Source: http://data.gov.bd/dataset/doctor-directory Doctor Directory

Health Knowledge about this information will help citizens be aware of the location and service facilities of various doctors across Bangladesh. This data will also help the government to stay updated and accordingly allocate resources where there’s deficiencies.

This dataset contains information on medical professionals across Bangladesh, with details such as their posts, providers, district, upazila, facility, professional designation, contact information, and more. It is intended to be a comprehensive directory of healthcare providers in the country. The data is sourced from the official data.gov.bd named Doctor Directory maintained by the Government of Bangladesh.

Source: The data is sourced from the official http://data.gov.bd/doctor-directory . 🌐

Features: Post: The designation of the doctor, including categories like Medical Officer, Medical Officer (IMO), and others. 🏥 Provider: The type of healthcare facility or organization the doctor is affiliated with. 🏨 Division: The division within Bangladesh where the doctor is located (e.g., Rajshahi). 📍 District: The district where the healthcare provider works. 🏘️ Upazila: The specific upazila or sub-district of the doctor’s location. 🗺️ Facility: The name of the medical facility or hospital. 🏥 Professional Designation: The professional status or title of the healthcare provider (e.g., Medical Officer, Specialist). 🩺 Contact No: The contact number of the healthcare provider for inquiries. 📞 Address: The physical address of the medical facility or healthcare provider. 🏠

Purpose: This dataset serves as a doctor directory for Bangladesh, offering detailed contact information and professional data for healthcare providers across the country. It can be used for a variety of purposes, including:

Research and Analysis: To study the distribution of medical professionals across Bangladesh, identify healthcare accessibility in different regions, and examine trends in medical professional designations. Public Health Studies: To support research on the healthcare infrastructure in Bangladesh and facilitate access to medical professionals for health initiatives. Healthcare Provider Lookup: To find specific doctors or facilities in different regions of Bangladesh for patients or researchers. How Researchers Can Use the Dataset: Healthcare Accessibility Studies: Researchers can study the distribution of medical professionals in various divisions and districts, comparing regions with higher and lower densities of healthcare providers. Trend Analysis: The dataset can be used to track trends in medical staffing and identify areas that may be underserved in terms of medical professionals. Public Health Research: Useful for projects focused on the availability of healthcare in different regions, especially for public health planning and policy development. Provider Lookup: Researchers or health professionals looking to collaborate or find specific doctors can use the dataset to locate them by name, location, and specialty.

Medical Doctors in the United States increased to 2.77 per 1000 people in 2019 from 2.74 per 1000 people in 2018. This dataset includes a chart with historical data for the United States Medical Doctors.

🩺 Overview This dataset is a simulated directory of doctors in Bangladesh, crafted for data analysis, machine learning projects, and educational purposes. It mimics a real-world healthcare directory, containing structured information about doctors, their qualifications, specializations, locations, and affiliations.

📌 Key Features Name of the doctor

Specialization (e.g., cardiology, dermatology, internal medicine)

Degrees/Qualifications (e.g., MBBS, FCPS, MD)

Hospital/Clinic Affiliation

Location (City/Division)

Contact/Phone (dummy format)

Note: All names, contacts, and institutions in this dataset are fictional or anonymized. This dataset is not intended for real-life use or medical reference.

🎯 Use Cases NLP tasks such as named entity recognition (NER) or text classification

Training models for appointment scheduling or doctor recommendation systems

Practicing data cleaning, visualization, and preprocessing

Geolocation-based analysis and healthcare access mapping

Development of dummy apps or dashboards for health tech projects

📂 File Format Format: CSV

Rows: (based on your file — please confirm the number)

Columns: Includes doctor name, specialization, qualification, hospital, chamber location, etc.

🛡️ Disclaimer This dataset is synthetic and solely for research and academic use. It does not represent any real individual or organization.

Description: The Doctors in Pakistan Dataset offers a detailed overview of healthcare professionals across the country, featuring a range of medical specialists in various locations. This dataset includes key information such as the doctor’s name, designation, specialty, location, and consultation fees. It serves as a vital resource for patients seeking medical services, helping them identify qualified professionals based on their specific needs and budget.

Data Structure: The dataset contains the following information:

• Name: The name of the doctor.
• Designation: The professional title or position held by the doctor.
• Specialty: The area of medical expertise, such as psychiatry, cardiology, or dentistry.
• Location: The clinic or hospital address where the doctor practices.
• Fee: The consultation fee charged by the doctor.

Usage: This dataset can be utilized by/for: - Patients: To find specialized medical professionals that meet their healthcare needs. - Researchers: To study healthcare accessibility and specialization trends within Pakistan. - Healthcare Providers: To analyze competition and market dynamics in the medical field.

• This dataset is not preprocessed to any degree, so if you are practicing data preprocessing then this dataset will prove to be useful for you.

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.

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.

Medical Doctors in Finland increased to 3.83 per 1000 people in 2020 from 3.79 per 1000 people in 2019. This dataset includes a chart with historical data for Finland Medical Doctors.

We offer a doctor database in India that is designed to help you generate highly qualified sales leads. With our Doctors Database, you can easily connect with the appropriate healthcare professionals in India and abroad. These doctor data are invaluable for promoting and selling products, services, and solutions within the healthcare industry, as well as for medical publications, travel-related services, insurance, banking, charity/donations, and memberships, among other areas. All India Doctors Database list consists of approximately 11 lakh doctors in India who are specialists in various fields of medicine. This database of doctors in India includes Doctors across various specialties/fields of medicine such as General Practitioners, Family Physicians, Obstetrics and Gynecology Cardiology, Gastroenterology, Endocrinology and Diabetes, Anesthesia, Pediatrics, Oncology, Emergency Medicine, Neurology, Nephrology, Haematology, Internal Medicine, Dermatology etc.

The Medicare Physician & Other Practitioners by Provider dataset provides information on use, payments, submitted charges and beneficiary demographic and health characteristics organized by National Provider Identifier (NPI).

Medical Doctors in Poland increased to 3.30 per 1000 people in 2019 from 2.38 per 1000 people in 2017. This dataset includes a chart with historical data for Poland Medical Doctors.

Medical Doctors in Slovenia increased to 3.30 per 1000 people in 2020 from 3.26 per 1000 people in 2019. This dataset includes a chart with historical data for Slovenia Medical Doctors.

The MedDialog dataset (English) contains conversations (in English) between doctors and patients.It has 0.26 million dialogues. The data is continuously growing and more dialogues will be added. The raw dialogues are from healthcaremagic.com and icliniq.com. All copyrights of the data belong to healthcaremagic.com and icliniq.com.

Context:

This synthetic healthcare dataset has been created to serve as a valuable resource for data science, machine learning, and data analysis enthusiasts. It is designed to mimic real-world healthcare data, enabling users to practice, develop, and showcase their data manipulation and analysis skills in the context of the healthcare industry.

Inspiration:

The inspiration behind this dataset is rooted in the need for practical and diverse healthcare data for educational and research purposes. Healthcare data is often sensitive and subject to privacy regulations, making it challenging to access for learning and experimentation. To address this gap, I have leveraged Python's Faker library to generate a dataset that mirrors the structure and attributes commonly found in healthcare records. By providing this synthetic data, I hope to foster innovation, learning, and knowledge sharing in the healthcare analytics domain.

Dataset Information:

Each column provides specific information about the patient, their admission, and the healthcare services provided, making this dataset suitable for various data analysis and modeling tasks in the healthcare domain. Here's a brief explanation of each column in the dataset - - Name: This column represents the name of the patient associated with the healthcare record. - Age: The age of the patient at the time of admission, expressed in years. - Gender: Indicates the gender of the patient, either "Male" or "Female." - Blood Type: The patient's blood type, which can be one of the common blood types (e.g., "A+", "O-", etc.). - Medical Condition: This column specifies the primary medical condition or diagnosis associated with the patient, such as "Diabetes," "Hypertension," "Asthma," and more. - Date of Admission: The date on which the patient was admitted to the healthcare facility. - Doctor: The name of the doctor responsible for the patient's care during their admission. - Hospital: Identifies the healthcare facility or hospital where the patient was admitted. - Insurance Provider: This column indicates the patient's insurance provider, which can be one of several options, including "Aetna," "Blue Cross," "Cigna," "UnitedHealthcare," and "Medicare." - Billing Amount: The amount of money billed for the patient's healthcare services during their admission. This is expressed as a floating-point number. - Room Number: The room number where the patient was accommodated during their admission. - Admission Type: Specifies the type of admission, which can be "Emergency," "Elective," or "Urgent," reflecting the circumstances of the admission. - Discharge Date: The date on which the patient was discharged from the healthcare facility, based on the admission date and a random number of days within a realistic range. - Medication: Identifies a medication prescribed or administered to the patient during their admission. Examples include "Aspirin," "Ibuprofen," "Penicillin," "Paracetamol," and "Lipitor." - Test Results: Describes the results of a medical test conducted during the patient's admission. Possible values include "Normal," "Abnormal," or "Inconclusive," indicating the outcome of the test.

Usage Scenarios:

This dataset can be utilized for a wide range of purposes, including: - Developing and testing healthcare predictive models. - Practicing data cleaning, transformation, and analysis techniques. - Creating data visualizations to gain insights into healthcare trends. - Learning and teaching data science and machine learning concepts in a healthcare context. - You can treat it as a Multi-Class Classification Problem and solve it for Test Results which contains 3 categories(Normal, Abnormal, and Inconclusive).

Acknowledgments:

• I acknowledge the importance of healthcare data privacy and security and emphasize that this dataset is entirely synthetic. It does not contain any real patient information or violate any privacy regulations.
• I hope that this dataset contributes to the advancement of data science and healthcare analytics and inspires new ideas. Feel free to explore, analyze, and share your findings with the Kaggle community.

Image Credit:

Image by BC Y from Pixabay

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The dataset comprises the yearwise and State/UT wise number of MBBS doctors registered with State Medical Councils/National Medical Commission as of 31 December of each year. NB: Data for 2021 is not available. Medical Council of India (MCI) stopped registration since 2015

This dataset comprises physician-level entries from the 1906 American Medical Directory, the first in a series of semi-annual directories of all practicing physicians published by the American Medical Association [1]. Physicians are consistently listed by city, county, and state. Most records also include details about the place and date of medical training. From 1906-1940, Directories also identified the race of black physicians [2].This dataset comprises physician entries for a subset of US states and the District of Columbia, including all of the South and several adjacent states (Alabama, Arkansas, Delaware, Florida, Georgia, Kansas, Kentucky, Louisiana, Maryland, Mississippi, Missouri, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, West Virginia). Records were extracted via manual double-entry by professional data management company [3], and place names were matched to latitude/longitude coordinates. The main source for geolocating physician entries was the US Census. Historical Census records were sourced from IPUMS National Historical Geographic Information System [4]. Additionally, a public database of historical US Post Office locations was used to match locations that could not be found using Census records [5]. Fuzzy matching algorithms were also used to match misspelled place or county names [6].The source of geocoding match is described in the “match.source” field (Type of spatial match (census_YEAR = match to NHGIS census place-county-state for given year; census_fuzzy_YEAR = matched to NHGIS place-county-state with fuzzy matching algorithm; dc = matched to centroid for Washington, DC; post_places = place-county-state matched to Blevins & Helbock's post office dataset; post_fuzzy = matched to post office dataset with fuzzy matching algorithm; post_simp = place/state matched to post office dataset; post_confimed_missing = post office dataset confirms place and county, but could not find coordinates; osm = matched using Open Street Map geocoder; hand-match = matched by research assistants reviewing web archival sources; unmatched/hand_match_missing = place coordinates could not be found). For records where place names could not be matched, but county names could, coordinates for county centroids were used. Overall, 40,964 records were matched to places (match.type=place_point) and 931 to county centroids ( match.type=county_centroid); 76 records could not be matched (match.type=NA).Most records include information about the physician’s medical training, including the year of graduation and a code linking to a school. A key to these codes is given on Directory pages 26-27, and at the beginning of each state’s section [1]. The OSM geocoder was used to assign coordinates to each school by its listed location. Straight-line distances between physicians’ place of training and practice were calculated using the sf package in R [7], and are given in the “school.dist.km” field. Additionally, the Directory identified a handful of schools that were “fraudulent” (school.fraudulent=1), and institutions set up to train black physicians (school.black=1).AMA identified black physicians in the directory with the signifier “(col.)” following the physician’s name (race.black=1). Additionally, a number of physicians attended schools identified by AMA as serving black students, but were not otherwise identified as black; thus an expanded racial identifier was generated to identify black physicians (race.black.prob=1), including physicians who attended these schools and those directly identified (race.black=1).Approximately 10% of dataset entries were audited by trained research assistants, in addition to 100% of black physician entries. These audits demonstrated a high degree of accuracy between the original Directory and extracted records. Still, given the complexity of matching across multiple archival sources, it is possible that some errors remain; any identified errors will be periodically rectified in the dataset, with a log kept of these updates.For further information about this dataset, or to report errors, please contact Dr Ben Chrisinger (Benjamin.Chrisinger@tufts.edu). Future updates to this dataset, including additional states and Directory years, will be posted here: https://dataverse.harvard.edu/dataverse/amd.References:1. American Medical Association, 1906. American Medical Directory. American Medical Association, Chicago. Retrieved from: https://catalog.hathitrust.org/Record/000543547.2. Baker, Robert B., Harriet A. Washington, Ololade Olakanmi, Todd L. Savitt, Elizabeth A. Jacobs, Eddie Hoover, and Matthew K. Wynia. "African American physicians and organized medicine, 1846-1968: origins of a racial divide." JAMA 300, no. 3 (2008): 306-313. doi:10.1001/jama.300.3.306.3. GABS Research Consult Limited Company, https://www.gabsrcl.com.4. Steven Manson, Jonathan Schroeder, David Van Riper, Tracy Kugler, and Steven Ruggles. IPUMS National Historical Geographic Information System: Version 17.0 [GNIS, TIGER/Line & Census Maps for US Places and Counties: 1900, 1910, 1920, 1930, 1940, 1950; 1910_cPHA: ds37]. Minneapolis, MN: IPUMS. 2022. http://doi.org/10.18128/D050.V17.05. Blevins, Cameron; Helbock, Richard W., 2021, "US Post Offices", https://doi.org/10.7910/DVN/NUKCNA, Harvard Dataverse, V1, UNF:6:8ROmiI5/4qA8jHrt62PpyA== [fileUNF]6. fedmatch: Fast, Flexible, and User-Friendly Record Linkage Methods. https://cran.r-project.org/web/packages/fedmatch/index.html7. sf: Simple Features for R. https://cran.r-project.org/web/packages/sf/index.html

The dataset contains a collection of frames extracted from videos captured within a hospital environment. The bounding boxes are drawn around the doctors, nurses, and other people who appear in the video footage. The dataset can be used for computer vision in healthcare settings and the development of systems that monitor medical staff activities, patient flow, analyze wait times, and assess the efficiency of hospital processes.

Doctors’ data is compiled by the Department of Health as part of the Non-Monetary Health Care Statistics, administered jointly by Eurostat, OECD and WHO in fulfilment of the European regulation (EU) 2022/2294. These statistics are compiled and published on an annual basis and refer to the stock of doctors according to country from where they obtained their first medical qualification, as at end of the referenced year.

Doctor email list is a list of emails of doctors. It helps people reach doctors fast. Companies use it for many reasons. They can send health news, medical tools, or event invites. However, a doctor, also known as a physician, is a licensed professional who practices medicine to maintain or restore human health. Their primary role is to diagnose and treat injuries, illnesses, and other medical conditions. People can know about names, job titles, and where doctors work. It may also show what kind of doctor they are. For example, heart doctors or eye doctors. Moreover, a good email list brings good results. It can also help in sales, jobs, and learning. It builds links between people. Even, it helps doctors stay up to date.

Doctor email list helps medicine companies a lot. They use it to share new medicine. Hospitals use it to find doctors for work. Moreover, that saves time, it is better than calling one by one. You can buy or rent a doctor email list. Some lists are free, but many are not. Wrong emails waste time. Good lists are always updated, our dataset must be correct. Privacy is key, our list must follow laws. People on the list should agree to get emails. We collect all datasets from trustworthy sources. We sell very big lists and other side make small lists for local needs. In short, this package is very useful. It is fast, easy, and smart. Hence, buy this lead. Doctor email database can give you more potential clients for your online marketing. As well as building numerous advantages for the company’s benefit. Any seller can promote their brand through this package. Hence, our professionals update these contact numbers monthly. Similarly, we provide a CSV and an Excel file that can run in any CRM software. Most importantly, it brings a great return on investment (ROI). So, if anyone needs any contacts for digital marketing, they can buy these from List to Data.