A Comparison of Automated Data Collection and Manual Data Collection for Toxicology Studies

Salem, Habib Haj; Roth, E.; Fornango, J.

doi:10.1007/978-3-642-69083-9_68

Cited by 4 publications

(3 citation statements)

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“…all of the intra-operative anesthesia care record) and manual validation may be needed to ensure that the automated algorithms are appropriately sensitive and specific. Automated collection, when carefully created and modeled from manual collection, can save time, improve accuracy and speed, and allow for ongoing and immediate response to surveillance as evidenced in medical and public health focused research [1,2,24]. Automation and re-use for physician and medical practice must continue to be examined to ensure that the findings carry over to nursing practice context.…”

Section: Scientificmentioning

confidence: 99%

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

Byrne¹,

Jordan²,

Welle³

2013

Appl Clin Inform

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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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Section: Scientificmentioning

confidence: 99%

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

Byrne¹,

Jordan²,

Welle³

2013

Appl Clin Inform

View full text Add to dashboard Cite

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“…Data can be manually abstracted from patient medical records into a customized data entry application ( 8 ). The use of a clearly written data abstraction manual combined with training of abstractors may decrease the potential for abstractors to apply subjective judgment and impair accuracy of data abstraction ( 9 , 10 ). A major limitation of data abstraction is transcription error due to manual data entry by abstractors ( 11 ).…”

Section: Introductionmentioning

confidence: 99%

Data Quality Improvement and Internal Data Audit of the Chinese Neonatal Network Data Collection System

Sun¹,

Cao²,

Hei³

et al. 2021

Front. Pediatr.

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Background: The Chinese Neonatal Network (CHNN) is a nationwide neonatal network that aims to improve clinical neonatal care quality and short- and long-term health outcomes of infants. This study aims to assess the quality of the Chinese Neonatal Network database by conducting an internal audit of data extraction.Methods: A data audit was performed by independently replicating the data collection and entry process in all 58 tertiary neonatal intensive care units (NICU) participating in the CHNN. Eighty-eight data elements selected for re-abstraction were classified into three categories (critical, important, less important), and agreement rates for original and re-abstracted data were predefined. Three to five records were randomly selected at each site for re-abstraction, including one short- (0–7 days), two medium- (8–28 days), and two long-stay (more than 28 days) cases. Agreement rates for each data item were calculated for individual NICUs and across the network, respectively.Results: A total of 283 cases and 24,904 data fields were re-abstracted. The agreement rates for original and re-abstracted data elements were 96.1% overall, and 97.2, 94.3, and 96.6% for critical, important, and less important data elements, respectively. Individual site variation for discrepancies ranged between 0.0 and 18.4% for all collected data elements.Conclusion: The completeness, precision, and quality of data in the CHNN database are high, providing assurance for multipurpose use, including health service evaluation, quality improvement, clinical trials, and other research.

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“…Although both methods follow a similar trajectory, each route presents with their own advantages and limitations. For example, stand-alone data collection applications require manual data entry which is subject to transcription error 6,7 and interrater variability, 8,9 particularly when data are abstracted by multiple individuals, as is the case in multicenter networks. These potential sources of disagreement often necessitate the need for validation checks to resolve inaccuracies in common data elements.…”

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confidence: 99%

Internal Audit of the Canadian Neonatal Network Data Collection System

et al. 2017

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Background Neonatal databases worldwide have become a prominent tool for benchmarking, evaluation of outcomes, and quality improvement initiatives. We aimed to assess the precision of the Canadian Neonatal Network (CNN) database by conducting an internal audit of data extraction. Methods An audit was conducted in all 31 neonatal units participating in the CNN. Ninety-five data items selected for reabstraction were classified into categories (critical, important, less important) based on predefined agreement rates. Five records were randomly selected at each site for reabstraction, including one short (3–7 days), two medium (8–12 days), and two long (18–22 days) stay cases. Agreement rates for each data item were calculated for individual units and across the network. Results A total of 155 cases and 14,725 data fields were reabstracted. The overall agreement rates for critical, important, and less important data items were 98.0, 96.1, and 96.3%, respectively. Individual site variation for discrepancies ranged between 0.2 and 12.8% for all collected data items. Conclusion Neonatal data extraction within the CNN database structure exhibited high precision; thereby, revealing the reliability of our data abstraction for neonatal demographic, processes of care, and outcomes information. An independent external audit of data extraction would be beneficial.

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A Comparison of Automated Data Collection and Manual Data Collection for Toxicology Studies

Cited by 4 publications

References 0 publications

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

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

Data Quality Improvement and Internal Data Audit of the Chinese Neonatal Network Data Collection System

Internal Audit of the Canadian Neonatal Network Data Collection System

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