This dataset provides values for MEDICAL DOCTORS reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Global Number of Medical Doctors by Country, 2023 Discover more data with ReportLinker!

According to Ahram online Egyptian doctors among top five foreign physicians who joined UK medical system in 2021: British report. Check: https://english.ahram.org.eg/News/478143.aspx In this dataset, we collected over 1000 doctor data from the appointment booking website.

You can use this data to practice:

1. Data cleaning
2. Data Analysis
3. Data visualization

Inspiration for the analysis:

1. Think about it as a patient and what is important for you to be a doctor like locations, fees, doctor rating, and so on.
2. think about it as a doctor if you want to buy a new clinic, about the fees, about waiting time, and so on.

Questions:

1. What are the most common specialties among Egyptian doctors in this dataset?
2. How many doctors are located in each governorate (province) of Egypt?
3. What is the average age of the doctors in this dataset?
4. How many male and female doctors are included in this dataset?
5. Which governorate has the highest number of doctors per capita?
6. What is the distribution of doctors across different specialties in each governorate?
7. Who are the top 10 doctors with the highest number of patients?
8. What is the average number of years of experience for doctors in each specialty?
9. How many doctors have international certifications or degrees?
10. What is the average consultation fee for doctors in each specialty?

Number of Doctors: Registered: Medical Council of India data was reported at 1,169.000 Person in 2014. This records a decrease from the previous number of 5,603.000 Person for 2013. Number of Doctors: Registered: Medical Council of India data is updated yearly, averaging 1,989.000 Person from Dec 2002 (Median) to 2014, with 13 observations. The data reached an all-time high of 5,603.000 Person in 2013 and a record low of 921.000 Person in 2004. Number of Doctors: Registered: Medical Council of India data remains active status in CEIC and is reported by Central Bureau of Health Intelligence. The data is categorized under India Premium Database’s Health Sector – Table IN.HLB001: Health Human Resources: Number of Doctors: Registered.

Egypt EG: Physicians: per 1000 People data was reported at 0.814 Ratio in 2014. This records a decrease from the previous number of 2.830 Ratio for 2010. Egypt EG: Physicians: per 1000 People data is updated yearly, averaging 1.364 Ratio from Dec 1960 (Median) to 2014, with 19 observations. The data reached an all-time high of 2.830 Ratio in 2010 and a record low of 0.391 Ratio in 1960. Egypt EG: Physicians: per 1000 People data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Egypt – Table EG.World Bank: Health Statistics. Physicians include generalist and specialist medical practitioners.; ; World Health Organization's Global Health Workforce Statistics, OECD, supplemented by country data.; Weighted average;

Medical Doctors in Japan increased to 2.67 per 1000 people in 2020 from 2.50 per 1000 people in 2016. This dataset includes a chart with historical data for Japan Medical Doctors.

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Electronic health records (EHRs) are a rich source of information for medical research and public health monitoring. Information systems based on EHR data could also assist in patient care and hospital management. However, much of the data in EHRs is in the form of unstructured text, which is difficult to process for analysis. Natural language processing (NLP), a form of artificial intelligence, has the potential to enable automatic extraction of information from EHRs and several NLP tools adapted to the style of clinical writing have been developed for English and other major languages. In contrast, the development of NLP tools for less widely spoken languages such as Swedish has lagged behind. A major bottleneck in the development of NLP tools is the restricted access to EHRs due to legitimate patient privacy concerns. To overcome this issue we have generated a citizen science platform for collecting artificial Swedish EHRs with the help of Swedish physicians and medical students. These artificial EHRs describe imagined but plausible emergency care patients in a style that closely resembles EHRs used in emergency departments in Sweden. In the pilot phase, we collected a first batch of 50 artificial EHRs, which has passed review by an experienced Swedish emergency care physician. We make this dataset publicly available as OpenChart-SE corpus (version 1) under an open-source license for the NLP research community. The project is now open for general participation and Swedish physicians and medical students are invited to submit EHRs on the project website (https://github.com/Aitslab/openchart-se), where additional batches of quality-controlled EHRs will be released periodically.

Dataset content

OpenChart-SE, version 1 corpus (txt files and and dataset.csv)

The OpenChart-SE corpus, version 1, contains 50 artificial EHRs (note that the numbering starts with 5 as 1-4 were test cases that were not suitable for publication). The EHRs are available in two formats, structured as a .csv file and as separate textfiles for annotation. Note that flaws in the data were not cleaned up so that it simulates what could be encountered when working with data from different EHR systems. All charts have been checked for medical validity by a resident in Emergency Medicine at a Swedish hospital before publication.

Codebook.xlsx

The codebook contain information about each variable used. It is in XLSForm-format, which can be re-used in several different applications for data collection.

suppl_data_1_openchart-se_form.pdf

OpenChart-SE mock emergency care EHR form.

suppl_data_3_openchart-se_dataexploration.ipynb

This jupyter notebook contains the code and results from the analysis of the OpenChart-SE corpus.

More details about the project and information on the upcoming preprint accompanying the dataset can be found on the project website (https://github.com/Aitslab/openchart-se).

🔗 This dataset was used in the following publication:

A. R. Mia, M. A. -A. -S. Chowdhury, A. A. Mamun, A. M. Ruddra and N. T. Tanny, "**A Deep Neural Network Approach with Pioneering Local Dataset to Recognize Doctor's Handwritten Prescription in Bangladesh**," 2024 International Conference on Advances in Computing, Communication, Electrical, and Smart Systems (iCACCESS), Dhaka, Bangladesh, 2024.

DOI: 10.1109/ACCESS.2024.10499631

💊 Prescription Word Dataset for Machine Learning

This dataset was created by extracting and processing prescription images to support educational research and experimentation in machine learning models, particularly for text recognition and classification in healthcare contexts.

📸 Dataset Creation Process

To transform the prescription images into a structured dataset suitable for machine learning, a specialized word detection algorithm was employed. This code segmented the prescription images into individual words, converting the data into a format that facilitates accurate recognition by ML models.

• Word segmentation was the first crucial step.
• Each word was manually screened to retain only pharmaceutical terms.
• All non-drug terms were discarded.
• Manual labelling was performed by multiple team members independently to ensure accuracy.

📊 Dataset Details

• Total Words Extracted: 4,680
• File Format: Excel (.xlsx) and CSV (.csv)
• Number of Classes (Medicines): 78

📚 Drug Names Included

Beklo, Maxima, Leptic, Esoral, Omastin, Esonix, Canazole, Fixal, Progut, Diflu, Montair, Flexilax, Maxpro, Vifas, Conaz, Fexofast, Fenadin, Telfast, Dinafex, Ritch, Renova, Flugal, Axodin, Sergel, Nexum, Opton, Nexcap, Fexo, Montex, Exium, Lumona, Napa, Azithrocin, Atrizin, Monas, Nidazyl, Metsina, Baclon, Rozith, Bicozin, Ace, Amodis, Alatrol, Napa Extend, Rivotril, Montene, Filmet, Aceta, Tamen, Bacmax, Disopan, Rhinil, Flamyd, Metro, Zithrin, Candinil, Lucan-R, Backtone, Bacaid, Etizin, Az, Romycin, Azyth, Cetisoft, Dancel, Tridosil, Nizoder, Ketoral, Ketocon, Ketotab, Ketozol, Denixil, Provair, Odmon, Baclofen, MKast, Trilock, Flexibac.

These classes represent commonly prescribed pharmaceutical names likely to appear in handwritten prescriptions.

🧪 Usage

This dataset is ideal for:

• Text classification
• Optical character recognition (OCR)
• Named entity recognition (NER)
• Deep learning model training and evaluation

⚠️ Note: This dataset is free to use for educational and research purposes only.

Update — December 7, 2014. – Evidence-based medicine (EBM) is not working for many reasons, for example: 1. Incorrect in their foundations (paradox): hierarchical levels of evidence are supported by opinions (i.e., lowest strength of evidence according to EBM) instead of real data collected from different types of study designs (i.e., evidence). http://dx.doi.org/10.6084/m9.figshare.1122534 2. The effect of criminal practices by pharmaceutical companies is only possible because of the complicity of others: healthcare systems, professional associations, governmental and academic institutions. Pharmaceutical companies also corrupt at the personal level, politicians and political parties are on their payroll, medical professionals seduced by different types of gifts in exchange of prescriptions (i.e., bribery) which very likely results in patients not receiving the proper treatment for their disease, many times there is no such thing: healthy persons not needing pharmacological treatments of any kind are constantly misdiagnosed and treated with unnecessary drugs. Some medical professionals are converted in K.O.L. which is only a puppet appearing on stage to spread lies to their peers, a person supposedly trained to improve the well-being of others, now deceits on behalf of pharmaceutical companies. Probably the saddest thing is that many honest doctors are being misled by these lies created by the rules of pharmaceutical marketing instead of scientific, medical, and ethical principles. Interpretation of EBM in this context was not anticipated by their creators. “The main reason we take so many drugs is that drug companies don’t sell drugs, they sell lies about drugs.” ―Peter C. Gøtzsche “doctors and their organisations should recognise that it is unethical to receive money that has been earned in part through crimes that have harmed those people whose interests doctors are expected to take care of. Many crimes would be impossible to carry out if doctors weren’t willing to participate in them.” —Peter C Gøtzsche, The BMJ, 2012, Big pharma often commits corporate crime, and this must be stopped. Pending (Colombia): Health Promoter Entities (In Spanish: EPS ―Empresas Promotoras de Salud).

1. Misinterpretations New technologies or concepts are difficult to understand in the beginning, it doesn’t matter their simplicity, we need to get used to new tools aimed to improve our professional practice. Probably the best explanation is here in these videos (credits to Antonio Villafaina for sharing these videos with me). English https://www.youtube.com/watch?v=pQHX-SjgQvQ&w=420&h=315 Spanish https://www.youtube.com/watch?v=DApozQBrlhU&w=420&h=315 ----------------------- Hypothesis: hierarchical levels of evidence based medicine are wrong Dear Editor, I have data to support the hypothesis described in the title of this letter. Before rejecting the null hypothesis I would like to ask the following open question:Could you support with data that hierarchical levels of evidence based medicine are correct? (1,2) Additional explanation to this question: – Only respond to this question attaching publicly available raw data.– Be aware that more than a question this is a challenge: I have data (i.e., evidence) which is contrary to classic (i.e., McMaster) or current (i.e., Oxford) hierarchical levels of evidence based medicine. An important part of this data (but not all) is publicly available. References
2. Ramirez, Jorge H (2014): The EBM challenge. figshare. http://dx.doi.org/10.6084/m9.figshare.1135873
3. The EBM Challenge Day 1: No Answers. Competing interests: I endorse the principles of open data in human biomedical research Read this letter on The BMJ – August 13, 2014.http://www.bmj.com/content/348/bmj.g3725/rr/762595Re: Greenhalgh T, et al. Evidence based medicine: a movement in crisis? BMJ 2014; 348: g3725. _ Fileset contents Raw data: Excel archive: Raw data, interactive figures, and PubMed search terms. Google Spreadsheet is also available (URL below the article description). Figure 1. Unadjusted (Fig 1A) and adjusted (Fig 1B) PubMed publication trends (01/01/1992 to 30/06/2014). Figure 2. Adjusted PubMed publication trends (07/01/2008 to 29/06/2014) Figure 3. Google search trends: Jan 2004 to Jun 2014 / 1-week periods. Figure 4. PubMed publication trends (1962-2013) systematic reviews and meta-analysis, clinical trials, and observational studies.
   Figure 5. Ramirez, Jorge H (2014): Infographics: Unpublished US phase 3 clinical trials (2002-2014) completed before Jan 2011 = 50.8%. figshare.http://dx.doi.org/10.6084/m9.figshare.1121675 Raw data: "13377 studies found for: Completed | Interventional Studies | Phase 3 | received from 01/01/2002 to 01/01/2014 | Worldwide". This database complies with the terms and conditions of ClinicalTrials.gov: http://clinicaltrials.gov/ct2/about-site/terms-conditions Supplementary Figures (S1-S6). PubMed publication delay in the indexation processes does not explain the descending trends in the scientific output of evidence-based medicine. Acknowledgments I would like to acknowledge the following persons for providing valuable concepts in data visualization and infographics:
4. Maria Fernanda Ramírez. Professor of graphic design. Universidad del Valle. Cali, Colombia.
5. Lorena Franco. Graphic design student. Universidad del Valle. Cali, Colombia. Related articles by this author (Jorge H. Ramírez)
6. Ramirez JH. Lack of transparency in clinical trials: a call for action. Colomb Med (Cali) 2013;44(4):243-6. URL: http://www.ncbi.nlm.nih.gov/pubmed/24892242
7. Ramirez JH. Re: Evidence based medicine is broken (17 June 2014). http://www.bmj.com/node/759181
8. Ramirez JH. Re: Global rules for global health: why we need an independent, impartial WHO (19 June 2014). http://www.bmj.com/node/759151
9. Ramirez JH. PubMed publication trends (1992 to 2014): evidence based medicine and clinical practice guidelines (04 July 2014). http://www.bmj.com/content/348/bmj.g3725/rr/759895 Recommended articles
10. Greenhalgh Trisha, Howick Jeremy,Maskrey Neal. Evidence based medicine: a movement in crisis? BMJ 2014;348:g3725
11. Spence Des. Evidence based medicine is broken BMJ 2014; 348:g22
12. Schünemann Holger J, Oxman Andrew D,Brozek Jan, Glasziou Paul, JaeschkeRoman, Vist Gunn E et al. Grading quality of evidence and strength of recommendations for diagnostic tests and strategies BMJ 2008; 336:1106
13. Lau Joseph, Ioannidis John P A, TerrinNorma, Schmid Christopher H, OlkinIngram. The case of the misleading funnel plot BMJ 2006; 333:597
14. Moynihan R, Henry D, Moons KGM (2014) Using Evidence to Combat Overdiagnosis and Overtreatment: Evaluating Treatments, Tests, and Disease Definitions in the Time of Too Much. PLoS Med 11(7): e1001655. doi:10.1371/journal.pmed.1001655
15. Katz D. A-holistic view of evidence based medicinehttp://thehealthcareblog.com/blog/2014/05/02/a-holistic-view-of-evidence-based-medicine/ ---

This dataset is the public medical text record (progress notes) written in Japanese.

Any researchers can use this dataset without privacy issues.

CC BY-NC 4.0

crowd.zip: 9,756 pseudo progress notes written by crowd workers

crowd_evaluated.zip: 83 pseudo progress notes with authentic quality written by crowd workers

MD.zip: 19 pseudo progress notes written by medical doctors

Reference:

Kagawa, R., Baba, Y., & Tsurushima, H. (2021, December). A practical and universal framework for generating publicly available medical notes of authentic quality via the power of crowds. In 2021 IEEE International Conference on Big Data (Big Data) (pp. 3534-3543). IEEE.

http://hdl.handle.net/2241/0002002333

The supplemental files of the paper are here: https://github.com/rinabouk/HMData2021

Nigeria NG: Physicians: per 1000 People data was reported at 0.395 Ratio in 2010. This records an increase from the previous number of 0.376 Ratio for 2009. Nigeria NG: Physicians: per 1000 People data is updated yearly, averaging 0.192 Ratio from Dec 1960 (Median) to 2010, with 19 observations. The data reached an all-time high of 0.395 Ratio in 2010 and a record low of 0.017 Ratio in 1960. Nigeria NG: Physicians: per 1000 People data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Nigeria – Table NG.World Bank: Health Statistics. Physicians include generalist and specialist medical practitioners.; ; World Health Organization's Global Health Workforce Statistics, OECD, supplemented by country data.; Weighted average;

This dataset was created for a project that assessed Twitter data from physicians posted anonymously by administrators of a specific Twitter user page to better understand physician perspectives and sentiments about COVID-19 in the United States.

Tweet identifiers are contained in the 'tweet_identifiers.csv file'

Other files contain sentiment analysis data; one file used vaderSentiment in Python 3, and the other file used NRC in R (see sources below for further information and use of these packages.

Hutto, C.J. & Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014.NRC Emotion Lexicon, Saif M. Mohammad and Peter D. Turney, NRC Technical Report, December 2013, Ottawa, Canada.Jockers ML (2015). Syuzhet: Extract Sentiment and Plot Arcs from Text. https://github.com/mjockers/syuzhet.Code used specifically for this project may be found at: https://github.com/sullkath/tweet_analysis

Government Doctor: West Bengal: Number of Doctors data was reported at 17,692.000 Person in 2022. This records an increase from the previous number of 90.000 Person for 2021. Government Doctor: West Bengal: Number of Doctors data is updated yearly, averaging 8,825.000 Person from Dec 2004 (Median) to 2022, with 11 observations. The data reached an all-time high of 17,692.000 Person in 2022 and a record low of 90.000 Person in 2021. Government Doctor: West Bengal: Number of Doctors data remains active status in CEIC and is reported by Central Bureau of Health Intelligence. The data is categorized under India Premium Database’s Health Sector – Table IN.HLB002: Health Human Resources: Number of Doctors: Government.

Pakistan PK: Physicians: per 1000 People data was reported at 0.978 Ratio in 2015. This records an increase from the previous number of 0.806 Ratio for 2014. Pakistan PK: Physicians: per 1000 People data is updated yearly, averaging 0.600 Ratio from Dec 1960 (Median) to 2015, with 28 observations. The data reached an all-time high of 0.978 Ratio in 2015 and a record low of 0.185 Ratio in 1960. Pakistan PK: Physicians: per 1000 People data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Pakistan – Table PK.World Bank: Health Statistics. Physicians include generalist and specialist medical practitioners.; ; World Health Organization's Global Health Workforce Statistics, OECD, supplemented by country data.; Weighted average;

Medical care for admitted patients is increasingly reallocated to physician assistants (PAs), because of an increased appreciation of continuity of care, pressure to deliver healthcare efficiently, and local shortages of medical doctors (MDs). A PA is a non-physician healthcare professional licensed to practice medicine in defined domains, with variable degrees of professional autonomy. PAs who are employed for medical care for admitted patients usually work in a team compromising both PAs and MDs (i.e. residents, staff physicians or hospitalists). Although there is a worldwide trend of an increase of PAs in the management of hospitalized patients, evidence about the consequences of reallocating inpatient care from MDs to PAs for healthcare outcomes is limited. This study aimed to determine the effects of substitution of inpatient care from MDs to PAs on patients’ lenght of stay, quality and safety of care, patient experiences and costs. Also the impact on guideline adherence on medication prescribing has been investigated. In a multicenter matched-controlled study, the traditional model in which only MDs are employed for inpatient care was compared with a mixed model in which besides MDs also PAs are employed. Thirty-four wards were recruited across the Netherlands. Patients were followed from admission till one month after discharge. In total, 2,307 patients were included

Objectives. Human resource management is the most important function in the management of organizations and significantly affects the quality of work-life (QWL). Recently, the health sector started to be interested in the QWL among doctors. The study aim was to assess the QWL among Polish medical residents. Methods. The questionnaire for the medical residents was prepared using data acquired from a review of the international literature. In October 2017, the questionnaires were completed twice by 10 residents with a 2-week interval to assess the inter-rater reliability. The online questionnaire was distributed between April and May 2018. Results. A total of 243 doctors responded, over one-third of whom were men. The QWL was very high for 2.06% of the participants, high for 23.87%, moderate for 27.16%, low for 38.27% and very low for 8.64%. Among the factors that significantly relate to the QWL are the number of working hours per week (p = 0.007) and the general quality of life (p = 0.000). Conclusion. Low QWL is the result of inadequate management in Polish hospitals and residents’ QWL still needs to be improved. We propose to conduct such a survey periodically among all young medical doctors to systematically improve their QWL.

Ukraine UA: Physicians: per 1000 People data was reported at 3.000 Ratio in 2014. This records a decrease from the previous number of 3.506 Ratio for 2013. Ukraine UA: Physicians: per 1000 People data is updated yearly, averaging 3.686 Ratio from Dec 1980 (Median) to 2014, with 28 observations. The data reached an all-time high of 4.407 Ratio in 1995 and a record low of 2.951 Ratio in 2003. Ukraine UA: Physicians: per 1000 People data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Ukraine – Table UA.World Bank: Health Statistics. Physicians include generalist and specialist medical practitioners.; ; World Health Organization's Global Health Workforce Statistics, OECD, supplemented by country data.; Weighted average;

AI virtual assistants have significant potential to alleviate the pressure on overly burdened healthcare systems by enabling patients to self-assess their symptoms and to seek further care when appropriate. For these systems to make a meaningful contribution to healthcare globally, they must be trusted by patients and healthcare professionals alike, and service the needs of patients in diverse regions and segments of the population. We developed an AI virtual assistant which provides patients with triage and diagnostic information. Crucially, the system is based on a generative model, which allows for relatively straightforward re-parameterization to reflect local disease and risk factor burden in diverse regions and population segments. This is an appealing property, particularly when considering the potential of AI systems to improve the provision of healthcare on a global scale in many regions and for both developing and developed countries. We performed a prospective validation study of the accuracy and safety of the AI system and human doctors. Importantly, we assessed the accuracy and safety of both the AI and human doctors independently against identical clinical cases and, unlike previous studies, also accounted for the information gathering process of both agents. Overall, we found that the AI system is able to provide patients with triage and diagnostic information with a level of clinical accuracy and safety comparable to that of human doctors. Through this approach and study, we hope to start building trust in AI-powered systems by directly comparing their performance to human doctors, who do not always agree with each other on the cause of patients’ symptoms or the most appropriate triage recommendation.

This dataset delves into the relationships between key indicators—Gross Domestic Product (GDP), Physician Density, and Literacy Rates. Understand how economic strength, healthcare access, and education intersect on a global scale.

Columns:

**Country: ** Names of the countries in focus.

GDP (Gross Domestic Product): - Shows a country's economic output. - Indicates overall economic health and productivity. - Presented in a standardized currency or index.

Physician Density: - Reveals the number of doctors per population. - Highlights a country's healthcare accessibility. - Gives insights into medical intervention capacity.

Literacy Rate: - Reflects the percentage of the population that can read and write. - Shows educational attainment and societal development. - Influences workforce productivity and social progress.

This dataset is a user-friendly resource for anyone curious about the connections between economic, healthcare, and educational factors across different countries. Ideal for researchers, policymakers, and enthusiasts looking to grasp the global dynamics of development.