Medlior was proud to be an exhibitor at the ISPOR 2023 conference in Boston last week. The event brought together our international community of colleagues, clients, and collaborators in HEOR.
ISPOR members tackle the evolving methods for evaluating innovative health technologies, including the use of RWE to inform decision-making. In fact, ISPOR ranked real-world evidence (RWE) as the top trend in 2022 for HEOR. The demand for RWE has increased in recent years to facilitate faster access to novel therapies, particularly for areas of high unmet need (e.g., rare diseases) and/or immature clinical trial data (e.g., promising phase II results). Although these remain topics of interest, other hot-button items from the ISPOR 2023 conference included the following:
- An emphasis on data quality
Transparency has been emphasized by the FDA, NICE, EMA, and CADTH RWE guidance documents. At the ISPOR 2023 RWE Summit, data provenance and quality were specifically emphasized by these agencies, to increase confidence in using RWE for decision-making. These organizations requested an increase in the transparency of how the data are collected, the quality of the data, and generalizability/transportability considerations for other jurisdictions. The takeaway message: Ask “Is the data fit for purpose?”
- Defining value
We noted an evolving view of “value” by including the perspectives of providers, patients, and caregivers into value-based assessments. For example, the value of novel interventions may include improved access to and equity in care, efficiency in care delivery, and non-clinical outcome measures, such as patient or caregiver quality of life. The takeaway message: Multistakeholder input is key to understanding value.
- Caution with AI
AI has growing applications in the field of HEOR. ISPOR sessions on the power of machine learning included real-world examples, emphasizing the need for practical application of such tools and caution regarding the interpretation. For example, one speaker stressed the need for predictive models used in clinical practice settings to be specific to the setting and included a relevant call to action for the end user to change behaviour. Another speaker provided examples of how machine learning algorithms can magnify biases, demonstrating the need to understand the source data and for continued refinement of algorithms with new data. The takeaway message: AI is a powerful tool that needs to be used responsibly.
How can Medlior help?
Our team was delighted by the keen interest in Canadian real-world data sources and Medlior’s capabilities to access and analyze it for industry-sponsored research studies. The advantages of Canadian data over other datasets include the data provenance and quality (i.e., it is not a commercial dataset but collected by the healthcare system). The data is population-based with long-term follow-up and includes linkages between comprehensive structured lab data, vital statistics, inpatient, outpatient, and drug claims datasets.
We are grateful for our opportunity to participate in such a thought-provoking conference and look forward to next year!