CONTEXT: To be successful in tomorrow's health care environment, to make the most appropriate decisions for their laboratories, to optimize training and continuing medical education opportunities, and to advance pathology as a professional specialty, pathologists must possess basic informatics knowledge and proficiency. Traditional areas of anatomic and clinical pathology residency training employ learning objectives, knowledge expectations, and skill sets, but such items have not been as well developed or widely implemented for pathology informatics training. OBJECTIVE: We present a proposal that defines a standard and specific set of learning (knowledge) objectives and skill set (proficiency) expectations for resident training in pathology informatics. DESIGN: The proposal includes a comprehensive and detailed set of knowledge applications and proficiencies that will assist residency programs in developing basic pathology informatics training for residents. The content of the proposal is based on and compiled from existing successful pathology informatics training programs. Learning objectives include those related to general and enterprise computing as well as objectives related specifically to pathology informatics. Skill set expectations include the ability to use software that facilitates and adds value to the work of pathologists, including the use of a laboratory information system and of productivity software and other tools. Other topics include guidelines for evaluating residents' informatics competency, suggestions regarding curriculum structure and implementation, and recommendations for residents' computing infrastructure. CONCLUSION: This proposal provides a foundation for building effective and standard curricula for residency training in pathology informatics. These curricula will be able to meet increasing expectations and needs for pathologists to contribute to clinical information management.
CONTEXT: To be successful in tomorrow's health care environment, to make the most appropriate decisions for their laboratories, to optimize training and continuing medical education opportunities, and to advance pathology as a professional specialty, pathologists must possess basic informatics knowledge and proficiency. Traditional areas of anatomic and clinical pathology residency training employ learning objectives, knowledge expectations, and skill sets, but such items have not been as well developed or widely implemented for pathology informatics training. OBJECTIVE: We present a proposal that defines a standard and specific set of learning (knowledge) objectives and skill set (proficiency) expectations for resident training in pathology informatics. DESIGN: The proposal includes a comprehensive and detailed set of knowledge applications and proficiencies that will assist residency programs in developing basic pathology informatics training for residents. The content of the proposal is based on and compiled from existing successful pathology informatics training programs. Learning objectives include those related to general and enterprise computing as well as objectives related specifically to pathology informatics. Skill set expectations include the ability to use software that facilitates and adds value to the work of pathologists, including the use of a laboratory information system and of productivity software and other tools. Other topics include guidelines for evaluating residents' informatics competency, suggestions regarding curriculum structure and implementation, and recommendations for residents' computing infrastructure. CONCLUSION: This proposal provides a foundation for building effective and standard curricula for residency training in pathology informatics. These curricula will be able to meet increasing expectations and needs for pathologists to contribute to clinical information management.
Authors: Lee A D Cooper; Alexis B Carter; Alton B Farris; Fusheng Wang; Jun Kong; David A Gutman; Patrick Widener; Tony C Pan; Sharath R Cholleti; Ashish Sharma; Tahsin M Kurc; Daniel J Brat; Joel H Saltz Journal: Proc IEEE Inst Electr Electron Eng Date: 2012-04 Impact factor: 10.961
Authors: David S McClintock; Bruce P Levy; William J Lane; Roy E Lee; Jason M Baron; Veronica E Klepeis; Maristela L Onozato; Jiyeon Kim; Anand S Dighe; Bruce A Beckwith; Frank Kuo; Stephen Black-Schaffer; John R Gilbertson Journal: J Pathol Inform Date: 2012-08-30
Authors: Bruce P Levy; David S McClintock; Roy E Lee; William J Lane; Veronica E Klepeis; Jason M Baron; Maristela L Onozato; Jiyeon Kim; Victor Brodsky; Bruce Beckwith; Frank Kuo; John R Gilbertson Journal: J Pathol Inform Date: 2012-08-30
Authors: John R Gilbertson; David S McClintock; Roy E Lee; Maristela Onozato; Frank C Kuo; Bruce A Beckwith; Yukako Yagi; Anand S Dighe; Tom M Gudewicz; Long P Le; David C Wilbur; Ji Yeon Kim; Victor B Brodsky; Stephen Black-Schaffer Journal: J Pathol Inform Date: 2012-03-16
Authors: Diana Mandelker; Roy E Lee; Mia Y Platt; Gregory Riedlinger; Andrew Quinn; Luigi K F Rao; Veronica E Klepeis; Michael Mahowald; William J Lane; Bruce A Beckwith; Jason M Baron; David S McClintock; Frank C Kuo; Matthew S Lebo; John R Gilbertson Journal: J Pathol Inform Date: 2014-03-28
Authors: Andrew M Quinn; Veronica E Klepeis; Diana L Mandelker; Mia Y Platt; Luigi K F Rao; Gregory Riedlinger; Jason M Baron; Victor Brodsky; Ji Yeon Kim; William Lane; Roy E Lee; Bruce P Levy; David S McClintock; Bruce A Beckwith; Frank C Kuo; John R Gilbertson Journal: J Pathol Inform Date: 2014-07-30