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Spencer Cooke is Director of Life Sciences Strategy & Transformation at Capgemini Invent. This article was coauthored with his colleague Jonathan Brandwine, Senior Consultant and Senior Strategist at Capgemini Invent.
Monitoring and optimizing pharmaceutical patient services programs can be extremely challenging. As the access landscape becomes more complex, it's more important than ever for program managers to leverage modern data strategies and modern analytical solutions to support speed-to-therapy, adherence and program efficiency.
Compliant AI models and proven processes can make a major positive difference while modernizing patient services program operations.
Complex programs complicate support efforts
More than 20% of all prescriptions in the U.S. are never filled, and when the prescription will cost the patient $500 or more out of pocket, it’s abandoned 60% of the time. Many pharmaceutical manufacturers offer robust patient services programs to address patients’ financial, logistical, and emotional needs.
These programs can include nurse navigators, financial assistance, free trial offers, bridge programs, in-home testing, mobile applications and more. The teams tasked with managing these programs are under constant pressure to demonstrate improvements in pull-through, adherence and efficiency, but the wide range of options can be overwhelming. What lever(s) should they pull? How can they deploy their resources most effectively?
To help them answer these important questions, pharmaceutical companies compile data from their hub, major specialty pharmacies and other syndicated data sources to create a comprehensive view of the patient journey.
The challenge is that too many programs are using outdated analytical platforms to parse the data.
New tools can recommend timely next steps
The great news for program managers is that a slew of new analytical solutions can help them turn data into timely actionable insights. A major challenge for manufacturers is identifying patients at risk of experiencing access barriers and providing support before they abandon their prescription.
Many patient services teams, saddled with outdated systems, tools and processes, have no alternative but to wait and see which patients get stuck before reaching out to advise the prescriber on next best steps.
But this step is sometimes just based on which patients have been waiting the longest. It can be a frustrating situation with patients’ health outcomes on the line.
Some organizations manually develop algorithms to estimate a patient’s abandonment risk at the time of enrollment, based on patient demographics, physician characteristics and payer trends. But these models are updated infrequently with historical data that can be many months old, which renders the risk estimates and next best action alerts inaccurate.
As a result, program outreach often comes too late, after patients and their prescribers have given up. AI-enabled algorithms can automatically estimate a patient’s risk of abandonment immediately upon enrollment with a high degree of accuracy, allowing patient services programs to offer timely, targeted support.
Advanced analytics offer multiple benefits
AI-enabled analytical platforms can dramatically improve the speed, effectiveness and efficiency of program outreach. Because AI is also being used by insurers to expand and strengthen barriers to care, it’s critical for providers to also leverage AI on behalf of their patients.
AI-powered insights also make patient services programs more efficient and cost-effective. For example, when a hub feeds patient data into your system the day it comes in, you quickly get insights on factors that increase the risk of abandonment, such as a provider without the staff resources to fill out patient paperwork for insurers, or insurance plans that are challenging to work with on approvals.
The ability to see this information while it’s fresh allows patient services teams to be proactive instead of reactive. You don’t need to wait to offer support until a patient gets stuck in the approval process or drops out. Instead, you can use analytics to flag high-risk patients, assign field representatives to work with their physician’s office, navigate insurance hurdles and help those patients access the therapy they need faster.
Knowing how to support patients better earlier in the process can have far-reaching benefits. For example, a patient who regularly forgets to take pills could receive a smart bottle that reminds them and tracks compliance, so they stay on their medication, their symptoms stay under control and they can delay or avoid disease progression and more intensive treatments.
The patient remains healthier for longer and spends less out of pocket on treatments. Meanwhile, the greater efficiency of the manufacturer’s patient services program frees up additional resources for research and development.
Overcoming internal barriers to better patient services
Improving patient services operations requires changing traditional approaches to technology and understanding today’s HIPAA-compliant solutions. Currently, many organizations use multiple legacy applications such as spreadsheets, data dashboards and slide decks to share information via email. This fragmented approach means that by the time that data is used to make patient-care decisions, it’s out of date.
Many organizations don’t realize the capabilities that today’s analytics systems can deliver when used with proven processes. It’s possible to start seeing data on a new patient as soon as they enter a program, instead of weeks or months later.
And any discussion of patient data must include compliance, which is another reason some HCOs hesitate to move away from older technology, even when it’s not serving them well. It’s now possible to work with anonymized, aggregated patient data that’s stored inside the HCO’s firewall to protect privacy while delivering insights.
For example, by assigning random strings of characters to new patients and getting outreach pre-authorization, patient services teams can see patients as they move from co-pay vendor to hub vendor to free goods pharmacy, or from one retail pharmacy to another.
That allows the team to offer support if a patient appears to stop taking their medicine, without compromising their privacy. With predictive analytics, they can also reach out before patients stop following their treatment plan.
Planning AI implementations in patient services
A successful AI pilot involves all relevant stakeholders, not only the IT team, because technical expertise needs to support business goals to deliver ROI.
The first step is a maturity assessment of the organization's patient services programs and analytics capabilities.
Next, acquire and organize patient journey data for the AI model to mine and analyze to detect patterns that indicate risk factors for program discontinuation. Over time, the model can use those insights to recommend next best steps for hub and field team members to support patients, upon enrollment and if difficulties arise later.
As the model amasses more data and feedback on the outcome of next best steps, it keeps getting smarter and supports better results.
Toward better patient and business outcomes
AI technology can feel new and experimental because it’s advancing day by day. However, the data mining solutions discussed here have been on the market for several years and are being used successfully in other industries.
That means these solutions offer patient services programs a reliable, compliant way to modernize data use and operations. In the near term, that can result in better patient adherence to treatment and more revenue for HCOs and pharmaceutical companies.
Over the longer term, modernizing now puts your organization in a better position to capitalize on new data-leveraging capabilities as they emerge, to keep improving patient outcomes and business results.
Spencer Cooke is the Patient Services and Product Launch lead for Capgemini Invent North America. He has supported leading pharmaceutical companies with US and global launches across a wide range of indications.
Jonathan Brandwine holds dual MBAs in Marketing and Management Systems from the Fordham University Graduate School of Business and has spent the past eight years driving value for clients in the life sciences sector.
