Impact of an integrated electronic handover tool on pediatric junior medical staff (JMS) handover.

BACKGROUND: Clinical medical handover between doctors forms a critical part of the patient care process. However, with the evolution of junior medical staff (JMS) working conditions, time pressure and increasing clinical and administrative loads mean that quality clinical handover is increasingly important yet more challenging to achieve. This study evaluated the impact of a newly integrated electronic handover tool on JMS adoption and usage of the tool, as well as impacts on the quality (accuracy and redundancy) of handover data, JMS perceived workflow (time management and communication) and JMS satisfaction. FINDINGS: The majority of JMS surveyed used the tool at 1 (87.0%) and 3 (67.4%) months post implementation. After the introduction of the electronic handover tool, 67.5% of users spent less than 15min updating handover data in the electronic handover tool, compared to just 6.7% prior to the introduction. 28.3% of respondents noted that there was >25% redundant data, compared to more than half (52.2%) prior to introduction of the electronic tool. Overall JMS satisfaction with their handover process was significantly higher post implementation of the integrated electronic handover report (17.4% pre, 80.4% at 1 month, 67.4% at 3 months). CONCLUSION: A newly introduced integrated electronic medical record handover tool had a high uptake amongst JMS, and resulted in improvement in perceived handover efficiency, a reduction in redundant data entry and improved JMS handover satisfaction.

A rational approach to legacy data validation when transitioning between electronic health record systems.

OBJECTIVE: The objective of this project was to use statistical techniques to determine the completeness and accuracy of data migrated during electronic health record conversion. METHODS: Data validation during migration consists of mapped record testing and validation of a sample of the data for completeness and accuracy. We statistically determined a randomized sample size for each data type based on the desired confidence level and error limits. RESULTS: The only error identified in the post go-live period was a failure to migrate some clinical notes, which was unrelated to the validation process. No errors in the migrated data were found during the 12- month post-implementation period. CONCLUSIONS: Compared to the typical industry approach, we have demonstrated that a statistical approach to sampling size for data validation can ensure consistent confidence levels while maximizing efficiency of the validation process during a major electronic health record conversion.