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Choosing Wisely Clinical Decision Support Adherence and Associated Inpatient Outcomes
Andrew M. Heekin, PhD; John Kontor, MD; Harry C. Sax, MD; Michelle S. Keller, MPH; Anne Wellington, BA; and Scott Weingarten, MD
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Choosing Wisely Clinical Decision Support Adherence and Associated Inpatient Outcomes

Andrew M. Heekin, PhD; John Kontor, MD; Harry C. Sax, MD; Michelle S. Keller, MPH; Anne Wellington, BA; and Scott Weingarten, MD
This analysis examines the associations between adherence to Choosing Wisely recommendations embedded into clinical decision support alerts and 4 measures of resource use and quality.
Alert Selection and Development

In 2013, we integrated CW recommendations as CDS alerts into the Epic EHR at CSMC. A clinical informatics team enabled the translation of the CW recommendations through a standardized process. First, clinicians reviewed the primary sources cited in each recommendation to define inclusion and exclusion criteria for the CDS rule. Once defined, the clinical logic was deployed in the EHR using standardly available alert tools. Finally, the team reviewed patient charts from encounters in which alerts were triggered and identified opportunities to refine the logic and reduce false positives.

To define the alerts for inclusion in the study, we initially reviewed all inpatient CW alerts that were active in the CSMC EHR at any point during the study period. For this analysis, we eliminated any low-volume alerts that sounded an average of less than once per month. The general definition of when an alert is adhered to is when a provider is advised against taking a particular action and complies with the request. Specifically, because our adherence criteria are used to evaluate EHR data to determine whether a particular order was signed within an hour after seeing an alert, we cannot accurately categorize adherence to alerts that either make recommendations about the appropriateness of individual orders within a series of identical orders (ie, repeat or standing laboratory testing) or that do not flag a particular order as inappropriate and instead are reminders unrelated to avoiding unnecessary care (eg, “Don’t delay palliative care for patients with advanced gynecological cancer.”). All remaining alerts were included in the data set (eAppendix Table 1 [eAppendix available at ajmc.com]).

Outcomes

We assessed the associations between alert adherence and 4 outcomes measures: encounter length of stay, 30-day readmissions, complications of care, and total direct costs. We defined 30-day readmission as an inpatient readmission to the same facility for any cause occurring within 30 days of discharge that was unplanned and deemed unavoidable. Complications of care were defined using the Agency for Healthcare Research and Quality (AHRQ) Healthcare Cost and Utilization Project (HCUP) classification system for complication codes.23 Total direct costs were defined as expenses directly associated with patient care, such as labor (wages, salaries, agency, and employee benefits), supplies (medical, implant, and nonmedical), professional fees, contracted services, equipment, and equipment depreciation.24 We selected these 4 outcomes measures due to their relevance to patients, health systems, and payers. As the industry shifts from fee-for-service to value-based contracts, cost containment and quality have become critical priorities for healthcare providers. Length of stay, readmission rates, and complication rates also merit evaluation, given their potential impact on patient outcomes and hospital value-based payment programs.25,26 Given that many low-value tests and procedures can result in a chain of additional tests and procedures, we theorized that reducing inappropriate and low-value services may lead to shorter lengths of stay, lower 30-day readmission rates, and lower complication rates.

Statistical Analysis

The adherent and nonadherent encounter groups were compared based on demographic characteristics, number of diagnoses, and case severity. The χ2 test was used for categorical variables, and the Wilcoxon rank sum test was used for continuous variables.

We estimated the association between alert adherence and the outcome measures using 4 generalized linear models. Alert adherence was measured as a dichotomous predictor. We adjusted for potential confounders, such as illness severity and case complexity, using demographic and clinical variables, such as gender, age, All Patient Refined Diagnosis Related Group (APR-DRG) severity level, number of diagnoses, expected length of stay, Elixhauser comorbidity index, Medicare status, and case mix index. A subset of all independent variables was used in each regression model to maximize the quality of fit of the model. Variable selection was performed using a backward stepwise method while minimizing the Akaike information criteria. In addition to alert adherence, variables were included in all models to adjust for the differences between the characteristics of the 2 groups. The continuous covariates generally had skewed distributions and were transformed prior to inclusion in the models. All statistical analyses were performed using R version 3.3.127 and the following packages: glm2,28 caret,29 and sqldf.30

Multiple logistic regression was used to estimate the odds of patient outcomes that were dichotomous (ie, 30-day readmissions and complications of care). The 2 continuous outcomes, length of stay and total cost, were estimated using multiple linear regression models, with the dependent variable log-transformed to correct for significant right skew in the distribution of each outcome. The outcome variables also appeared as independent variables in other models. Statistical tests were 2-sided, with P <.05 considered statistically significant. More detailed discussion of the regression models is included in the eAppendix.


 
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