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Verma AA, Pasricha SV, Jung HY, Kushnir V, Mak DYF, Koppula R, Guo Y, Kwan JL, Lapointe-Shaw L, Rawal S, Tang T, Weinerman A, Razak F. Assessing the quality of clinical and administrative data extracted from hospitals: the General Medicine Inpatient Initiative (GEMINI) experience. J Am Med Inform Assoc 2021; 28:578-587. [PMID: 33164061 DOI: 10.1093/jamia/ocaa225] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2020] [Revised: 08/27/2020] [Accepted: 09/14/2020] [Indexed: 12/20/2022] Open
Abstract
OBJECTIVE Large clinical databases are increasingly used for research and quality improvement. We describe an approach to data quality assessment from the General Medicine Inpatient Initiative (GEMINI), which collects and standardizes administrative and clinical data from hospitals. METHODS The GEMINI database contained 245 559 patient admissions at 7 hospitals in Ontario, Canada from 2010 to 2017. We performed 7 computational data quality checks and iteratively re-extracted data from hospitals to correct problems. Thereafter, GEMINI data were compared to data that were manually abstracted from the hospital's electronic medical record for 23 419 selected data points on a sample of 7488 patients. RESULTS Computational checks flagged 103 potential data quality issues, which were either corrected or documented to inform future analysis. For example, we identified the inclusion of canceled radiology tests, a time shift of transfusion data, and mistakenly processing the chemical symbol for sodium ("Na") as a missing value. Manual validation identified 1 important data quality issue that was not detected by computational checks: transfusion dates and times at 1 site were unreliable. Apart from that single issue, across all data tables, GEMINI data had high overall accuracy (ranging from 98%-100%), sensitivity (95%-100%), specificity (99%-100%), positive predictive value (93%-100%), and negative predictive value (99%-100%) compared to the gold standard. DISCUSSION AND CONCLUSION Computational data quality checks with iterative re-extraction facilitated reliable data collection from hospitals but missed 1 critical quality issue. Combining computational and manual approaches may be optimal for assessing the quality of large multisite clinical databases.
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Affiliation(s)
- Amol A Verma
- Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ontario, Canada.,Department of Medicine, University of Toronto, Toronto, Ontario, Canada.,Institute for Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
| | - Sachin V Pasricha
- Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ontario, Canada.,School of Medicine, Faculty of Health Sciences, Queen's University, Kingston, Ontario, Canada
| | - Hae Young Jung
- Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ontario, Canada
| | - Vladyslav Kushnir
- Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ontario, Canada
| | - Denise Y F Mak
- Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ontario, Canada
| | - Radha Koppula
- Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ontario, Canada
| | - Yishan Guo
- Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ontario, Canada
| | - Janice L Kwan
- Department of Medicine, University of Toronto, Toronto, Ontario, Canada.,Department of Medicine, Mount Sinai Hospital, Toronto, Ontario, Canada
| | - Lauren Lapointe-Shaw
- Department of Medicine, University of Toronto, Toronto, Ontario, Canada.,Division of General Internal Medicine, University Health Network, Toronto, Ontario, Canada.,Institute for Clinical and Evaluative Sciences, Toronto, Ontario, Canada
| | - Shail Rawal
- Department of Medicine, University of Toronto, Toronto, Ontario, Canada.,Division of General Internal Medicine, University Health Network, Toronto, Ontario, Canada
| | - Terence Tang
- Department of Medicine, University of Toronto, Toronto, Ontario, Canada.,Institute for Better Health, Trillium Health Partners, Toronto, Ontario, Canada
| | - Adina Weinerman
- Department of Medicine, University of Toronto, Toronto, Ontario, Canada.,Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
| | - Fahad Razak
- Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ontario, Canada.,Department of Medicine, University of Toronto, Toronto, Ontario, Canada.,Institute for Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
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