Development of an inpatient operational pharmacy productivity model

The development and evaluation of a predictive productivity model in the ambulatory oncology infusion setting

PURPOSE

Accurately describing pharmacy productivity in the ambulatory oncology infusion setting is important to ensure appropriate labor utilization. The purpose of this study was to develop a productivity model utilizing weighted medication complexity and prospective schedule data to determine if predicted productivity corresponds to actual productivity across 6 ambulatory oncology infusion sites.

METHODS

This study was a 2-part analysis. Part 1 was to modify the historic productivity model from dispense code weighting to individual medication complexity weighting. Medication-specific relative value units were determined by analyzing 12 months of historic timestamp data from the electronic health record and gravimetric technology software. The productivity model containing updated relative value units was compared to the historic model to determine if the difference in total calculated full-time equivalents (FTEs) was within 2.0 FTEs. Part 2 applied prospective infusion schedule data to the updated model to determine if predicted productivity corresponded to actual productivity (within 2.0 FTEs) for pharmacy infusion services.

RESULTS

The mean difference in total calculated FTEs for infusion during the study period was 2.46 (standard deviation = 1.87) and was within the range of 2.0 FTEs (P = 0.54), indicating that the updated model was not statistically different from the historic model. The mean difference in total calculated FTEs between the predictive and actual productivity model for infusion was 18.28 (standard deviation = 1.00) and was out of the range of 2.0 FTEs (P < 0.001), indicating that predicted productivity was statistically different from the actual productivity.

CONCLUSION

Medication complexity weighting can be used to provide a comprehensive assessment of workload and productivity across pharmacy infusion services. The methodology used to assess predictive productivity should be explored further.

Decision-making framework for an acute care clinical pharmacist productivity model: Part 1

Disclaimer

In an effort to expedite the publication of articles related to the COVID-19 pandemic, AJHP is posting these manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time.

Purpose

Clinical pharmacist productivity assessment has long been challenging, as a standard definition does not exist. A multistep project was undertaken with the intent to develop, validate, and implement an acute care clinical pharmacist productivity model. The initial step of the project was designed to identify, define, prioritize, and weight a comprehensive list of daily pharmacist responsibilities stratified by relative time spent on each function via consensus.

Methods

Delphi methodology applied by a panel of experts was used to identify a comprehensive list of acute care pharmacist responsibilities ranked in order of time intensity. Twenty-three acute care clinical pharmacists participated in the process. The consensus list was validated by time observation studies. Each responsibility was assigned a weight and corresponding work outputs by a consensus panel. Weighting of each responsibility was assigned according to the relative time intensity and complexity of each task.

Results

The results of the Delphi consensus process included the top 20 time-intensive responsibilities identified by the acute care clinical pharmacists. Timed observations of acute care clinical pharmacists yielded results similar to those of the consensus process. Selection of corresponding work outputs and weights for each responsibility provided the final requirements for the productivity model.

Conclusion

The development of an acute care clinical pharmacist productivity model first requires the selection of appropriate work outputs and weighting. The consensus process provided a newly identified comprehensive list of pharmacist responsibilities that will serve as the foundation of the clinical productivity model. Validated consensus methodology can be useful for engaging clinical pharmacists in decision-making and the development of a clinical productivity model.

Formation and validation of an acute care clinical pharmacist productivity model: Part 2

Purpose

The purpose of the project described here was to use the work outputs identified in part 1 of a 2-part research initiative to build and validate an acute care clinical pharmacist productivity model.

Methods

Following the identification of work outputs in part 1 of the project, relative weighting was assigned to all outputs based on the time intensity and complexity of each task. The number of pharmacists verifying an inpatient medication order each day was selected to represent the labor input. A multivariable linear regression was performed to determine the final work outputs for inclusion in the model. Productivity and productivity index values were calculated for each day from July 1, 2018, through June 30, 2019.

Results

Of the 27 work outputs identified via consensus by the clinical pharmacist working team, 17 work outputs were ultimately included in the productivity model. The average productivity during the period July 2018 through June 2019 was derived from the model and will serve as the baseline productivity for acute care clinical pharmacists.

Conclusion

Validated consensus methodology can be useful for engaging clinical pharmacist in decision-making and developing a clinical productivity model. When thoughtfully designed, the model can replace obsolete measures of productivity that do not account for the responsibilities of clinical pharmacists.

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

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.

Evaluation of medication safety resources in pediatric hospitals

PURPOSE

As health systems continue to expand pharmacy and clinical services, the ability to evaluate potential medication safety risks and mitigate errors remains a high priority. Workload and productivity monitoring tools for the assessment of operational and clinical pharmacy services exist. However, such tools are not currently available to justify medication safety pharmacy services. The purpose of this study is to determine methods used to assess, allocate, and justify medication safety resources in pediatric hospitals.

METHODS

A 32-question survey was designed and distributed utilizing the Research Electronic Data Capture (REDCap) tool. The survey was disseminated to 46 pediatric hospitals affiliated with the Children's Hospital Association (CHA). The survey was distributed in October 2018, and the respondents were given 3 weeks to submit responses. Data analysis includes the use of descriptive statistics. Categorical variables were summarized by frequencies and percentages to distinguish the differences between pediatric health systems.

RESULTS

Of 26 respondents, 15.4% utilized metrics to justify medication safety resources. Metrics utilized were based on medication dispenses, projects, and error coding. Twenty-three percent of respondents were dissatisfied with current pharmacy-based medication safety resources within the organization. There was variability of medication safety resources within pediatric hospitals, including the number of dedicated full-time equivalents, time spent on tasks, and task prioritization.

CONCLUSION

Assessing medication safety resources at various pediatric hospitals highlights several potential barriers and opportunities. This information will serve as the foundation for the creation of a standardized workload assessment tool to assist pharmacy leaders with additional resource justification.

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

BACKGROUND AND OBJECTIVES

Current productivity assessment models lack the ability to measure the quality of pharmacy services through workload validation. The goal of our efforts was to create a model to more accurately assess workload at multiple outpatient infusion centers.

METHOD

Current procedural terminology codes were identified as representative of the key drivers of pharmacy workload. Fourteen current procedural terminology codes representing medication orders were selected and categorized into eight distinctive groups associated with varying amounts of pharmacy workload. A three-month average of current procedural terminology volumes were calculated and used to create a workload baseline.

RESULTS

Our study found a usable productivity assessment and coefficient to compare relevant clinical workload across outpatient oncology sites. The current procedural terminology codes were readily retrievable from our system electronic medical record. By assigning activities, e.g. clinical review, verification, barcoding, batch preparation, we were able to compute a workload and then adjust staffing to achieve a median coefficient across sites.

DISCUSSION

This study evaluated the use of administration current procedural terminology codes for an outpatient oncology productivity model. Based upon our analysis, it can be successfully used to determine workload for pharmacists and technicians across variable locations. We believe it is the first study to demonstrate a productivity model for this setting.

Evolving Pharmacist Productivity Models

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.