5 Pharmacy Data Analytics Blind Spots in Your Supply Chain Today

As a health system or hospital pharmacy leader, you know there are gaps in your supply chain operations. Take a moment to consider the end-to-end item level drug tracking required to comply with the CMS 340B Drug Pricing Program or Drug Supply Chain Security Act (DSCSA) requirements and the breaks in the chain are evident.  

Pharmacy data analytics blind spots aren’t just a problem when it comes to the nuances of regulatory compliance; rather, they cloud the decisions and actions made every day by your team members and anyone else who touches your processes and inventory. This includes: 

• Buyers making duplicate drug purchases through separate channels 

• Pharmacy staff members subjectively setting PAR levels/stocking ADCs without usage data on which to make these decisions  

• Clinicians in one department believing they have stocked out of a critical drug product – and raising alarm bells – when the same drug is sitting in a storage area somewhere else in the hospital 

• Technicians managing drug inventory without knowing which items are nearing expiration and where they are stored 

• Staff ordering without knowledge of strategies regarding purchasing patterns, vendor relations, or contract terms and conditions 
• The inability to leverage pharmacy data analytics tools for demand forecasting and other strategic decisions because there are data gaps everywhere  

Let’s explore the root cause of pharmacy data analytics blind spots, where they commonly occur, their impact on your pharmacy’s operational, clinical and financial performance, and how to address them.  

The problem: Standalone technologies and data silos

Over the years, pharmacy operations have grown increasingly complex, with pharmacy leaders today navigating widespread drug shortages, staggering prices, workforce challenges and compliance burdens.  

This has spurred the development of pharmacy information technology (IT) systems and solutions meant to replace inefficient and error-prone manual processes. On the surface it makes sense:  

Use a drug carousel in the pharmacy for fast order fulfillment, place automated dispensing cabinets (ADC) in clinical departments for convenient but secure drug access, implement a scanning system to document when drug products are received into a hospital and another system at the patient bedside to track when drugs are administered – and the list goes on.  

This all seemed well and good until pharmacy leaders, wanting to perform pharmacy data analytics for more strategic, proactive decision making, find the data they need is locked up in all these different IT systems.  

Most technologies used in pharmacy operations today are designed to be standalone solutions — they can’t integrate, nor can they talk to each other (share data) easily. Then there’s manual data entry and workarounds still prevalent in pharmacies today (more on that later). 

With critical information for pharmacy data analytics missing, inconsistent, outdated or inaccessible, pharmacy leaders can’t forecast accurately, manage inventory effectively and meet regulatory demands for supply chain transparency.  

5 common pharmacy data analytics blind spots 

1. Purchasing: Who’s buying what?

Considering how hospital pharmacies buy the bulk of their drug products through their primary wholesaler, it would seem purchasing is a relatively centralized and standardized process.  

The complexity becomes clear when you consider all the other procurement channels — secondary wholesalers, direct vendor purchases for specialty drugs or vaccines, consignment, 503A and B, and department-level buys made outside the pharmacy’s oversight. 

Without a unified system, purchasing data remains siloed, and buyers operate in isolation — leading to duplicative orders, overstock and waste, and missed opportunities for tiered or volume-based discounts. 

Perhaps one of the biggest risks of siloed purchasing is buying drugs under the wrong pricing model — CMS 340B, WAC or GPO — potentially leading to audit failures or financial penalties. 

2. Inventory management: What drugs are where?

Without real-time pharmacy data analytics on drugs stored across numerous locations — warehouses, pharmacies, ADCs, carousels, storage areas, satellite pharmacies, other departments, lock boxes, clinical areas — inventory management becomes a major challenge for pharmacy teams. 

They struggle to strike a balance between overstocking and understocking and redistributing stock as needed. Case in point: A community hospital is close to stocking out of a drug, so the pharmacy buyer places a costly rush order not knowing there are excess quantities of the same drug stored in a nearby, offsite clinic.  

Complicating matters, technicians tasked with filling automated dispensing machines can inadvertently input incorrect item level data or fail to enter this data at all. With the DSCSA requiring pharmacies to track prescription drugs by serial number, lot number, expiration date, and National Drug Code (NDC), failing to document this data presents significant risk to the healthcare organization.  

Also, consider the challenges of finding and removing recalled drugs from inventory when there is no clear system-wide view into where these products are stored.  

3. Expiration tracking: What’s expiring and when?

Expiry waste is rampant throughout U.S. health systems and hospitals, adding unnecessary costs and risks to the pharmacy supply chain. Higher drug prices coupled with lower accessibility (there are currently 200+ drugs on ASHP Shortage list) are driving pharmacy leaders to make the best use of their drug inventory assets. 

From a clinical, operational and financial perspective, it’s critical for pharmacy teams to manage drug products by expiry date, but often this data is inaccurate, non-existent or inaccessible to decision makers.  

If a technician is manually checking expiration dates on drug products, it’s easy to overlook an expired item at the back of a bin. If they are loading drugs into a dispensing cabinet and having to type in expiration dates, a simple slip — such as inputting the wrong month or the wrong year — will skew expiry reports generated from that technology solution.  

The inability to perform pharmacy data analytics for organization-wide expiry management prevents pharmacy leaders from driving standardization and best practices. While one department or facility might pull meds 30 days before expiry to prioritize their use, another might pull at 10 days. Failure to pull medications in a timely and accurate matter can lead to loss in contracted reimbursements with manufacturers through a reverse distributor. 

This variability not only complicates inventory management, but it also increases the risk for an expired drug reaching the patient bedside.  

4. Usage patterns: What’s being used?

Individual facilities and departments using separate IT systems to track administration of drugs to patients exposes another critical data blind spot in the pharmacy supply chain. How can a pharmacy leader track drug usage hospital or health system-wide when no overarching solution is in place to unite data from disparate systems for pharmacy data analytics?  

Even if medications are dispensed from a central pharmacy, tracking them to the patient and confirming they were consumed is still a challenge with no end-to-end solution in place. A clinician might scan a drug to a patient but the patient refuses to take it. The pill then ends up in the clinician’s pocket where they forget about it. Or maybe the clinician drops a drug vial and neglects to record it as wasted.  

If there’s no easy and automated way to document the status of that drug at the patient bedside, it will fly under the radar and be unaccounted for.  

5. Demand forecasting: What will we need?

Demand forecasting in the pharmacy realm — predicting future demand for drugs to maintain patient care continuity and avoid disruptions — only works when there is complete and accurate data on which to perform pharmacy data analytics.  

A pharmacy leader needs to know what drugs have been ordered, if they have been received (or are on backorder), where they are stored in what quantities, when they will expire, whether they have been administered to a patient or wasted, etc. Disjointed systems and data blind spots obscure this enterprise-wide view, offering only small segments of the intelligence puzzle.  

Pharmacy teams might attempt to manually access data from the different systems and cobble it together for use in demand forecasting, but there are two major problems with this approach:  

• Lack of data integrity: Manual workarounds lead to errors and delays, making data outdated by the time it’s analyzed — hindering accurate, real-time demand forecasting. 
• Resource limitations: Staffing shortages force pharmacy staff to focus solely on meeting immediate drug supply needs, leaving little or no capacity for data analysis. 

Given the staggering challenges and complexities facing pharmacy teams today, it’s no surprise that survival mode has become the norm. Without the data or resources to perform forward-looking projections aimed at getting ahead of the demand curve, staff are constantly in reactive mode. Conversely, with an integrated inventory management system in place, pharmacy teams have global visibility into demand that they can use to proactively plan.  

How to bridge pharmacy data analytics gaps 

As pharmacy supply chains evolve, success will depend on strategic planning, data-driven purchasing decisions and organization-wide transparency. Health systems need a unified solution that eliminates piecemeal workarounds and enables all stakeholders to operate from a single source of truth.