Data Sources

We gathered the data used for this API from a variety of sources. Many of the data points were calculated by ProPublica by combining data from different sources.

Data sources we used in the creation of this API include:

• CMS NPPES NPI Registry: The NPI Registry Public Search is a free directory of all active National Provider Identifier (NPI) records, provided by the Center for Medicare and Medicaid Services (CMS). Healthcare providers apply for their unique 10-digit NPIs through the CMS National Plan and Provider Enumeration System (NPPES), in order to identify themselves in a standard way throughout their industry.

  Other information published with the NPI includes the provider’s name, speciality, and practice address.

• U.S. Department of Health and Human Services Office of Inspector General’s List of Excluded Individuals and Entities Database: This database provides information about providers who have been excluded from participation in Medicare, Medicaid and all other Federal health care programs. Individuals and entities that are excluded may be reinstated at a future date.

  A significant part of HHS OIG’s mission is combating fraud, waste and abuse within HHS programs.

  To read more about the process that HHS OIG uses to determine exclusions, you can read their FAQ.

• ProPublica’s Treatment Tracker: ProPublica’s Treatment Tracker provides data about Medicare Part B payments to health care providers, and also shows how providers compare to their peers — that is, providers in the same state and specialty — in terms of the services they perform. Medicare Part B covers the services delivered to seniors and the disabled.

  Note: Medicare only released data on services and referral patterns if they applied to at least 11 patients. If a service was performed on 10 or fewer patients, CMS redacted it and excluded it from aggregate totals.

  Note: The data does not include Medicare Advantage plans, which are the health plans Medicare beneficiaries can choose in place of the traditional program. Nor does it include services delivered to patients with other coverage, such as private health insurance or Medicaid.

  A more in-depth listing of the limitations of the data is available in the Treatment Tracker methodology.

• ProPublica’s Dollars for Docs: ProPublica’s Dollars for Docs database contains payments to doctors and teaching hospitals from pharmaceutical and medical device companies made between August 2013 and December 2014. The disclosures were required under the Physician Payment Sunshine Act, a part of the 2010 Affordable Care Act.

  ProPublica’s analysis found that most doctors take payments, and that doctors who receive payments are, on average, more likely to prescribe a higher percentage of brand-name drugs.

• ProPublica’s Prescriber Checkup: Prescriber Checkup examines the prescribing patterns of physicians and other providers in Medicare’s drug benefit program, known as Part D. No patient information was disclosed. Prescribers include any health professional who wrote prescriptions filled by beneficiaries in Medicare Part D. In addition to doctors, nurses, physician assistants, dentists and others with prescribing authority are included.

  • Note: The data may not tell us everything. ProPublica interviewed many high-volume prescribers to better understand their patients and their practices. Some told us their numbers were high because they were credited with prescriptions by others working in the same practice. In addition, providers who primarily work in long-term care facilities or busy clinics with many patients naturally may write more prescriptions.

  • Note: The type of patients some doctors see may affect their rate of prescribing name-brand drugs. Some of the physicians who prescribe name brands at far higher rates than their peers specialize in treating HIV/AIDS.

• ProPublica’s Surgeon Scorecard: Surgeon Scorecard uses Medicare billing records for in-patient hospital stays to calculate a figure called the Adjusted Complication Rate (ACR) for individual surgeons.

  To calculate a surgeon’s raw rate of complications for a given procedure, we divided the number of the surgeon’s patients who suffered a complication by the total number of surgeries he or she performed. We then factored in a variety of other elements, such as differences in patient health, age and hospital quality.

  The result is an ACR with a confidence interval with an upper and lower bound: the more data we have, the higher our confidence and the narrower the confidence interval.