title: Advantages and limitations of anticipating laboratory test results from regression- and tree-based rules derived from electronic health-record data.
abstract: Laboratory testing is the single highest-volume medical activity, making it useful to ask how well one can anticipate whether a given test result will be high, low, or within the reference interval ("normal"). We analyzed 10 years of electronic health records--a total of 69.4 million blood tests--to see how well standard rule-mining techniques can anticipate test results based on patient age and gender, recent diagnoses, and recent laboratory test results. We evaluated rules according to their positive and negative predictive value (PPV and NPV) and area under the receiver-operator characteristic curve (ROC AUCs). Using a stringent cutoff of PPV and/or NPV≥0.95, standard techniques yield few rules for sendout tests but several for in-house tests, mostly for repeat laboratory tests that are part of the complete blood count and basic metabolic panel. Most rules were clinically and pathophysiologically plausible, and several seemed clinically useful for informing pre-test probability of a given result. But overall, rules were unlikely to be able to function as a general substitute for actually ordering a test. Improving laboratory utilization will likely require different input data and/or alternative methods.
authors.affiliation: Department of Pathology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States of America; Harvard Medical School, Boston, Massachusetts, United States of America.
authors.forename: Fahim
authors.initials: F
authors.lastname: Mohammad
authors.affiliation: Division of General Medicine and Primary Care, Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States of America.
authors.forename: Jesse C
authors.initials: JC
authors.lastname: Theisen-Toupal
authors.affiliation: Department of Pathology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States of America; Division of Clinical Informatics, Beth Israel Deaconess Medical Center, Boston, Massachusetts, United States of America; Harvard Medical School, Boston, Massachusetts, United States of America.
authors.forename: Ramy
authors.initials: R
authors.lastname: Arnaout
mesh_terms: D019411:Clinical Laboratory Techniques; D057286:Electronic Health Records; D006801:Humans; D016014:Linear Models; D011237:Predictive Value of Tests; D012044:Regression Analysis; D013223:Statistics as Topic
references.citation: Transfusion. 2013 Jun;53(6):1205-16
references.pmid: 22934792
references.citation: Clin Chem. 2012 Jun;58(6):986-8
references.pmid: 22522222
references.citation: Stud Health Technol Inform. 2001;84(Pt 2):1399-403
references.pmid: 11604957
references.citation: Int J Med Inform. 2008 Feb;77(2):81-97
references.pmid: 17188928
references.citation: Comput Biol Med. 2006 Dec;36(12):1351-77
references.pmid: 16375883
references.citation: Ann Intern Med. 1997 Dec 15;127(12):1109-23
references.pmid: 9412316
references.citation: Clin Lab Med. 2008 Jun;28(2):305-19, vii
references.pmid: 18436073
references.citation: Med Care. 2013 Mar;51(3):251-8
references.pmid: 23269109
references.citation: BMC Med Inform Decis Mak. 2005 Feb 15;5:3
references.pmid: 15713231
references.citation: Am J Med. 2013 Oct;126(10):926.e1-5
references.pmid: 23953876
references.citation: J Neurosci Methods. 2007 Apr 15;161(2):342-50
references.pmid: 17156848
references.citation: Can Med Assoc J. 1972 Jan 22;106(2):122-3
references.pmid: 5058884
references.citation: J Wound Ostomy Continence Nurs. 2011 Jan-Feb;38(1):77-87
references.pmid: 21287773
references.citation: PLoS One. 2013 Nov 15;8(11):e78962
references.pmid: 24260139
references.citation: Artif Intell Med. 2006 Nov;38(3):275-89
references.pmid: 16730961
references.citation: Ann Emerg Med. 2004 Jan;43(1):17-22
references.pmid: 14707935
references.citation: Proc Innov Appl Artif Intell Conf. 2012;2012:2341-2347
references.pmid: 25360347
references.citation: Magn Reson Med. 2003 Jul;50(1):92-8
references.pmid: 12815683
references.citation: Health Policy. 2005 Mar;71(3):315-31
references.pmid: 15694499