Clinical Conversion Toolkit

Introducing the Galen Clinical Conversion Toolkit, a solution and process designed to guide clients through converting from legacy systems and existing EHRs to the Allscripts Enterprise EHR. As Managed Service Organizations (MSOs) seek to expand their footprint through acquisitions, conversions to consolidate to a standardized system are often desirable to avoid the Healthcare Information System Mosaic.  Leveraging experience gained from previous clinical conversions, the Clinical Conversion Toolkit streamlines the process and provides a means of converting clinical data safely and efficiently. It should be noted that while conversions can be extremely useful in the sense that they save duplicate data entry, clients need to exercise caution in that any conversion data should be reviewed.

Discrete Conversion Types

  • Allergies

  • Immunizations

  • Medications

  • Documents

  • Results

  • Problems

  • Vitals

  • Dictionaries

  • Process Overview

    • Data Extraction

    • Data Analysis: Cross-Referencing

    • Design: Data Filtering, Matching (Provider, Patient Item), and Exceptions/Errors

    • Testing

    • Go-Live

    Data Extraction

    Whether the historical system is an EHR such as NextGen, Greenway or eClinicalWorks, a PM such as GE Centricity, or a legacy in-house clinical information system, one of the most important aspect of the conversion will be acquisition of the data. Often times, this will require assistance with the current vendor to trigger a bulk-load export of the data. The preference is to output to flat-file, thus facilitating an intermediary step of data analysis and cross-referencing to prep the data before it is loaded into the EHR.

    Data Analysis – Cross-Referencing:

    The most important part to cross-referencing is to ensure that the corresponding AE-EHR dictionary dependency exists in the dictionary. If starting from a “blank-slate,” it is prudent to extract the compendium information for the exported data file from the source/reference system. In the case that dictionary entries already exist in the AE-EHR (for instance, in the scenario of a client that is an MSO, multi-org, and single EHRDB), it will be important to setup cross references so that the codes in the reference system match up to the corresponding values in AE-EHR. This is often realized through deployment of translation tables within the Clinical Conversion Toolkit.

    • Provider code – Recommendation to use default “dummy” provider to identify clinical items loaded from conversion
    • Document type
    • Allergy code
    • Immunization name, provider, route of admin, body site and manufacturer
    • Problem and procedure codes
      • These codes will need to be cross referenced with the more-granular Medcin nomenclature.
      • This presents a challenge in that an ICD9 or CPT code could have a one-to-many relationship with Medcin.
      • The Clinical Conversion Toolkit provides a translation tool utilized in previous problem conversions to assist in the cross-reference.
    • Vital sign names
    • Order/Result Item Codes

    Design – Filtering:

    Filtering can be performed up-stream as part of the source/reference system extraction process, or it can be realized within the Clinical Conversion Toolkit logic. Typical filtering options include the following (but are not limited to):

    • Items that have been entered in error in vendor system
    • Allergies – NKA and NKDA entries are typically excluded to avoid incorrect reporting of NKA and NKDA
    • Exclusion of non-finalized documents or results

    Design – Matching:

    Patient Matching: Patient matching is crucial to the exchange of clinical information between systems.  The Allscripts Enterprise EHR has strict matching criteria – in summary, matching on three of the four of the following: Name, Date of Birth, Medical Record Number (MRN) and Social Security Number (SSN).  As there is a move away from using SSN, by patients at least, this leaves us with only three fields to match on for many patients: Name, Date of Birth and MRN. In certain cases, more advanced options for patient matching can be deployed such as fuzzy matching as previously described on our blog.

    Provider Matching: Clinical items will need to tie back to the provider who originally entered, ordered or authorized.  Typically, a short numeric or alphanumeric identifier is used to link a provider entry in the Other Vendor’s system to the provider entry in the EHR.  The current trend is to use a provider’s National Provider Identifier (NPI) as the identifier that the two systems exchange.

    Design – Exceptions:

    Not all of the data will load to the EHR without error. Some records will require manual assistance via add-on tools such as Allscripts Patient Bridge, while others may require more advanced troubleshooting and re-file. The Clinical Conversion Toolkit takes care of logging those transactions which generated error for future reference.


    Please contact if you or your organization would like to learn more about Galen’s Clinical Conversion Toolkit for the Allscripts Enterprise EHR.

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