BACKGROUND: A practical data point for assessing information quality and value in the Electronic Health Record (EHR) is the professional category of the EHR author. We evaluated and compared free form electronic signatures against LOINC note titles in categorizing the profession of EHR authors. METHODS: A random 1000 clinical document sample was selected and divided into 500 document sets for training and testing. The gold standard for provider classification was generated by dual clinician manual review, disagreements resolved by a third reviewer. Text matching algorithms composed of document titles and author electronic signatures for provider classification were developed on the training set. RESULTS: Overall, detection of professional classification by note titles alone resulted in 76.1% sensitivity and 69.4% specificity. The aggregate of note titles with electronic signatures resulted in 95.7% sensitivity and 98.5% specificity. CONCLUSIONS: Note titles alone provided fair professional classification. Inclusion of author electronic signatures significantly boosted classification performance.
BACKGROUND: A practical data point for assessing information quality and value in the Electronic Health Record (EHR) is the professional category of the EHR author. We evaluated and compared free form electronic signatures against LOINC note titles in categorizing the profession of EHR authors. METHODS: A random 1000 clinical document sample was selected and divided into 500 document sets for training and testing. The gold standard for provider classification was generated by dual clinician manual review, disagreements resolved by a third reviewer. Text matching algorithms composed of document titles and author electronic signatures for provider classification were developed on the training set. RESULTS: Overall, detection of professional classification by note titles alone resulted in 76.1% sensitivity and 69.4% specificity. The aggregate of note titles with electronic signatures resulted in 95.7% sensitivity and 98.5% specificity. CONCLUSIONS: Note titles alone provided fair professional classification. Inclusion of author electronic signatures significantly boosted classification performance.
Authors: S H Brown; M Lincoln; S Hardenbrook; O N Petukhova; S T Rosenbloom; P Carpenter; P Elkin Journal: J Am Med Inform Assoc Date: 2001 Jul-Aug Impact factor: 4.497
Authors: Sookyung Hyun; Jason S Shapiro; Genevieve Melton; Cara Schlegel; Peter D Stetson; Stephen B Johnson; Suzanne Bakken Journal: J Am Med Inform Assoc Date: 2009-03-04 Impact factor: 4.497
Authors: Donna Barron; Lauren Blumenthal; Suzonne Bourque; Natasha Brovarny; Jennifer Childress; Jill S Clark; Dawn L Criswell; Julie Dillard; Michelle Dougherty; Marie Gardenier; Darice Gryzbowski; Terri Hall; Marla Hardison; Janice Hecht; Beth Hjort; Kim Jackson; Mary Johnson; Diane M Lerch; Dorothy W Maxim; David Ike Mozie; Indra Osi; Deanna Panzarella; Janis L Pavlick; Ulkar Qazen; Sharron Ray; Linda Spurrell; Delores Stephens; Susan Sugg; Kim Vernon; Traci E Waugh; Lou Ann Wiedemann Journal: J AHIMA Date: 2009 Nov-Dec