Identifying Unit-Specific Nursing Demands Using Nursing Interventions Classification (NIC)

摘要

There is evidence that increased nurse staffing is associated with better outcomes. However, there has been little research evidence to assist staffing decisions at the unit level. This study is part of a larger project in which we are exploring the use of a queuing model to inform unit-specific nurse staffing. A queuing model is a mathematical description of a queuing system. Queuing models have been used to assist staffing decisions that require timely guidance in various settings such as police patrol and emergency department physician staffing. In order to apply queuing models to nurse staffing specific data elements are needed as inputs for the model including demands for nursing service. The purpose of this sub-study was to identify unit-specific nursing demands using a standardized nursing terminology, the Nursing Interventions Classification (NIC). Two separate focus groups with 8 nurses and 8 managers were conducted in an orthopedic in-patient unit in October, 2007. Prior to the focus groups, the NIC list was reviewed and domains not related to care on the unit (e.g., family and community) were eliminated. The focus group participants were asked to review potentially applicable NIC interventions and rate the frequency that they occur on the unit. As a result, 42 nursing demands (e.g., Emergency Care, Pain Management) were identified from NIC. Twenty-four of the 42 (57%) NIC labels were modified to better represent the demand (Table 1). Participants did not specify need for interventions that were not included. NIC definitions were refined for three interventions (care coordination, communication, and critical thinking) based upon participants' comments. NIC provided a useful foundation for selecting potential parameters for the queuing model. Next, a Delphi study is planned to gain consensus on the frequency of the interventions as well as obtain data on other needed inputs (e.g., average duration of time-sensitive interventions). Once all inputs are obtained an evaluation study will compare staffing plans informed by the queuing models to those currently used.