Taikyoung Kim

Predictors of Medication Errors Among Elderly Hospitalized Patients

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. (Am J Med Qual 2008; 23:115-127)

Estimating effects of nursing intervention via propensity score analysis.

Background: Lack of randomization of nursing intervention in outcome effectiveness studies may lead to imbalanced covariates. Consequently, estimation of nursing intervention effect can be biased as in other observational studies. Propensity score analysis is an effective statistical method to reduce such bias and further derive causal effects in observational studies. Objectives: The objective of this study was to illustrate the use of propensity score analysis in quantitative nursing research through an example of pain management effect on length of hospital stay. Methods: Propensity scores are generated through a regression model treating the nursing intervention as the dependent variable and all confounding covariates as predictor variables. Then, propensity scores are used to adjust for this nonrandomized assignment of nursing intervention through three approaches: regression covariance adjustment, stratification, and matching in the predictive outcome model for nursing intervention. Results: Propensity score analysis reduces the confounding covariates into a single variable of propensity score. After stratification and matching on propensity scores, observed covariates between nursing intervention groups are more balanced within each stratum or in the matched samples. The likelihood of receiving pain management is accounted for in the outcome model through the propensity scores. Both regression covariance adjustment and matching methods report a significant pain management effect on length of hospital stay in this example. The pain management effect can be regarded as causal when the strongly ignorable treatment assignment assumption holds. Discussion: Propensity score analysis provides an alternative statistical approach to the classical multivariate regression, stratification, and matching techniques for examining the effects of nursing intervention with a large number of confounding covariates in the background. It can be used to derive causal effects of nursing intervention in observational studies under certain circumstances.

The Effect of High Nursing Surveillance on Hospital Cost

PURPOSE The purpose of this study was to determine the cost of one nursing treatment, surveillance, for older, hospitalized adults at risk for falling. DESIGN An observational study using information from data repositories at one Midwestern tertiary hospital. The inclusion criteria included patients age>60 years, admitted to the hospital between July 1, 1998 and June 31, 2002, at risk for falls or received the nursing treatment of fall prevention. METHODS Data came from clinical and administrative data repositories that included Nursing Interventions Classification (NIC). The nursing treatment of interest was surveillance and total hospital cost associated with surveillance was the dependent variable. Propensity-score analysis and generalized estimating equations (GEE) were used as methods to analyze the data. Independent variables related to patient characteristics, clinical conditions, nurse staffing, medical treatments, pharmaceutical treatments, and other nursing treatments were controlled for statistically. FINDINGS The total median cost per hospitalization was

The unique contribution of the nursing intervention pain management on length of stay in older patients undergoing hip procedures

9,274 for this sample. The median cost was different (p=0.050) for patients who received high versus low surveillance. High surveillance delivery cost

Cost of care for seniors hospitalized for hip fracture and related procedures

191 more per hospitalization than did low surveillance delivery. CONCLUSION Propensity scores were applied to determine the cost of surveillance among hospitalized adults at risk for falls in this observational study. The findings show the effect of high surveillance delivery on total hospital cost compared to low surveillance delivery and provides an example of a useful method of determining cost of nursing care rather than including it in the room rate. More studies are needed to determine the effects of nursing treatments on cost and other patient outcomes in order for nurses to provide cost-effective care. Propensity scores were a useful method for determining the effect of nursing surveillance on hospital cost in this observational study. CLINICAL RELEVANCE The results of this study along with possible clinical benefits would indicate that frequent nursing surveillance is important and might support the need for additional nursing staff to deliver frequent surveillance.

Overview of Nursing Outcomes Effectiveness Research

The purpose of this study was to examine the unique contribution of the nursing intervention pain management on length of stay (LOS) for 568 older patients hospitalized for hip procedures. Propensity-score-adjusted analysis was used to determine the effect of pain management on LOS. The LOS for hospitalizations that received pain management was 0.78 day longer than that for hospitalizations that did not receive pain management. Other variables that were predictors of LOS included several context-of-care variables (e.g., time spent in the intensive care unit, registered nurse skill mix, etc.), number of medical procedures and unique medications, and several other nursing interventions.