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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.

Here are a few use cases for this project:

1. Medical Transcription Assistant: This computer vision model can be used in transcription services, helping transcribe handwritten prescriptions into digital text. Doctors, pharmacists, and healthcare professionals can use such transcriptions for digital record-keeping, data analysis, sharing medical information, and patient follow-ups.

2. Medicine Inventory Management: The model can help pharmacies automate their drug inventory management. By identifying medicines names in prescriptions, the software can update inventory data in real time, ensuring that stocks are always updated and adequate.

3. Drug Interaction Analysis: The model can be applied in an application that identifies potential drug interactions for a given patient's multiple prescriptions. By recognizing the names of medicines, it could cross-check them with a database of known drug interactions, alerting the pharmacist or patient about potential risks.

4. Telemedicine Applications: This model can be useful in telemedicine scenarios where patients send images of their prescriptions. It can analyze the prescription, identify the drug names, and forward the information to online pharmacies for home deliveries or to doctors/nurses for tele-consultations.

5. Pharma Market Research: Companies can use this model to analyze prescriptions to understand the most commonly prescribed drugs, aiding in market research and trending analysis in pharmaceutical industries.

Malaysia Number of Doctors: Selangor data was reported at 11,711.000 Person in 2017. This records an increase from the previous number of 9,483.000 Person for 2016. Malaysia Number of Doctors: Selangor data is updated yearly, averaging 4,420.000 Person from Dec 1997 (Median) to 2017, with 21 observations. The data reached an all-time high of 11,711.000 Person in 2017 and a record low of 1,834.000 Person in 1997. Malaysia Number of Doctors: Selangor data remains active status in CEIC and is reported by Department of Statistics. The data is categorized under Global Database’s Malaysia – Table MY.G057: Health Statistics: Number of Doctor, Dentists & Nurses.

Laos LA: Physicians: per 1000 People data was reported at 0.491 Ratio in 2014. This records an increase from the previous number of 0.447 Ratio for 2013. Laos LA: Physicians: per 1000 People data is updated yearly, averaging 0.277 Ratio from Dec 1960 (Median) to 2014, with 16 observations. The data reached an all-time high of 0.729 Ratio in 1985 and a record low of 0.020 Ratio in 1960. Laos LA: Physicians: per 1000 People data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Laos – Table LA.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;

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.

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/ ---

Stop relying on outdated and inaccurate databases and lists and let Wiza be your source of truth for all plastics outreach.

Why we're different: Healthcare Professionals are not easy to get in contact with - Wiza is not a static database that gets refreshed on occasion. Every datapoint is sourced and verified the moment that you receive the information. We verify deliverability of every single email ahead of providing the data, and we ensure that each person in your dataset has 100% data accuracy by leveraging Linkedin Data sourced through their live Linkedin profile.

Key Features:

Comprehensive Data Coverage: Stop contacting the same healthcare professionals as everyone else. Wiza's search fund Data is sourced live, not stored in a limited database. We source the contact data in real-time based on everyone who is currently a plastic surgeon on Linkedin at the time of request.

High-Quality, Accurate Data: Wiza ensures accuracy of all datapoints by taking a few key steps that other data providers fail to take: (1) Every email is SMTP verified ahead of delivery, ensuring they will not bounce (2) Every person's Linkedin profile is checked live to ensure we have 100% job title, company, location, etc. accuracy, ahead of providing any data (3) Phone numbers are constantly being verified with AI to ensure accuracy

Linkedin Data: Wiza is able to provide Linkedin Data points, sourced live from each person's Linkedin profile, including Subtitle, Bio, Job Title, Job Description, Skills, Languages, Certifications, Work History, Education, Open to Work, Premium Status, and more!

Personal Data: Wiza has access to industry leading volumes of B2C Contact Data, meaning you can find gmail/yahoo/hotmail email addresses, and mobile phone number data to contact your plastic surgeons.

Here are a few use cases for this project:

1. Medication Transcription: This computer vision model can be used by pharmacies or hospitals to transcribe doctors' handwritten prescriptions into digital form, ensuring that the correct medication and dosage information is captured and understood accurately.

2. Drug Adherence Verification: The model could be employed to verify if patients are taking their prescribed medication on time. It could be used to scan images of medication bottles or packaging, confirming that the correct medication is being taken and identifying any potential medication errors.

3. Pharmaceutical Research: Researchers can use this model to analyze large volumes of prescriptions, helping them understand patterns in medications prescribed for specific diseases. This can lead to valuable insights into treatment outcomes, overprescription, or underprescription practices.

4. Integrated Healthcare Systems: In a more integrated healthcare system, this model could work as part of a comprehensive platform. It could allow the instant uploading of prescriptions into a patient's digital health record, helping doctors to track medication history and potentially flagging drug interactions.

5. Drug Inventory Management: Pharmacies could use the model to automate the process of inventory management by scanning doctors' prescriptions. The system could automatically update stocks of various drugs, helping with demand forecasting and ensuring vital drugs are always available.

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;

The purpose of the study: to analyse Lithuanian residents opinion about peculiarities of communication between doctors and patients. Major investigated questions: respondents were asked when was the last time they went to see a doctor. It respondents visited a doctor in the last 12 months, they were asked to indicate how long was the time period between registration to see a doctor and actual visit. It was analysed how long respondents had to wait at their last visit from the time appointed or from the time when they got to a health institution to the time a doctor got to them. It was questioned what was the main purpose for respondents last visit to a doctor. After question block was presented, respondents were asked for what complains, illnesses they visited a doctor the last time. Respondents were asked to evaluate a doctor whom they visited the last time. After question block was presented, peculiarities of doctor interaction with respondents (patients) at the time of their last visit was analysed. Respondents were asked if they think that a doctor can take care of their health. It was questioned how likely is that respondents would search for information about health using electronic tools. Respondents were asked if they are currently using medicines which were prescribed by a doctor to whom they visited the last time. Respondents, who uses prescribed medicines, were asked to indicate how often they use them exactly like doctor prescribed. Socio-demographic characteristics: gender, age, monthly family income per one family member, employment.

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 28 January 2022.

--- 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 ---

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

The number of physicians in Ghana was forecast to continuously increase between 2024 and 2029 by in total 2.1 thousand physicians (+26.92 percent). After the tenth consecutive increasing year, the number of physicians is estimated to reach 9.94 thousand physicians and therefore a new peak in 2029. Depicted here is the estimated number of physicians in the geographical unit at hand. Thereby physicians include medical specialists as well as general practitioners.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 up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of physicians in countries like Ivory Coast and Nigeria.

ABSTRACT The Welcoming and Assessment Module (Módulo de Acolhimento e Avaliação) is the first contact of the candidate for the More Doctors Program with the Brazilian health system and composes one of the formative cycles of the More Doctors for Brazil Project, the provision of physicians in priority areas of the Unified Health System (SUS). It is a selective training for Brazilian physicians graduated abroad and for foreigners who wish to participate in the More Doctors Program. The organization and success of the Welcoming and Assessment Module are essential for the continuity of the subsequent formative cycles, foreseen in the Project. This article describes and analyzes the planning and operationalization of the Welcoming and Assessment Modules taken place in Brazil and in Cuba, from 2014 to 2017, recommending adjustments in the management of education so that health work can be qualified and resolutive and, thus, contribute to the strengthening of primary care in Brazil.

The number of physicians in the United Arab Emirates was forecast to continuously increase between 2024 and 2029 by in total 8.2 thousand physicians (+23.71 percent). After the fifteenth consecutive increasing year, the number of physicians is estimated to reach 42.78 thousand physicians and therefore a new peak in 2029. Notably, the number of physicians of was continuously increasing over the past years.Depicted here is the estimated number of physicians in the geographical unit at hand. Thereby physicians include medical specialists as well as general practitioners.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 up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of physicians in countries like Bahrain and Israel.

The average number of physicians per 1,000 inhabitants in Colombia was forecast to continuously increase between 2024 and 2029 by in total 0.2 physicians (+8.2 percent). After the seventh consecutive increasing year, the number of physicians is estimated to reach 2.68 physicians and therefore a new peak in 2029. Depicted here is the average number of physicians per one thousand people. Thereby physicians include medical specialists as well as general practitioners. A data point thereby denotes the weighted average across the depicted geographical unit.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 up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

Workplace violence (WPV) is a globally prevailing public health concerns among healthcare workers. Workplace violence includes occupational abuse (physical, sexual, verbal and psychological), threats or harm among health workers, and workplace harassment. It is important to identify the prevalence of workplace violence at the workplace. Therefore, this study aimed to assess workplace violence and its associated factors among healthcare workers at a tertiary hospital in Kathmandu, Nepal. A descriptive cross-sectional study was carried out among 369 health care workers in a tertiary hospital in Kathmandu. A semi-structured questionnaire was used for data collection. Data was entered and analyzed using SPSS v20. Descriptive statistics were used to assess workplace violence and other independent variables. Bivariate and multivariate logistic regression model was used to examine the factors associated with workplace violence. The prevalence of verbal violence was highest among doctors (34.3%) and nurses (52.8%) followed by bullied/mobbed among doctors (11.9%) and nurses (17%) any time in the past. Experience of any type of workplace violence in the past among doctor was 45.5% and among nurses was 54% while 35.8% doctors and 46.8% nurses had experienced it in the past 12 months. Patients and relatives of patient were major perpetrator for physical and verbal violence while management and staff members were major perpetrators for bullying/mobbing. Participants marital status, work experience, posted department, nature of work shift, frequency of night shift and working hours per week showed statistically significant association with the experience of workplace violence within past 12 months (p

This statistic shows a ranking of the estimated average number of physicians per 1,000 inhabitants in 2020 in Latin America, differentiated by country.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).

The share of the population with overweight in Saudi Arabia was forecast to continuously increase between 2024 and 2029 by in total 2.2 percentage points. After the fifteenth consecutive increasing year, the overweight population share is estimated to reach 74.67 percent and therefore a new peak in 2029. Notably, the share of the population with overweight of was continuously increasing over the past years.Overweight is defined as a body mass index (BMI) of more than 25.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 up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the share of the population with overweight in countries like Kuwait and Jordan.