Healthcare fraud is considered a challenge for many societies. Health care funding that could be spent on medicine, care for the elderly, or emergency room visits is instead lost to fraudulent activities by materialistic practitioners or patients. With rising healthcare costs, healthcare fraud is a major contributor to these increasing healthcare costs.

Try out various unsupervised techniques to find the anomalies in the data.

Detailed Data File:

The following variables are included in the detailed Physician and Other Supplier data file (see Appendix A for a condensed version of variables included)).

npi – National Provider Identifier (NPI) for the performing provider on the claim. The provider NPI is the numeric identifier registered in NPPES.

nppes_provider_last_org_name – When the provider is registered in NPPES as an individual (entity type code=’I’), this is the provider’s last name. When the provider is registered as an organization (entity type code = ‘O’), this is the organization's name.

nppes_provider_first_name – When the provider is registered in NPPES as an individual (entity type code=’I’), this is the provider’s first name. When the provider is registered as an organization (entity type code = ‘O’), this will be blank.

nppes_provider_mi – When the provider is registered in NPPES as an individual (entity type code=’I’), this is the provider’s middle initial. When the provider is registered as an organization (entity type code= ‘O’), this will be blank.

nppes_credentials – When the provider is registered in NPPES as an individual (entity type code=’I’), these are the provider’s credentials. When the provider is registered as an organization (entity type code = ‘O’), this will be blank.

nppes_provider_gender – When the provider is registered in NPPES as an individual (entity type code=’I’), this is the provider’s gender. When the provider is registered as an organization (entity type code = ‘O’), this will be blank.

nppes_entity_code – Type of entity reported in NPPES. An entity code of ‘I’ identifies providers registered as individuals and an entity type code of ‘O’ identifies providers registered as organizations.

nppes_provider_street1 – The first line of the provider’s street address, as reported in NPPES.

nppes_provider_street – The second line of the provider’s street address, as reported in NPPES.

nppes_provider_city – The city where the provider is located, as reported in NPPES.

nppes_provider_zip – The provider’s zip code, as reported in NPPES.

nppes_provider_state – The state where the provider is located, as reported in NPPES. The fifty U.S. states and the District of Columbia are reported by the state postal abbreviation. The following values are used for all other areas:

'XX' = 'Unknown' 'AA' = 'Armed Forces Central/South America' 'AE' = 'Armed Forces Europe' 'AP' = 'Armed Forces Pacific' 'AS' = 'American Samoa' 'GU' = 'Guam' 'MP' = 'North Mariana Islands' 'PR' = 'Puerto Rico' 'VI' = 'Virgin Islands' 'ZZ' = 'Foreign Country'

nppes_provider_country – The country where the provider is located, as reported in NPPES. The country code will be ‘US’ for any state or U.S. possession. For foreign countries (i.e., state values of ‘ZZ’), the provider country values include the following: AE=United Arab Emirates IT=Italy AG=Antigua JO= Jordan AR=Argentina JP=Japan AU=Australia KR=Korea BO=Bolivia KW=Kuwait BR=Brazil KY=Cayman Islands CA=Canada LB=Lebanon CH=Switzerland MX=Mexico CN=China NL=Netherlands CO=Colombia NO=Norway DE= Germany NZ=New Zealand ES= Spain PA=Panama FR=France PK=Pakistan GB=Great Britain RW=Rwanda GR=Greece SA=Saudi Arabia HU= Hungary SY=Syria IL= Israel TH=Thailand IN=India TR=Turkey IS= Iceland VE=Venezuela

provider_type – Derived from the provider specialty code reported on the claim.

medicare_participation_indicator – Identifies whether the provider participates in Medicare and/or accepts the assigned assignment of Medicare allowed amounts.

place_of_service – Identifies whether the place of service submitted on the claims is a facility (value of ‘F’) or non-facility (value of ‘O’). Non-facility is generally an office setting; however other entities are included in non-facility.

hcpcs_code – HCPCS code used to identify the specific medical service furnished by the provider.

hcpcs_description – Description of the HCPCS code for the specific medical service furnished by the provider.

hcpcs_drug_indicator –Identifies whether the HCPCS code for the specific service furnished by the provider is an HCPCS listed on the Medicare Part B Drug Average Sales Price (ASP) File.

line_srvc_cnt – Number of services provided; note that the metrics used to count the number provided can vary from service to service.

bene_unique_cnt – Number of distinct Medicare beneficiaries rec...

Generally, do the health care apps, websites, or online patient portals you use make managing your health care easier, more difficult, or does it not make a difference?

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.

If you found value in this dataset, an upvote would be appreciated! It helps others find and benefit from it too. Thank you!

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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.

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Context

Health workforce shortage areas are geographic areas, populations, and facilities that have a shortage of outpatient primary care, dental, and mental health providers and services. These areas are designated by the Health Resources and Services Administration (HRSA), a federal agency in the United States Department of Health and Human Services.

There are several types of shortage designations including: - Health Professional Shortage Areas (HPSAs) - Medically Underserved Areas and Populations (MUAPs) - Exceptional Medically Underserved Population (Exceptional MUPs) - Governor's-Designated Secretary-Certified Shortage Areas for Rural Health Clinics

HRSA's Bureau of Health Workforce operates a cooperative agreement and evaluates applications submitted by the Primary Care Office (PCO) of each U.S. state and territory as part of the process to designate some types of shortage areas. These applications are reviewed by HRSA to determine if they meet specific designation criteria which differs by the type of shortage area. Other shortage area types are automatically designated by federal statute or at the request of a state governor. Once HPSAs are designated, score is calculated which represents a relative measure of need for health care services for that discipline. Both HPSAs and MUAPs can be designated to indicate a shortage of primary care services while only HPSAs can be designated to indicate a shortage of dental or mental health services. Shortage area designations and scores are used by various federal programs for distributing resources. Some shortage area designations may also be used by state programs.

See the shortage designation website for more information.

Content

The health workforce shortage area data in the included files represent the HPSA and MUAP (including Exceptional MUP) designation information at a single point in time. The dataset is refreshed weekly from the source data files on data.hrsa.gov.

HPSAs All three file contain the same columns but represent only a single healthcare discipline. Each record represents either a "component" (county, county subdivision or census tract) of a Geographic/Population HPSA service area or represents the physical location of facility HPSA.

Files: - BCD_HPSA_FCT_DET_PC.csv: Primary Care HPSAs - BCD_HPSA_FCT_DET_DH.csv: Dental Health HPSAs - BCD_HPSA_FCT_DET_MH.csv: Mental Health HPSAs

Fields of interest: - [HPSA ID]: Unique identifier for each HPSA designation - [Designation Type]: Type of HPSA Designation. Types for areas designated for a geographic area include "Geographic HPSA", "High Needs Geographic HPSA" and "HPSA Population" - [HPSA Discipline Class]

MUAPs Each record in this file represents a "component" (county, county subdivision or census tract) of a Medically Underserved Area or Medically Underserved Population Group service area

Files: - MUA/_DET.csv: Medically Underserved Areas/Populations

Fields of interest:

Acknowledgements

Inspiration

Graph and download economic data for Economic Policy Uncertainty Index: Categorical Index: Health care (EPUHEALTHCARE) from Jan 1985 to May 2026 about healthcare, uncertainty, health, World, and indexes.

The Agency for Healthcare Research and Quality (AHRQ) created SyH-DR from eligibility and claims files for Medicare, Medicaid, and commercial insurance plans in calendar year 2016. SyH-DR contains data from a nationally representative sample of insured individuals for the 2016 calendar year. SyH-DR uses synthetic data elements at the claim level to resemble the marginal distribution of the original data elements. SyH-DR person-level data elements are not synthetic, but identifying information is aggregated or masked.

The Medicare & Medicaid Electronic Health Record (EHR) Incentive Programs provide incentives to eligible ambulatory and inpatient providers to adopt electronic health records. This dataset provides the counts of health care providers that have reported a developer's product through participation in the Medicare EHR Incentive Program. The data are provided beginning in 2011. This dataset enables the tracking of trends in the adoption of healthIT by developer and by both office-based health care providers and non-federal acute-care hospitals. Filter the data by Program Year to get the most recent counts by health care provider type. The most recent data is available through the 2016 Program Year.

Graph and download economic data for Per Capita Personal Consumption Expenditures: Services: Health Care for South Dakota (SDPCEPCHLTHCARE) from 1997 to 2024 about healthcare, SD, health, PCE, consumption expenditures, consumption, per capita, personal, services, and USA.

Percent who say they have used a health care app or website either on their smartphone, tablet, or computer to do each of the following:

US Healthcare Readmissions and Mortality

Evaluating Hospital Performance

By Health [source]

About this dataset

This dataset contains detailed information about 30-day readmission and mortality rates of U.S. hospitals. It is an essential tool for stakeholders aiming to identify opportunities for improving healthcare quality and performance across the country. Providers benefit by having access to comprehensive data regarding readmission, mortality rate, score, measure start/end dates, compared average to national as well as other pertinent metrics like zip codes, phone numbers and county names. Use this data set to conduct evaluations of how hospitals are meeting industry standards from a quality and outcomes perspective in order to make more informed decisions when designing patient care strategies and policies

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How to use the dataset

This dataset provides data on 30-day readmission and mortality rates of U.S. hospitals, useful in understanding the quality of healthcare being provided. This data can provide insight into the effectiveness of treatments, patient care, and staff performance at different healthcare facilities throughout the country.

In order to use this dataset effectively, it is important to understand each column and how best to interpret them. The ‘Hospital Name’ column displays the name of the facility; ‘Address’ lists a street address for the hospital; ‘City’ indicates its geographic location; ‘State’ specifies a two-letter abbreviation for that state; ‘ZIP Code’ provides each facility's 5 digit zip code address; 'County Name' specifies what county that particular hospital resides in; 'Phone number' lists a phone contact for any given facility ;'Measure Name' identifies which measure is being recorded (for instance: Elective Delivery Before 39 Weeks); 'Score' value reflects an average score based on patient feedback surveys taken over time frame listed under ' Measure Start Date.' Then there are also columns tracking both lower estimates ('Lower Estimate') as well as higher estimates ('Higher Estimate'); these create variability that can be tracked by researchers seeking further answers or formulating future studies on this topic or field.; Lastly there is one more measure oissociated with this set: ' Footnote,' which may highlight any addional important details pertinent to analysis such as numbers outlying National averages etc..

This data set can be used by hospitals, research facilities and other interested parties in providing inciteful information when making decisions about patient care standards throughout America . It can help find patterns about readmitis/mortality along county lines or answer questions about preformance fluctuations between different hospital locations over an extended amount of time. So if you are ever curious about 30 days readmitted within US Hospitals don't hesitate to dive into this insightful dataset!

Research Ideas

• Comparing hospitals on a regional or national basis to measure the quality of care provided for readmission and mortality rates.
• Analyzing the effects of technological advancements such as telemedicine, virtual visits, and AI on readmission and mortality rates at different hospitals.
• Using measures such as Lower Estimate Higher Estimate scores to identify systematic problems in readmissions or mortality rate management at hospitals and informing public health care policy

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: Readmissions_and_Deaths_-_Hospital.csv | Column name | Description | |:-------------------------|:---------------------------------------------------------------------------------------------------| | Hospital Name ...

The Healthcare Analytics Market Report is Segmented by Analytics Type (Descriptive, Diagnostic, and More), Component (Hardware, Software, and Services), Delivery Mode (On-Premise, Cloud-Based, and Hybrid), Application (Clinical, Financial/Revenue-Cycle, Operational/Administrative, and More), End User (Providers, Payers, and More), and Geography (North America, and More). Market Forecasts are Provided in Terms of Value (USD).

How worried, if at all, are you about being able to afford each of the following for you and your family?

Market size, estimate, forecast and CAGR for the Home Healthcare Market Size, Growth Report, 2026-2033.

This dataset is real-time physiological and network-level data collected from a Secure Healthcare IoT Monitoring System involving 2000 patients. It includes biometric readings such as heart rate, body temperature, and blood pressure, alongside metadata like device ID, IP address, access type, and action performed.

Each record is labeled with a target value:

0 for normal activity

1 for anomalous events

This dataset contains The Consumer Assessment of Healthcare Providers and Systems (CAHPS) Home Health Care Survey results. This survey is designed to measure the experiences of people receiving home health care from Medicare-certified home health agencies. The Home Health Care Consumer Assessment of Healthcare Providers and Systems (HHCAHPS) is conducted for home health agencies by approved HHCAHPS Survey vendors.

Graph and download economic data for Labor Productivity for Health Care and Social Assistance: Medical and Diagnostic Laboratories (NAICS 62151) in the United States (IPURN62151L000000000) from 1994 to 2024 about diagnostic labs, healthcare, medical, social assistance, productivity, health, NAICS, IP, labor, and USA.

Graph and download economic data for Job Openings: Health Care and Social Assistance (JTS6200JOL) from Dec 2000 to May 2026 about job openings, social assistance, vacancy, health, and USA.

This dataset contains data for the Healthcare Payments Data (HPD) Healthcare Measures report. The data cover three measurement categories: Health conditions, Utilization, and Demographics. The health condition measurements quantify the prevalence of long-term illnesses and major medical events prominent in California’s communities like diabetes and heart failure. Utilization measures convey rates of healthcare system use through visits to the emergency department and different categories of inpatient stays, such as maternity or surgical stays. The demographic measures describe the health coverage and other characteristics (e.g., age) of the Californians included in the data and represented in the other measures. The data include both a count or sum of each measure and a count of the base population so that data users can calculate the percentages, rates, and averages in the visualization. Measures are grouped by year, age band, sex (assigned sex at birth), payer type, Covered California Region, and county.