Considerations when evaluating real‐world data quality in the context of fitness for purpose

Reynolds, Matthew W.; Bourke, Alison; Dreyer, Nancy A

doi:10.1002/pds.5010

Cited by 26 publications

(24 citation statements)

References 3 publications

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“…Understanding how medications are captured in data sources used for future studies, and recognizing situations when they may not be completely captured, especially within specific care settings (e.g., intra‐operatively administered medications) 24 is not a challenge unique to this study. Considerations for ensuring real‐world data are fit for purpose have been commented on previously 2,25 …”

Section: Discussionmentioning

confidence: 99%

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Using inpatient electronic medical records to study influenza for pandemic preparedness

Fuller

Cosgrove

Sands

et al. 2021

Influenza Resp Viruses

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Background We assessed the ability to identify key data relevant to influenza and other respiratory virus surveillance in a large‐scale US‐based hospital electronic medical record (EMR) dataset using seasonal influenza as a use case. We describe characteristics and outcomes of hospitalized influenza cases across three seasons. Methods We identified patients with an influenza diagnosis between March 2017 and March 2020 in 140 US hospitals as part of the US FDA's Sentinel System. We calculated descriptive statistics on the presence of high‐risk conditions, influenza antiviral administrations, and severity endpoints. Results Among 5.1 million hospitalizations, we identified 29,520 hospitalizations with an influenza diagnosis; 64% were treated with an influenza antiviral within 2 days of admission, and 25% were treated >2 days after admission. Patients treated >2 days after admission had more comorbidities than patients treated within 2 days of admission. Patients never treated during hospitalization had more documentation of cardiovascular and other diseases than treated patients. We observed more severe endpoints in patients never treated (death = 3%, mechanical ventilation [MV] = 9%, intensive care unit [ICU] = 26%) or patients treated >2 days after admission (death = 2%, MV = 14%, ICU = 32%) than in patients treated earlier (treated on admission: death = 1%, MV = 5%, ICU = 23%, treated within 2 days of admission: death = 1%, MV = 7%, ICU = 27%). Conclusions We identified important trends in influenza severity related to treatment timing in a large inpatient dataset, laying the groundwork for the use of this and other inpatient EMR data for influenza and other respiratory virus surveillance.

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Section: Discussionmentioning

confidence: 99%

“…Considerations for ensuring real‐world data are fit for purpose have been commented on previously. 2 , 25 …”

Section: Discussionmentioning

confidence: 99%

Using inpatient electronic medical records to study influenza for pandemic preparedness

Fuller

Cosgrove

Sands

et al. 2021

Influenza Resp Viruses

View full text Add to dashboard Cite

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“…On this point, it is important to acknowledge some considerations for quality checking RWE in the context of influenza vaccine effectiveness. While evaluating quality of evidence is particularly important for informing health decisions, appropriate and cautious selection of quality assessment tools and strategies is needed in this field [20]. For instance, some frameworks for grading quality of evidence and making recommendations are unlikely to be adequate for understanding the value and reliability of RWE regarding influenza vaccine effectiveness.…”

Section: Randomized Controlled Trial (Rct) Vs Real-wordl Data (Rwd)mentioning

confidence: 99%

“…Conversely, assessment of RWE in public health interventions should be conducted in the context of a complete overview of the research question and fitness for purpose [20]. While availability of and access to appropriate RWD sources are increasing, the use of a specific tool to assess influenza vaccine effectiveness is required to ensure that RWE is relevant in informing stakeholders and influencing public health policy.…”

Section: Randomized Controlled Trial (Rct) Vs Real-wordl Data (Rwd)mentioning

confidence: 99%

The Importance of Real-World Evidence in Understanding Influenza Vaccine Effectiveness

Ferrara

Mantovani

2022

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“…For example, some researchers might want to know the process or operational aspects of data collection (eg, Were the data collected under the bring-your-own-device policy or were devices provided?) [36]. These factors were not captured as a data quality dimension, but it is an aspect that might need to be considered when assessing the fitness-for-use of a data set.…”

Section: Limitationsmentioning

confidence: 99%

Identifying Data Quality Dimensions for Person-Generated Wearable Device Data: Multi-Method Study

Cho¹,

Weng²,

Kahn³

et al. 2021

JMIR Mhealth Uhealth

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Background There is a growing interest in using person-generated wearable device data for biomedical research, but there are also concerns regarding the quality of data such as missing or incorrect data. This emphasizes the importance of assessing data quality before conducting research. In order to perform data quality assessments, it is essential to define what data quality means for person-generated wearable device data by identifying the data quality dimensions. Objective This study aims to identify data quality dimensions for person-generated wearable device data for research purposes. Methods This study was conducted in 3 phases: literature review, survey, and focus group discussion. The literature review was conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guideline to identify factors affecting data quality and its associated data quality challenges. In addition, we conducted a survey to confirm and complement results from the literature review and to understand researchers’ perceptions on data quality dimensions that were previously identified as dimensions for the secondary use of electronic health record (EHR) data. We sent the survey to researchers with experience in analyzing wearable device data. Focus group discussion sessions were conducted with domain experts to derive data quality dimensions for person-generated wearable device data. On the basis of the results from the literature review and survey, a facilitator proposed potential data quality dimensions relevant to person-generated wearable device data, and the domain experts accepted or rejected the suggested dimensions. Results In total, 19 studies were included in the literature review, and 3 major themes emerged: device- and technical-related, user-related, and data governance–related factors. The associated data quality problems were incomplete data, incorrect data, and heterogeneous data. A total of 20 respondents answered the survey. The major data quality challenges faced by researchers were completeness, accuracy, and plausibility. The importance ratings on data quality dimensions in an existing framework showed that the dimensions for secondary use of EHR data are applicable to person-generated wearable device data. There were 3 focus group sessions with domain experts in data quality and wearable device research. The experts concluded that intrinsic data quality features, such as conformance, completeness, and plausibility, and contextual and fitness-for-use data quality features, such as completeness (breadth and density) and temporal data granularity, are important data quality dimensions for assessing person-generated wearable device data for research purposes. Conclusions In this study, intrinsic and contextual and fitness-for-use data quality dimensions for person-generated wearable device data were identified. The dimensions were adapted from data quality terminologies and frameworks for the secondary use of EHR data with a few modifications. Further research on how data quality can be assessed with respect to each dimension is needed.

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Considerations when evaluating real‐world data quality in the context of fitness for purpose

Cited by 26 publications

References 3 publications

Using inpatient electronic medical records to study influenza for pandemic preparedness

Using inpatient electronic medical records to study influenza for pandemic preparedness

The Importance of Real-World Evidence in Understanding Influenza Vaccine Effectiveness

Identifying Data Quality Dimensions for Person-Generated Wearable Device Data: Multi-Method Study

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