This interactive healthcare dashboard leverages patient-level data from Johns Hopkins University to offer a comprehensive, data-driven analysis of both clinical and financial performance in a healthcare setting. Developed using Power BI, the dashboard integrates diverse data sources—such as diagnoses, procedures, lab results, and billing records—to provide stakeholders with a unified view of population health, operational efficiency, and revenue cycles.

At the core of the dashboard is a risk stratification model, categorizing patients into Low Risk, Medium Risk, and High Risk segments based on parameters such as chronic conditions, abnormal lab values, and age. This segmentation enables a focused evaluation of healthcare delivery patterns, clinical burdens, and cost implications across risk categories. For instance, High-Risk patients, while representing a small proportion of the population, account for a disproportionately high share of average billing and extended lengths of stay—underscoring the need for proactive care management strategies.

Key features of the dashboard include:

Patient Risk Scoring based on clinical and demographic factors

Revenue Attribution by Diagnosis, highlighting top cost drivers like Type 2 diabetes, heart failure, and hypertension

Monthly Revenue Trends to monitor changes in financial performance

Department-Level Length of Stay Metrics, helping optimize bed utilization and throughput

Correlation of Age and Chronic Conditions with Revenue, revealing the impact of aging and multimorbidity on healthcare expenditure

The dashboard also presents a granular view of patient encounters, lab abnormalities, and procedure volumes—supporting decision-making for clinical operations, resource allocation, and preventive care initiatives. From a financial perspective, it highlights average revenue per patient and per encounter across risk levels, guiding strategic planning and budgeting efforts.

By combining clinical, operational, and financial metrics into one interactive platform, this dashboard empowers healthcare administrators, analysts, and clinicians to identify trends, assess performance, and take timely action. It supports value-based care initiatives by focusing on outcomes, efficiency, and high-cost patient cohorts, helping institutions achieve better health outcomes while maintaining financial sustainability.

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...

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.

Graph and download economic data for Total Revenue for 621: Ambulatory Health Care Services, All Establishments (REV621APSA) from Q2 2009 to Q2 2026 about ambulatory, healthcare, revenue, establishments, health, services, and USA.

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Age, Race/Ethnicity

Income, Insurance

Disability, LGBT+

Do you have an ongoing health condition that...

In 2024, approximately 13.48 million adults were insured by military health care (including TRICARE, VA or CHAMPVA) in the United States. This statistic presents the number of U.S. adults insured by military health care from 1990 to 2024.

Limit state-purchased health care cost growth to 2% less than the projected national health expenditures average every year through 2019.

Percent of voters who say they trust each of the following on addressing health care costs:

Percent who say they skipped or delayed care due to cost in the past year:

Complete dataset included in the full report. Detailed tables, regional splits, forecasts, and methodologies are available with purchase.

The Healthcare_Dataset is a structured collection of patient health-related records compiled to support data analysis, machine learning applications, and clinical research. It typically includes information such as patient demographics, medical history, vital signs, laboratory test results, diagnoses, treatments, and health outcomes. The dataset is organized in tabular format, with each row representing an individual patient or clinical visit and each column describing specific attributes related to health status or healthcare services.

This dataset is widely used for building predictive models, identifying disease patterns, analyzing risk factors, and performing exploratory data analysis in healthcare analytics.

A 2025 survey found that over half of U.S. individuals indicated the cost of accessing treatment was the biggest problem facing the national healthcare system. This is much higher than the global average of 33 percent and is in line with the high cost of health care in the U.S. compared to other high-income countries. Bureaucracy along with a lack of staff were also considered to be pressing issues.

Graph and download economic data for Personal Consumption Expenditures: Services: Health Care for Pennsylvania (PAPCEHLTHCARE) from 1997 to 2025 about healthcare, health, PA, PCE, consumption expenditures, consumption, personal, services, and USA.

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.

In 2022, across all states in the United States, Vermont was ranked as having the best mental health care, followed by Massachusetts and Rhode Island. On the other hand, Texas had the worst mental health care in the U.S., ranking last on the list. The best mental health care ranking was based on scores that measured ***** healthcare metrics relevant to access to treatment, services, and insurance for mental illness in the U.S. in 2022.

This table contains data on the perceived state of health and on contacts with providers of medical care of the Dutch population from 0 years on in private households. These data can be grouped by several personal characteristics. For several topics a different age demarcation applies. The age boundaries are listed at the relevant topics.

Data available from: 2014

Status of the data: final.

Changes by September 4, 2026 The figures regarding the percentage of people with a GALI limitation (‘GALI limitation’, ‘GALI limitation, severe’, and ‘GALI limitation, non-severe’) were incorrect. People whose limitation was not long-standing had been included in the wrong figures. This error affected the figures from 2019 onwards. The incorrect figures have been corrected.

When will new data be published? Data on reporting year 2026 will be published in the second quarter of 2027.

Limit state-purchased health care cost growth to 2% less than the projected national health expenditures average every year through 2019.

A 2024 survey found that over half of individuals in Great Britain indicated that access to treatment and long waiting times were the biggest problem facing the national healthcare system. Access to treatment and/or long waiting times were also considered to be pressing issues. This statistic reveals the share of individuals who said select problems were the biggest facing the health care system in Great Britain in 2024.

OCR breach portal record for Health Care Service Corporation (Illinois) — 220,913 individuals affected.

When it comes to the people who feel patients overpay for healthcare in selected countries worldwide, Poland ranks the highest in this list. 62 percent of consumer in Poland share this attitude. Following in the second place is Hungary, while South Korea can be found at the bottom of the ranking.Statista Consumer Insights offer you all results of our exclusive Statista surveys, based on more than 2,000,000 interviews.