Comparison of Manual versus Automated Data Collection Method for an Evidence-Based Nursing Practice Study

Abstract

SummaryObjective: The objective of this study was to investigate and improve the use of automated data collection procedures for nursing research and quality assurance. Methods: A descriptive, correlational study analyzed 44 orthopedic surgical patients who were part of an evidence-based practice (EBP) project examining post-operative oxygen therapy at a Midwestern hospital. The automation work attempted to replicate a manually-collected data set from the EBP project. Results: Automation was successful in replicating data collection for study data elements that were available in the clinical data repository. The automation procedures identified 32 "false negative" patients who met the inclusion criteria described in the EBP project but were not selected during the manual data collection. Automating data collection for certain data elements, such as oxygen saturation, proved challenging because of workflow and practice variations and the reliance on disparate sources for data abstraction. Automation also revealed instances of human error including computational and transcription errors as well as incomplete selection of eligible patients. Conclusion: Automated data collection for analysis of nursing-specific phenomenon is potentially superior to manual data collection methods. Creation of automated reports and analysis may require initial up-front investment with collaboration between clinicians, researchers and information technology specialists who can manage the ambiguities and challenges of research and quality assurance work in healthcare. The expanded need for integrating evidence into clinical nursing practice, particularly through the use of the Electronic Health Records (EHR) and Clinical Data Repository (CDR), necessitates the exploration of new methodologies for quickly gathering, evaluating, and responding to clinical agency data. The traditional, solitary use of manual data collection may inhibit the ability of an organization to promptly inform practice changes and continuously measure the impact of these changes on patient outcomes. Recent research has suggested that automated data collection may be a synergistic approach that will help to reduce the time and effort required to collect and review data, while also increasing the quality of the data [1, 2]. Automation of Data CollectionData collection for quality assurance and research can be an intensive use of resources. There are multiple potential sources of error for studies relying on manual data collection, particularly if data collection is done across multiple care units and with multiple data collectors, even if the data collection is done within one clinical agency. Automation of data collection has been shown to conserve limited temporal resources, improve quality and accuracy of data, and potentially allow for the ongoing and rapid evaluation of policy or practice changes through report generation [2]. Automated data collection can be an excellent means of re-using clinical data that may have initially been captured only for care docume...

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