AI-Enabled Automation of Prescription Renewal Systems: Addressing Healthcare’s Hidden Burden
The management of prescription renewals represents one of healthcare’s most pressing yet understated challenges. This administrative burden not only strains medical practices but also creates significant barriers to patient care and medication adherence. As healthcare systems struggle to balance efficiency with quality of care, the need for innovative solutions in prescription renewal management has become ... Read More
The management of prescription renewals represents one of healthcare’s most pressing yet understated challenges. This administrative burden not only strains medical practices but also creates significant barriers to patient care and medication adherence. As healthcare systems struggle to balance efficiency with quality of care, the need for innovative solutions in prescription renewal management has become increasingly urgent.
The scale of this challenge is striking. Physicians can spend up to two hours daily managing prescription renewals, with larger medical practices handling upwards of 100 renewal requests each day. This considerable time investment diverts healthcare providers from direct patient care and contributes to the growing problem of physician burnout. The impact extends beyond healthcare providers to affect patient outcomes directly. Studies show that up to 40% of patients experience delays or disruptions in their medication regimens due to renewal-related challenges. For patients with chronic conditions, these disruptions can have serious consequences. Diabetic patients, for instance, face a two to three times higher rate of adverse events when they do not persist with their medication regimen.
Current prescription renewal processes are usually a simple set of delegation rules. Here, staff evaluate renewal requests against predetermined criteria. These criteria typically encompass medication type, basic clinical parameters, and if there is a scheduled follow-up appointment already set. For example, when considering the renewal of anti-diabetic medication, staff may evaluate the patient’s blood sugar control as measured by hemoglobin A1c and confirm if the patient has any upcoming follow-up appointments within a six-month window. This simple rule may be documented in some procedure manual, online or on paper, or may just be institutional knowledge. Either way, there is often not any formal, measured, tracked system in place.
This “low-tech” approach suffers from several limitations. The manual review process against criteria first and foremost is costly. Humans must pull data from the chart, compare it against checklists and/or flowcharts, make a decision, and then act accordingly. Time is money! Thus, if this workflow is to be at all practical, it must only leverage easily accessible/viewable data in the EMR. Further, as non-physicians are often doing such reviews, the criteria must be easy to understand, with little risk of misinterpretation. For example, general concepts like “well controlled” do not work. The criteria need to be parametric in nature, e.g., “if this number is less than X or more than Y.” Finally, because the checklists need to be simple, they cannot be easily personalized to account for patient-specific factors, e.g., polychronicity, polypharmacy, or compliance issues. Thus, all too often, the physician needs to get involved, even when with a better workflow, she need not.
Recent technological advances in three key areas—artificial intelligence, EMR interoperability, and Software-as-a-Service (SaaS)—have opened new possibilities for automating prescription renewals more effectively. Such automated approval systems can reduce staff time requirements by up to 80% through extensive automation, making them highly cost-effective. Second, these systems can incorporate sophisticated logic that simultaneously evaluates multiple clinical parameters, laboratory results, and patient history, leading to smarter decision-making. Third, they support a broader range of medications and patient types through enhanced data access, including complex medication regimens and chronic condition management. Finally, they provide improved safety by utilizing comprehensive EMR and Health Information Exchange data to assess medication appropriateness, potential drug interactions, contraindications, and patient-specific risk factors. In doing so, the renewal process becomes an important element is an overall pharmaco-vigilance program.
This future state of prescription renewals lies in a comprehensive automated solution built around four key capabilities:
- Extracting all relevant data from the EMR.
- Incorporating data from Health Information Exchanges.
- Configuring the renewal logic based on a provider’s specific preferences.
- Creating actionable, automated responses to requests.
- Continually monitoring program performance for safety and efficiency.
The first step in automating renewals is extracting EMR data to establish a patient’s clinical baseline. This includes a thorough review of patient history, current medications, and laboratory results. As Kharrazi et al. quantify, many clinical data and electronic medical record systems are captured purely as text. Just because these data are not structured in databases does not mean they are not important. Systems need to factor this in as well. For example, per Kharrazi et al., more often than not, a history of falls only exists in the EMR as text, and even dementia is underrepresented in problem lists or claims. Both of these may be very relevant in renewal decisions for certain medications.
The second capability for next-generation renewal automation is gathering information from health information exchanges to complete the clinical picture. The fragmented nature of US healthcare means that a patient’s clinical data is typically split among numerous EMRs. Further, for pharmacy data, it is important to look at past fill data and state prescription monitoring programs. Of course, gathering this data is just half the work. Cleaning it so it can be incorporated into the decision-making process is the hard part.
The third crucial capability is allowing clinicians to configure business rules that determine appropriate intervention levels based on their specific clinical criteria. Ultimately, a renewal, like any other prescription, will be written under the doctor’s license. It must be their treatment decision. Such clinical rules can of course be compared with current clinical guidelines by clinical leadership. This flexibility to allow the doctors to make the decision rules can reduce the friction so often encountered with automated clinical decision support systems.
The fourth capability focuses on automating renewal summaries around what can be termed the “3As”—Appropriateness, Access, and Availability. Appropriateness means alignment with provider-specific guidelines, including careful consideration of contraindications, drug interactions, and patient-specific factors. Access examines benefit design changes that might affect renewal, such as prior authorization requirements and formulary updates. Availability monitoring tracks supply constraints affecting medication access, providing proactive alerts and alternative recommendations when necessary. Simply put, to ensure the medication gets into the patient’s hands, just saying “a renewal is clinically indicated” is often not enough. Too many times, this just leads to downstream questions on access. Automating the workflow can mitigate some of the burden.
Finally, automation creates transparency as well as efficiency. By tracking renewal requests and dispositions at a granular level, guidelines can be revised over time. Staff and providers can be further trained to address gaps, and access can be measured. Not only can renewals themselves be measured, but so can the overall impact of the “renewal patient touchpoint” on patient engagement and compliance.
Organizations have made significant progress in developing these auto-medication-renewal capabilities, particularly in areas of data integration and advanced search technology. AI-powered platforms demonstrates the potential for intelligent renewal decisions based on the real-time processing of complex clinical data. This technological advancement represents a crucial step toward transforming prescription renewal management from a burdensome administrative task into a streamlined, efficient process that enhances both provider workflow and patient care.
About Kevin Agatstein
Kevin Agatstein is the founder and CEO of KAID Health, an AI-powered health care data analysis and provider engagement platform. Prior to KAID, Kevin founded Agate Consulting, and held roles at McKinsey & Company and Arthur Andersen where he advised providers, payers, healthcare IT companies, life-sciences organizations, and healthcare venture-capital and private-equity firms. Kevin also led operations for CareKEY, Inc., from its early years through its acquisition by The TriZetto Group.
