Context

Open Payments is a federal program that collects and makes information public about financial relationships between the health care industry, physicians, and teaching hospitals. The Centers for Medicare & Medicaid Services (CMS) collects information from manufacturers of drugs and devices about payments and other transfers of value they make to physicians and teaching hospitals. These payments and other transfers of value can be for many purposes, like research, consulting, travel, and gifts. We’ll make this data publicly available and searchable on this site each year. More information about it can be found here.

Content

The data has been cleaned slightly to remove all fields that had 5% or more null values. This was done in order to decrease the file size and make it slightly more understandable.

Acknowledgements

The original datasets can be found here.

Inspiration

Inspired by the 2013 version of this dataset that was upload by Centers for Medicare & Medicaid Services , found here

Every year, CMS will update the Open Payments data at least once after its initial publication. The refreshed data will include updates to data disputes and other data corrections made since the initial publication of this data documenting payments or transfers of value to physicians and teaching hospitals, and physician ownership and investment interests. This financial data is submitted by applicable manufacturers and applicable group purchasing organizations (GPOs). #### What data is collected? Applicable manufacturers and GPOs submit data to Open Payments about payments or other transfers of value between applicable manufacturers and GPOs and physicians or teaching hospitals: 1. Paid directly to physicians and teaching hospitals (known as direct payments) 2. Paid indirectly to physicians and teaching hospitals (known as indirect payments) through an intermediary such as a medical specialty society 3. Designated by physicians or teaching hospitals to be paid to another party (known

Background: Open Paymentsis a United States federal program mandating reporting of medical industry payments to physicians, thereby increasing transparency of physician conflicts of interest (COI).Study objectives were to assess industry payments to physician-editors, and to compare their financial COI rate to all physicians within the specialty.

Methods and Findings: We performed a retrospective analysis of prospectively collected data, reviewing Open Paymentsfrom August 1, 2013 to December 31, 2016. We reviewed general payments ("payments… not made in connection with a research agreement") and research funding to "top tier" physician-editors of highly-cited medical journals. We compared payments to physician-editors and to physicians-by-specialty. In 35 journals, 333 (74.5%) of 447 "top tier" editors met inclusion criteria (US-based physician-editors). Of these, 212 (63.7%) received industry-associated payments in the study period. In an average year, 141 (42.3%) of physician-editors received any direct payments (to themselves rather than their institutions; includes general payments and research payments), 66 (19.8%) received direct payments >$5,000 (threshold designated by the National Institutes of Health as a Significant Financial Interest) and 51 (15.3%) received direct payments >$10,000. Mean annual general payments to physician-editors was $55,157 (median 3,512, standard deviation 561,885, range 10-10,981,153). Median general payments to physician-editors were mostly higher compared to all physicians within their specialty. Mean annual direct research payment to the physician-editor was $14,558 (median 4,000, standard deviation 34,471, range 15-174,440), and mean annual indirect research funding to the physician-editor's institution was $175,282 (median 49,107, standard deviation 479,480, range 0.18-5,000,000). The main study limitation was difficulty in identifying "top tier" physician-editors. Though we aimed to study physician-editors primarily responsible for making manuscript decisions, we were unable to confirm each editor's role.

Conclusions: A substantial minority of physician-editors receive payments from industry within any given year, and most editors received payment of some kind during the four-year study period. There were significant outliers. Given the extent of editors' influences on the medical literature, more robust and accessible editor financial COI declarations are recommended.

Analysis of ‘World Bank WDI 2.12 - Health Systems’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/danevans/world-bank-wdi-212-health-systems on 21 November 2021.

--- Dataset description provided by original source is as follows ---

World Bank - World Development Indicators: Health Systems

This is a digest of the information described at http://wdi.worldbank.org/table/2.12# It describes various health spending per capita by Country, as well as doctors, nurses and midwives, and specialist surgical staff per capita

Content

Notes, explanations, etc. 1. There are countries/regions in the World Bank data not in the Covid-19 data, and countries/regions in the Covid-19 data with no World Bank data. This is unavoidable. 2. There were political decisions made in both datasets that may cause problems. I chose to go forward with the data as presented, and did not attempt to modify the decisions made by the dataset creators (e.g., the names of countries, what is and is not a country, etc.).

Columns are as follows: 1. Country_Region: the region as used in Kaggle Covid-19 spread data challenges. 2. Province_State: the region as used in Kaggle Covid-19 spread data challenges. 3. World_Bank_Name: the name of the country used by the World Bank 4. Health_exp_pct_GDP_2016: Level of current health expenditure expressed as a percentage of GDP. Estimates of current health expenditures include healthcare goods and services consumed during each year. This indicator does not include capital health expenditures such as buildings, machinery, IT and stocks of vaccines for emergency or outbreaks.

1. Health_exp_public_pct_2016: Share of current health expenditures funded from domestic public sources for health. Domestic public sources include domestic revenue as internal transfers and grants, transfers, subsidies to voluntary health insurance beneficiaries, non-profit institutions serving households (NPISH) or enterprise financing schemes as well as compulsory prepayment and social health insurance contributions. They do not include external resources spent by governments on health.

2. Health_exp_out_of_pocket_pct_2016: Share of out-of-pocket payments of total current health expenditures. Out-of-pocket payments are spending on health directly out-of-pocket by households.

3. Health_exp_per_capita_USD_2016: Current expenditures on health per capita in current US dollars. Estimates of current health expenditures include healthcare goods and services consumed during each year.

4. per_capita_exp_PPP_2016: Current expenditures on health per capita expressed in international dollars at purchasing power parity (PPP).

5. External_health_exp_pct_2016: Share of current health expenditures funded from external sources. External sources compose of direct foreign transfers and foreign transfers distributed by government encompassing all financial inflows into the national health system from outside the country. External sources either flow through the government scheme or are channeled through non-governmental organizations or other schemes.

6. Physicians_per_1000_2009-18: Physicians include generalist and specialist medical practitioners.

7. Nurse_midwife_per_1000_2009-18: Nurses and midwives include professional nurses, professional midwives, auxiliary nurses, auxiliary midwives, enrolled nurses, enrolled midwives and other associated personnel, such as dental nurses and primary care nurses.

8. Specialist_surgical_per_1000_2008-18: Specialist surgical workforce is the number of specialist surgical, anaesthetic, and obstetric (SAO) providers who are working in each country per 100,000 population.

9. Completeness_of_birth_reg_2009-18: Completeness of birth registration is the percentage of children under age 5 whose births were registered at the time of the survey. The numerator of completeness of birth registration includes children whose birth certificate was seen by the interviewer or whose mother or caretaker says the birth has been registered.

10. Completeness_of_death_reg_2008-16: Completeness of death registration is the estimated percentage of deaths that are registered with their cause of death information in the vital registration system of a country.

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Inspiration

Does health spending levels (public or private), or hospital staff have any effect on the rate at which Covid-19 spreads in a country? Can we use this data to predict the rate at which Cases or Fatalities will grow?

--- Original source retains full ownership of the source dataset ---

Every year, CMS will update the Open Payments data at least once after its initial publication. The refreshed data will include updates to data disputes and other data corrections made since the initial publication of this data documenting payments or transfers of value to physicians and teaching hospitals, and physician ownership and investment interests. This financial data is submitted by applicable manufacturers and applicable group purchasing organizations (GPOs). #### What data is collected? Applicable manufacturers and GPOs submit data to Open Payments about payments or other transfers of value between applicable manufacturers and GPOs and physicians or teaching hospitals: 1. Paid directly to physicians and teaching hospitals (known as direct payments) 2. Paid indirectly to physicians and teaching hospitals (known as indirect payments) through an intermediary such as a medical specialty society 3. Designated by physicians or teaching hospitals to be paid to another party (known

ART data are made available as part of the National ART Surveillance System (NASS) that collects success rates, services, profiles and annual summary data from fertility clinics across the U.S. There are four datasets available: ART Services and Profiles, ART Patient and Cycle Characteristics, ART Success Rates, and ART Summary. All four datasets may be linked by “ClinicID.” ClinicID is a unique identifier for each clinic that reported cycles. The Summary dataset provides a full snapshot of clinic services and profile, patient characteristics, and ART success rates. It is worth noting that patient medical characteristics, such as age, diagnosis, and ovarian reserve, affect ART treatment’s success. Comparison of success rates across clinics may not be meaningful because of differences in patient populations and ART treatment methods. The success rates displayed in this dataset do not reflect any one patient’s chance of success. Patients should consult with a doctor to understand their chance of success based on their own characteristics.

Abstract (en): The National Ambulatory Medical Care Surveys (NAMCS) supply data on ambulatory medical care provided in physicians' offices. The 2006 survey contains information from 29,392 patient visits to 1,455 physicians' offices. Data are available on the patient's smoking habits, reason for the visit, expected source of payment, the physician's diagnosis, and the kinds of diagnostic and therapeutic services rendered. Other variables cover drugs/medications ordered, administered, or provided during office visits, with information on medication code, generic name and code, brand name, entry status, prescription status, federal controlled substance status, composition status, and related ingredient codes. Information is also included on the physician's specialization and geographic location. Demographic information on patients, such as age, sex, race, and ethnicity, was also collected. In addition, the 2006 survey contains two new sampling strata which are from 104 Community Health Centers (CHCs) and 200 oncologists. Microdata file users should be fully aware of the importance of the "patient visit weight" (PATWT) and how it must be used. Information about the patient visit weight is presented in the codebook. If more information is needed, the staff of the Ambulatory Care Statistics Branch can be consulted by calling (301) 458-4600 during regular working hours. Prior to this data release, researchers could not make physician-level estimates with publicly available NAMCS data. For 2006, a new "physician weight" (PHYSWT) variable was added to the first record for each individual physician in the dataset. Office visits made within the United States by patients of nonfederally-employed physicians who were primarily involved in office-based patient care activities, but not engaged in the specialties of radiology, pathology, or anesthesiology. The 2006 NAMCS utilized a multistage probability sample design. Primary sampling units (PSUs) were selected in the first stage, physician practices within PSUs in the second stage, and patient visits to selected physicians in the third stage. 2011-10-12 Changes to the CPSUM variable have been made within the dataset. Funding insitution(s): United States Department of Health and Human Services. National Institutes of Health. National Cancer Institute. Per agreement with the National Center for Health Statistics (NCHS), ICPSR distributes the data file and text of the technical documentation for this collection as prepared by NCHS.A portion of NAMCS 2006, the sampling stratum of 200 oncologists, was made possible through funding from the National Cancer Institute.The Stata dataset (.dta file) made available by ICPSR does not contain all of the value labels found within the .do file supplied by ICPSR. Specifically, the value labels that are composed primarily of ICD-9 codes have been omitted from the .dta file. Those data users interested in applying the value labels to the dataset will be able to edit the Stata setup files, which include the aforementioned labels, provided by ICPSR.

Every year, CMS will update the Open Payments data at least once after its initial publication. The refreshed data will include updates to data disputes and other data corrections made since the initial publication of this data documenting payments or transfers of value to physicians and teaching hospitals, and physician ownership and investment interests. This financial data is submitted by applicable manufacturers and applicable group purchasing organizations (GPOs). #### What data is collected? Applicable manufacturers and GPOs submit data to Open Payments about payments or other transfers of value between applicable manufacturers and GPOs and physicians or teaching hospitals: 1. Paid directly to physicians and teaching hospitals (known as direct payments) 2. Paid indirectly to physicians and teaching hospitals (known as indirect payments) through an intermediary such as a medical specialty society 3. Designated by physicians or teaching hospitals to be paid to another party (known

ART data are made available as part of the National ART Surveillance System (NASS) that collects success rates, services, profiles and annual summary data from fertility clinics across the U.S. There are four datasets available: ART Services and Profiles, ART Patient and Cycle Characteristics, ART Success Rates, and ART Summary. All four datasets may be linked by “ClinicID.” ClinicID is a unique identifier for each clinic that reported cycles. The Summary dataset provides a full snapshot of clinic services and profile, patient characteristics, and ART success rates. It is worth noting that patient medical characteristics, such as age, diagnosis, and ovarian reserve, affect ART treatment’s success. Comparison of success rates across clinics may not be meaningful because of differences in patient populations and ART treatment methods. The success rates displayed in this dataset do not reflect any one patient’s chance of success. Patients should consult with a doctor to understand their chance of success based on their own characteristics.

Snapshot img

Online Doctor Consultation Market Size 2024-2028

The online doctor consultation market size is forecast to increase by USD 38.87 billion at a CAGR of 29.6% between 2023 and 2028. The market is experiencing significant growth due to several key drivers. The increasing incidence and prevalence of infectious diseases have led to an increase in demand for convenient and accessible healthcare solutions. Advanced technologies, such as telemedicine and artificial intelligence, are enabling online doctor consultations, offering patients the ability to connect with healthcare professionals from the comfort of their homes. However, privacy and data security concerns are major challenges in this market, requiring strong security measures to protect sensitive patient information. The market is expected to continue its growth trajectory, driven by these factors and the ongoing digital transformation of the healthcare industry.

What will be the Size of the Market During the Forecast Period?

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The market is witnessing significant growth due to the increasing internet usage and the younger generation's preference for digital healthcare services. With lifestyle disorders on the rise, people are turning to technology for convenient and accessible healthcare solutions. Artificial intelligence (AI) is playing a pivotal role in this sector, enabling telehealth, telemedicine, mHealth, and digital healthcare services. Hospitals and clinics are also adopting these technologies to provide E-OPD, E-Pathology, and remote consultations. E-commerce services, retail clinics, and urgent care centers are also leveraging online doctor consultations to expand their reach and offer round-the-clock services. The use of laptops, tablets, smartphones, high-speed internet, and video conferencing software has made consultations more accessible, allowing doctors to connect with patients from anywhere. The population growth and the availability of technological aid have further fueled the market's growth. The use of smartphone cameras for virtual consultations and data connection for sharing medical records have also streamlined the process, making it more efficient and patient-friendly.

Moreover, e-commerce services, retail clinics, and urgent care centers are also adopting online doctor consultations to expand their reach and offer more convenient services to patients. Mobile-based health applications, laptops, and tablets are the preferred devices for accessing these services, while smartphone cameras and video conferencing software facilitate virtual consultations. Population growth and the increasing prevalence of chronic conditions necessitate the need for more accessible and affordable healthcare services. Online doctor consultations offer a cost-effective and convenient solution, enabling patients to receive medical advice and treatment plans without the need for lengthy travel or wait times. With data connection becoming increasingly reliable and affordable, online doctor consultations are set to become a mainstream component of the US healthcare system.

In conclusion, the trend towards online doctor consultations is transforming the US healthcare landscape, offering patients more convenient, accessible, and affordable healthcare services. With the widespread availability of high-speed internet and digital devices, as well as the increasing adoption of AI and telehealth technologies, online doctor consultations are poised to become an integral part of the US healthcare system.

Market Segmentation

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Product

  Services
  Software


Type

  Audio chat
  Video chat


Geography

  North America

    US


  Europe

    Germany
    UK
    France


  Asia

    China


  Rest of World (ROW)

By Product Insights

The services segment is estimated to witness significant growth during the forecast period. The proliferation of high-speed internet and the availability of smartphone cameras have facilitated the growth of the market. Video conferencing software enables doctors to conduct virtual consultations with their patients, providing them with convenient access to medical care. The use of a reliable data connection ensures secure transmission of medical information between medical practitioners and their clients. The medical fraternity has increasingly embraced telehealth services, including live video consultations and remote patient monitoring, to expand their reach and improve patient outcomes. As more healthcare providers adopt these services, the demand for comprehensive telehealth solutions has surged.

While the benefits of online doctor consultations are numerous, it is essential to prioritize data secu

BackgroundRelatively little is known about American medical student’s attitudes toward caring for the uninsured, limiting physician reimbursement and the role of cost-effectiveness data in medical decision-making. We assessed American medical student’s attitudes regarding these topics as well as demographic predictors of those attitudes, and compared them to practicing physicians.Methods and FindingsA survey instrument was explicitly designed to compare medical student attitudes with those previously reported by physicians. Between December 1st 2010 and March 27th 2011 survey responses were collected from more than 2% of the total estimated 2010–2011 US medical student population enrolled at 111 of 159 accredited US medical schools within the 50 United States (n = 2414 of possible 98197). Medical students were more likely to object to reimbursement cuts, and more likely to object to the use of cost effectiveness data in medical decision making than current physicians according to the literature. Specialty preference, political persuasion, and medical student debt were significant predictors of health policy attitudes. Medical students with anticipated debt in excess of $200,000 were significantly less willing to favor limiting reimbursement to improve patient access (OR: 0.73 [95% confidence interval (CI): 0.59–0.89]), and significantly more likely to object to using cost effectiveness data to limit treatments (OR 1.30, 95% CI 1.05–1.60) when compared to respondents with anticipated debt less than $200,000.ConclusionsWhen compared to physicians in the literature, future physicians may be less willing to favor cuts to physician reimbursements and may be more likely to object to the use of cost effectiveness data. Political orientation, specialty preference and anticipated debt may be important predictors of health policy attitudes among medical students. Early career medical providers with primary care ambitions and those who anticipate less debt may be more likely to support healthcare cost containment.

Data were updated on September 11, 2024. ART data are made available as part of the National ART Surveillance System (NASS) that collects success rates, services, profiles and annual summary data from fertility clinics across the U.S. There are four datasets available: ART Services and Profiles, ART Patient and Cycle Characteristics, ART Success Rates, and ART Summary. All four datasets may be linked by “ClinicID.” ClinicID is a unique identifier for each clinic that reported cycles. The Summary dataset provides a full snapshot of clinic services and profile, patient characteristics, and ART success rates. It is worth noting that patient medical characteristics, such as age, diagnosis, and ovarian reserve, affect ART treatment’s success. Comparison of success rates across clinics may not be meaningful because of differences in patient populations and ART treatment methods. The success rates displayed in this dataset do not reflect any one patient’s chance of success. Patients should consult with a doctor to understand their chance of success based on their own characteristics.

This dataset tracks the updates made on the dataset "Weekly Cumulative Estimated Number of RSV Vaccinations Administered in Retail Pharmacies and Physicians’ Medical Offices, Adults 75 years and older, United States" as a repository for previous versions of the data and metadata.

************Tuberculosis (TB) Chest X-ray Database************ A team of researchers from Qatar University, Doha, Qatar, and the University of Dhaka, Bangladesh along with their collaborators from Malaysia in collaboration with medical doctors from Hamad Medical Corporation and Bangladesh have created a database of chest X-ray images for Tuberculosis (TB) positive cases along with Normal images. In our current release, there are 700 TB images publicly accessible and 2800 TB images can be downloaded from NIAID TB portal[3] by signing an agreement, and 3500 normal images.

Note: -The research team managed to classify TB and Normal Chest X-ray images with an accuracy of 98.3%. This scholarly work is published in IEEE Access. Please make sure you give credit to us while using the dataset, code, and trained models.

Credit should go to the following: Tawsifur Rahman, Amith Khandakar, Muhammad A. Kadir, Khandaker R. Islam, Khandaker F. Islam, Zaid B. Mahbub, Mohamed Arselene Ayari, Muhammad E. H. Chowdhury. (2020) "Reliable Tuberculosis Detection using Chest X-ray with Deep Learning, Segmentation and Visualization". IEEE Access, Vol. 8, pp 191586 - 191601. DOI. 10.1109/ACCESS.2020.3031384. Paper Link

To view images please check image folders and references of each image are provided in the metadata.csv.

Research Team members and their affiliation Muhammad E. H. Chowdhury, PhD (mchowdhury@qu.edu.qa) Department of Electrical Engineering, Qatar University, Doha-2713, Qatar Tawsifur Rahman (tawsifurrahman.1426@gmail.com) Department of Electrical Engineering, Qatar University, Doha-2713, Qatar Amith Khandakar (amitk@qu.edu.qa) Department of Electrical Engineering, Qatar University, Doha-2713, Qatar Rashid Mazhar, MD Thoracic Surgery, Hamad General Hospital, Doha-3050, Qatar Muhammad Abdul Kadir, PhD Department of Biomedical Physics & Technology, University of Dhaka, Dhaka-1000, Bangladesh Zaid Bin Mahbub, PhD Department of Mathematics and Physics, North South University, Dhaka-1229, Bangladesh Khandakar R. Islam, MD Department of Orthodontics, Bangabandhu Sheikh Mujib Medical University, Dhaka-1000, Bangladesh

Contribution - This dataset contains CXR images of Normal (3500) and patients with TB (700 TB images in publicly accessible and 2800 TB images can be downloaded from NIAID TB portal[3] by signing an agreement). The TB database is collected from the source: 1. NLM dataset: National Library of Medicine (NLM) in the U.S. [1] has made two lung X-ray datasets publicly available: the Montgomery and Shenzhen datasets. 2. Belarus dataset: Belarus Set [2] was collected for a drug resistance study initiated by the National Institute of Allergy and Infectious Diseases, Ministry of Health, Republic of Belarus. 3. NIAID TB dataset: NIAID TB portal program dataset [3], which contains about 3000 TB positive CXR images from about 3087 cases. -Note: Due to the data-sharing restriction, we have to direct the potential user to NIAID website where you can get a data-sharing agreement signing option and you can get DICOM images from there easily. Weblink: https://tbportals.niaid.nih.gov/download-data 4. RSNA CXR dataset: RSNA pneumonia detection challenge dataset [4], which is comprised of about 30,000 chest X-ray images, where 10,000 images are normal and others are abnormal and lung opacity images.

This database has been used in the paper titled “Reliable Tuberculosis Detection using Chest X-ray with Deep Learning, Segmentation and Visualization” published in IEEE Access in 2020.

Objective - Researchers can use this database to produce useful and impactful scholarly work on TB, which can help in tackling this issue.

Citation - Please cite this database if you are using it for any scientific purpose: Tawsifur Rahman, Amith Khandakar, Muhammad A. Kadir, Khandaker R. Islam, Khandaker F. Islam, Zaid B. Mahbub, Mohamed Arselene Ayari, Muhammad E. H. Chowdhury. (2020) "Reliable Tuberculosis Detection using Chest X-ray with Deep Learning, Segmentation and Visualization". IEEE Access, Vol. 8, pp 191586 - 191601. DOI. 10.1109/ACCESS.2020.3031384.

References: [1] S. Jaeger, S. Candemir, S. Antani, Y.-X. J. Wáng, P.-X. Lu, and G. Thoma, "Two public chest X-ray datasets for computer-aided screening of pulmonary diseases," Quantitative imaging in medicine and surgery, vol. 4 (6), p. 475(2014) [2] B. P. Health. (2020). BELARUS TUBERCULOSIS PORTAL [Online]. Available: http://tuberculosis.by/. [Accessed on 09-June-2020] [3] NIAID TB portal program dataset [Online]. Available: https://tbportals.niaid.nih.gov/download-data. [4] kaggle. RSNA Pneumonia Detection Challenge [Online]. Available: https://www.kaggle.com/c/rsna-pneumonia-detection-challenge/data. [Accessed on 09-June-2020]

Data were updated on September 11, 2024.

ART data are made available as part of the National ART Surveillance System (NASS) that collects success rates, services, profiles and annual summary data from fertility clinics across the U.S. There are four datasets available: ART Services and Profiles, ART Patient and Cycle Characteristics, ART Success Rates, and ART Summary. All four datasets may be linked by “ClinicID.” ClinicID is a unique identifier for each clinic that reported cycles. The Summary dataset provides a full snapshot of clinic services and profile, patient characteristics, and ART success rates. It is worth noting that patient medical characteristics, such as age, diagnosis, and ovarian reserve, affect ART treatment’s success. Comparison of success rates across clinics may not be meaningful because of differences in patient populations and ART treatment methods. The success rates displayed in this dataset do not reflect any one patient’s chance of success. Patients should consult with a doctor to understand their chance of success based on their own characteristics.