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Factual Makes a House Call with Improved Healthcare Provider Data

In the U.S., we have many choices when considering healthcare providers for ourselves or loved ones. Unfortunately, much of this information is scattered, disjointed, and unreliable.  If you’re shopping around for a specific type of doctor or specialist based on the insurance you have, that information is not easily available via the big search engines or major health web portal or apps.  Insurance companies have made it fairly difficult for both consumers and application developers to get data on the doctors they reimburse.

Fortunately, Factual has spent months pouring over numerous sources and tens of millions of inputs to build enhanced Healthcare Provider data which now includes the critical “insurance carried” field.  Additionally, this 1.8 million record strong dataset includes other attributes such as medical affiliations, gender, languages spoken, and education.

Among the initial launch partners for this data will be, a leading health site helping consumers connect with the healthcare providers best suited to address their specific needs and preferences.

Factual is focused on delivering high quality data to its customers, such as, and we’re constantly improving our data quality.  This new release of Healthcare Providers has more sources of data, better rules for ensuring you see only high quality, sensible data, and of course it’s served through our ultra-fast API.

Querying Data

As a developer, you can use the API which employs the same filter context with which you’re familiar (these examples are formatted for readability — remember to URL-encode your request values).  These examples show the HTTP requests to make, but be sure to make life easy and check out the API drivers.

This query returns the first twenty female doctors in Omaha (use the offset parameter to pull the next set).

The text fields in the table have been indexed, which enables full text search against them.  Developers may find it easier to just create ad hoc queries, rather than querying against specific attributes.  This query produces almost the identical results to the one above, with a few extra lines because words can appear in fields other than the expected one – such as “Omaha Blvd”.

Here we do a combination of filters and free text search:

Now we have a precise location, and fairly precise query without having to know that the column names were degrees and gender.

Let’s make sure she’s an experienced doctor, with at least 10 years of experience. To do this, we need to add a filter attribute with an inequality (see the Core API documentation on filters for further technical details):

Learning More

We hope that you’re healthy and your users are too.  But when it’s time to see a doctor, dentist, chiropractor, or a multitude of other providers, we think this data will prove to be a helpful resource.