Role of Pharmacy Analytics in Creating a Data-Driven Culture for Frontline Management

Abstract

Purpose: The purpose of this article is to offer key recommendations based on the authors’ experiences for utilizing pharmacy analytics to support moving beyond standard-of-practice operational metrics towards high impact reporting to drive day-to-day decisions for frontline leaders. Summary: There is a continuous and vast amount of data generated through all facets of a health system’s daily operations, yet many data elements go unused and fail to contribute to value creation and increased performance at an organizational level. It is critical, therefore, for departments of pharmacy to identify and implement effective strategies to leverage data through robust business analytics and reporting, ensuring managers at every level are provided the information they need to support data-driven decisions and meaningful interventions in the day-to-day operations of the organization. At the authors’ institution, development and growth of a dedicated Pharmacy Analytics (PA) team has been instrumental to the pharmacy department for generating value and proactively supporting a business intelligence strategy that focuses on a data-driven management culture. Key recommendations to leverage pharmacy analytics are provided within four overarching themes: building transparency, leveraging synergy, optimizing actionability, and prioritizing partnerships. Conclusion: Through creation of a data-driven management culture, the authors provide recommendations for leveraging pharmacy analytics to reduce costs and impact outcomes across a range of hospital pharmacy operations.

Introduction

The importance of enterprise analytics, defined as an organization’s ability to analyze and process the data it uses for all of its activities, is increasingly being recognized for its role in creating value and driving performance of the organization. ¹ There is a continuous and vast amount of data generated through all facets of a health system’s daily operations. However, despite a significant amount of these data existing in machine readable formats and available for reporting, many data elements go unused. ² It is important for departments of pharmacy to evaluate how they can leverage these data through robust business analytics and reporting. In an ideal state, managers at every level are provided the information they need to support data-driven decisions in the day-to-day operations of the organization. In addition, with recognition for the need of increased continuous quality improvement efforts within health care, business intelligence also plays a role in measuring current status, identifying trends, and providing data to support impactful interventions.

Sophisticated data analytics requires that organizations consider how they use data beyond baseline reporting such as budgeting, financial forecasting, and supply chain management. ³ At our institution, for example, initial use of data revolved around one-time reports, return on investment analysis, and capturing pharmacy workload through metrics such as dispense volume. While these products are important, this should not be the ceiling of pharmacy analytics (PA) deliverables. One-time reports can miss the insights gained by tracking key performance measures over time. Likewise, advances in data management and visualization can enable pharmacy leaders to identify new gaps in care not previously captured in traditional operational reports. However, despite a recognized and compelling need for improved data utilization, there is no agreed-upon model that maps business analytics capabilities to business performance. ¹ In addition, there is no industry standard within health care currently available regarding frontline manager utilization of pharmacy data to guide day-to-day decisions.

At our institution, development and growth of a dedicated PA team has been instrumental to the pharmacy department’s ability to generate value. This is in part because it enables a business intelligence strategy that focuses on a data-driven management culture. ⁴ This culture emphasizes collaboration between analytics expertise and management to foster idea generation, provide insight into data availability, and identify the best data for the end-user’s goal.

The PA team serves as a system-level service that supports the departments of pharmacy across our 11-hospital health care system. Upon creation in 2013, the PA team was comprised of a 0.5 dedicated full-time equivalent (FTE). Recognizing the value of this team, the institution invested in continuous growth. The current team includes 9 FTEs: a pharmacist manager (1 FTE), pharmacists (3 FTEs), business analysts (4 FTEs), and a biostatistician (1 FTE).

The PA team handles more than 700 data requests per fiscal year, ranging from one-time reports to complex, recurring reports and dashboards. Data sources used for reporting include electronic health record (EHR) data as well as data sources external to the EHR, which are housed in our institution’s enterprise data warehouse and queried through SAP BusinessObjects Business Intelligence software. Robust data warehousing plays a key role in supporting our institution’s commitment to moving beyond standard-of-practice metrics and toward high impact reporting analytics.

Our institution’s goal is to create a reporting and analytics infrastructure to produce data-driven insights that can be used by managers and frontline staff in near real-time to support operational decisions, promote financial accountability and transparency, and drive clinical outcomes and research. This article offers key recommendations based on our institution’s experience leveraging pharmacy analytics support.

Recommendations for the Development of Data-Driven Management

Build Transparency

Analytics reporting can provide leadership with insight and create accountability

As health systems across the country struggled to manage the intravenous (IV) fluid shortage after the devastation in Puerto Rico caused by Hurricane Maria in September 2017, our institution began mobilizing its resources to tackle the problem with a data-driven strategy. Optimizing fluid supplies required the ability to monitor inventory and utilization at a level more granular than previously done. At the time, inventory management was tracked primarily through monthly purchasing and utilization reports. However, with IV fluid allocations changing almost weekly, our health system needed a way to determine which hospital services and pharmacy dispensing sites were the highest utilizers. This allowed reallocation of supplies and targeted purchasing, identification of a fluid conservation strategy, and assessment of compliance and impact with the new approach.

For this level of operational insight, monthly reports were found to be insufficient. Purchasing data and quantity on hand provide a historical estimate of utilization but reveal little in the way of near real-time use. The PA team instead built reports based on actual dispense data from our EHR. It was an iterative process to build an accurate accounting for fluid usage. Compounded medication orders were broken down into their components and tracked at the National Drug Code (NDC) level. For example, in Table 1, the central pharmacy reduced use of 100 mL normal saline (NS) IV piggyback bags from 844 to 370 over the rolling 4-week reporting period. The report was run on a weekly basis and sent to all managers, providing transparency and accountability for compliance with the institution’s conservation efforts. In addition, resources could be shifted between pharmacy satellite sites based on need. For example, after analyzing the highest movers for the 250 and 500 mL bags, our institution had to create specific allocation plans for the chemotherapy pharmacy, given that many products dispensed from this location are subject to maximum drug concentration limitations.

Table 1.

Fluid Utilization Report.

Product	Size (mL)	Weekly total dispenses				Sum
		Week 1	Week 2	Week 3	Week 4
Sodium chloride 0.9% intravenous solution	50	1475	2544	2138	2064	8221
Sodium chloride 0.9% intravenous piggyback	100	844	419	365	370	1998
Sodium chloride 0.9% intravenous solution	100	760	720	699	625	2804
Sodium chloride 0.9% intravenous solution	150	2		2	3	7
Sodium chloride 0.9% intravenous solution	250	325	294	273	345	1237
Sodium chloride 0.9% intravenous bolus	250				1	1
Sodium chloride 0.9% intravenous solution	500	171	193	172	175	711
Sodium chloride 0.9% intravenous solution	1000	23	13	21	9	66
Sodium chloride 0.9% intravenous bolus	1000	1				1

The granularity of the data also allowed improved estimation of how long current supplies would last. When clinical pharmacy personnel were provided updates on current shortage status and given conservation protocols to follow, managers included a specific estimation of the days’ supply of current stock. For example, when provided with information that the hospital had enough IV potassium chloride 50 mL bags to last 5 days at current use and new supplies may not be received for another week, it created a tangible sense of urgency. This gave clinical pharmacists strong evidence to foster provider buy-in for our vigilant conservation efforts, even in care areas with the highest utilization of the product.

Leverage Synergy

Combine individual reports to glean insights beyond which either report alone can provide

Dispensing data have long been used by pharmacy managers as a way to monitor pharmacy volume, assess staffing, and adjust workflow limitations. However, dispensing reports may be reviewed in isolation, which limits their potential for generating insight for frontline managers and driving day-to-day decisions. For any reporting and analysis, an important feature that must be determined is the optimal frequency to receive the data so that the information is actionable. At our institution, the PA team provides daily operational dashboards to operations managers, which are viewed in conjunction with daily revenue reports. By providing both reports daily, one should be able to see at a glance whether the number of dispenses (Figure 1) aligns with expected revenue (Figure 2).

Figure 1.

The Daily Dispense Dashboard shows the number of medications dispensed daily from a single pharmacy location, broken down by medication type and name. Example dashboard for illustrative purposes only.

Figure 2.

The Daily Charge Dashboard shows the number of charges generated, which can be filtered by pharmacy location. Used in conjunction with the Daily Dispense Dashboard (Figure 1), erroneous or missing charges can be detected by identifying discrepancies between the number of charges and number of dispenses. Example dashboard for illustrative purposes only.

It is important to note the difference in how pharmacy financial data are provided compared with financial reporting from other departments. Within pharmacy, the date that matters most for charges is the service date, as opposed to the post-date used by other billing departments. In the traditional revenue cycle, financial reports are provided at a monthly cadence. Although monthly reports provide an aggregate view of performance, one cannot seamlessly identify performance issues, as expenses and dispense volume can vary significantly day to day. Producing both of these reports daily allows leaders to identify synergistic trends not apparent by monthly roll-ups.

For example, at our institution, the Infusion Manager routinely reviewed the 2 daily reports. The manager was therefore able to identify an issue which occurred when a new pharmacy dispense location was created and revenue mapping to that cost center did not appropriately occur. The revenue mapping logic was corrected in a timely fashion, minimizing the financial impact of the error. While our Infusion Manager uses the 2 daily reports in this manner, they can similarly be used in conjunction across any operational area, including acute care.

Optimize Actionability

Identify key stakeholders and streamline access to the individual or group best positioned to take action from the data

Not all reports are best utilized by members of the management team. By automating report provisioning to the individuals able to take direct action as a result of the data, organizations can gain efficiency. For example, at our institution, the PA team developed a Financial Assistance Missed Opportunities report. This report identifies uninsured patients who received an outpatient infusion the previous day with a charge greater than $1000. On a daily basis, it pulls patient information, drug information, and claim detail in which the patient is listed as self-pay. Our institution has a dedicated Medication Assistance Program team to help patients get access to the medications they need by finding and coordinating enrollment into manufacturer programs. The majority of qualifying patients are identified prior to starting treatment; however, there is the potential for patients to slip through the cracks due to a lapse in insurance coverage. The most common scenario in which this might occur is when a patient has expired coverage information in their profile that is not identified and updated until after their outpatient infusion encounter. Once medication assistance is obtained, the patient’s bill is reprocessed so that the patient is not charged. When utilized this way, the report also gauges the accuracy of the program’s primary patient identification workflow. This helps mitigate patient financial burden as well as a likely organizational revenue loss.

Originally, the report was provided daily to the department manager. If a missed opportunity was identified on the report, the manager forwarded it to the team lead. However, this created unnecessary work for the manager and delayed information getting to those who would ultimately take action. The report routing was changed so that it routes directly to the team lead, improving efficiency and increasing frontline personnel ownership to pursue this missed opportunity.

Prioritize Partnerships

Invest in strengthening collaboration and communication between analytics resources and departmental leadership to improve reporting efficiency and accuracy

Central to the value of analytics is the strength of the collaborative partnership between the PA team and end-users, including integration of routine and consistent communication channels between pharmacy department managers and analysts. Analytics resources can be engaged prior to the “data request” submission, with management partnering with an analyst on the front end to assess what data are available and determine the best data to meet their goal. Our institution has found value in this for idea generation and analysis plan formulation. Figure 3 shows the closed-loop relationship between the analyst and pharmacy manager end-user.

Figure 3.

Closed-loop relationship between analysts and end-user.

Note. PDSA = Plan, Do, Study, Act.

In addition, as the value of a report is represented by its utility, lack of trust in the data by the end-user negates a report’s potential value by hindering its utilization. Taking the time to assess for the presence of and address any sources of mistrust can drive significant gains in reporting value. At our institution, one of the first strategic objectives prioritized by the PA team at its inception in 2013 was to address the counterproductive culture of mistrust around operational reporting that had taken root. Prior to the installation of the Carolina Data Warehouse for Health (CDW-H, our institution’s enterprise data warehouse), it was extremely challenging, if not impossible, to pull data from the EHR for pharmacy operations reporting. This limited reporting capacity, coupled with the inherently complex nature of pharmacy-related data, led to minimal buy-in from frontline managers when the newly formed PA team began ramping up its production. Reports and dashboards perceived to be incorrect were dismissed as unhelpful. For example, it was not uncommon to discover reports that were not being used because they were considered inaccurate by the end-user.

To address the issue, the PA team focused on creating a robust validation phase with the end-user during the initial build. Prior to this, reports might be built and deployed with little follow-up. Now, one of the key steps of the PA team process in provisioning any report is a detailed, up-front validation phase with the end-user. If the end-user does not believe the data are accurate, then it will have little value in informing decisions. Even if it is possible for other analysts or pharmacy personnel to validate report accuracy, our institution has determined that it is important for the management end-user to be a part of the process by validating the report within their workflow.

The next step to optimize the reporting life cycle was establishing workflows and mechanisms to facilitate ongoing communication about the reporting. Users are encouraged to promptly alert the analytics team of any inaccuracies they observe and question potential anomalies, which may need additional investigation. By rejecting the initial reaction that a report is incorrect in favor of partnering to perform a timely investigation into the root cause, collaboration and trust are built.

An example from our institution that demonstrates the importance of this collaboration was the transition of the drug purchasing process for an outpatient infusion pharmacy. On the monthly financial report provided to management, the drug expense line for the new site was unexpectedly high considering a recent change in purchasing practices. When subsequent reports continued to show a mismatch between expected drug expense and known dispense volume, the PA team was asked to help investigate the possibility of a reporting error. The investigation confirmed the report’s accuracy and unveiled an issue with how contracts for the new site had been loaded. Due to the collaborative investigation, the issue was identified quickly enough to correct, allowing the site to recoup a significant portion of its annual budget.

Conclusions

The performance of an organization is increasingly contingent on its ability to leverage its data and analytics resources. It is critical, therefore, for health system pharmacy departments to build infrastructure and strategy around effective use of incoming data streams to generate analytics and reporting that can drive day-to-day decisions at every level of management.