|ProviderPoint®: Master Provider File Cleansing and Augmentation|
Enclarity ProviderPoint is a secure, hosted service specifically designed to cleanse, standardize, consolidate, de-duplicate, augment and integrate provider data from across multiple systems within an enterprise. Enclarity delivers the highest quality information by tapping the best data sources; assembling the right information via our innovative AcuSync® process; and systematically verifying the results. The result is a storehouse of correct, current, comprehensive information that Enclarity calls its Master Provider Referential Database.
The highest quality information
Too many vendors attempt to impress prospective clients by focusing on the number of data sources they draw from or high counts of healthcare providers. But, neither of those measures is, in the end, an accurate measure of the quality of information clients receive - and that’s what drives the value you get.
The starting place for the Master Provider Referential Database is thousands of referential and transactional data sources, which currently includes information on over 6.5 million providers from more than 140 million records. Whenever possible, Enclarity uses original, authority sources, such as state certification boards, rather than compiled sources, which tend to be less current and more prone to errors. Enclarity updates the Master Provider Referential Database each time a source is changed to ensure that the healthcare provider (health care provider) information you receive is as up-to-date as possible. Enclarity also bridges sources, recognizing that although a particular source may be the authority for one data element, it may be not have the best data quality for other elements.
In creating our Master Provider Referential Database, we bring together hundreds of sources of provider information on a daily, weekly, monthly and quarterly basis. It was critical to be able to accurately identify each unique provider in order to build this system. Enclarity has the most robust and sophisticated matching algorithm available on the market today. Hundreds of matching rules and techniques are deployed on each match, ensuring accurate results.
AcuSync is Enclarity’s "secret sauce." AcuSync uses advanced analytical and database methods both to create the Master Provider Referential Database and to match health care provider legacy files to it. Although AcuSync’s foundation is time-tested techniques used in the financial industry, it is purpose-built for working with health care provider information. The process includes steps such as file transformation, standardization of data elements such as name and address, element validation, and determining if two records represent the same health care provider. Because of AcuSync, Enclarity can quickly and accurately match, update, join and augment health care provider records, and can identify duplicate records. The AcuSync process steps are depicted in the following diagram:
To ensure that the information it provides is the highest quality, Enclarity uses a combination of analytical models and phone calls to verify provider demographic information. We perform two types of telephonic verification. The first is a campaign that contacts office-based physicians roughly twice per year. The second is a campaign that uses a combination of client-driven contacts and analytical models that identify the data that is most likely to have changed, to establish call priorities that drive accuracy across all provider types.
Each year, we make over two million phone calls to verify the quality of our provider data. To make it easy for clients to decide which Enclarity-proposed changes to accept and which to research further, Enclarity records data about the quality of each health care provider data element in the Master Provider Referential Database. On average, there are approximately five fields dedicated to recording data about each element’s quality.
Enclarity utilizes statistical techniques in our matching process. Our standard data flow utilizes the following processes:
Once a match is found, we calculate and measure how valid and accurate your input information is as we compare it to our Master Referential Provider Database. We can tell our clients if an address in their provider file is incorrect or out of date, if the office is an active office, if the provider’s phone number is incorrect, if the provider is sanctioned, has an out of date license or in the worst case, if the provider is deceased. Confidence scores are calculated on every single record to determine the relative accuracy of our validation.
Upon determining that we have the right provider, we can augment the record with supplemental information such as other active practice addresses, phone numbers, license numbers, etc. We do this by linking the incoming record to the Master Provider Referential Database then chaining all the relevant data sources to the input record to validate, cleanse and ID each record.
However, Enclarity is more than a company with advanced solutions for provider data accuracy. While our core competency is cleansing provider data to the most accurate levels achievable, Enclarity is much more than that. We use proven, best-of-breed techniques of Customer Data Integration, long established in consumer marketing and financial services, and have adopted these techniques for health care provider data matching.
Our ProviderPoint solution can offer a single, enterprise-wide view of each provider by enabling the identification, linking and synchronization of provider information across disparate silos of data. This view of the provider should be accessible throughout the enterprise and automatically updated as provider information changes to ensure data integrity.
One of the significant differentiators Enclarity has to offer in this space is our tremendous passion for quality. Systematically, Enclarity has created several application audit tools to validate, verify or reject suspect data. Enclarity generates “scores” using probabilistic algorithms for each element to help determine a weighed rank of the input data. If the input data does not exceed this minimum quality threshold, the record is rejected. One example of an assignment of a lower score would be state license information does not line up with the provider’s practice address state. Other examples include:
The only before- and after- measurement of information quality and associated ROI
Everyone in the industry knows that poor health care provider information quality affects administrative efficiency, and marketing effectiveness. But, it has always been very difficult to accurately measure information quality at a point in time, track changes over time, and to attach a dollar value to those measurements.
Enclarity has changed all that. Enclarity has developed EQI®, a standardized score that enables an organization to measure its information quality at a point-in-time, compare quality over time, and to compare quality across sources and even with other organizations.
Most Enclarity clients establish a baseline for information quality during their buying process. Enclarity can then accurately estimate how your information quality should improve over time and the value you will receive. This engagement gives you a new understanding of the state-of-the-state of your health care provider information quality, provides a baseline for monitoring changes and continuous improvement, and enables the development of a sound business case, including expected return on investment.
Enclarity’s standard turnaround time is 48 to 72 hours after file receipt, depending on file size. As our solution processes several hundred thousand records per hour, we can work with our clients to meet their specific needs if a more aggressive turn-around time is required. We currently have production clients on daily cycles.
Enclarity has a business rules engine and can provider output files in almost any desired layout and format. Generally, our customers receive a series of pipe delimited flat files that are linked by the Enclarity unique Provider ID. This format allows for the many-to-one relationship common in provider data.