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H1 Blog
OCTOBER 16, 2023
AI can help in speeding up various phases like target identification, molecule optimization, and pre-clinical testing, thereby reducing the cost and time to bring a drug to market. Faster Time-to-Market: AI-driven predictive maintenance can reduce machine downtime, ensuring smoother manufacturing processes and quicker product delivery.
H1 Blog
DECEMBER 11, 2024
While this approach aims to improve ROI before potential price controls kick in, it complicates trial design and recruitment, increasing the time it takes to get treatments to market. This makes it more difficult for patients to afford treatments and contributes to health inequity.
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H1 Blog
NOVEMBER 6, 2023
By utilizing a single healthcare data platform that allows for seamless information sharing between teams, medical affairs can gain access to valuable market insights from the commercial team while also being able to provide feedback on HCP engagement to support sales efforts–all supported with real-world, structured data insights.
H1 Blog
JULY 24, 2023
Strategies for Market Access Teams One thing the healthcare ecosystem lacks in the United States is a system of shareable, actionable healthcare data. Understanding which treatments are being utilized, by whom, and for what length of time can help inform pharmaceutical operations on how best to meet patient needs.
H1 Blog
NOVEMBER 29, 2023
Every day, patients around the globe are waiting for life-saving treatments to reach the market. Without access to all the relevant data, researchers are unable to fully understand the complexities of diseases and develop effective treatments.
H1 Blog
JULY 5, 2023
From trial site feasibility to successful trial recruitment, diversity within a trial population helps ensure the safety and efficacy of drugs that reach the market. To learn more about how H1 can help diversify your HCP engagement strategy, request a demo.
H1 Blog
JUNE 7, 2023
Performance data on past trials can help identify patient groups that tend to respond better to certain treatments, recommend specific principal investigators based on their track record in similar trials, and even suggest optimal trial sites based on historical performance. The Michael J.
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