PURPOSE To estimate the time to perform and type of personnel to deliver each of the 486 interventions listed and described in the third edition of NIC. METHODS Small groups of research team members rated selected interventions in their area of expertise on education and time needed for each intervention. Education needed was defined as the minimal educational level necessary to perform the intervention in most cases in most states. Rating categories were (a) nursing assistant (NA/LPN/LVN/technician), (b) RN (basic education whether baccalaureate, associate degree, or diploma), or (c) RN with post‐basic education or certification. Time needed was defined as the average time needed to perform the intervention. Raters selected one of five possible time estimates: (a) <15 minutes, (b) 16–30 minutes, (c) 31–45 minutes, (d) 46–60 minutes, or (e) >1 hour. All ratings were reviewed across groups to ensure overall consistency. FINDINGS Results of this exercise provide beginning estimates of the time and education needed for 486 NIC interventions. Twenty percent required <15 minutes, 30% required 16–30 minutes, 17% required 31–45 minutes, 12% required 46–60 minutes, and 21% required >1 hour. More than 70% of the interventions were judged as needing basic RN education to perform. Raters judged RN post‐basic education to be required to performi 16% of the interventions, and 14% were deemed appropriate for personnel with NA/LPN education to perform. A monograph, Estimated Time and Educational Requirements to Perform 486 Nursing Interventions, available from http://www.nursing@uiowa.ed/cnc, includes lists of interventions appropriate for each time and education category, as well as time and education ratings according to NIC domains and classes. DISCUSSION The estimates of time and education provided by expert ratings provide a good beginning for cost estimates, resource planning, and reimbursement. The results of this study add to the small but growing body of literature that demonstrates that estimates of time to perform interventions by nurses who are familiar with the interventions is an accurate and efficient method to determine time values. A description of how this information can be used in a costing model is in the July/August 2001 issue of Nursing Economics. CONCLUSIONS NIC has identified the interventions that nurses perform. This study of time to perform and type of personnel to deliver each of the NIC interventions can help nurse leaders make better‐informed decisions about cost‐effective nursing care.

Medication errors are a serious safety concern and most errors are preventable. A retrospective study design was employed to describe medication errors experienced during 10187 hospitalizations of elderly patients admitted to a Midwest teaching hospital between July 1, 1998 and December 31, 2001 and to determine the factors predictive of medication errors. The model considered patient characteristics, clinical conditions, interventions, and nursing unit characteristics. The dependent variable, medication error, was measured using a voluntary incident reporting system. There were 861 medication errors; 96% may have been preventable. Most errors were omissions errors (48.8%) and the source was administration (54%) or transcription errors (38%). Variables associated with a medication error included unique number of medications (polypharmacy), patient gender and race, RN staffing changes, medical and nursing interventions, and specific pharmacological agents. Further validation of this explanatory model and focused interventions may help decrease the incidence of medication errors.

Objective. To determine the impact of patient characteristics, clinical conditions, hospital unit characteristics, and health care interventions on hospital cost of patients with heart failure. Data Sources/Study Setting. Data for this study were part of a larger study that used electronic clinical data repositories from an 843-bed, academic medical center in the Midwest. Study Design. This retrospective, exploratory study used existing administrative and clinical data from 1,435 hospitalizations of 1,075 patients 60 years of age or older. A cost model was tested using generalized estimating equations (GEE) analysis. Data Collection/Extraction Methods. Electronic databases used in this study were the medical record abstract, the financial data repository, the pharmacy repository; and the Nursing Information System repository. Data repositories were merged at the patient level into a relational database and housed on an SQL server. Principal Findings. The model accounted for 88 percent of the variability in hospital costs for heart failure patients 60 years of age and older. The majority of variables that were associated with hospital cost were provider interventions. Each medical procedure increased cost by $623, each unique medication increased cost by $179, and the addition of each nursing intervention increased cost by $289. One medication and several nursing interventions were associated with lower cost. Nurse staffing below the average and residing on 2-4 units increased hospital cost. Conclusions. The model and data analysis techniques used here provide an innovative and useful methodology to describe and quantify significant health care processes and their impact on cost per hospitalization. The findings indicate the importance of conducting research using existing clinical data in health care.

The results indicate the types of information that can result from analysis of actual clinical nursing data documented with standardized language (Nursing Interventions Classification) in a nursing information system. The knowledge of nursing interventions used in clinical practice has major implications for staff development and nursing education. The information is also useful in making staffing decisions for different types of patient populations.

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