Migrating from ICD-9 to ICD-10 will be a high stakes, costly, and highly complex effort impacting a large part of every provider or payer enterprise. Apart from the complexity of mapping from ICD-9 to ICD-10 (many are not 1:1 mappings), major changes to business processes, policies, retraining of medical coders, education of clinical staff, etc., – system remediation in itself will be a significant undertaking.
In his earlier piece titled “ICD10 Migration Approach“, one of our senior solution architects, Mike Berard, states “Systems Remediation: Insurers must determine all business processes and applications impacted by ICD-10. Applications that capture, store, send, receive, or edit diagnosis or procedure codes must be modified. Fields must support alphanumeric characters and expanded to support extra digits. The new specificity of IDC-10 codes will impact corresponding application logic, business rules, system interfaces and data reporting.” Of course, providers must follow a similar process regarding system remediation for ICD-10.
Using Effective Meta Data Management to Identify Changes Needed for ICD-10 Migration
If your IT department can quickly identify and visualize all of the changes needed for ICD-10 migration – you don’t need to read any further. Ok, let’s make it easier – if your IT department can quickly identify and visualize all of the changes needed for changing just the data type and length of diagnosis and procedure code fields across the enterprise – you don’t need to read any further. Diagnosis and procedure code fields must support alphanumeric characters and must support an extra two digits (from five to seven). This is probably the most mundane and easiest change, right? It definitely isn’t rocket science to change the data type and length of a field – but of course you have to find all the fields that need to be changed – in databases, application code, models, reports, cubes, interfaces, screen forms, etc., (anyone remember Y2K?). We are talking about a profound change – and of course the risk of not identifying downstream system impacts may mean serious disruptions and possible system downtime.
This is where effective meta data management comes in. Meta data describes our data, processes, and systems. Meta data can be technical, business, or operational in nature – examples of meta data are database schemas, data definitions, KPI metric definitions, interface specifications, report layouts, source to target mappings, process execution statistics – to name a few. Without an effective meta data management solution (where meta data from many sources is integrated to provide a holistic view of the enterprise) – identifying all the changes needed for the migration from ICD-9 to ICD-10 is going to be an expensive, labor-intensive, and error prone proposition. Consider for a moment all the well documented (sarcasm) application code, interface specifications, models, schemas, etc., that you’ll have to browse through… Wouldn’t it be more advantageous and efficient to be able to use a meta data scanner to scan application code (for example) and extract data flows, transformations, and business logic? Aren’t we in the 21st century after all!
The Power of Enterprise Meta Data Management Systems
Enterprise meta data management systems and repositories are nothing new, and are being used by leading providers (e.g., Mayo Clinic) and payers (e.g., HCSC) to support enterprise information management initiatives such as Data Warehousing / Business Intelligence and Master Data Management. Organizations with an existing enterprise meta data management system might look at incorporating new meta data subject areas (e.g., business process models, data exchange meta data) in order to support the ICD-10 migration, and organizations new to enterprise meta data management should integrate meta data from the systems most likely to be affected by ICD-10 first (e.g., claims processing systems), and then continue to incorporate additional meta data sources (as the ICD-10 migration will have a major ripple effect throughout the enterprise). Those organizations with an enterprise meta data management system supported by solid Data Governance and Data Stewardship organizations will be better positioned for the ICD-10 migration, as these organizations can help provide alignment across the enterprise and ensure a more standardized approach, at least for the data management aspects of the ICD-10 migration.
With an effective meta data management system that provides a holistic view of the enterprise’s data structures, systems, applications, and business processes – system remediation can be performed in a timelier, less costly, and more accurate process. Analyses which can take months can be performed in hours or days (depending on the level of meta data integration). Implementing an enterprise meta data management system is not inexpensive or effortless, however the benefits provided may be outstanding for those visionary provider or payer organizations that recognize that the only constant is change (e.g., Meaningful Use, ACO, and other changes being envisioned) , and that the pace of change is accelerating at an increasing rate.
The costs of implementing an enterprise meta data management system may be recovered by the benefits realized from the ICD-9 to ICD-10 migration alone! However, once in place, the enterprise meta data management system will provide many other benefits for data and application management, business intelligence, system consolidation/migration, application development, SOA, etc., well into the future to help enable a more nimble and effective enterprise.
So what do you think? I’d love to hear from you with questions and comments below.
