Healthcare has a provider data problem.

OrderlyData is the solution.

How it Works
BackForward
STEP 1Ingest Your Data

We know your systems are complex. OrderlyData is a solution optimized for flexibility. We work with every client to ingest and transfer data in a way that does not disrupt your current ecosystem and workflows.

STEP 2Combine with Orderly's Data

OrderlyData leverages 30+ public and proprietary data sources. We score each data source by field (e.g., address) based on its likeliness to be correct and use this information to algorithmically compare your data to ours.

STEP 3Use ML to Repair Data

We then use supervised, non-parametric machine learning algorithms to identify the fields in your provider records most likely to be erroneous and update those fields only with cleaned and up-to-date data.

STEP 4Return Corrected Data

Lastly, we return your updated, accurate provider data in the structure in which you provided it to us and/or in the format and interval of your choosing.

STEP 1Ingest Your Data

We know your systems are complex. OrderlyData is a flexible solution optimized for flexibility. We work with every client to ingest and transfer data in a way that does not disrupt your current ecosystem and workflows.

STEP 2Combine with Orderly's Data

OrderlyData leverages 30+ public and proprietary data sources. We score each data source by field (e.g. address) based on its likeliness to be correct and use this information to algorithmically compare your data to ours.

STEP 3Use ML to Repair Data

We then use supervised, non-parametric machine learning algorithms to identify the fields in your provider records most likely to be erroneous and update those fields only with cleaned and up-to-date data.

STEP 4Return Corrected Data

Lastly, we return your updated, accurate provider data in the structure in which you provided it to us and/or in the format and interval of your choosing.

How OrderlyData is different
EXISTING SOLUTIONS
VS
OrderlyData
DATA SOURCES
Use only a handful of sources to validate provider directory data. On average, provider data is still 50% inaccurate.
Leverages 30+ public and proprietary data sources to ensure you recieve the most accurate, up-to-date provider data.
TECHNOLOGY
Rely on provider attestation to source directory data. Fewer than 20% of providers, however, complete data verification processes, despite outreach attempts.
Uses supervised machine learning to identify erroneous provider records, clean and update the data, all without provider feedback.
COST
Pay call centers, on average, four dollars per record to validate provider data and wait between 6-8 weeks to receive the evaluation results.
Automates validation through machine learning, allowing us to update information faster and more accurately than existing solutions at a fraction of the price.
DATA FIELDS
Validate only a small number of data fields, such as addresses and phone numbers.
Validates nearly any data field attributed to a provider and can be customized to the needs of your organization and the fields most important to you.
OUTPUT
Ingest and return your data in the format of their choosing, leaving the hard work of integration up to you.
Ingests and returns your data in the format of your choosing, integrating seamlessly into your existing workflows.