J Michel1, A Hsiao2, A Fenick2. 1. Children's Hospital of Philadelphia, Pediatrics , Philadelphia, Pennsylvania, United States. 2. Yale School of Medicine, Pediatrics and Emergency Medicine , New Haven, Connecticut, United States.
Abstract
BACKGROUND: Transitioning between Electronic Medical Records (EMR) can result in patient data being stranded in legacy systems with subsequent failure to provide appropriate patient care. Manual chart abstraction is labor intensive, error-prone, and difficult to institute for immunizations on a systems level in a timely fashion. OBJECTIVES: We sought to transfer immunization data from two of our health system's soon to be replaced EMRs to the future EMR using a single process instead of separate interfaces for each facility. METHODS: We used scripted data entry, a process where a computer automates manual data entry, to insert data into the future EMR. Using the Center for Disease Control's CVX immunization codes we developed a bridge between immunization identifiers within our system's EMRs. We performed a two-step process evaluation of the data transfer using automated data comparison and manual chart review. RESULTS: We completed the data migration from two facilities in 16.8 hours with no data loss or corruption. We successfully populated the future EMR with 99.16% of our legacy immunization data - 500,906 records - just prior to our EMR transition date. A subset of immunizations, first recognized during clinical care, had not originally been extracted from the legacy systems. Once identified, this data - 1,695 records - was migrated using the same process with minimal additional effort. CONCLUSIONS: Scripted data entry for immunizations is more accurate than published estimates for manual data entry and we completed our data transfer in 1.2% of the total time we predicted for manual data entry. Performing this process before EMR conversion helped identify obstacles to data migration. Drawing upon this work, we will reuse this process for other healthcare facilities in our health system as they transition to the future EMR.
BACKGROUND: Transitioning between Electronic Medical Records (EMR) can result in patient data being stranded in legacy systems with subsequent failure to provide appropriate patient care. Manual chart abstraction is labor intensive, error-prone, and difficult to institute for immunizations on a systems level in a timely fashion. OBJECTIVES: We sought to transfer immunization data from two of our health system's soon to be replaced EMRs to the future EMR using a single process instead of separate interfaces for each facility. METHODS: We used scripted data entry, a process where a computer automates manual data entry, to insert data into the future EMR. Using the Center for Disease Control's CVX immunization codes we developed a bridge between immunization identifiers within our system's EMRs. We performed a two-step process evaluation of the data transfer using automated data comparison and manual chart review. RESULTS: We completed the data migration from two facilities in 16.8 hours with no data loss or corruption. We successfully populated the future EMR with 99.16% of our legacy immunization data - 500,906 records - just prior to our EMR transition date. A subset of immunizations, first recognized during clinical care, had not originally been extracted from the legacy systems. Once identified, this data - 1,695 records - was migrated using the same process with minimal additional effort. CONCLUSIONS: Scripted data entry for immunizations is more accurate than published estimates for manual data entry and we completed our data transfer in 1.2% of the total time we predicted for manual data entry. Performing this process before EMR conversion helped identify obstacles to data migration. Drawing upon this work, we will reuse this process for other healthcare facilities in our health system as they transition to the future EMR.
Entities:
Keywords:
Immunization; automatic data processing; electronic data processing; electronic health records; organizational efficiency
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