TY - JOUR
T1 - Repurposing Clinical Decision Support System Data to Measure Dosing Errors and Clinician-Level Quality of Care
AU - Chin, David L.
AU - Wilson, Michelle H.
AU - Trask, Ashley S.
AU - Johnson, Victoria T.
AU - Neaves, Brittanie I.
AU - Gojova, Andrea
AU - Hogarth, Michael A
AU - Bang, Heejung
AU - Romano, Patrick S
N1 - Funding Information:
We thank Michael Fong and Terry Viera for providing the alert override data; Drs. Eduard Poltavskiy and Brian Chan for data management; Gary Tabler for his computational support; Rebecca Davis for guiding our literature search; and Regan Scott-Chin, for her critical contributions drafting this manuscript. Drs. Chin and Wilson are the guarantors of the manuscript; concept and design: DLC, MHW, AST, VTJ, PSR; acquisition, analysis, or interpretation of data: DLC, MHW, AST, VTJ, BIN, AG, MAH, HB, PSR; final approval of the article: DLC, MHW, AST, VTJ, BIN, AG, MAH, HB, PSR. All of the authors provided substantial contributions to the design, acquisition, analysis, or interpretation of the work; revised it critically for important intellectual content; and approved of the final version.
PY - 2020/10/1
Y1 - 2020/10/1
N2 - We aimed to develop and validate an instrument to detect hospital medication prescribing errors using repurposed clinical decision support system data. Despite significant efforts to eliminate medication prescribing errors, these events remain common in hospitals. Data from clinical decision support systems have not been used to identify prescribing errors as an instrument for physician-level performance. We evaluated medication order alerts generated by a knowledge-based electronic prescribing system occurring in one large academic medical center’s acute care facilities for patient encounters between 2009 and 2012. We developed and validated an instrument to detect medication prescribing errors through a clinical expert panel consensus process to assess physician quality of care. Six medication prescribing alert categories were evaluated for inclusion, one of which – dose – was included in the algorithm to detect prescribing errors. The instrument was 93% sensitive (recall), 51% specific, 40% precise, 62% accurate, with an F1 score of 55%, positive predictive value of 96%, and a negative predictive value of 32%. Using repurposed electronic prescribing system data, dose alert overrides can be used to systematically detect medication prescribing errors occurring in an inpatient setting with high sensitivity.
AB - We aimed to develop and validate an instrument to detect hospital medication prescribing errors using repurposed clinical decision support system data. Despite significant efforts to eliminate medication prescribing errors, these events remain common in hospitals. Data from clinical decision support systems have not been used to identify prescribing errors as an instrument for physician-level performance. We evaluated medication order alerts generated by a knowledge-based electronic prescribing system occurring in one large academic medical center’s acute care facilities for patient encounters between 2009 and 2012. We developed and validated an instrument to detect medication prescribing errors through a clinical expert panel consensus process to assess physician quality of care. Six medication prescribing alert categories were evaluated for inclusion, one of which – dose – was included in the algorithm to detect prescribing errors. The instrument was 93% sensitive (recall), 51% specific, 40% precise, 62% accurate, with an F1 score of 55%, positive predictive value of 96%, and a negative predictive value of 32%. Using repurposed electronic prescribing system data, dose alert overrides can be used to systematically detect medication prescribing errors occurring in an inpatient setting with high sensitivity.
KW - Decision support systems, clinical
KW - Electronic health records
KW - Medical informatics applications
KW - Medication errors
KW - Outcome and process assessment (health care)
KW - Quality of health care
UR - http://www.scopus.com/inward/record.url?scp=85090614352&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85090614352&partnerID=8YFLogxK
U2 - 10.1007/s10916-020-01603-9
DO - 10.1007/s10916-020-01603-9
M3 - Article
C2 - 32897483
AN - SCOPUS:85090614352
VL - 44
JO - Journal of Medical Systems
JF - Journal of Medical Systems
SN - 0148-5598
IS - 10
M1 - 185
ER -