In the patient services space, there is no shortage of collected data. When utilized properly, this valuable data can help reduce speed to therapy, increase adherence rates, and optimize the patient journey with information we already have at our fingertips. However, fully realizing this goal requires an analytics solution that enables full-scale data democratization.
Enter the AI-powered analytics platform, coming soon to a hub near you.
AI-Powered Analytics: What Is it?
When it comes to modern analytics platforms, they – at a minimum – are capable of the following:
- Producing reports and dashboards
- Providing visual, interactive analytics
Where the artificial intelligence (AI) component comes in is in its ability to seamlessly incorporate generative AI (GenAI) technology. This enables the software itself to create automated analytics and visual dashboards in plain language, identify correlations, and recommend additional data points to explore. By entering a question, the application will not only provide an answer but anticipate related considerations and questions you may be interested in to provide a full picture for your review.
What this results in is a secure, generative, pre-training transformer (GPT) experience for visual dashboards and analytics. Secure is the key word here in that the trust layer does not compromise data security and privacy by only providing insights the user is authorized to see and leveraging only approved, governed data sources. This also results in optimized data integrity as the sources used to generate insights and text are governed and tested at multiple levels before even being made available for use as a source. Finally, as with any AI solution, the ability to detect and correct anomalies, otherwise known as AI hallucinations, is a critical feature that your patient services service provider needs to include when using such technologies.
How the Hub Can Benefit
There are four key areas that a full-service AI-powered analytics platform unlocks to ensure we’re not just collecting data but generating mission-critical insights.
- Descriptive Analytics
- This answers the question “what happened?”
- Goals
- Establish the core program management reports and dashboards. This will be a combination of interactive visual dashboards, paginated reports, and emailed data alerts.
- Make key metrics and operational monitoring available to all program leaders and ensure full education and adoption.
- Use Cases:
- Understand and routinely align on data points like enrollment counts, case outcomes, work in progress (WIP).
- Drive monthly or quarterly business reviews (MBR/QBR) to ensure the program sponsor and hub leadership are aware and engaged with the health of the program
- Diagnostic Analytics
- This answers the question “why did it happen?”
- Goals:
- Identify the root cause of an identified trend
- Automate data discovery and data mining
- Use Cases:
- Understand correlations between metrics to help explain patient, prescriber, and payer behavior
- Identify process or workflow inefficiencies.
- Predictive Analytics
- This answers the question “what will happen?”
- Goals:
- Use historical data to predict future behavior and outcomes
- Leverage regression, classification, clustering, and time-series techniques for automated statistical modeling.
- Use Cases
- Forecast case volumes and staffing needs.
- Predict case outcomes, prior authorization (PA) and appeal approval likelihood, and benefits investigation (BI) turnaround time (TAT).
- Prescriptive Analytics
- This answers the question “what do we do about it?”
- Goals:
- Perform built-in “what-if” analysis to explore different proposed actions to predict the outcome of said action
- Use cases:
- Explore process or engagement changes without having to invest time or money in costly pilots.
There are many other considerations and uses for a full-service AI-powered analytics platform in a hub including, but not limited to:
- Scalable, cloud-based architecture
- Easy-to-use drag-and-drop extract-transform-load (ETL) functionality to ingest and cleanse hub datasets
- The ability to embed live dashboards within stakeholder portals and third party collaboration tools (Slack, Teams, etc.)
- Built-in data integrity checks and alerts
- Mobile app-enablement
Domain Expertise Matters More than Ever
As powerful as AI can be to transform how we manage a hub, these tools are precisely that – a means to an end. There is no substitution for domain expertise and sharp program leadership with a dedicated focus on the patient population. However, by combining strong industry leaders with optimized technology and data-driven processes, the value of the hub is higher than ever.
UBC is proud to partner with Tableau and Microsoft to deliver the best available data assets for our hubs. The UBC Analytics suite of data solutions provides AI-powered analytics in a single, easy-to-navigate, visual platform. All our Patient Access programs are backed by UBC’s data and analytics teams, providing the needed insights for continuous improvements so your patients can get the right therapy at the right time with the right adherence plan.
About the Author
Steve Liford is the Senior Director of Analytics for UBC. He has over 20 years of data analytics strategy and leadership experience with an emphasis on process and workflow optimization. Steve and the UBC Analytics team work directly with Patient Services program leaders, manufacturers, and third parties to deliver full-service business intelligence and analytics solutions to seamlessly monitor and manage the patient journey.
