title: Outlier-based detection of unusual patient-management actions: An ICU study.
abstract: Medical errors remain a significant problem in healthcare. This paper investigates a data-driven outlier-based monitoring and alerting framework that uses data in the Electronic Medical Records (EMRs) repositories of past patient cases to identify any unusual clinical actions in the EMR of a current patient. Our conjecture is that these unusual clinical actions correspond to medical errors often enough to justify their detection and alerting. Our approach works by using EMR repositories to learn statistical models that relate patient states to patient-management actions. We evaluated this approach on the EMR data for 24,658 intensive care unit (ICU) patient cases. A total of 16,500 cases were used to train statistical models for ordering medications and laboratory tests given the patient state summarizing the patient's clinical history. The models were applied to a separate test set of 8158 ICU patient cases and used to generate alerts. A subset of 240 alerts generated by the models were evaluated and assessed by eighteen ICU clinicians. The overall true positive rates for the alerts (TPARs) ranged from 0.44 to 0.71. The TPAR for medication order alerts specifically ranged from 0.31 to 0.61 and for laboratory order alerts from 0.44 to 0.75. These results support outlier-based alerting as a promising new approach to data-driven clinical alerting that is generated automatically based on past EMR data.
authors.affiliation: Department of Computer Science, University of Pittsburgh, Pittsburgh, PA, USA; The Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, USA. Electronic address: milos@pitt.edu.
authors.forename: Milos
authors.initials: M
authors.lastname: Hauskrecht
authors.affiliation: Department of Computer Science, University of Pittsburgh, Pittsburgh, PA, USA; Yahoo Research, San Francisco, CA, USA.
authors.forename: Iyad
authors.initials: I
authors.lastname: Batal
authors.affiliation: Department of Computer Science, University of Pittsburgh, Pittsburgh, PA, USA.
authors.forename: Charmgil
authors.initials: C
authors.lastname: Hong
authors.affiliation: Department of Computer Science, University of Pittsburgh, Pittsburgh, PA, USA; Siemens Research, Princeton, NJ, USA.
authors.forename: Quang
authors.initials: Q
authors.lastname: Nguyen
authors.affiliation: The Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, USA; Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.
authors.forename: Gregory F
authors.initials: GF
authors.lastname: Cooper
authors.affiliation: The Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, USA; Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA.
authors.forename: Shyam
authors.initials: S
authors.lastname: Visweswaran
authors.affiliation: CRISMA Center, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
authors.forename: Gilles
authors.initials: G
authors.lastname: Clermont
mesh_terms: D003422:Critical Care; D057286:Electronic Health Records; D006801:Humans; D007362:Intensive Care Units; D000071117:Laboratory Critical Values; D019300:Medical Errors; D015233:Models, Statistical; D013223:Statistics as Topic
keywords: Clinical monitoring and alerting; ICU care; Machine learning; Medical errors; Outlier detection