Every year, CMS will update the Open Payments data at least once after its initial publication. The refreshed data will include updates to data disputes and other data corrections made since the initial publication of this data documenting payments or transfers of value to physicians and teaching hospitals, and physician ownership and investment interests. This financial data is submitted by applicable manufacturers and applicable group purchasing organizations (GPOs). #### What data is collected? Applicable manufacturers and GPOs submit data to Open Payments about payments or other transfers of value between applicable manufacturers and GPOs and physicians or teaching hospitals: 1. Paid directly to physicians and teaching hospitals (known as direct payments) 2. Paid indirectly to physicians and teaching hospitals (known as indirect payments) through an intermediary such as a medical specialty society 3. Designated by physicians or teaching hospitals to be paid to another party (known

We conducted a cross-sectional study of the publicly available 2022 Open Payments data to characterize and quantify sponsored events (available for download at: https://www.cms.gov/priorities/key-initiatives/open-payments/data/dataset-downloads). Data sources We downloaded the 2022 dataset ZIP files from the Open Payments website on June 30th, 2023. We included all records for nurse practitioners, clinical nurse specialists, certified registered nurse anesthetists, and certified nurse-midwives (hereafter advanced practiced registered nurses (APRNs)); and allopathic and osteopathic physicians (hereafter, ‘physicians’). To ensure consistency in provider classification, we linked Payments data to the National Plan and Provider Enumeration System data (June 2023) by National Provider Identifier (NPI) and the National Uniform Claim Committee (NUCC) and excluded individuals with an ambiguous provider type. Event-centric analysis of Open Payments records: Creating an event typology We included only payments classified as “food and beverage” to reliably identify distinct sponsored events. We reasoned that food and beverage would be consumed on the same day in the same place, thus assumed that records for food and beverage associated with the same event would share the date of payment and location. We also assumed that the reported value of a food and beverage payment is the total cost of the hospitality divided by the number of attendees, thus grouped payment records with the same amount, rounded to the nearest dollar. Inferring which Open Payment records relate to the same sponsored event requires analytic decisions regarding the selection and representation of variables that define an event. To understand the impact of these choices, we undertook a sensitivity analysis to explore alternative ways to group Open Payments records for food and beverage, to determine how combination of variables, including date (specific date or within the same calendar week), amount (rounded to nearest dollar), and recipient’s state, affected the identification of sponsored events in the Open Payments data set. We chose to define a sponsored event as a cluster of three or more individual payment records for food and beverage (nature of payment) with the following matching Open Payments record variables: • Submitting applicable manufacturer (name) • Product category or therapeutic area • Name of drug or biological or device or medical supply • Recipient state • Total amount of payment (USD, rounded to nearest dollar) • Date of payment (exact) After examining the distribution of the data, we classified events in terms of size (≥20 attendees as “large” and 3-<20 as “small”) and amount per person. We categorized events <$10 as “coffee”, $10-<$30 as “lunch”, $30-<$150 as “dinner”, and ≥$150 as “banquet”.

Context Financial relationships between manufacturers of drugs, devices, biologicals, and medical supplies and healthcare providers (physicians, non-physician practitioners, and teaching hospitals) are common and often serve important functions. However, these ties can also create potential conflicts of interest.

The CMS (Centers for Medicare & Medicaid Services) Open Payments program is a U.S. federal initiative designed to increase transparency around these financial relationships. By publicly reporting data on payments and other transfers of value, the program helps patients and researchers better understand the nature and extent of these collaborations.

While the raw data is incredibly rich, its comprehensive and detailed structure can be challenging for quick analysis or machine learning applications. This dataset is a cleaned, processed, and user-friendly version of the original Open Payments data, specifically prepared to facilitate straightforward data exploration, visualization, and predictive modeling.

Content This dataset contains records of payments made by manufacturers to healthcare providers in the United States. The original, multi-part fields for products, specialties, and licenses have been simplified to focus on the primary entry for each record, and columns with low variance or sparse data have been removed.

The dataset includes the following columns: | Column Name | Description | | ------------------------- | -------------------------------------------------------------------------------------------------------- | | payment_id | System-assigned unique identifier for the payment transaction. | | payment_amount | The total value of the payment in U.S. Dollars. | | payment_number | The number of individual payments included in the total amount. | | address_full | The full primary business street address of the payment recipient. | | address_country | The primary business country of the recipient. | | address_state | The primary business state of the recipient (2-letter abbreviation). | | address_city | The primary business city of the recipient. | | zip_code | The 5 or 9-digit zip code for the recipient's primary business location. | | payment_day | The day of the month the payment was made. | | payment_month | The month the payment was made. | | payment_year | The year the payment was made. | | publication_day | The day of the month the payment record was published. | | publication_month | The month the payment record was published. | | publication_year | The year the payment record was published. | | change_type | An indicator showing if the record is new or added (NEW, ADD). | | indicator_third_party | Indicates if payment was made to a third party (ENTITY, INDIVIDUAL, NO THIRD PARTY PAYMENT). | | indicator_related_product | Indicates if the payment was related to a specific product (YES, NO). | | indicator_covered | Indicates if the related product is "covered" under Open Payments rules (UNKNOWN, NON-COVERED, COVERED). | | identity_type | The professional designation of the payment recipient (NON-PHYSICIAN PRACTITIONER, PHYSICIAN). | | first_name | The first name of the covered recipient. | | last_name | The last name of the covered recipient. | | manufacturer_name | The name of the company that made the payment. | | manufacturer_state | The state where the paying company is located. | | manufacturer_country | The country where the paying compan...

Every year, CMS will update the Open Payments data at least once after its initial publication. The refreshed data will include updates to data disputes and other data corrections made since the initial publication of this data documenting payments or transfers of value to physicians and teaching hospitals, and physician ownership and investment interests. This financial data is submitted by applicable manufacturers and applicable group purchasing organizations (GPOs). #### What data is collected? Applicable manufacturers and GPOs submit data to Open Payments about payments or other transfers of value between applicable manufacturers and GPOs and physicians or teaching hospitals: 1. Paid directly to physicians and teaching hospitals (known as direct payments) 2. Paid indirectly to physicians and teaching hospitals (known as indirect payments) through an intermediary such as a medical specialty society 3. Designated by physicians or teaching hospitals to be paid to another party (known

Payments to suppliers made by City of York Council from April 2011 onwards. Resources are split according to financial years. Date: The date shown is the date the transaction was input to the system, not the payment date. Transaction number: Our internal reference number to enable us to identify an individual transaction. Transaction numbers beginning with CR relate to entries from the creditor system, usually straightforward payments or credit notes. Transaction numbers beginning with J relate to journal entries, which are usually an accounting entry to correct a miscoding error. Amount: All payments shown exclude VAT. Negative amounts relate to credit notes or corrections. Corrections: Miscoding errors may occur, for example the allocation of a payment to an incorrect expense area or expense type. These are usually corrected in the next month. One of the principles of the spending guidance is to make the data available quickly and to reflect how each individual item was originally recorded in the financial system. Therefore since this report includes only one months data it is likely to include some miscoding errors which have not been corrected yet. These corrections will not be back dated so will appear in the next months report. In the month that the correction occurs a credit (negative) amount will show against the incorrect expense area/ expense type and the corresponding payment will show against the correct expense area/expense type. Supplier Name: The name of the supplier or recipient of the payment. Payments to individuals which may contain sensitive information have been redacted. Supplier ID: Our internal reference number to enable us to identify the supplier. Expense Area: The department where the expenditure is incurred. Expense Type: The description of the nature of the spend.

Variability in mean payment per physician, number of physicians, and aggregated payments for transactions in the Open Payments database, 2014–2018, for each top-category specialty available for allopathic and osteopathic physicians.

See the data reports required under the Physician Payments Sunshine Act.

Open Payments (otherwise known as the Sunshine Act) - Open Payments is a Congressionally-mandated transparency program that increases awareness of financial relationships between the health care industry and physicians by collecting and reporting any payments or transfers of value medical manufacturers make to physicians or teaching hospitals.

This dataset contains data for the Healthcare Payments Data (HPD) Snapshot visualization. The Enrollment data file contains counts of claims and encounter data collected for California's statewide HPD Program. It includes counts of enrollment records, service records from medical and pharmacy claims, and the number of individuals represented across these records. Aggregate counts are grouped by payer type (Commercial, Medi-Cal, or Medicare), product type, and year. The Medical data file contains counts of medical procedures from medical claims and encounter data in HPD. Procedures are categorized using claim line procedure codes and grouped by year, type of setting (e.g., outpatient, laboratory, ambulance), and payer type. The Pharmacy data file contains counts of drug prescriptions from pharmacy claims and encounter data in HPD. Prescriptions are categorized by name and drug class using the reported National Drug Code (NDC) and grouped by year, payer type, and whether the drug dispensed is branded or a generic.

This table provides statistics on the Distribution of Physician Payments by Type of Service and Specialty, based on fee-for-service payments under the Alberta Health Care Insurance Plan (AHCIP). This table is an Excel version of a table in the “Alberta Health Care Insurance Plan Statistical Supplement” report published annually by Alberta Health.

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.

This dataset shows all payments made by state agencies from Fiscal Years 2008 to 2023. All parties receiving funds are shown: private businesses, local governments, non-profit organizations, and individuals. If you have any questions please contact service.desk@maryland.gov. This dataset combines multiple data sources from the Department of Budget and Management to show spending across multiple years. Source data can be found at spending.dbm.maryland.gov. This same data is mirrored in pre-existing datasets on data.maryland.gov, one for each fiscal year.

Information on Open Payments managed by the Centers for Medicare & Medicaid Services (CMS), which is a national disclosure program created by the Affordable Care Act (ACA) that promotes transparency and accountability by helping consumers understand the financial relationships between pharmaceutical and medical device industries and physicians and teaching hospitals.

This dataset contains data for the Healthcare Payments Data (HPD): Medical Out-of-Pocket Costs and Chronic Conditions report. The data covers three measurement categories: annual member count, annual median out-of-pocket count, annual median claim count. The annual member count quantify the number of unique individuals who received at least one medical service in the reporting year. Annual median out-of-pocket measurements quantifies the sum of copay, coinsurance, and deductible incurred by members. Annual median claim count measurements quantifies the number of distinct claims or encounters associated with members. Both 25th and 75th percentiles for out-of-pocket cost and claim count are also included. Measures are grouped by payer types, chronic conditions flag, chronic condition types, and chronic condition numbers.

Texas Code, Chapter 380 Payments & Compliance Dataset

This dataset is pre-filtered based on the most frequent searches of Open Payments data.

This dataset contains data for the Healthcare Payments Data (HPD) Services report. The term "Services" refers to individual procedures reported on the service lines of healthcare claims in California, categorized using the Restructured Berenson-Eggers Type of Services (BETOS) Classification System (RBCS) from the Centers for Medicare & Medicaid Services (CMS). The data in the report includes three main metrics: Total services, the total member count, and the service rate per 1,000 members. Total services represents the total number of services received by members during the reporting year. The member count reports the total number of unique individuals who received at least one service during the reporting year. The service rate per 1,000 members is calculated by dividing the total number of services during the reporting year by the total sum of monthly member enrollments (provided in the data) and multiplying the result by 12,000. The metrics can be grouped by year, age, sex (assigned at birth), county of residence (including an option for Los Angeles Service Planning Areas, or SPAs), Covered California Region, and payer.

Users can choose to view the data at two different levels. The most aggregate level groups the data by the eight main RBCS categories: Anesthesia, Durable Medical Equipment (DME), Evaluation and Management (E&M), Imaging, Procedure, Test, Treatment and Other. The second level breaks the eight aggregate RBCS categories into more specific subcategories. Data files are provided for each choice.

SUMMARY

DDOD use case to link Centers for Medicare and Medicaid Services (CMS) Open Payments dataset to a physician's National Provider Identifier (NPI).

WHAT IS A USE CASE?

A “Use Case” is a request that was made by the user community because there were no available datasets that met their particular needs. If this use case is similar to your needs, we ask that you add your own requirements to the specifications section.

The concept of a use case falls within the Demand-Driven Open Data (DDOD) program and gives you a formalized way to identify what data you need. It’s for anyone in industry, research, media, nonprofits or other government agencies. Each request becomes a DDOD use case, so that it can be prioritized and worked on.

Use Cases also provide a wealth of insights about existing alternative datasets and tips for interpreting and manipulating data for specific purposes.

PURPOSE

Linking the Open Payments dataset to NPI would allow for matching to other datasets which use NPI.

VALUE

A common physician identifier across all datasets would allow for provider cost and quality analytics across datasets.

USE CASE SPECIFICATIONS & SOLUTION

Information about this use cases is maintained in a wiki: http://hhs.ddod.us/wiki/Use_Case_29:_Link_Open_Payments_dataset_to_NPI

It serves as a knowledge base.

USE CASE DISCUSSION FORUM

All communications between Data Users, DDOD Administrators and Data Owners are logged as discussions within GitHub issues: https://github.com/demand-driven-open-data/ddod-intake/issues/29

It aims to provide complete transparency into the process and ensure the same message gets to all participants.

CASE STATUS

Closed, as the legislation that made Open Payments possible explicitly forbids reporting of the NPI.

BackgroundWhile the rise in opioid analgesic prescribing and overdose deaths was multifactorial, financial relationships between opioid drug manufacturers and physicians may be one important factor.MethodsUsing national data from 2013 to 2015, we conducted a retrospective cohort study linking the Open Payments database and Medicare Part D drug utilization data. We created two cohorts of physicians, those receiving opioid-related payments in 2014 and 2015, but not in 2013, and those receiving opioid-related payments in 2015 but not in 2013 and 2014. Our main outcome measures were expenditures on filled prescriptions, daily doses filled, and expenditures per daily dose. For each cohort, we created a comparison group that did not receive an opioid-related payment in any year and was matched on state, specialty, and baseline opioid expenditures. We used a difference-in-differences analysis with linear generalized estimating equations regression models.ResultsWe identified 6,322 physicians who received opioid-related payments in 2014 and 2015, but not in 2013; they received a mean total of $251. Relative to comparison group physicians, they had a significantly larger increase in mean opioid expenditures ($6,171; 95% CI: 4,997 to 7,346), daily doses dispensed (1,574; 95%CI: 1,330 to 1,818) and mean expenditures per daily dose ($0.38; 95% CI: 0.29 to 0.47). We identified 8,669 physicians who received opioid-related payments in 2015, but not in 2013 or 2014; they received a mean total of $40. Relative to comparison physicians, they also had a larger increase in mean opioid expenditures ($1,031; 95% CI: 603 to 1,460), daily doses dispensed (557; 95% CI: 417 to 697), and expenditures per daily dose ($0.06; 95% CI: 0.002 to 0.13).ConclusionsOur findings add to the growing public policy concern that payments from opioid drug manufacturers can influence physician prescribing. Interventions are needed to reduce such promotional activities or to mitigate their influence.

2025: The CY 2025 Medicare Physician Fee Schedule has been updated by the annual PFS Final Rule for various new policies and for new and existing CPT/HCPCS codes for this latest calendar year, which becomes effective starting January 1, 2025, with PFS conversion factor of $32.3465.