Taikyoung Kim

Bio: Taikyoung Kim is an academic researcher from University of Iowa. The author has contributed to research in topics: Nursing Interventions Classification & Propensity score matching. The author has an hindex of 5, co-authored 5 publications receiving 191 citations.

Predictors of medication errors among elderly hospitalized patients.

Abstract: 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.

Estimating effects of nursing intervention via propensity score analysis.

Abstract: > 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 non-randomized 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.

Abstract: 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 $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 $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.

Polypharmacy Status as an Indicator of Mortality in an Elderly Population

Abstract: Increased use of drugs has raised concern about the risks of polypharmacy in elderly populations. Adverse outcomes, such as hospitalizations and falls, have been shown to be associated with polypharmacy. So far, little information is available on the association between polypharmacy status and mortality. To assess whether polypharmacy (six to nine drugs) or excessive polypharmacy (ten or more drugs) could be indicators of mortality in elderly persons. This was a population-based cohort study conducted between 1998 and 2003 with mortality follow-up through to 2007. The data in this study were derived from the population-based Kuopio 75+ Study, which involved elderly persons aged ≥75 years living in the city of Kuopio, Finland. The initial sample (sample frame n=4518, random sample n=700) was drawn from the population register. For the purpose of this study, two separate analyses were carried out. In the first phase, participants (aged ≥75 years, n=601) were followed from 1998 (baseline) to 2002. In the second phase, survivors (aged ≥80 years, n=339) were followed from 2003 to 2007. Current medications were determined from drug containers and prescriptions during interviews conducted by a trained nurse. The Kaplan-Meier method and Cox proportional hazards regression were used to examine the association between polypharmacy status and mortality. In the first phase, 28% (n=167) belonged to the excessive polypharmacy group, 33% (n=200) to the polypharmacy group, and the remaining 39% (n=234) to the non-polypharmacy (0–5 drugs) group. The corresponding figures in the second phase were 28% (n=95), 39% (n=132) and 33% (n=112), respectively. The mortality rate was 37% in the first phase and 40% in the second phase. In both phases, the survival curves showed a significant difference in all-cause mortality between the three polypharmacy groups. In the first phase, the univariate model showed an association between excessive polypharmacy and mortality (hazard ratio [HR] 2.53, 95% CI 1.83, 3.48); however, after adjustment for demographics and other variables measuring functional and cognitive status, this association did not remain statistically significant (HR 1.28, 95% CI 0.86, 1.91). In the second phase, the association between excessive polypharmacy and mortality (HR 2.23, 95% CI 1.21, 4.12) remained significant after adjustments. Age, male sex and dependency according to the Instrumental Activities of Daily Living screening instrument were associated with mortality in both phases. This study points to the importance of excessive polypharmacy as an indicator for mortality in elderly persons. This association needs to be confirmed following adjustment for co-morbidities.

The Societal Burden of Osteoporosis

Abstract: Osteoporosis currently affects 10 million Americans and is responsible for more than 1.5 million fractures annually. The financial burden of osteoporosis is substantial, with annual direct medical costs estimated at 17 to 20 billion dollars. Most of these costs are related to the acute and rehabilitative care following osteoporotic fractures, particularly hip fractures. The societal burden of osteoporosis includes these direct medical costs and the monetary (eg, caregiver time) and nonmonetary costs of poor health. The aging of the US population is expected to increase the prevalence of osteoporosis and the number of osteoporotic fractures. Growth of the older adult population will pose significant challenges to Medicare and Medicaid, which bear most of the cost of osteoporosis. Efforts to address the looming financial burden must focus on reducing the prevalence of osteoporosis and the incidence of costly fragility fractures.

Medication errors: the importance of an accurate drug history.

Abstract: 1. Medication histories are important in preventing prescription errors and consequent risks to patients. Apart from preventing prescription errors, accurate medication histories are also useful in detecting drug-related pathology or changes in clinical signs that may be the result of drug therapy. A good medication history should encompass all currently and recently prescribed drugs, previous adverse drug reactions including hypersensitivity reactions, any over-the counter medications, including herbal or alternative medicines, and adherence to therapy. 2. Medication history errors, such as omitting drugs erroneously, are common and often have the potential to harm the patient. Hypersensitivity reactions are often poorly documented or not explored in detail, which may lead to unnecessary avoidance of a drug. Accurate documentation of concomitant herbal or alternative therapies is rare, despite the importance they may have in causing adverse effects or drug-drug interactions. Polypharmacy, specific drugs, and clinical specialty can affect the risk of medication history errors. 3. There are various strategies to reduce medication history errors. Pharmacists are better at taking an accurate medication history than many physicians or nurses. In the context of acute hospital admissions they reduce error, the risks of adverse drug reactions, and prescription costs. Electronic prescribing may reduce transcription errors, but it can facilitate other errors and still depends on an accurate medication history. 4. Education of prescribers, both in clinical pharmacology and in taking accurate medication histories is vitally important in reducing errors.

Quality and Cost Analysis of Nurse Staffing, Discharge Preparation, and Postdischarge Utilization

Abstract: Readmission and emergency department (ED) use within the first 30 days following hospital discharge represent adverse, potentially avoidable, and costly outcomes of hospitalization (Friedman and Basu 2004; Goldfield et al. 2008; Jencks, Williams, and Coleman 2009). The aims of this study were to determine the following: (1) the direct effect of nursing unit staffing structure onpostdischarge utilization of readmissions and ED visits within 30 days; (2) the indirect effect through its influence on the quality of discharge teaching and patient's perception of readiness for discharge; and (3) the cost-benefit of adjustments to unit-level nurse staffing on postdischarge utilization.