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Develop Pharmacy Productivity Metrics

November 2019 - Vol. 16 No. 11 - Page #34

As health care becomes increasingly costly and complex, there is a heightened need to manage labor costs, which often comprise the majority of a department’s operating budget. Identifying methods to evaluate and justify these expenses is critically important. One way to control labor costs while expanding pharmacy services is by implementing departmental productivity measures. Productivity tools compare the amount of time and resources used to produce a service or a good.1 As a process improvement tool, productivity monitoring is used to evaluate current processes and develop best practices through the assessment of the outputs from day-to-day operations compared to the input.

Historically, departments primarily evaluated labor costs to identify efficiencies; however, it is critical to note that productivity measurement systems provide more sophistication than simply targeting a reduction in labor costs. For example, consider that measuring productivity does not only compute labor dollars per hour, but rather labor dollars per product or output, which allows for more robust, informed decision-making with regard to business operations.1,2

A pharmacy department that can detail and justify its labor costs is in the best position to apply for additional budget to expand departmental services; however, this is not always a simple proposition. The ability of pharmacy to develop a standard metric, calculate productivity, and hone an optimal productivity model will be significant factors in obtaining these objectives.

Calculation Options

Productivity calculations differ depending on the services and goods produced by a department. Typically, the calculation is represented as Units of Output divided by Units of Input.1 TABLE 1 highlights different approaches to calculating productivity statistics.

Oftentimes productivity is calculated as a percentage, with 100% as the baseline; percentages less than that amount represent less productive than expected and percentages over 100% represent more productive than expected. The productivity calculation, using one of the equations described in TABLE 1, is calculated and compared to a historical average of output. For example, if a department is 100% productive, it has the same output as expected when compared with historical norms; a department that is 105% productive is 5% more efficient than expected based on historical norms.1

The Impact of Improved Productivity

In health care organizations, the money saved simply by moving from 99% to 100% productivity can be significant, so health-system leaders strive to maintain 100% or higher productivity in their departments. For example, consider a health system that is budgeted to generate $500 million in revenue, with a productivity metric of revenue dollars per worked hours. Based on historical experience, budgeting is based on the assumption that the department is 100% productive. If a health system demonstrates 99% productivity, the expected revenue would be only $495 million; therefore, the health system would miss the opportunity for an additional $5 million in revenue.1,3,4 Thus, maintaining or surpassing 100% productivity is a crucial goal.

Productivity measurement systems have been widely adopted in many industries, particularly in manufacturing, as a way to gain efficiencies in workflow.1 Measurement of departmental productivity is also gaining popularity among hospital administrators as a way to reduce costs by incentivizing departments that improve their efficiency. Departmental productivity is important to evaluate staffing needs, to manage labor, and to facilitate flexible scheduling in times when there is less need for extra staff, thus helping control labor costs using an objective, predictive method.2-4

Developing a Standardized Productivity Metric

The accuracy of current measures used to capture operational productivity in the pharmacy department, and particularly the work of clinical pharmacists, is limited.3,4 Multiple factors contribute to this dearth of information, including the complexity of pharmacy’s job responsibilities, which comprise diverse duties such as clinical pursuits, drug verification, dispensing, teaching, and more.

Departments of pharmacy also are unique in that labor costs make up only a portion of departmental costs, as pharmacy is typically responsible for shouldering the entire weight of the hospital’s pharmaceutical budget. Thus, a pharmacy productivity monitoring system must be sophisticated enough to quantify the impact of reducing staff on the pharmaceutical budget, as well as on patient safety and quality of care. It is important to note that reducing staff based solely on labor efficiencies can be counterintuitive and may lead to an increase in the pharmaceutical budget. Many reports have been published describing the cost savings pharmacists and technicians can provide through reducing medication cost, inventory management, or avoidance of side effects. Given pharmacy’s unique responsibility to simultaneously manage both labor and medication expenses, inefficiencies in one piece of the equation can cause increases in the other.3,4

The Value of Benchmarking

While productivity monitoring is often undertaken internally to assess performance against an organization’s historical production, benchmarking is a similar concept that is used by hospital administrators to compare operations to those of closely related peers. The goal of benchmarking is to identify and implement best practices.1,3

Many benchmarking services provide both internal and external metrics that can be used to evaluate a department’s services. Internal benchmarking metrics can be used to calculate a productivity percentage. For health-system departments of pharmacy, these measures often limit departments to simple metrics, such as doses dispensed or adjusted discharges, which do not comprehensively account for the significant clinical work conducted by inpatient pharmacists. Additionally, the lack of ease in reporting clinical data and interventions makes evaluating productivity surrounding clinical knowledge work difficult; it is particularly challenging for external benchmarking companies to provide useful productivity reports. However, the literature has noted success when health systems internally evaluate their services and develop productivity metrics, which are then reported outside of external benchmarking companies.3,4

Barriers to Standardizing Metrics

The nature of pharmacy practice complicates the development of pharmacy productivity metrics. For example, the variety of clinical practices, the challenge of accurately measuring the value of cognitive pharmacy work, different practice settings, and the move toward value-based services can make it difficult to create a standardized measurement tool.

  • Dissimilar Clinical Practices. One of the major challenges in developing a standardized productivity metric across the pharmacy enterprise is the vastly different clinical practices that exist at various work sites. Elements that can affect the ability to generalize productivity from department to department include the presence or absence of technology and automation, state laws, clinical services offered, and the size of the hospital or health system.

Due to the lack of generalizability, each pharmacy must individually assess which productivity metric best suits its operation. An effective way to accomplish this is to utilize the staff’s expertise to develop a consensus as to what the workload drivers are for the day-to-day clinical practice at the institution.2-4

  • Measuring the Value of Cognitive Work. Calculating the value of pharmacists’ cognitive work is challenging. Organizations must identify a method of measuring the value of cognitive work, while the work itself may not always be readily apparent. There are few validated methods in the literature on capturing value of clinical work. Possible methods include having pharmacists document the type of clinical interventions made daily, or developing institution-wide key performance indicators (KPIs), which may be easier to automate.
  • Inpatient vs Outpatient. Pharmacy should also consider the operational and clinical value of both inpatient and outpatient pharmacy services.
  • Value-Based Reimbursement. Finally, as health care moves toward value-based reimbursement, it will become more critical to calculate reimbursement based on value and outcomes, rather than simply services provided.

The complexities associated with developing an ideal productivity metric are highlighted by a review of the literature (see SIDEBAR2-10).

Characteristics of Optimal Productivity Models

In an increasingly technologically advanced world, productivity metrics must be easy to produce and seamless to analyze. The following characteristics should be considered when developing an ideal productivity metric.1,3,4,11

  • Automated Data Capture. Many productivity models described in the literature are inefficient in capturing data. Productivity metrics must strike a balance between being simple enough to automate but also comprehensive enough to capture a majority of daily work activities. Leveraging tools, including corporate data analytics departments and instrumentation in the EMR, can help automate workload statistics.
  • Data-Driven. Productivity models and metrics should be driven by data and should demonstrate the value the pharmacy department provides to the organization. Next-generation productivity models should use advanced analytics to forecast volumes and make staffing decisions that are informed by data analysis.
  • The Intended Audience. When developing a productivity metric, consider the intended audience. Whether the metric is being used for departmental decision-making or is being presented to senior financial leaders, the metric must be tailored to meet the needs of the organization and demonstrate the productivity of the pharmacy department.
  • Internal and External Use. An ideal productivity metric is applicable to both internal and external benchmarking. However, due to the wide-ranging differences in pharmacy practice models across the country, this can be a challenging proposition. Nevertheless, efforts should be undertaken to develop industry standards for productivity in order to accurately compare similar institutions and drive best practices.

Future Steps

It is critical to note several key directions for developing productivity metrics for pharmacy. Looking to the future, productivity metrics must accurately quantify the value of cognitive work, calculate both operational and clinical value, include outpatient pharmacy services, and analyze reimbursement for value-based services.

Pharmacy has evolved to encompass both operational responsibilities and clinical services, providing value to patients via clinical education and quality improvement. As such, productivity metrics must calculate both operational and clinical value. In addition, both inpatient and outpatient pharmacy services should be considered when developing productivity metrics. Much attention has been paid to acute care services; however, pharmacy departments are increasingly developing comprehensive ambulatory care services as well. Incorporating research into both areas of pharmacy practice is necessary.

Finally, it is critical that pharmacy embrace the industry trend toward reimbursement for value rather than reimbursement for services rendered. To accomplish this objective, pharmacy productivity metrics should demonstrate value-based outcomes for patients. Future productivity metrics that can capture clinical outcomes and quality can help signify quality patient care.11


Pharmacy productivity models can serve to articulate the work that is provided by pharmacy in order to justify labor costs and expand services. Validated metrics are needed to continue this work, especially for clinical and cognitive pharmacy services. While some pharmacy-specific productivity metrics exist, additional research is necessary.

With evolving practice models, pharmacy must continue to demonstrate its value in new and unique practice settings. Developing standardized, robust productivity monitoring tools will be crucial for the sustainability of the profession in the future in order to demonstrate pharmacy’s contributions in an increasingly value-based care environment.

Tyler A. Vest, PharmD, MS, BCPS, is a pharmacy manager of automated dispensing cabinetry and controlled substances at Duke University Hospital in Durham, North Carolina. He completed his health-system pharmacy administration residency at the University of North Carolina (UNC) Medical Center and received his Masters degree from the UNC Eshelman School of Pharmacy. Tyler earned his Doctor of Pharmacy degree from the University of Cincinnati James L. Winkle College of Pharmacy. His professional interests include acute care operations, leadership development, practice advancement, pharmacy practice models, productivity and monitoring, the medication use process, and oncology.

Nicholas P. Gazda, PharmD, MS, BCPS, is the assistant director of specialty pharmacy at Cone Health in Greensboro, North Carolina. He received his Masters Degree and Doctor of Pharmacy from the UNC Eshelman School of Pharmacy. Nick’s professional interests include ambulatory care, specialty pharmacy, productivity, pharmacy operations, and financial management.


        1. Chew BW. No-nonsense guide to measuring productivity. Harvard Business Review 1988;66(1):110.
        2. Louden L, Lopez BR, Naseman RW, Weber RJ. Evolving pharmacist productivity models. Hosp Pharm. 2016:89-93.
        3. Rough SS, McDaniel M, Rinehart JR. Effective use of workload and productivity monitoring tools in health-system pharmacy, part 1. Am J Health Syst Pharm. 2010;67(4):300-311.
        4. Rough SS, McDaniel M, Rinehart JR. Effective use of workload and productivity monitoring tools in health-system pharmacy, part 2. Am J Health Syst Pharm. 2010;67(5):380-388.
        5. Naseman RW, Lopez BR, Forrey RA, et al. Development of an inpatient operational pharmacy productivity model. Am J Health Syst Pharm. 2015;72(3):206-211.
        6. Pawloski P, Cusick D, Amborn L. Development of clinical pharmacy productivity metrics. Am J Health Syst Pharm. 2012;69(1):49-54.
        7. Reichard JS, Garbarz DM, Teachey AL, Allgood J, Brown MJ. Pharmacy workload benchmarking: Establishing a health-system outpatient infusion productivity metric. J Oncol Pharm Pract. Jan 2017.
        8. Gupta SR, Wojtynek JE, Walton SM, et al. Monitoring of pharmacy staffing, workload, and productivity in community hospitals. Am J Health Syst Pharm. 2006;63(18):1728-1734.
        9. Granko RP, Poppe LB, Savage SW, et al. Method to determine allocation of clinical pharmacist resources. Am J Health Syst Pharm. 2012;69(16):1398-1404.
        10. Achey TS, Riffle AR, Rose RM, et al. Development of an operational productivity tool within a cancer treatment center pharmacy. Am J Health Syst Pharm. 2018;75(21):1736-1741.
        11. Lo E, Rainkie D, Semchuk WM, et al. Measurement of clinical pharmacy key performance indicators to focus and improve your hospital pharmacy practice. Can J Hosp Pharm. 2016;69(2):149-155.


Significant Publications on Developing a Pharmacy Department Productivity Metric

These two essential articles by Rough et al evaluate existing productivity models that are relevant in pharmacy practice, and outline future directions concerning optimal productivity models that warrant further evaluation. Published in 2010, these studies have become landmark pharmacy productivity articles.

The papers outline the lack of pharmacy productivity models validated in the literature, and highlight some of the metrics commonly used by departments. The studies highlight several methods to consider when developing an internal productivity model, including self-observation of pharmacy activities, self-reporting, sampling work activities, and estimating the time required to conduct such activities.

         PART 1 examines the value of benchmarking, including:

         PART 2 addresses key points in productivity monitoring, including:

The authors of this 2016 article detail the productivity model utilized at The Ohio State University Wexner Medical Center, providing an academic center’s perspective on which factors to consider when building a productivity model. The authors also comment on the evolution of pharmacy practice models, in addition to referencing key productivity model publications.

This article describes the utilization of automated reporting from the EMR to capture pharmacist interventions, including order verifications, discontinued orders, patient profile reviews, progress notes, and ADE reporting. The focus is on the development of an automated tool to quantify decentralized clinical pharmacist productivity at a large metropolitan hospital.

This 2015 study by Naseman et al evaluated a novel measure of weighted verifications by determining a weighted relative value unit (RVU) for each medication verification. This allowed the investigators to directly compare different types of verifications by assigning them a weighted intensity based on the amount of time it took to complete that verification. Assigning pharmacy responsibilities an RVU facilitates comparison of dissimilar activities through a common denominator. This model was deployed for clinical pharmacists with order verification as their workload driver, and focuses on operational rather than clinical productivity.

This 2017 article evaluated productivity for outpatient pharmacists in a cancer center. Through evaluating workloads, the authors were able to assign responsibilities and intensities for CPT procedure codes that quantify pharmacist involvement for each procedure code. This process addresses many challenges in previous metrics, as it allows for easily reportable information in CPT procedural codes and assigns entity-specific workload considerations to those codes.

Offering an innovative perspective on how to allocate clinical pharmacy resources, this article describes the pharmacy practice model at the University of North Carolina Medical Center. This piece is a particularly helpful resource for initiating productivity conversations.

The need for easily reportable, accurate pharmacy productivity models is discussed in this pilot productivity study. The survey-based study highlights the various productivity metrics used across different pharmacy departments, demonstrating the wide variety that are utilized; no one model was used in more than 20% of institutions. Innovative, accurate models have been developed, but there is still debate as to whether these new models capture enough information to accurately assess productivity. The article suggests that although community hospitals have access to productivity monitoring, most models fail to capture relevant clinical workload data.

This article discusses the development and implementation of an operational productivity tool in a cancer center at an academic medical center. The study used the productivity metric of weighted dispense type, as the authors concluded that this was the best indicator of mixed-skill workflow. The authors mapped workflows and observed pharmacist order verification and technician activities. RVUs were assigned to be representative of pharmacist and pharmacy technician workflows. The authors weighted each individual medication, which allowed for enhanced evaluation of productivity. The weighting of operational metrics allowed the authors to assess performance of the cancer center pharmacy.



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