Prescription management has undergone a remarkable evolution in recent decades.
While physicians once relied on cumbersome manuals like the Physicians' Desk Reference or unwieldy PDF guides, today's clinicians have a wealth of digital resources at their fingertips.
However, effectively navigating the flood of pharmaceutical data during prescription management is an ongoing challenge.
Prescribing decisions require weighing complex tradeoffs between efficacy, safety, cost, and other factors for each patient. This process demands targeted, up-to-date data that is comprehensive and easy to access.
As past experiences have shown, there is still work to do to fully realize that ideal, but the industry is getting there.
In this post, we’ll explore some of the biggest challenges and opportunities in prescription management, and discuss how artificial intelligence (AI) fits into the larger picture.
To begin, let’s explore what the future of optimized prescribing could look like.
Envisioning the Future of Optimized Prescription Management
While prescribing practices have come a long way, “ideal” prescription management remains aspirational for many health systems. High-performing prescription management systems will need to:
- Seamlessly integrate into clinician workflows via electronic health records.
- Synthesize vast data into precisely timed, patient-specific insights and alerts to enhance safety.
- Optimize therapy with machine learning
Real progress has been made, but realizing the full promise of data-enabled prescribing remains on the horizon.
It will take continued collaboration across healthcare stakeholders along with user-centered design of clinical decision support. But the building blocks are falling into place to create more effective, personalized prescription management for easier, safer care delivery.
Let’s take a closer look at some of the challenges and opportunities in this area of care delivery.
The Three Biggest Challenges to Creating Optimized Prescription Management
While technology has unlocked enormous potential to optimize processes, there are still many roadblocks to truly modernized prescription management. Key challenges include:
Challenge #1: Data Overload and the Goldilocks Dilemma
Prescribers today face a deluge of pharmaceutical data from disparate sources. The volume can quickly become overwhelming. Human clinicians need data to make optimal prescribing decisions, and AI tools need information to help support those decisions. Both groups need the right amount of data — not too much, and not too little.
With endless streams of drug data, clinical guidelines, and formulary lists, it is impossible for clinicians to synthesize it all effectively. Even the most advanced AI requires properly processed data as input.
To combat information overload, we must be able to synthesize prescribing data into targeted, actionable insights. Platforms need intuitive interfaces that highlight only the most relevant data for each scenario. They must filter out noise and spotlight critical considerations around efficacy, safety, cost-effectiveness, and more. With the right information curation, data can enlighten rather than overwhelm.
Challenge #2: Blind Spots: How Incomplete Records Undermine Prescribing
Prescription management software is only as effective as the data it relies on. If a patient's medication list is incomplete, it creates blind spots. Important clinical decisions could be made without accounting for drugs prescribed elsewhere or missing information on over-the-counter medications or dietary supplements.
Likewise, prescribers can only make fully informed choices if they have access to complete medication histories. Fragmented records across institutions, gaps in reconciliation, or patient omissions can all limit prescribing accuracy. Small missing pieces of the puzzle can lead to dangerous oversights.
In addition, clinicians need to know if specific medications are on a patient’s formulary list, so they can recommend cost-effective choices.
Breaking down silos through better interoperability and data integration is imperative.
With more complete data, clinicians can make safer, more effective prescribing decisions, and AI algorithms can deliver more accurate recommendations.
This brings us to the next challenge — it’s easier to get a comprehensive picture of each patient if pharmacists and clinicians can seamlessly exchange data.
Challenge #3: Tackling the “Dirty Data” Dilemma to Deliver Better Patient Outcomes
High-quality data is the foundation of improved clinical decision-making and patient care — but in the push to digitize healthcare, data quality was often an afterthought.
Clinicians are used to coping with messy, incomplete, or inaccurate health records out of necessity — but now that we’re asking AI to support decision-making, data quality matters more than ever before.
Dirty data severely limits the utility of modern analytics and decision support tools. As the adage goes — garbage in, garbage out.
Organizations across healthcare systems must now prioritize cleaning up their data. This will require investment in auditing records, mapping to standards, deduplication, and implementing governance programs.
Three Opportunities for Advancing Prescription Management
Looking ahead, there are key areas of opportunity to drive advancements in prescription management.
Opportunity #1: Escaping Healthcare Data Silos by Creating Frictionless Data Exchange
To enable optimal prescribing, we need seamless, secure, bi-directional healthcare data exchange between pharmacists and other healthcare providers. Right now, pharmacy data often remains siloed, with little integration into patients’ broader health records.
In recent years, we’ve seen an enormous amount of progress in elevating pharmacists’ roles in technology-enabled care delivery. Pharmacists play critical roles in:
- Care transitions
- Monitoring high-risk medications
- Patient education (particularly around COVID-19 and vaccinations)
- Assessing social determinants of health
- Providing care in underserved areas
However, the healthcare system does not consistently share the full range of pharmacists' interventions and clinical insights across the care continuum. Sharing this data with clinicians could provide more comprehensive patient information to inform prescribing.
Breaking down data silos will require continued collaboration between pharmacists, physicians, health systems, regulators, and health IT vendors to enable secure interoperability. With more robust data exchange, prescribers and pharmacists can access a more holistic view of each patient for safer, more effective care.
Opportunity #2: Tackling Drug Data Consistency
There are a number of standardization and reliability challenges to accessing and using drug information, including:
- Terminology issues: The terminology around medications is complex, and there are often a number of different ways to describe the same drug. While standards exist, adoption is uneven, which results in inconsistencies.
- Rapidly-evolving drug data: As new medications are approved and knowledge of existing ones advances, details on side effects, interactions, and more are constantly changing. Drug databases are often out of date.
- Multiple users: Prescription information flows between diverse healthcare roles, and each role requires a customized view of the data. Nurses, doctors, pharmacists, and others need targeted insights from the underlying data. Presenting the right information to the right person in the right way is difficult.
Standardizing both content and structure — ensuring providers can expect drug data in consistent, compatible formats across systems — is key. Physicians need to trust they have the full context to make informed comparisons and decisions, without having to mentally remap disjointed data points.
Opportunity #3: Simplifying Clinical Decision Support Tools
While the goal of automated decision support tools is enhancing safety, suboptimal design often hinders effectiveness. Alert fatigue from excessive low-value, overly broad warnings leads many clinicians to simply ignore notifications.
User experience issues also reduce efficacy. Tools that display dense blocks of information can overwhelm prescribers who are already facing information overload. More intuitive visual interfaces could dramatically improve usability — for example, organizing potential adverse effects by frequency rather than alphabetically allows quicker identification of common issues. Similarly, color-coding drug interactions by severity enables rapid risk assessment.
Consolidating all the alerts for a prescription into one simple interface makes it easier for prescribers to review critical information. Having the key interaction warnings together in a focused dashboard means they don't have to hunt through multiple screens to identify risks. Streamlining the alerts from disparate systems cuts through clutter and drives better visibility of what matters most.
Finally, flawed timing means prescribers get spammed with irrelevant alerts rather than receiving targeted guidance when they need it most. More adaptive systems could analyze prescribing patterns and deliver insights precisely when risks arise or knowledge gaps occur.
By focusing on the quality, quantity, and timing of insights, future decision support tools can become trusted advisors for clinicians, not annoyances. This will require continued user-centered design thinking and the integration of predictive analytics to make alerts more selective, helpful, and personalized.
Balancing AI and Human Expertise in Prescription Management
AI is already offering huge benefits in prescription management by synthesizing vast data into actionable insights. Algorithms can rapidly analyze data to highlight key risks, spotlight optimal therapies, and identify care gaps.
However, bias lurks within the underlying data, and humans must remain in the loop. Thoughtful oversight and judicious use of AI recommendations are critical as we expand our use of automation in healthcare.
Certain repetitive tasks are ripe for intelligent automation, but for higher-risk decisions around drug selection and dosing, human clinical judgment must have the final say. AI's full value in enhancing prescribing will only be realized through ongoing collaboration between its designers and end-user healthcare professionals.
When designed to augment professional judgment instead of replacing it, AI can be an invaluable aid to clinicians and pharmacists — but again, data quality will be a critical factor in maximizing the potential of decision support tools.
The Future of Prescription Management: Easy-to-Use Clinical Decision Support Systems
At Synapse Medicine, our mission is to deliver targeted clinical decision support precisely when and where it is needed most. Our company was founded by two public health physicians who are seeking to improve prescribing through better data utilization.
We believe user experience is the make-or-break factor that will decide how useful clinical decision support systems can be for providers and pharmacists.
Our solutions seamlessly integrate into e-prescribing and EHR workflows to provide patient-specific warnings, optimal therapy suggestions, and consolidated interaction alerts. Intuitive design principles like sorting potential adverse events by frequency and color coding drug interactions by severity also make our decision systems more useful for time-pressed clinicians.
We also believe accessing clinical decision support shouldn't require advanced technical expertise or a Ph.D. in pharmacology. Through user-friendly APIs and ready-to-use Components, we simplify integration so anyone can deliver state-of-the-art drug information and prescribing guidance. Our dedicated team provides personalized help to optimize integration with your existing systems and clinical workflows.
The future of prescription management lies in targeted, actionable, and easy-to-use clinical decision support. To see our solutions in action and discuss your specific needs, contact us today to schedule a demo.
