TY - JOUR
T1 - Analysis of clinical decision support system malfunctions
T2 - A case series and survey
AU - Wright, Adam
AU - Hickman, Thu Trang T.
AU - McEvoy, Dustin
AU - Aaron, Skye
AU - Ai, Angela
AU - Andersen, Jan Marie
AU - Hussain, Salman
AU - Ramoni, Rachel
AU - Fiskio, Julie
AU - Sittig, Dean F.
AU - Bates, David W.
N1 - Publisher Copyright:
© The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association.
PY - 2016/11/1
Y1 - 2016/11/1
N2 - Objective To illustrate ways in which clinical decision support systems (CDSSs) malfunction and identify patterns of such malfunctions. Materials and Methods We identified and investigated several CDSS malfunctions at Brigham and Women's Hospital and present them as a case series. We also conducted a preliminary survey of Chief Medical Information Officers to assess the frequency of such malfunctions. Results We identified four CDSS malfunctions at Brigham and Women's Hospital: (1) an alert for monitoring thyroid function in patients receiving amiodarone stopped working when an internal identifier for amiodarone was changed in another system; (2) an alert for lead screening for children stopped working when the rule was inadvertently edited; (3) a software upgrade of the electronic health record software caused numerous spurious alerts to fire; and (4) a malfunction in an external drug classification system caused an alert to inappropriately suggest antiplatelet drugs, such as aspirin, for patients already taking one. We found that 93% of the Chief Medical Information Officers who responded to our survey had experienced at least one CDSS malfunction, and two-thirds experienced malfunctions at least annually.Discussion CDSS malfunctions are widespread and often persist for long periods. The failure of alerts to fire is particularly difficult to detect. A range of causes, including changes in codes and fields, software upgrades, inadvertent disabling or editing of rules, and malfunctions of external systems commonly contribute to CDSS malfunctions, and current approaches for preventing and detecting such malfunctions are inadequate.Conclusion CDSS malfunctions occur commonly and often go undetected. Better methods are needed to prevent and detect these malfunctions.
AB - Objective To illustrate ways in which clinical decision support systems (CDSSs) malfunction and identify patterns of such malfunctions. Materials and Methods We identified and investigated several CDSS malfunctions at Brigham and Women's Hospital and present them as a case series. We also conducted a preliminary survey of Chief Medical Information Officers to assess the frequency of such malfunctions. Results We identified four CDSS malfunctions at Brigham and Women's Hospital: (1) an alert for monitoring thyroid function in patients receiving amiodarone stopped working when an internal identifier for amiodarone was changed in another system; (2) an alert for lead screening for children stopped working when the rule was inadvertently edited; (3) a software upgrade of the electronic health record software caused numerous spurious alerts to fire; and (4) a malfunction in an external drug classification system caused an alert to inappropriately suggest antiplatelet drugs, such as aspirin, for patients already taking one. We found that 93% of the Chief Medical Information Officers who responded to our survey had experienced at least one CDSS malfunction, and two-thirds experienced malfunctions at least annually.Discussion CDSS malfunctions are widespread and often persist for long periods. The failure of alerts to fire is particularly difficult to detect. A range of causes, including changes in codes and fields, software upgrades, inadvertent disabling or editing of rules, and malfunctions of external systems commonly contribute to CDSS malfunctions, and current approaches for preventing and detecting such malfunctions are inadequate.Conclusion CDSS malfunctions occur commonly and often go undetected. Better methods are needed to prevent and detect these malfunctions.
KW - Anomaly detection
KW - Clinical decision support
KW - Electronic health records
KW - Machine learning
KW - Safety
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U2 - 10.1093/jamia/ocw005
DO - 10.1093/jamia/ocw005
M3 - Article
AN - SCOPUS:84994783028
SN - 1067-5027
VL - 23
SP - 1068
EP - 1076
JO - Journal of the American Medical Informatics Association
JF - Journal of the American Medical Informatics Association
IS - 6
M1 - ocw005
ER -