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.

The National Ambulatory Medical Care Survey (NAMCS), conducted by the National Center for Health Statistics (NCHS), collects data on visits to physician offices to describe patterns of ambulatory care delivery in the United States. As part of NAMCS, the Physician Induction Interview collects information about practice characteristics at physician offices. Partway through the 2020 NAMCS, NCHS added questions to the Physician Induction Interview to assess physician experiences related to COVID-19 in office-based settings. The data include nationally representative estimates of experiences related to COVID-19 among office-based physicians in the United States, including: shortages of personal protective equipment (PPE) in the past 3 months; the ability to test for COVID-19 in the past 3 months; providers testing positive for COVID-19 in the past 3 months; turning away COVID-19 patients in the past 3 months; and telemedicine or telehealth technology use before and after March 2020. Estimates were derived from interviews with physicians in periods 3 and 4 of 2020 NAMCS and periods 1 through 4 of 2021 NAMCS, which occurred between December 15, 2020 and May 6, 2022. The data are considered preliminary, and the results may change with the final data release.

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.

These are the test and training data used for experiments presented in BioNLP 2017.

Licence The data are only aimed for research, educational and non-commercial purposes.

How to cite If you use these data, please cite our contribution to BioNLP 2017 as follows:

Automatic classification of doctor-patient questions for a virtual patient record query task Leonardo Campillos-Llanos, Sophie Rosset, Pierre Zweigenbaum Proc. of BioNLP 2017, August 4 2017, Vancouver, Canada, pp. 333-341

Note that these data were manually collected from books aimed at medical consultation and clinical examination, as well as resources for medical translation. These sources also need to be referenced as follows:

Barbara Bates and Lynn S Bickley. 2014. Guide de l’examen clinique-Nouvelle édition 2014. Arnette- John Libbey Eurotext.

Claire Coudé, Franois-Xavier Coudé, and Kai Kassmann. 2011. Guide de conversation médicale - français-anglais-allemand. Lavoisier, Médecine Sciences Publications.

Owen Epstein, David Perkin, John Cookson, and David P. de Bono. 2015. Guide pratique de l’examen clinique. Elsevier Masson, Paris.

Félicie Pastore. 2015. How can I help you today? Guide de la consultation médicale et paramédicale en anglais. Ellipses, Paris.

UMVF/Medical English Portal UFR Médecine de Dijon (Last access: May 2017)

We generated this dataset to train a machine learning model for automatically generating psychiatric case notes from doctor-patient conversations. Since, we didn't have access to real doctor-patient conversations, we used transcripts from two different sources to generate audio recordings of enacted conversations between a doctor and a patient. We employed eight students who worked in pairs to generate these recordings. Six of the transcripts that we used to produce this recordings were hand-written by Cheryl Bristow and rest of the transcripts were adapted from Alexander Street which were generated from real doctor-patient conversations. Our study requires recording the doctor and the patient(s) in seperate channels which is the primary reason behind generating our own audio recordings of the conversations.

We used Google Cloud Speech-To-Text API to transcribe the enacted recordings. These newly generated transcripts are auto-generated entirely using AI powered automatic speech recognition whereas the source transcripts are either hand-written or fine-tuned by human transcribers (transcripts from Alexander Street).

We provided the generated transcripts back to the students and asked them to write case notes. The students worked independently using a software that we developed earlier for this purpose. The students had past experience of writing case notes and we let the students write case notes as they practiced without any training or instructions from us.

NOTE: Audio recordings are not included in Zenodo due to large file size but they are available in the GitHub repository.

LukeGPT88/patient-doctor-text-classifier-eng-dataset-0523 dataset hosted on Hugging Face and contributed by the HF Datasets community

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). Note: This full dataset contains more records than most spreadsheet programs can handle, which will result in an incomplete load of data. Use of a database or statistical software is required.

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).

Medical Question Pairs (MQP) Dataset This repository contains a dataset of 3048 similar and dissimilar medical question pairs hand-generated and labeled by Curai's doctors. The dataset is described in detail in our paper.

Methodology We present our doctors with a list of 1524 patient-asked questions randomly sampled from the publicly available crawl of HealthTap. Each question results in one similar and one different pair through the following instructions provided to the labelers:

Rewrite the original question in a different way while maintaining the same intent. Restructure the syntax as much as possible and change medical details that would not impact your response. e.g. "I'm a 22-y-o female" could become "My 26 year old daughter" Come up with a related but dissimilar question for which the answer to the original question would be WRONG OR IRRELEVANT. Use similar key words.

The first instruction generates a positive question pair (similar) and the second generates a negative question pair (different). With the above instructions, we intentionally frame the task such that positive question pairs can look very different by superficial metrics, and negative question pairs can conversely look very similar. This ensures that the task is not trivial.

Dataset format The dataset is formatted as dr_id, question_1, question_2, label. We used 11 different doctors for this task so dr_id ranges from 1 to 11. The label is 1 if the question pair is similar and 0 otherwise.

Dataset statistics The final dataset contains 4567 unique questions. The minimum, maximum, median and average number of tokens in these questions are 4, 81, 20 and 22.675 respectively showing there is reasonable variance in the length of the questions. The shortest question is Are fibroadenomas malignant?

An off-the-shelf medical entity recognizer finds around 1000 unique medical entities in the questions. Some of the top entity mentions were: physician, pregnancy, pain, lasting weeks, menstruation, emotional state, cancer, visual function, headache, bleeding, fever, sexual intercourse

Medical Doctors in Turkey increased to 2.18 per 1000 people in 2021 from 2.05 per 1000 people in 2020. This dataset includes a chart with historical data for Turkey Medical Doctors.

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Data on visits to physician offices, hospital outpatient departments and hospital emergency departments by selected population characteristics. Please refer to the PDF or Excel version of this table in the HUS 2019 Data Finder (https://www.cdc.gov/nchs/hus/contents2019.htm) for critical information about measures, definitions, and changes over time. Note that the data file available here has more recent years of data than what is shown in the PDF or Excel version. Data for 2017 physician office visits are not available. SOURCE: NCHS, National Ambulatory Medical Care Survey and National Hospital Ambulatory Medical Care Survey. For more information on the National Ambulatory Medical Care Survey and the National Hospital Ambulatory Medical Care Survey, see the corresponding Appendix entries at https://www.cdc.gov/nchs/data/hus/hus17_appendix.pdf.

Medical Doctors in Germany increased to 4.98 per 1000 people in 2021 from 4.90 per 1000 people in 2020. This dataset includes a chart with historical data for Germany Medical Doctors.

######################### Medical scope, Prompt #1 61K instruction PubMED-Unlabeled ###################################################################Instruction: You are a language model specialized in generating JSON or YAML output. Given an instruction, you should generate a valid JSON or YAML object according to the provided guidelines. Your output should conform to the following JSON format: { "key1": "value1", "key2": "value2", ... } Make sure to adhere to the specified key-value pairs and maintain the correct syntax throughout your response. Your goal is to generate accurate and properly structured JSON or YAML output in response to the given instructions withoutany extra comment. Input: As a doctor that only speak in JSON, provide a "answer", "context and "keywords" about following question: Is naturopathy as effective as conventional therapy for treatment of menopausal symptoms? Output:{"answer" :Naturopathy appears to be an effective alternative for relief of specific menopausal symptoms compared to conventional therapy.,"context" :Although the use of alternative medicine in the United States is increasing, no published studies have documented the effectiveness of naturopathy for treatment of menopausal symptoms compared to women receiving conventional therapy in the clinical setting."keywords" :['Anxiety', 'Cohort Studies', 'Confidence Intervals', 'Estrogen Replacement Therapy', 'Female', 'Health Status', 'Hot Flashes', 'Humans', 'Menopause', 'Middle Aged','Naturopathy', 'Odds Ratio', 'Patient Satisfaction', 'Retrospective Studies', 'Risk Factors', 'Severity of Illness Index', 'Sleep Initiation and Maintenance Disorders','Surveys and Questionnaires', 'Time Factors', 'Urinary Incontinence', 'Vaginal Diseases', 'Washington']} ############################### Medical scope, Prompt #2 symptom-disease dataset 1K instruction ######################################################Instruction: You are a language model specialized in generating JSON or YAML output. Given an instruction, you should generate a valid JSON or YAML object according to theprovided guidelines. Your output should conform to the following JSON format: { "key1": "value1", "key2": "value2", ... } Make sure to adhere to the specified key-value pairs and maintain the correct syntax throughout your response. Your goal is to generate accurate and properly structured JSON or YAML output in response to the given instructions without any extra comment. Input: You are a doctor that only speak in JSON. Do not output that is'nt properly formated JSON. Provide following information about Panic disorder disease. Fill information about "symptom", "reason", "TestsAndProcedures" and "commonMedications" about Panic disorder disease in JSON format. Output: {"symptom" :['Anxiety and nervousness', 'Depression', 'Shortness of breath', 'Depressive or psychotic symptoms', 'Sharp chest pain', 'Dizziness', 'Insomnia', 'Abnormal involuntary movements', 'Chest tightness', 'Palpitations', 'Irregular heartbeat', 'Breathing fast'],"reason" :Panic disorder is an anxiety disorder characterized by recurring severe panic attacks. It may also include significant behavioral changes lasting at least a month and of ongoing worry about the implications or concern about having other attacks. The latter are called anticipatory attacks (DSM-IVR). Panic disorder is not the same as agoraphobia (fear of public places), although many afflicted with panic disorder also suffer from agoraphobia. Panic attacks cannot be predicted, therefore an individual may become stressed, anxious or worried wondering when the next panic attack will occur. Panic disorder may be differentiated as a medical condition, or chemical imbalance. The DSM-IV-TR describes panic disorder and anxiety differently. Whereas anxiety is preceded by chronic stressors which build to reactions of moderate intensity that can last for days, weeks or months, panic attacks are acute events triggered by a sudden, out-of-the-blue cause: duration is short and symptoms are more intense. Panic attacks can occur in children, as well as adults. Panic in young people may be particularly distressing because children tend to have less insight about what is happening, and parents are also likely to experience distress when attacks occur. "TestsAndProcedures" : ['Psychotherapy', 'Mental health counseling', 'Electrocardiogram', 'Depression screen (Depression screening)', 'Toxicology screen', 'Psychological and psychiatric evaluation and therapy']"commonMedications" : ['Lorazepam', 'Alprazolam (Xanax)', 'Clonazepam', 'Paroxetine (Paxil)', 'Venlafaxine (Effexor)', 'Mirtazapine', 'Buspirone (Buspar)', 'Fluvoxamine (Luvox)', 'Imipramine', 'Desvenlafaxine (Pristiq)', 'Clomipramine', 'Acamprosate (Campral)']} ############################### Medical scope, Prompt #3 194K instruction MED-MCQA dataset ######################################################Instruction: You are a language model specialized in generating JSON or YAML output. Given an instruction, you should generate a valid JSON or YAML object according to the provided guidelines. Your output should conform to the following JSON format: { "key1": "value1", "key2": "value2", ... } Make sure to adhere to the specified key-value pairs and maintain the correct syntax throughout your response. Your goal is to generate accurate and properly structured JSON or YAML output in response to the given instructions without any extra comment. Input: You are a medical teacher that only speak in JSON. Do not output that is'nt properly formated JSON. Generate a multichoice question and answer inAnatomy field and Urinary tract topic with following format: { "question": "expresion" , "OPA": "expresion", "OPB" : "expresion", "OPC": "expresion","OPD": "expresion", "Answer": "3"}. Output: { "question": Chronic urethral obstruction due to benign prismatic hyperplasia can lead to the following change in kidney parenchyma, "opa": Hyperplasia, "opb": Hyperophy, "opc": Atrophy, "opd": Dyplasia, "Answer": 2. ############################### Finance scope, Prompt #4 38K instruction ######################################################Instruction: You are a language model specialized in generating JSON or YAML output. Given an instruction, you should generate a valid JSON or YAML object according to the provided guidelines. Your output should conform to the following JSON format: { "key1": "value1", "key2": "value2", ... } Make sure to adhere to the specified key-value pairs and maintain the correct syntax throughout your response. Your goal is to generate accurate and properly structured JSON or YAML output in response to the given instructions without any extra comment. Input: You are a financial news analyzer that only speaks in JSON. Do not output that isn't properly formatted JSON. Analyze this news title "Here Why Bitcoin is Likely to See Historic Volatility in the Next Few Days". Provide sentiment as a probability distribution also indicate target market and related asset to the news title in JSON format. Output:{"sentiment": {"positive": 0.4463904500007629, "negative": 0.06607405841350555,"neutral":0.4875355064868927}, "market": "cryptocurrency" , "relatedAsset": ['BTC/USDT']}.

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 synthetic healthcare dataset serves 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 practise, develop, and showcase their data manipulation and analysis skills within the healthcare industry. The inspiration behind this dataset stems from the need for practical and diverse healthcare data for educational and research purposes, addressing the challenge of accessing sensitive real-world healthcare information. Generated using Python's Faker library, it mirrors the structure and attributes commonly found in healthcare records, aiming to foster innovation, learning, and knowledge sharing in healthcare analytics.

Columns

• Name: 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, such as "A+" or "O-."
• Medical Condition: Specifies the primary medical condition or diagnosis, for example, "Diabetes," "Hypertension," or "Asthma."
• 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: Indicates the patient's insurance provider, such as "Aetna," "Blue Cross," "Cigna," "UnitedHealthcare," or "Medicare."
• Billing Amount: The monetary amount billed for the patient's healthcare services during their admission, expressed as a floating-point number.
• Room Number: The room number where the patient was accommodated.
• Admission Type: Specifies the type of admission, which can be "Emergency," "Elective," or "Urgent."
• Discharge Date: The date on which the patient was discharged, based on the admission date and a realistic range of days.
• Medication: Identifies a medication prescribed or administered to the patient, including examples like "Aspirin," "Ibuprofen," "Penicillin," "Paracetamol," and "Lipitor."
• Test Results: Describes the results of a medical test conducted during admission, with possible values being "Normal," "Abnormal," or "Inconclusive."

Distribution

This dataset is typically provided as a data file in CSV format. It is structured with columns providing specific information about the patient, their admission, and the healthcare services received. While the exact number of rows or records is not specified, it is designed to be a synthetic dataset suitable for various data analysis and modelling tasks in the healthcare domain.

Usage

This dataset is ideal for a wide range of applications, including: * Developing and testing healthcare predictive models. * Practising data cleaning, transformation, and analysis techniques. * Creating data visualisations to gain insights into healthcare trends. * Learning and teaching data science and machine learning concepts in a healthcare context. It can specifically be treated as a Multi-Class Classification Problem for predicting 'Test Results', which contains three categories: Normal, Abnormal, and Inconclusive.

Coverage

The dataset has a global geographic region. The time range for admissions and discharges, as indicated by the 'Date of Admission' and 'Discharge Date' columns, spans across several years, with examples observed from 2019 to 2024. Demographic scope is covered by patient 'Name', 'Age', 'Gender', and 'Blood Type' information. As this is a synthetic dataset, it does not contain real patient information and is created to mirror common healthcare record structures.

License

CCO

Who Can Use It

This dataset is intended for data science, machine learning, and data analysis enthusiasts. It is particularly useful for those looking to engage in learning and experimentation within the healthcare analytics domain. The dataset encourages exploration, analysis, and sharing of findings within communities like Kaggle.

Dataset Name Suggestions

• Healthcare Dataset
• Healthcare Insurance & Costs Data
• Synthetic Patient Records
• Medical Admissions Data for Analytics
• Healthcare Analytics Training Dataset

Attributes

Original Data Source: Healthcare Dataset

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.

This dataset consists of 405 transcriptions of audio recorded physician-patient interactions conducted at Veterans Health Administration (VHA) medical center primary care clinics. The recordings were collected utilizing concealed (except where indicated) audio recorders by patients. The protocol was approved by VHA Institutional Review Boards, and participating physicians and patients consented to participate in the study. The interactions were analyzed using Content Coding for Contextualization of Care ("4C"). An excel spreadsheet with the coding of the original audio of each transcript is included. All data has been de-identified. "xxx" indicates PHI was removed. "@@@" indicates transcriber did not understand audio. These transcripts are a resource to medical educators and research scientists seeking transcriptions of primary care encounters, as well as those interested in 4C coding in its early stages. Their acquisition was supported with research funding from the Department of Veterans Affairs, Veterans Health Administration, Office of Research and Development, Health Services Research & Development.

MQuAD

The Medical Question and Answering dataset(MQuAD) has been refined, including the following datasets. You can download it through the Hugging Face dataset. Use the DATASETS method as follows.

  Quick Guide

from datasets import load_dataset dataset = load_dataset("danielpark/MQuAD-v1")

Medical Q/A datasets gathered from the following websites.

eHealth Forum iCliniq Question Doctors WebMD Data was gathered at the 5th of May 2017.

The MQuAD provides embedded question… See the full description on the dataset page: https://huggingface.co/datasets/danielpark/mquad-v1.

Received 17 February 2025: ‘may I have details of your independent doctor so I can check them out.’ Received 25 February 2025: ‘Please could you arrange for me to receive the Freedom of Information Act so that I can check the qualifications of your independent doctors.’ Our response I can confirm that the NHS Business Services Authority (NHSBSA) holds some of the information you have requested. Question 1 I can confirm that we do hold information on the names and General Medical Council numbers for independent medical assessors. Please note that this response does not relate to a specific claim or claimant. The request is being answered more generally given requests under FOIA are requester-blind, that is to say the identity of the requester is not taken into account when considering a request for information under FOIA. We consider the name and GMC number to be personal data under the Data Protection Act 2018. Disclosure of medical assessors’ names or GMC numbers would result in the identification of the medical assessors when entered into the GMC public register. Please be aware that I have decided not to release the names and GMC numbers of the medical assessors as this information falls under the exemption in section 40 subsections 2 and 3(A)(a) of the FOIA. As the requested information would allow a medical assessor to be identified, I consider this information is exempt. This is because it would breach the first data protection principle as: A. it is not fair to disclose medical assessors’ personal details to the world and is likely to cause damage or distress. B. these details are not of sufficient interest to the public to warrant an intrusion into the privacy of the medical assessor. The requested information is exempt if disclosure would contravene any of the data protection principles. For disclosure to comply with the lawfulness, fairness, and transparency principle, we either need the consent of the data subject(s) or there must be a legitimate interest in disclosure. In addition, the disclosure must be necessary to meet the legitimate interest and finally, the disclosure must not cause unwarranted harm. This means that the NHSBSA is therefore required to conduct a balancing exercise between the legitimate interest of the applicant in disclosure against the rights and freedoms of the medical assessor. While I acknowledge that you have a legitimate interest in disclosure of the information, the disclosure of the requested information would cause unwarranted harm. Disclosure under FOIA is to the world and therefore the NHSBSA has to consider the overall impact of the disclosure and its duty of care. The expectation of the medical assessors is that they will remain anonymous and will therefore not be subject to contact or pressure from claimants or campaigning groups. Given the certainty that the name and/or GMC number will identify the medical assessor there is a reasonable expectation that this information will not be disclosed under the FOIA. Disclosing this information would be unfair and as such this would breach the UK General Data Protection Regulation first data protection principle. Please see the following link to view the section 40 exemption in full: https://www.legislation.gov.uk/ukpga/2000/36/section/40 Question 2 I have established that the NHSBSA does not hold this information. This is because the medical qualifications and experience of the medical assessors are the responsibility of the third-party medical assessment supplier. I hope, however, that the following information provides reassurance on this point. All claims are assessed by the independent medical assessment supplier with a consistent approach. Each case is considered on its own merits, by an experienced independent medical assessor. The contract with our supplier does not require them to tell us details of the qualifications of the medical assessors or their experience. The contract requires that all assessments carried out are undertaken by suitably qualified and experienced registered medical practitioners. This includes being registered on the UK General Medical Council register, with a licence to practise and meet or exceed the following requirements: • they are a registered medical practitioner with at least five years’ post graduate experience; and • they have experience of the performance of medical and/ or disability assessment, addressing questions of causation and impact in the context of legislative or policy requirements to assist the decision maker