Wednesday, July 11, 2007
This presentation is part of : Creating tools and building evidence to evaluate the Outcome-Present State-Test (OPT) Model of clinical reasoning
Reasoning Into the Future: Complexity Thinking and The OPT Model of Clinical Reasoning
Daniel J. Pesut, PhD, APRN, BC, FAAN, Graduate Programs, Indiana University School of Nursing, Indianapolis, IN, USA and RuthAnne Kuiper, RN, PhD, Department of Nursing, Winston-Salem State University, Winston-Salem, NC, USA.

As the health care industry shifts to an outcomes orientation and embraces electronic medical and health records, the nursing process and nurses’ clinical reasoning are likely to change and evolve. Contemporary trends and forces suggest that another transformation of the process is needed. Contemporary nursing practice, with its focus on outcomes and on the complex analysis of multiple client conditions, requires critical, creative, systems, and complexity thinking. Nursing classification systems and taxonomies provide the clinical vocabulary for clinical reasoning in nursing. The OPT model provides structure for embracing the nursing knowledge work of the past thirty years. It is a structure that supports clinical thinking about relationships among nursing diagnoses, interventions, and outcomes. The continued evolution and development of nursing knowledge classification systems, as well as continued research into the dynamics of clinical reasoning, set the stage for further developments in nursing knowledge work. Clinical reasoning regardless of discipline is both science and art. Edward Tufte (2006) in his book, Beautiful Evidence, suggests the common analytic task in nearly all disciplines is to help people understand causality, make multivariate comparisons, examine relevant evidence and assess the credibility of evidence and conclusions. OPT is a meta- model of clinical reasoning that requires clinicians  to consider many problems at the same time, and discern which problem or issue is most important. As information systems and electronic medical records provide ways for nurses to capture data related to diagnoses, interventions, and outcomes, there will certainly be ways to discover and mine the data associated with nursing knowledge that is embedded in health-related information technology systems. Such data mining activities contribute to our knowledge building and modeling and will support the nursing work of the future.