Background: Chemotherapy ordering and administration, in which errors have potentially severe consequences, was quantitatively and qualitatively evaluated by employing process formalism (or formal process definition), a technique derived from software engineering, to elicit and rigorously describe the process, after which validation techniques were applied to confirm the accuracy of the described process. Methods: The chemotherapy ordering and administration process, including exceptional situations and individuals' recognition of and responses to those situations, was elicited through informal, unstructured interviews with members of an interdisciplinary team. The process description (or process definition), written in a notation developed for software quality assessment purposes, guided process validation (which consisted of direct observations and semistructured interviews to confirm the elicited details for the treatment plan portion of the process). Results: The overall process definition yielded 467 steps; 207 steps (44%) were dedicated to handling 59 exceptional situations. Validation yielded 82 unique process events (35 new expected but not yet described steps, 16 new exceptional situations, and 31 new steps in response to exceptional situations). Process participants actively altered the process as ambiguities and conflicts were discovered by the elicitation and validation components of the study. Chemotherapy error rates declined significantly during and after the project, which was conducted from October 2007 through August 2008. Discussion: Each elicitation method and the subsequent validation discussions contributed uniquely to understanding the chemotherapy treatment plan review process, supporting rapid adoption of changes, improved communication regarding the process, and ensuing error reduction.
|Original language||English (US)|
|Number of pages||9|
|Journal||Joint Commission Journal on Quality and Patient Safety|
|State||Published - Nov 2012|
Bibliographical noteFunding Information:
This article is based on work supported by the National Science Foundation (NSF) under Awards CCF-0820198 and CCF-0427071 and by Rays of Hope of Springfield, Massachusetts. Any opinions, findings, conclusions, or recommendations expressed are those of the authors and do not necessarily reflect the views of the NSF or Rays of Hope.