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AI Is the Remedy to Pharmacies’ Operational Headaches
From staffing to supply shortages, these seven artificial intelligence use cases can help pharmaceutical retailers enhance efficiencies and patient experiences.
4 Min Read
Ekkasit Keatsirikul via Alamy Stock
American pharmacies are continuing to struggle with the challenges they faced throughout the COVID-19 pandemic. A limited talent pool, medication shortages, and expanded scopes have strained pharmacies across the nation and caused widespread burnout. Couple these COVID-induced issues with the industry’s longstanding technical debt dilemma and challenging role in the payor-provider-patient ecosystem and you have a recipe for disruption.
Inefficient operations do not only impact pharmaceutical retailers’ profit margins, but they can also compromise patient care and affect health outcomes. Millions of Americans rely on their local pharmacists, pharmacy assistants, and technicians to provide valuable medical advice, liaise with payors and providers, and administer critical vaccinations.
In order to help patients and their frontline staff, pharmacies must first help themselves by enhancing the quality and increasing the quantity of their AI-enabled digital solutions. And we’re finding pharmacists desperately need to leverage technologies that provide them with real-time and predictive analytics.
Exploring AI Use Cases
Pharmacists at most drugstores -- including national chains with larger tech budgets -- must manually search, input, and assimilate patient, medication, insurance, and provider information in addition to tracking the changing regulatory and pharmacovigilance landscapes. This is because pharmacy software is typically outdated and not enabled by advanced AI models. As a result, they are unable to expedite time-consuming tasks like monitoring which customers are filling which prescriptions at what time and synthesizing complex prescription coverage parameters across multiple providers.
There are dozens of examples of AI-enabled solutions vastly improving an array of functions, but there are arguably seven leading use cases that today’s pharmaceutical retail leaders should begin utilizing to solve their most pressing frontline issues:
Prescription substitution explanation: Identify chemically equivalent alternatives that could be substituted for the prescribed medication and explain the rationale for changing drugs.
Prescription coverage explanation: Confirm which drugs are covered in patients’ plans and formularies.
Prescription substitution option: Determine if there are cheaper, in-plan options for substituted formularies.
Script complexity ranking: Assess the most complex patient formularies to ensure compliance and proper usage.
Adherence monitoring: Evaluate patients’ medication adherence by tracking if a patient is taking medication as prescribed and, if not, create custom content to address associated risks and medical concerns to get them back on track.
Adherence agent: Interact with a custom-built, patient-centric chatbot that can determine an individual’s adherence challenges, offer solutions, and connect them to the right provider as needed.
Comprehensive medication review: Compile a list of potential medications and compare it to a list of a patient’s prescribed medications to evaluate each drug’s appropriateness. This includes potentially identifying discrepancies, drug interactions, adverse effects, and duplications as well as ensuring medication adherence. Comprehensive medication reviews also help pharmacy staff to suggest and educate patients on necessary lifestyle adjustments and to monitor the patient throughout the course of medication.
How to Integrate AI Into Your Digital Ecosystem
Many pharmaceutical retailers are either not aware of, or struggle to understand, the benefits of AI-enabled “front office” functions. Unsurprisingly, they also don’t know how to integrate AI across their digital ecosystems.
The foundational element of any successful AI integration is clear alignment between IT and business teams. Every AI use case must address a clearly defined business goal. Both IT and business stakeholders must not only establish and track key performance indicator (KPIs) for their AI implementations, but they should come to an agreement on budgets, timelines, and accountable project leaders.
Data is also key when revamping AI across an organization. After all, you can’t have AI without data. Pharmacies traditionally struggle with data readiness, reliability, and availability. Like many companies across industries, they don’t have enough of the right data to train their AI models. Advanced AI solutions can help address these data challenges by creating copies of production data without sensitive fields that can expose HIPAA or personal identifiable information (PII) data.
Ensuring AI trust is another key pillar of any implementation. Solutions not only require baseline accuracy, timeliness, and consistency metrics to ensure quality, but also continuous monitoring of bias, toxicity, decision transparency, and related measures to meet audit and compliance needs.
Many Americans are becomingly increasingly frustrated with pharmacies -- as are their frontline staff. Pharmacists, pharmacy assistants, and technicians are critical medical experts that will play an even more essential role as the country grapples with an aging population and an extreme healthcare provider shortage that impacts nearly one-third of the US population. In order to reduce operational inefficiencies, help alleviate their short-staffed workforce, ensure quality patient care, and drive growth, pharmaceutical leaders must adopt AI across their digital ecosystems.
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