) education level, (i) presence of chronic health conditions, (j) self-reported height and weight, (k) overall health status, (l) native language and proficient language(s), (m) number of health care visits in the past year, and (n) perceived weight. -Media and Technology Usage and Attitudes Scale (MTUAS). The Media and Technology Usage and Attitudes Scale is a 60-item scale used to measure the frequency of use from specific forms of media and attitudes toward technology (Rosen, Whaling, Carrier, Cheever, & Rokkum, 2013). The scale consists of eleven media usage subscales and four attitude subscales. For the purposes of this study, only the smartphone usage subscale will be included (9 items). Prompts assessing the frequency of technology use stated: “Please indicate how often you do each of the following…” and asked about smartphone usage habits on a scale from 1(Never) to 10 (All the time). Higher scores are indicative of more technology use. The MTUAS was found to show sufficient proof of reliability for smartphone usage subscale (α = .93). Validity has also been shown through comparisons with measures of daily media usage hours, technology-related anxiety, and the Internet Addiction Test (Rosen et al., 2013). -The Sedentary Behavior Questionnaire (SBQ). The Sedentary Behavior Questionnaire is an 18-item scale designed to assess nine different sedentary behaviors including the use of technological devices, hobbies, and sitting due to transportation and work (Rosenberg et al., 2010). The measure is designed to assess sedentary behaviors over weekdays as well as the weekend and then are multiplied to estimate the sum amounts of sedentary hours during a week/weekend. The scale consisted of nine items with answer choices ranging from 1 (None) to 9 (6 hours or more). The current study will slightly alter the SBQ as some of the items may be dated in regards to the technology. An example is “sitting listening to music on the radio, tapes, or CDs.” The examples used in the items will be reflective of sedentary forms of technology used nowadays. The SBQ has been found to be a reliable measure for sedentary behaviors as intraclass correlation coefficients found that the items were sufficient for both weekday (.64-.90) and weekends (.51-.93). Validity of the measure was also sufficient as partial correlations were used to compare the self-reported ratings of the SBQ to accelerometer measures of activity. The study also found that in comparison to the International Physical Activity Questionnaire and body mass index, there were significant correlations with both male and female samples (Rosenberg et al., 2010). -PHQ-9- English: The Patient Health Questionnaire (PHQ-9). The PHQ-9 is a 9-item instrument that measures depressive symptoms (Kroenke, Spitzer, & Williams, 2001). Instructions on the PHQ-9 are as follows: “Over the last 2 weeks, how often have you been bothered by any of the following problems?” The assessment uses a 4-point Likert-type scale with responses ranging from 0 (not at all) to 3 (nearly every day). Scores for PHQ-9 scale are determined by assigning a score to each response ranging from 0 to 3 and then summing the responses. The PHQ-9 score can range from 0 to 27. Higher scores on the measure indicate higher levels of depressive symptoms. -Health Promoting Behaviors – Health Promoting Lifestyle Profile II (HPLP-II). The HPLP-II is a 52-item inventory designed to measure engagement in behaviors that characterize a health-promoting lifestyle (Walker, Sechrist, Pender, 1995). The HPLPII is comprised of a scale and six subscales, which include Spiritual Growth, Interpersonal Relations, Nutrition, Physical Activity, Health Responsibility, and Stress Management. Only the Nutrition (9 items) and Physical Activity (8 items) subscales will be used for the current study. Instructions on the HPLP-II are to indicate level of engagement in each listed behavior using a Likert-type scale, with responses ranging from 1 (never) to 4 (routinely). Scores for the HPLP-II scale and subscale are determined by calculating means for each. Higher scores on the scale and subscales indicate higher levels of engagement in the assessed health promoti... Visit https://dataone.org/datasets/sha256%3A947312a2e719300f2006c0c8f48294d38a5b6a63ad0f31869ed48ea690048cde for complete metadata about this dataset.

description:

Medically Unlikely Edits (MUEs) define for each HCPCS / CPT code the maximum units of service (UOS) that a provider would report under most circumstances for a single beneficiary on a single date of service.

Practitioner services also refers to ambulatory surgical centers.
DME refers to provider claims for durable medical equipment.

The CMS National Correct Coding Initiative (NCCI) promotes national correct coding methodologies and reduces improper coding which may result in inappropriate payments of Medicare Part B claims and Medicaid claims. NCCI procedure-to-procedure (PTP) edits define pairs of Healthcare Common Procedure Coding System (HCPCS)/Current Procedural Terminology (CPT) codes that should not be reported together for a variety of reasons. The purpose of the PTP edits is to prevent improper payments when incorrect code combinations are reported. The edits in this dataset are active for the dates indicated within. This file should NOT be used by state Medicaid programs as their edit file. Current Procedural Terminology (CPT) codes, descriptions and other data only are copyright 2017 American Medical Association. All rights reserved. CPT is a registered trademark of the American Medical Association. Applicable FARSDFARS Restrictions Apply to Government Use. Fee schedules, relative value units, conversion factors and/or related components are not assigned by the AMA, are not part of CPT, and the AMA is not recommending their use. The AMA does not directly or indirectly practice medicine or dispense medical services. The AMA assumes no liability for the data contained or not contained herein.

Medically Unlikely Edits (MUEs) define for each HCPCS / CPT code the maximum units of service (UOS) that a provider would report under most circumstances for a single beneficiary on a single date of service.

Practitioner services also refers to ambulatory surgical centers.
DME refers to provider claims for durable medical equipment.

The CMS National Correct Coding Initiative (NCCI) promotes national correct coding methodologies and reduces improper coding which may result in inappropriate payments of Medicare Part B claims and Medicaid claims. NCCI procedure-to-procedure (PTP) edits define pairs of Healthcare Common Procedure Coding System (HCPCS)/Current Procedural Terminology (CPT) codes that should not be reported together for a variety of reasons. The purpose of the PTP edits is to prevent improper payments when incorrect code combinations are reported. The edits in this dataset are active for the dates indicated within. This file should NOT be used by state Medicaid programs as their edit file. Current Procedural Terminology (CPT) codes, descriptions and other data only are copyright 2017 American Medical Association. All rights reserved. CPT is a registered trademark of the American Medical Association. Applicable FARSDFARS Restrictions Apply to Government Use. Fee schedules, relative value units, conversion factors and/or related components are not assigned by the AMA, are not part of CPT, and the AMA is not recommending their use. The AMA does not directly or indirectly practice medicine or dispense medical services. The AMA assumes no liability for the data contained or not contained herein.

This statistic shows a ranking of the estimated per capita consumer spending on healthcare in 2020 in Latin America and the Caribbean, differentiated by country. Consumer spending here refers to the domestic demand of private households and non-profit institutions serving households (NPISHs) in the selected region. Spending by corporations or the state is not included. Consumer spending is the biggest component of the gross domestic product as computed on an expenditure basis in the context of national accounts. The other components in this approach are consumption expenditure of the state, gross domestic investment as well as the net exports of goods and services. Consumer spending is broken down according to the United Nations' Classification of Individual Consumption By Purpose (COICOP). The shown data adheres broadly to group 06. As not all countries and regions report data in a harmonized way, all data shown here has been processed by Statista to allow the greatest level of comparability possible. The underlying input data are usually household budget surveys conducted by government agencies that track spending of selected households over a given period.The data is shown in nominal terms which means that monetary data is valued at prices of the respective year and has not been adjusted for inflation. For future years the price level has been projected as well. The data has been converted from local currencies to US$ using the average exchange rate of the respective year. For forecast years, the exchange rate has been projected as well. The timelines therefore incorporate currency effects.The shown forecast is adjusted for the expected impact of the COVID-19 pandemic on the local economy. The impact has been estimated by considering both direct (e.g. because of restrictions on personal movement) and indirect (e.g. because of weakened purchasing power) effects. The impact assessment is subject to periodic review as more data becomes available.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).

Context

The Healthcare Common Procedure Coding System (HCPCS, often pronounced by its acronym as "hick picks") is a set of health care procedure codes based on the American Medical Association's Current Procedural Terminology (CPT).

HCPCS includes three levels of codes: Level I consists of the American Medical Association's Current Procedural Terminology (CPT) and is numeric. Level II codes are alphanumeric and primarily include non-physician services such as ambulance services and prosthetic devices, and represent items and supplies and non-physician services, not covered by CPT-4 codes (Level I). Level III codes, also called local codes, were developed by state Medicaid agencies, Medicare contractors, and private insurers for use in specific programs and jurisdictions. The Health Insurance Portability and Accountability Act of 1996 (HIPAA) instructed CMS to adopt a standard coding systems for reporting medical transactions. The use of Level III codes was discontinued on December 31, 2003, in order to adhere to consistent coding standards.

Content

Classification of procedures performed for patients is important for billing and reimbursement in healthcare. The primary classification system used in the United States is Healthcare Common Procedure Coding System (HCPCS), maintained by Centers for Medicare and Medicaid Services (CMS). This system is divided into two levels: level I and level II.

Level I HCPCS codes classify services rendered by physicians. This system is based on Common Procedure Terminology (CPT), a coding system maintained by the American Medical Association (AMA). Level II codes, which are the focus of this public dataset, are used to identify products, supplies, and services not included in level I codes. The level II codes include items such as ambulance services, durable medical goods, prosthetics, orthotics and supplies used outside a physician’s office.

Given the ubiquity of administrative data in healthcare, HCPCS coding systems are also commonly used in areas of clinical research such as outcomes based research.

Update Frequency: Yearly

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Acknowledgements

https://bigquery.cloud.google.com/table/bigquery-public-data:cms_codes.hcpcs

https://cloud.google.com/bigquery/public-data/hcpcs-level2

Dataset Source: Center for Medicare and Medicaid Services. This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy — and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

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Inspiration

What are the descriptions for a set of HCPCS level II codes?