Journalism in the Public Interest

How We Analyzed Medicare’s Drug Data

ProPublica obtained Medicare Part D data from the Centers for Medicare and Medicaid Services (CMS) under the Freedom of Information Act. Here follows more information about the data and how we analyzed it.

Updated June 2015

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.

ProPublica has published Prescriber Checkup since May 2013. We initially obtained the data under the Freedom of Information Act. (You can download older data in our data store.) In April 2015, the Centers for Medicare and Medicaid Services began releasing the information on its website.

There is a lag between when prescriptions are written and when the data is processed and released. The data currently available covers calendar year 2013.

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.

In 2013, 1.3 million providers wrote nearly 1.4 billion prescriptions, including refills. The data does not include prescriptions that were written during hospital or short skilled nursing home stays because those are paid for under different parts of Medicare.

For each provider and drug, the data included the total number of claims, including refills dispensed, the number of beneficiaries who received the drug, the retail cost of the drug, and the days of supply. CMS did not release any information in cases where a provider wrote 10 or fewer prescriptions for a specific drug, to protect patient privacy. In addition, if the number of beneficiaries who received the drug was under 11, that field was left blank to protect patient privacy.

The file also broke down those prescriptions written for patients 65 and older in 2013.

Prescriber Checkup displays about 410,000 providers who wrote 50 or more prescriptions for at least one drug in 2013.

CMS also provided data on overall spending and prescriptions for each drug nationally and in each state.

To understand how best to analyze and categorize the drugs, we consulted dozens of experts. They included pharmacologists, academics, government officials who work with prescribing data and specialists in geriatrics and psychiatry.

In our early analyses, we relied on data from First Databank, a company that sells and analyzes health information, to classify drugs by category, such as narcotics or antipsychotics. In the current version of the tool, we rely on lists from CMS to characterize drugs as Schedule 2 controlled substances, Schedule 3 controlled substances and antipsychotics. We have used our own research to identify benzodiazepines and drugs that are considered particularly risky for older patients by the American Geriatrics Society. (The AGS compiles what's called the Beers list of drugs that may be inappropriate for seniors; often, safer alternatives exist.)

The Medicare Part D data was attributed to doctors both by name and by a unique federal health care ID called an NPI, or National Provider Identifier. Providers’ information is current as of March 2015.

The data could not tell us everything. We 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.

In addition, 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. The drugs for these patients are expensive and there aren’t comparable generics.

When a provider writes a prescription, the pharmacy filling it enters the information into a computer system. That is transmitted to a health plan for payment and then relayed to Medicare. Medicare compiles all of the prescriptions into a massive database. (Prescriptions written by doctors but not filled by patients wouldn’t be included.)

Prescriptions can vary in length, from days to months. Some researchers who analyze prescribing patterns adjust the number of prescriptions to a 30-day standard to allow for easier comparisons between providers. This app does not. Rather, it notes when the average length of a provider’s prescriptions for a given drug is higher or lower than normal. In some cases, a provider may write shorter prescriptions while adjusting a patient’s medication regimen, for example. That could mean that the provider’s prescription count may appear higher than his or her peers. On the other end, if providers write prescriptions for more days than average, they may have fewer prescriptions. Prescriber Checkup indicates that a prescriber’s average prescription length was significantly higher or lower from his or her peers if it was more than two standard deviations from providers in the same specialty and state for that drug.

Prescriber Checkup features a chart comparing prescribers to others in their state and specialty. It clusters prescribers based on their drug preferences and volume and represents them visually under the heading “Another View.” A provider who appears far to the right has drug preferences and prescribing volume that are markedly differ from others.

If there are 10 or fewer providers in a given specialty and state, comparisons aren’t shown.

In a few cases, we use estimates instead of precise counts for prescription totals. This is because of CMS redactions in the underlying data to protect patient privacy. The actual number may be higher or lower by as many as five prescriptions. These instances are marked in the app.

Finally, a small number of drugs appear more than once in the tool because of slight differences in their generic names, which are not shown. For purposes of aggregation, we have combined them into a single brand name.

See our FAQ for more information about this project. If you have questions or need more information please email

Tracy Weber contributed to this report.


The FAQ link directs me to a “404 Not Found” error. Thanks!

Amazing release! The entire Propublica/Post team should be applauded for their efforts here. Getting this scale of identifiable data is non-trivial.

I would love to hear the war story: How long did it take you to get it, did you have to sue, etc etc…

Of course, I am very curious to see about if/when/how you might make the data available for secondary analysis…


I’m not surprised (and in fact relieved) that allegedly self-described ophthalmologist Nevin Mehta of New York has prescribing patterns far to the right of his peers. He’s an otolaryngologist NOT an ophthalmologist.

At least you got the first letter of his specialty correct.

Wayne Elliott

May 14, 2013, 6:10 p.m.

Big Brother is here and watching you - by name and number.

Only in this country do folks with nothing to do find ways to make people suffer.  These bigots clearly have never been or lived with someone in pain 24/7.  We demand help for our pets but yet we want our people to suffer.  The true addicts will always get their drugs one way or another but the honest person will most likely just end their life if made to feel as though society has cast them aside and in pain with no relief.

This article is part of an ongoing investigation:

The Prescribers: Inside the Government's Drug Data

Medicare’s failure to monitor what doctors are prescribing has wasted billions of taxpayer dollars on excessive use of brand-name medication and exposed the elderly and disabled to drugs they should avoid.

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