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?

This table contains 267456 series, with data for years 2000 - 2000 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (199 items: Canada; Health and Community Services Eastern Region; Newfoundland and Labrador (Peer group D); Health and Community Services St. John's Region; Newfoundland and Labrador (Peer group H); Newfoundland and Labrador ...) Age group (14 items: Total; 12 years and over; 12-19 years; 15-19 years; 12-14 years ...) Sex (3 items: Both sexes; Females; Males ...) Contact with medical doctors (4 items: Total population for the variable contact with medical doctors; Contact with medical doctors in past 12 months; Contact with medical doctors; not stated; No contact with medical doctors in past 12 months ...) Characteristics (8 items: Number of persons; Low 95% confidence interval - number of persons; High 95% confidence interval - number of persons; Coefficient of variation for number of persons ...).

This dataset contains three healthcare datasets in Hindi and Punjabi, translated from English. The datasets cover medical diagnoses, disease names, and related healthcare information. The data has been carefully cleaned and formatted to ensure accuracy and usability for various applications, including machine learning, NLP, and healthcare analysis.

Diagnosis: Description of the medical condition or disease. Symptoms: List of symptoms associated with the diagnosis. Treatment: Common treatments or recommended procedures. Severity: Severity level of the disease (e.g., mild, moderate, severe). Risk Factors: Known risk factors associated with the condition. Language: Specifies the language of the dataset (Hindi, Punjabi, or English). The purpose of these datasets is to facilitate research and development in regional language processing, especially in the healthcare sector.

Column Descriptions: Original Data Columns: patient_id – Unique identifier for each patient. age – Age of the patient. gender – Gender of the patient (e.g., Male/Female/Other). Diagnosis – The diagnosed medical condition or disease. Remarks – Additional notes or comments from the doctor. doctor_id – Unique identifier for the doctor treating the patient. Patient History – Medical history of the patient, including previous conditions. age_group – Categorized age group (e.g., Child, Adult, Senior). gender_numeric – Numeric encoding for gender (e.g., 0 = Female, 1 = Male). symptoms – List of symptoms reported by the patient. treatment – Recommended treatment or medication. timespan – Duration of the illness or treatment period. Diagnosis Category – General category of the diagnosis (e.g., Cardiovascular, Neurological). Pseudonymized Data Columns: These columns replace personally identifiable information with anonymized versions for privacy compliance:

Pseudonymized_patient_id – An anonymized patient identifier. Pseudonymized_age – Anonymized age value. Pseudonymized_gender – Anonymized gender field. Pseudonymized_Diagnosis – Diagnosis field with anonymized identifiers. Pseudonymized_Remarks – Anonymized doctor notes. Pseudonymized_doctor_id – Anonymized doctor identifier. Pseudonymized_Patient History – Anonymized version of patient history. Pseudonymized_age_group – Anonymized version of age groups. Pseudonymized_gender_numeric – Anonymized numeric encoding of gender. Pseudonymized_symptoms – Anonymized symptom descriptions. Pseudonymized_treatment – Anonymized treatment descriptions. Pseudonymized_timespan – Anonymized illness/treatment duration. Pseudonymized_Diagnosis Category – Anonymized category of diagnosis.

Background The purpose of this study is to explore laypersons' attitudes toward the use of archived (existing) materials such as medical records and biological samples and to compare them with the attitudes of physicians who are involved in medical research. Methods Three focus group interviews were conducted, in which seven Japanese male members of the general public, seven female members of the general public and seven physicians participated. Results It was revealed that the lay public expressed diverse attitudes towards the use of archived information and samples without informed consent. Protecting a subject's privacy, maintaining confidentiality, and communicating the outcomes of studies to research subjects were regarded as essential preconditions if researchers were to have access to archived information and samples used for research without the specific informed consent of the subjects who provided the material. Although participating physicians thought that some kind of prior permission from subjects was desirable, they pointed out the difficulties involved in obtaining individual informed consent in each case. Conclusions The present preliminary study indicates that the lay public and medical professionals may have different attitudes towards the use of archived information and samples without specific informed consent. This hypothesis, however, is derived from our focus groups interviews, and requires validation through research using a larger sample.

Despite decades of low utilization, telemedicine adoption expanded at an unprecedented rate during the COVID-19 pandemic. This study examined quantitative and qualitative data provided by a national online sample of 228 practicing physicians (64% were women, and 75% were White) to identify facilitators and barriers to the adoption of telemedicine in the United States (U.S.) at the beginning of the COVID-19 pandemic. Logistic regressions were used to predict the most frequently endorsed (20% or more) barriers and facilitators based on participant demographics and practice characteristics. The top five reported barriers were: lack of patient access to technology (77.6%), insufficient insurance reimbursement (53.5%), diminished doctor-patient relationship (46.9%), inadequate video/audio technology (46.1%), and diminished quality of delivered care (42.1%). The top five reported facilitators were: better access to care (75.4%), increased safety (70.6%), efficient use of time (60.5%), lower cost for patients (43%), and effectiveness (28.9%). Physicians’ demographic and practice setting characteristics significantly predicted their endorsement of telemedicine barriers and facilitators. Older physicians were less likely to endorse inefficient use of time (p < 0.001) and potential for medical errors (p = 0.034) as barriers to telemedicine use compared to younger physicians. Physicians working in a medical center were more likely to endorse inadequate video/audio technology (p = 0.037) and lack of patient access to technology (p = 0.035) as a barrier and more likely to endorse lower cost for patients as a facilitator (p = 0.041) than providers working in other settings. Male physicians were more likely to endorse inefficient use of time as a barrier (p = 0.007) than female physicians, and White physicians were less likely to endorse lower costs for patients as a facilitator (p = 0.012) than physicians of color. These findings provide important context for future implementation strategies for healthcare systems attempting to increase telemedicine utilization.

This table contains 6720 series, with data for years 1994 - 1998 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (not all combinations are available): Geography (5 items: Territories; Yukon; Northwest Territories including Nunavut; Northwest Territories ...), Age group (14 items: Total; 12 years and over; 12-19 years; 12-14 years; 15-19 years ...), Sex (3 items: Both sexes; Females; Males ...), Contact with medical doctors (4 items: Total population for the variable contact with medical doctors; Contact with medical doctors in past 12 months; Contact with medical doctors; not stated; No contact with medical doctors in past 12 months ...), Characteristics (8 items: Number of persons; High 95% confidence interval - number of persons; Coefficient of variation for number of persons; Low 95% confidence interval - number of persons ...).

This table contains 127008 series, with data for years 2005 - 2005 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (126 items: Canada; Newfoundland and Labrador; Central Regional Integrated Health Authority; Newfoundland and Labrador; Eastern Regional Integrated Health Authority; Newfoundland and Labrador ...) Age group (6 items: Total; 12 years and over; 12 to 19 years ...) Sex (3 items: Both sexes; Males; Females ...) Regular medical doctor (7 items: Total population for the variable regular medical doctor; Has not looked for a regular medical doctor; Cannot find a regular medical doctor; Has a regular medical doctor ...) Characteristics (8 items: Number of persons; Coefficient of variation for number of persons; High 95% confidence interval; number of persons; Low 95% confidence interval; number of persons ...).

Associations between the gender of physicians and the perspective of patients on their opportunity to engage with SDM.

The dataset contains detailed records of hospital admissions and discharges. It includes information about the type of admission, source of shift, patient demographics, diagnosis, admission and discharge dates, length of stay, and consulting doctors. Here is a detailed description of each column:

adm_type: Indicates the type of admission (e.g., "Shift From"). shift_from: Specifies the source from where the patient was shifted (e.g., "ER" for Emergency Room, "Neu" for Neurology). ssc: Sehat sahulat card insurance yes or no. yr_nae: Likely represents the year of the admission event. m_no: Monthly Admision number. mrn: Medical record number, a unique identifier for each patient. pt_name: Name of the patient. sex: Gender of the patient (e.g., "M" for male, "F" for female). disease: Diagnosis or condition of the patient (e.g., "STEMI", "ADHF"). D.O.A: Date of admission in various formats. D.O.D: Date of discharge or death, also in various formats. status: Discharge status of the patient (e.g., "Discharge"). consultant: Name of the consulting doctor responsible for the patient. L.O.S: Length of stay in the hospital, measured in days. This dataset can be used to analyze trends and patterns in hospital admissions, lengths of stay, common diseases, and patient outcomes over time.

Data Column adm_type: Indicates the type of admission (e.g., "Shift From"). shift_from: Specifies the source from where the patient was shifted (e.g., "ER" for Emergency Room, "Neu" for Neurology). ssc: Sehat Sahulat Card insurance or Health card of KPK province. yr_nae: Likely represents the year of the admission event. m_no: Month number of the admission event. mrn: Medical record number, a unique identifier for each patient. pt_name: Name of the patient. sex: Gender of the patient (e.g., "M" for male, "F" for female). disease: Diagnosis or condition of the patient (e.g., "STEMI", "ADHF"). D.O.A: Date of admission in various formats. D.O.D: Date of discharge or death, also in various formats. status: Discharge status of the patient (e.g., "Discharge"). consultant: Name of the consulting doctor responsible for the patient. L.O.S: Length of stay in the hospital, measured in days.

This table contains 12960 series, with data for years 2000 - 2000 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (15 items: Canada; Nova Scotia; Prince Edward Island; Newfoundland and Labrador ...) Age group (12 items: Total; 15 years and over; 20-24 years; 20-34 years; 15-19 years ...) Sex (3 items: Both sexes; Females; Males ...) Patient satisfaction - family doctor or other physician care (3 items: Received family doctor or other physician care in past 12 months; Very or somewhat satisfied with family doctor or other physician care received; Quality of family doctor or other physician care received rated as excellent or good ...) Characteristics (8 items: Number of persons; Low 95% confidence interval - number of persons; Coefficient of variation for number of persons; High 95% confidence interval - number of persons ...).

This table contains 267456 series, with data for years 2000 - 2000 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (199 items: Canada; Health and Community Services Eastern Region; Newfoundland and Labrador (Peer group D); Health and Community Services St. John's Region; Newfoundland and Labrador (Peer group H); Newfoundland and Labrador ...) Age group (14 items: Total; 12 years and over; 12-19 years; 15-19 years; 12-14 years ...) Sex (3 items: Both sexes; Females; Males ...) Contact with medical doctors (4 items: Total population for the variable contact with medical doctors; Contact with medical doctors in past 12 months; Contact with medical doctors; not stated; No contact with medical doctors in past 12 months ...) Characteristics (8 items: Number of persons; Low 95% confidence interval - number of persons; High 95% confidence interval - number of persons; Coefficient of variation for number of persons ...).

Context

Content

A few years ago, the United States District Court of Houston had a case that arises under Title VII of the Civil Rights Act of 1964, 42 U.S.C. 200e et seq. The plaintiffs in this case were all female doctors at Houston College of Medicine who claimed that the College has engaged in a pattern and practice of discrimination against women in giving promotions and setting salaries. The Lead plaintiff in this action, a pediatrician and an assistant professor, was denied for promotion at the College. The plaintiffs had presented a set of data to show that female faculty at the school were less likely to be full professors, more likely to be assistant professors, and earn less money than men, on average.

1 Dept 1=Biochemistry/Molecular Biology 2=Physiology 3=Genetics 4=Pediatrics 5=Medicine 6=Surgery

2 Gender 1=Male, 0=Female

3 Clin 1=Primarily clinical emphasis, 0=Primarily research emphasis

4 Cert 1=Board certified, 0=not certified

5 Prate Publication rate (# publications on cv)/(# years between CV date and MD date)

6 Exper # years since obtaining MD

7 Rank 1=Assistant, 2=Associate, 3=Full professor (a proxy for productivity)

8 Sal94 Salary in academic year 1994

9 Sal95 Salary after increment to 1994

Acknowledgements

We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

One countermeasure against the increasing prevalence of multimorbidity is the need to provide clinical education and training that considers the characteristics of physicians. We conducted a questionnaire survey to determine the relationship between physicians’ characteristics and their approach to treating older patients with multimorbidity. A total of 3300 geriatric specialists and primary care specialists in Japan were enrolled. A 4-point Likert scale was used to score the following items: difficult diseases (43 items), difficult patient backgrounds (14 items), important clinical factors (32 items), and important clinical management (32 items). Exploratory factor analysis was performed to examine the constructs in each of the scales Diseases, Backgrounds, Clinical Factors, and Clinical Management, and group comparisons by physician characteristics were conducted. A total of 778 respondents were included in the analysis. Six factors for Diseases, two factors for Patient Background, four factors for Clinical Factors, and two factors for Clinical Management were explored as patterns. Group comparison between mean scores for each factor and the characteristics of responding physicians showed statistically significant differences in at least one factor for all patterns in terms of years of experience as a physician (26 years or less, 27 years or more), the clinical setting (providing or not providing home medical care), and sex (male or female). Our results suggest a need for clinical education and training that takes into account not only physicians’ experience and clinical setting, but also their sex.

Demographic characteristics of study sample.

This dataset includes detailed burnout assessments and related factors for 1,434 Bangladeshi physicians, representing one of the largest studies of physician burnout in a lower-middle-income country. The breadth of variables (demographic, occupational, and personal factors alongside standardized burnout measures) offers a rich resource for understanding contributors to burnout in healthcare settings. Researchers can reuse these data to perform secondary analyses, such as cross-cultural comparisons of burnout prevalence and predictors, meta-analyses, or validation studies of burnout interventions. The data can also be leveraged to test new hypotheses (e.g. mediating factors or subgroup differences) beyond the scope of the initial analysis. Stakeholders in healthcare management and policy can utilize the aggregated insights from this dataset to identify critical risk factors for physician burnout in Bangladesh and similar contexts. By highlighting factors like workload, workplace conflicts, job satisfaction, and personal well-being, the data can guide targeted interventions (such as wellness programs or organizational policy changes) to mitigate burnout. The burnout levels in this dataset were measured using the validated Maslach Burnout Inventory (MBI-HSS), ensuring reliability and comparability with international burnout research. In addition, the dataset contains responses to Likert-scale items on job-related sentiments (e.g., feeling annoyed with patients, perceived job security), which can facilitate nuanced analysis of how these sentiments correlate with burnout. Data were collected from 62 hospitals across urban and rural settings, capturing a diverse physician population from various specialties and job ranks. The large sample size and multicenter design enhance the generalizability of findings and allow subgroup analysis (for example, comparing burnout between different hospital types or between male and female physicians). Such granular analysis could help tailor interventions to specific groups of physicians most at risk.

This dataset is one of the sources of data visualisations available on the [Liberal Health Professionals] website(https://data.ameli.fr/pages/data-professionnels-sante-liberaux/). ### General information: The liberal health professions available in this dataset are: * the doctors (with more than twenty medical specialties); * dental surgeons** (including dentofacial orthopaedic specialists – ODF); * the women; * medical assistants with five professions: nurses, massage therapists, speech therapists, orthoptists, pedicures-podologists. They are health professionals active on 31 December of the year concerned and: * exercising their activity as a liberal; * in metropolitan France, Guadeloupe, French Guiana, Reunion, Martinique and Mayotte; * having received at least EUR 1 in fees; * whether they are contracted with the Sickness Insurance or not (when they generate a prescription reimbursed by the Sickness Insurance); * professionals in employment-retirement cumulation are counted in the workforce as long as they meet the previous conditions. This dataset presents information on the numbers of liberal health professionals: * by age group; * by sex. It also presents the densities of liberal health professionals. This dataset is complementary to the following dataset: Liberal health professionals: average ages, male/female share. Several territorial levels are available: national level (whole France), region, department. The data are derived from the National Health Data System (NSDS). For more information (source, field, definitions of modalities), visit the Method page of this site. ### Data update: The data proposed for download in the “Export” tab is updated every year (data from the whole of France since 2010). ### Export data in Excel format: Given the large number of rows in this dataset, export in Excel format is not possible, except by using upstream filters. Privilege export in CSV or JSON format.

В этом наборе данных представлены данные о количестве врачей в разбивке по возрастным группам и полу. Возрастные группы включают менее 35, 35-44, 45-54, 55-64, 65-74, 75+, в общей сложности врачей-женщин и врачей-мужчин. Данные в разбивке по возрасту и полу приведены для практикующих врачей. Если это невозможно, данные приводятся для профессионально активных врачей или врачей, имеющих лицензию на практику. This dataset provides data on the number of physicians by age groups and gender. Age groups include less than 35, 35-44, 45-54, 55-64, 65-74, 75+, for total, female and male physicians. The breakdown by age and gender is provided for practising physicians. If not possible, the data are reported for professionally active physicians or physicians licensed to practise.

Context

2017 India primary health care data. Includes health/patients/doctors/workers data , at state level.

Content

• allo-doc-PHCS_2017.csv -Allopathic doctor data 2017 with expanded columns.
• assistant-female-PHCS_2017.csv - Female assistant data 2017 with expanded columns. *assistant-male-PHCS_2017.csv - Male assistant data 2017 with expanded form.
• dis-subdis-doctors_2017.csv - Dis & Sub dis doctors data 2017 with expanded form. *facilities-CHCS_2017.csv - CHC facilities data 2017 with expanded form. *facilities-PHCS_2017.csv - PHC facilities 2017 with expanded form. *functioning-PHCS_2017.csv - PHC functioning data 2017 with expanded form. *infant-mortality-rate_2017.csv - Infant mortality rate data 2017 with expanded form. *nursing-staff-PHCS-CHCS_2017.csv - Nursing staff PHC data 2017 with expanded form. *pharmacists-PHCS-CHCS_2017.csv - Pharmacist PHC & CHC data 2017 with expanded form. *physician-CHCS_2017.csv - Physician CHC data 2017 with expanded form. *radiographers-CHCS_2017.csv - Radiographer CHC data 2017 with expanded form. *rural-area-covered-centre_2017.csv - Rural area covered b y centre data 2017 with expanded form. *rural-population-centre_2017.csv - Rural polulation covered data 2017 with expanded form. *surgeons-CHCS_2017.csv - Surgeons CHC data 2017 with expanded form. *villg-coveredby-centre_2017.csv - Villages covered by centres data 2017 with expanded form. *worker-female-subcen_2017.csv - Female workers data 2017 with expanded form. *worker-male-subcen_2017.csv - Male workers data 2017 with expanded form. *year-wise-CHCS.csv - Year wise CHC functioning data with expanded form. *year-wise-PHCS.csv - Year wise PHC functioning data with expanded form. *year-wise-Subcentre.csv - Year wise sub-centre functioning data with expanded form.

Acknowledgements

https://data.gov.in/catalog/rural-health-statistics-2017

Inspiration

What are the health care facilities in different states of India?

Early accurate diagnosis of patellofemoral pain syndrome (PFPS) is important to prevent the further development of the disease. However, traditional diagnostic methods for PFPS mostly rely on the subjective experience of doctors and subjective feelings of the patient, which do not have an accurate-unified standard, and the clinical accuracy is not high. With the development of artificial intelligence technology, artificial neural networks are increasingly applied in medical treatment to assist doctors in diagnosis, but selecting a suitable neural network model must be considered. In this paper, an intelligent diagnostic method for PFPS was proposed on the basis of a one-dimensional convolutional neural network (1D CNN), which used surface electromyography (sEMG) signals and lower limb joint angles as inputs, and discussed the model from three aspects, namely, accuracy, interpretability, and practicability. This article utilized the running and walking data of 41 subjects at their selected speed, including 26 PFPS patients (16 females and 10 males) and 16 painless controls (8 females and 7 males). In the proposed method, the knee flexion angle, hip flexion angle, ankle dorsiflexion angle, and sEMG signals of the seven muscles around the knee of three different data sets (walking data set, running data set, and walking and running mixed data set) were used as input of the 1D CNN. Focal loss function was introduced to the network to solve the problem of imbalance between positive and negative samples in the data set and make the network focus on learning the difficult-to-predict samples. Meanwhile, the attention mechanism was added to the network to observe the dimension feature that the network pays more attention to, thereby increasing the interpretability of the model. Finally, the depth features extracted by 1D CNN were combined with the traditional gender features to improve the accuracy of the model. After verification, the 1D CNN had the best performance on the running data set (accuracy = 92.4%, sensitivity = 97%, specificity = 84%). Compared with other methods, this method could provide new ideas for the development of models that assisted doctors in diagnosing PFPS without using complex biomechanical modeling and with high objective accuracy.

This study is about how people think and act in different situations. We asked 44 people some questions to understand their feelings and behaviors. The questions helped us learn how they react to different things and how they treat others. Each person gave answers based on what they felt was true for them. The study looked at three important traits. The first trait is impulsivity, which means acting without thinking. Some people make quick decisions without considering the consequences. The second trait is aggression, which means getting angry or frustrated easily. Some people react strongly to problems, while others stay calm. The third trait is a lack of empathy, which means not caring much about others’ feelings. Some people understand and care about others, while some do not. Each person answered 30 questions, with 10 questions for each trait. They used a scale from 1 to 5 to show how much they agreed or disagreed with each statement. A score of 1 meant "I do not agree," while a score of 5 meant "I strongly agree." This helped us measure how strongly each person showed these traits. We also asked about their age and gender. The people in the study were between 18 and 60 years old. They could choose to identify as male, female, or other. This information helped us see if there were any differences based on age or gender. When we looked at the answers, we found some interesting patterns. The average score for impulsivity was 3.5, meaning many people sometimes act without thinking. The average score for aggression was 3.2, which shows that some people get angry more quickly than others. The average score for lack of empathy was 2.9, meaning that most people still care about others, but some do not as much. These scores helped us see how common these traits are among different people. This study is important because it helps us understand human behavior better. Teachers can use this information to help students who struggle with anger or impulsive actions. Doctors can use it to support people who need help making better choices. Scientists can learn more about why people act in different ways and what influences their behavior. All the answers were collected in a table. Each person had their own row in the table, and each column showed their age, gender, and answers to the questions. The data was saved in a file that researchers can use to study personality traits. The file format allows them to open it in programs like Excel or special tools for data analysis. Now that we have this data, scientists can compare it with studies from other places. They can check if people in different countries or cultures answer in similar or different ways. They can also see if younger and older people have different patterns in their responses. This can help us learn more about personality traits across different groups. This study is helpful because it shows us how people act and feel. It helps us understand who thinks before acting, who gets angry easily, and who cares about others. By learning more about these traits, we can help people make better choices and improve their relationships with others. This information can be useful in schools, workplaces, and daily life. Understanding people better can help everyone live and work together in a more positive way.