Development of an inpatient operational pharmacy productivity model

Naseman, Ryan W.; Lopez, Ben; Forrey, Ryan A.; Weber, Robert J.; Kipp, Kris M.

doi:10.2146/ajhp130803

Cited by 10 publications

(6 citation statements)

References 5 publications

(7 reference statements)

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“…3 Furthermore, tools that do incorporate medication therapies to predict pharmacist workload have been developed but not specifically for the ICU setting or for external use beyond site-specific academic medical centers. [4][5][6] Thus, the MRC-ICU is the first and only validated tool using medication regimen complexity in critically ill patients with the intent to predict both patient outcomes and pharmacist interventions; however, further validation is necessary. 7 Ideally, the developed scoring value would build on existing patient acuity scores (eg, APACHE III) providing enhanced and more pharmacy-oriented information.…”

Section: Introductionmentioning

confidence: 99%

Development of Machine Learning Models to Validate a Medication Regimen Complexity Scoring Tool for Critically Ill Patients

2020

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Introduction: The Medication Regimen Complexity -Intensive Care Unit (MRC-ICU) is the first tool for measuring medication regimen complexity in critically ill patients. This study tested machine learning (ML) models to investigate the relationship between medication regimen complexity and patient outcomes. Methods: This study was a single-center, retrospective observational evaluation of 130 adults admitted to the medical ICU. The MRC-ICU score was utilized to improve the inpatient model’s prediction accuracy. Three models were proposed: model I, demographic data without medication data; model II, demographic data and medication regimen complexity variables; and model III: demographic data and the MRC-ICU score. A total of 6 ML classifiers was developed: k-nearest neighbor (KNN), naïve Bayes (NB), random forest, support vector machine, neural network, and logistic classifier (LC). They were developed and tested using electronic health record data to predict inpatient mortality. Results: The results demonstrated that adding medication regimen complexity variables (model II) and the MRC-ICU score (model III) improved inpatient mortality prediction.. The LC outperformed the other classifiers (KNN and NB), with an overall accuracy of 83%, sensitivity (Se) of 87%, specificity of 67%, positive predictive value of 93%, and negative predictive value of 46%. The APACHE III score and the MRC-ICU score at the 24-hour interval were the 2 most important variables. Conclusion and Relevance: Inclusion of the MRC-ICU score improved the prediction of patient outcomes on the previously established APACHE III score. This novel, proof-of-concept methodology shows promise for future application of the MRC-ICU scoring tool for patient outcome predictions.

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Section: Introductionmentioning

confidence: 99%

Development of Machine Learning Models to Validate a Medication Regimen Complexity Scoring Tool for Critically Ill Patients

2020

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“…The development of an inpatient operational pharmacy productivity model by Naseman and colleagues provided an innovative way to more accurately measure the operational productivity of medication order management. 7 This model was compared with commonly used pharmacy productivity metrics including pharmacy-adjusted admissions (PAA)/case-mix index (CMI) and PAA/pharmacy intensity score (PIS). Although the PIS can provide a more accurate measure of pharmacy costs compared to CMI, it still assumes that the total cost of medications a patient receives may have some correlation to the total time required for a pharmacy department to manage those medications.…”

Section: Future Of Productivy Operational Productivity Modelmentioning

confidence: 99%

Evolving Pharmacist Productivity Models

et al. 2016

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Leaders in health-system pharmacy are challenged to minimize costs, maximize revenue, and maintain or improve quality while simultaneously expanding services. Strong command of productivity and workload measurement is necessary to achieve these goals. This article reviews foundational pharmacy productivity concepts and key terminology, reviews historical pharmacy productivity models and their limitations, and considers new and evolving pharmacist productivity models.

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“…Verification weights were combined to create a multiplier based on relative value units (RVUs), which measure the value of a service based on work performed, the cost of the service, and liability associated with the service. 5 Each RVU corresponds to a current procedural terminology (CPT™) code that is associated with a specific procedure or service. In addition to RVUs, CPT™ codes can play a role in assessments of productivity.…”

Section: Introductionmentioning

confidence: 99%

Pharmacy workload benchmarking: Establishing a health-system outpatient infusion productivity metric

Reichard

Garbarz

Teachey

et al. 2017

J Oncol Pharm Pract

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Development of an inpatient operational pharmacy productivity model

Abstract: The WV model yielded productivity data that correlated closely with values calculated using two validated workload management models. The model may be used as an alternative measure of pharmacy operational productivity.

Cited by 10 publications

References 5 publications

Development of Machine Learning Models to Validate a Medication Regimen Complexity Scoring Tool for Critically Ill Patients

Development of Machine Learning Models to Validate a Medication Regimen Complexity Scoring Tool for Critically Ill Patients

Evolving Pharmacist Productivity Models

Pharmacy workload benchmarking: Establishing a health-system outpatient infusion productivity metric

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