Five Expert Insights from the HealthXL Community

By HealthXL Team & Community 

Over the past month, our HealthXL community members discussed the value of RWD, integrating oncology digital health solutions into hospital workflows and end points selection for clinical trials among other topics. We’ve put together a snapshot of expert insights from these meetings.

#1 On building trust with clinicians: When digital health companies are trying to convince clinicians, it is very important they don’t over-promise clinical benefits that are not well-validated yet. The key is to make clinicians trust the solution by showing robust evidence and data that proves the positive impact of the digital health tool on the outcomes of their patients. As they do with drugs, if clinicians see positive results after prescribing the digital health tool, they will keep prescribing it. It’s important to set realistic expectations and be transparent about clinical benefits. 

#2 On involving clinicians in product development: Episodic user research in product development is not enough. Continuously involving clinicians along the product development journey and in product iterations is critical to ensure the solution being built will meet clinicians’ expectations and needs. Clinicians need to trust the product will be beneficial to their patients before recommending it to them. Therefore, winning over this key stakeholder group will drive adoption of the product. 

#3 On the value of RWD generated by DTx: The RWD market is growing fast and the expectations are that it will continue to grow. While there are many sources of RWD, what can make DTx-generated data unique and valuable is; 1) the data collected is continuous, not periodic, 2) the marginal cost required to generate RWD data from DTx is lower than other RWD sources and, 3) the data is generated for large samples sizes and across the patient journey.

#4 On endpoints selection for clinical trials: It is not one-size-fits-all. When designing a clinical trial for a digital health solution, selecting the right endpoints is important to maximize the likelihood of commercial success. To do that, a clear understanding of the solution and the company's objectives as well as the clinical research question being investigated is required. This will help define the appropriate quantitative measures (endpoints) required to achieve the objectives. Additionally, it’s important to bear in mind what the trial is looking to achieve and who the intended stakeholders are, for example is the trial for payer reimbursement, to convince HCPs, or for patient adoption?

#5 On big tech working with pharma: There is an opportunity for impactful collaborations between big tech and pharma to unlock greater value within healthcare data. Big Tech’s expertise with artificial intelligence and machine learning, cloud computing and interoperability can enable siloed and unstructured data to be translated into actionable insights for pharma. Pharma can then utilize these insights and incorporate them into research efforts on treatment and prevention. 

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