An Effective Healthcare Data Solution with ProviderClenz

Curated & Enriched Provider Data

ProviderClenz from Curatus can help reduce the administrative burden and close crucial revenue leakage points by 'scrubbing' your provider data in preparation for your critical data collection, analytics, and submission events.

Our proprietary cleansing process is an easy to implement healthcare data solution and requires no contact with your providers, thereby removing the fear of provider abrasion.

Your provider data and claims data are uploaded into our ProviderClenz platform.
From there, your information is run through our sophisticated data curation and
enrichment algorithms.
The result is a pristine provider data set for your HEDIS, Risk Analytics, and Encounter
Reporting submission efforts.

Download the ProviderClenz PDF

Simply Put, Bad Provider Data Costs Health Plans & Health Systems Money

ProviderClenz Use Cases

Select a hexagon on our interactive graphic below to learn more:

Public Datasets

These primarily include national and state-based data from government agencies responsible for medical licensing, clinical practice oversight, and dispensing controlled substances; such as NPPES, DEA, and State Medical Licensing Boards.

Risk Analytics

Many false positives and false negatives for provider/member combinations can be caused by bad provider data. The results include a missed chart, an upset provider office staff, and unnecessary expense.

Private Datasets

These include data gathered and curated by private companies serving both healthcare and other industries with complementary data needs; such as telecommunications, credit-rating and monitoring, mapping and geo-coding, etc.

Encounter Reporting

Provider data errors are a leading cause of Encounter Data and EDGE Submission rejections. Excessive disallowed or rejected encounters can cause revenue leakage, which is easily avoided with ProviderClenz.

Client Datasets

These include information from various high-volume management information systems within health plan and health systems such as claims, care management, and provider contracting. We prefer to receive blinded data.

Quality Metrics

Bad provider data going into critical quality analytics can result in false denominator inclusion and no matching chart to validate the numerator. This results in negative impact to quality measure performance and revenue.

Looking for more custom outputs and use cases?

LEARN MORE ABOUT PROVIDERLENZ ➥