Clinical Decision Support and Alerting Mechanisms
More than 55 % of US hospitals have electronic health records (EHRs); frequently these contain computerized decision support (CDS) in the form of alerts. Alerts are a common form of CDS often implemented for medication ordering and decision support to imp
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Clinical Decision Support and Alerting Mechanisms Judith W. Dexheimer, Philip Hagedorn, Eric S. Kirkendall, Michal Kouril, Thomas Minich, Rahul Damania, Joshua Courter, and S. Andrew Spooner
Abstract More than 55 % of US hospitals have electronic health records (EHRs); frequently these contain computerized decision support (CDS) in the form of alerts. Alerts are a common form of CDS often implemented for medication ordering and decision support to improve patient care. EHRs implement rules supplied by thirdparty vendors to help guide the dosing process include weight-based dosing. Since many of these rules are conservative, they result in noisy alerting and are therefore
J.W. Dexheimer, Ph.D. (*) Departments of Pediatrics and Biomedical Informatics, Divisions of Emergency Medicine and Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati College of Medicine, 3333 Burnet Ave, ML-2008, Cincinnati, OH 45229, USA e-mail: [email protected] P. Hagedorn, M.D. Department of Pediatrics, Division of Hospital Medicine, Cincinnati Children’s Hospital Medical Center, University of Cincinnati College of Medicine, 3333 Burnet Ave, ML-9016, Cincinnati, OH 45229, USA E.S. Kirkendall, M.D. Departments of Pediatrics and Biomedical Informatics, Divisions of Hospital Medicine and Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati College of Medicine, 3333 Burnet Avenue, MLC-3024, Cincinnati, OH 45229, USA M. Kouril, Ph.D. Departments of Pediatrics and Biomedical Informatics, Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati College of Medicine, 3333 Burnet Avenue, ML-7024, Cincinnati, OH 45229-3039, USA T. Minich, RPh • J. Courter, Pharm.D. Division of Pharmacy, Cincinnati Children’s Hospital Medical Center, 3333 Burnet Ave, ML-15010, Cincinnati, OH 45229, USA R. Damania, B.S. Northeast Ohio Medical University, 4209 OH-44, Rootstown, OH 44272, USA S.A. Spooner, M.D., M.S., FAAP Departments of Pediatrics and Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, University of Cincinnati College of Medicine, 3333 Burnet Avenue, MLC-9009, Cincinnati, OH 45229, USA e-mail: [email protected] © Springer Science+Business Media Singapore 2016 J.J. Hutton (ed.), Pediatric Biomedical Informatics, Translational Bioinformatics 10, DOI 10.1007/978-981-10-1104-7_9
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overridden by users. Alert fatigue is commonly studied and reported by providers. EHR implementers customize these rules to reduce noise. Adverse drug events are a common occurrence, prevalent in both adult and pediatric populations. However, there are few automated ways to identify adverse drug events. Weight-based dosing guidance for medication orders has limited functionality if the patient’s body weight is entered incorrectly. Despite safeguards intended to prevent weight data-entry errors, erroneous weights exist in patients’ charts. These pose a safety threat to patients, e
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