You've chosen a new system; now what do you do with the old data? You want to retire the legacy system, but need to retain end-user access to the vital detailed information from the old application. Traditional options for clinical systems include full data conversion and new system implementation, delaying a go-live, or running new and legacy systems in parallel. Each of these approaches has its advantages and disadvantages. If managed incorrectly, users do not have access to time critical information, or even worse, data is lost and IT costs skyrocket. Managing the legacy data effectively will maintain production operations, reduce costs and headaches for the IT department and keep the healthcare provider compliant with Federal data retention requirements.

Traditional options for managing healthcare legacy data all have inherent and significant risks affecting cash flow, effective operation of the new system and the legally required retention of healthcare legacy data. These risks can be mitigated with the help of our proven team of professionals.

Data migration in healthcare is a high-impact, time critical activity for any system transition. As an activity, it is often over-simplified, and even experienced managers have often overlooked its importance. Elektra Inc is ready to assist you in your data migration efforts. Our highly experienced professionals have extensive experience in migrations in transportation and government sectors.

Some of the symptoms of improper data migration are

• Loss of production data
• Loss of granularity of data
• Inability to switch completely to a new target system

Some of the ramifications of improper data migration are

• Increased workload at all levels - test, dr, qa and prod
• Increased inefficiency
• Lack of productivity
• Inability to improve efficiency or productivity despite optimization in business processes and/or infrastructure upgrade
• Increased costs
• Longer response time
• Potential legal issues

A more specific explanation would involve an ETL process (Extract, Transform and Load).

• Extract - As suggests extract data from a source system
• Transform - Manipulation of extracted data
• Loading - Loading the transformed data into the target system Challenges
• Identifying true source of data
• Developing an extraction engine
• Identifying duplicate data and its amalgamation
• Identifying data types - random, sequential, file and block.
• Developing a loading strategy