Nursing Outcomes Classification (NOC): Measurement of Health Outcomes, 5e

Book Preface

The fifth edition of Nursing Outcomes Classification (NOC) contains 490 outcomes and represents over 20 years of work by the Iowa Outcomes team. The classification standardizes the outcome names and definitions for use in practice, education, and research. Each outcome includes a label name; a definition; a set of indicators that describe specific states, perceptions, or behaviors related to the outcome; a 5-point Likert measurement scale or scales; and selected references used in the development of the outcome. The outcomes assist nurses and other health care providers to evaluate and quantify the status of the patient, caregiver, family, or community.

The classification focuses on the measurement of outcomes across a variety of specialties and settings and has outcomes for use across the lifespan. Nurses incorporating NOC in their practice are able to quantify the change in patient status after interventions and monitor progress. Feedback from clinicians using the outcome measures in clinical settings has been positive, and their suggestions have helped improve the classification.

This edition contains 490 outcomes and includes 107 new outcomes. A complete list of new outcomes and changes in previously published outcomes can be found in Appendix A. We have added a new class to the taxonomy focused on self-management of acute and chronic health conditions. This edition provides practical guidance on how to use NOC in clinical practice. Chapter 1 describes the current classification presented in this edition. Definition of terms, frequently asked questions, and new features are highlighted. Chapter 2 discusses how to use NOC in clinical practice, education, and research. Linkages between the NANDA-I diagnoses and the NOC outcomes are included in the book. The reader will note that the NANDA-I diagnoses are listed by key concept, in alphabetical order, and consistent with the terminology used in the 2012-2014 edition of the NANDA International Classification. Also included in this section are linkages to Gordon’s Functional Patterns. It is important to note that these linkages are not prescriptive and need to be validated with clinical data across settings and populations. They are suggested to assist nurses with identifying possible outcomes when a diagnosis is made or to develop a framework for clinical information systems. The nurse’s clinical judgment remains the most important factor in selecting outcomes.

The need for nursing to define the patient outcomes that are responsive to nursing care has continued to increase since the first edition of this book was published.

The growth of managed care, the emphasis on cost containment and safety, and the need for evidence-based practice continue to bring concerns about effectiveness of nursing interventions and health care quality to the attention of nurses, consumers, health care organizations, payers, and policy makers. Nursing plays a key role in the delivery of safe, cost-effective care in every health care setting; therefore it is imperative that nursing data be included in the evaluation of health care effectiveness. The NOC completes the nursing process elements of the Nursing Minimum Data Set (NMDS). NOC is a companion language to the NIC interventions and the NANDA International diagnoses. Standardized nursing languages are required to ensure that the nursing elements identified in the NMDS are included in electronic patient databases. They also facilitate the study and teaching of diagnostic reasoning and the development of mid-range theory as linkages between patient characteristics, nursing diagnoses, nursing interventions, and nursing-sensitive outcomes are tested.

The editors of this book thank the many nurses who have contributed to the development of NOC. The team has worked diligently to continue to expand and evaluate the NOC outcomes. Many individuals have shared their knowledge and work with us or have agreed to review an outcome related to their specialty. Without them, this edition would not be possible.

Sue Moorhead
Marion Johnson
Meridean L. Maas
Elizabeth Swanson