Early Stage Drug Adoption
Pharmaceutical companies are facing major challenges with new drug launches. Although companies are increasingly innovative and efficient in producing new drugs, the implementation of these innovations and strategies is often delayed. Where new drugs expand therapeutics in areas of yet unmet clinical need, accelerated adoption benefits both medicine and society – innovative new drugs should be offered fast and appropriately to the population in need. Understanding the characteristics of early adopters may help us to not only forecast utilization by providing start market coverage and overall sales growth in the life cycle of the drug, but also promote cost-efficient prescribing habits, and develop targeted intervention strategies. In many cases, newly marketed drugs only bring a marginal or insignificant contribution to the conventional therapeutic arsenal, often at a substantial cost increase. Accurate prediction is not only important for pharmaceutical companies, but also for healthcare professionals and policymakers in charge of healthcare budget planning.
Our solution, designed as an explicit user-friendly tool, helped multiple pharmaceutical customers in identifying the early adopters of their drug along with the important factors which classify a prescriber as an early adopter. The tool utilizes advanced ML algorithms to identify early adopters under each segment and various factors classifying physicians as an early adopter. Upon implementation, the solution gave a lift of 3X in the adoption rate as compared to the control group. Moreover, a 22% sales growth for the drug has been observed in the first 6 months post implementing the solution! Early adopter solution would require customization of the tool in terms of defining the adoption rate, enhancing hypothesis based on product profile, tuning model development/deployment process and creating market/drug-specific dashboards. D Cube will hypothesize business contextualize features for early adopters within the therapeutic market. Exploratory data analysis will be done to finalize the features that will ultimately go into the model.
Next, the Targeting Engine is setup to provide the propensity score of a physician for adopting the drug under each segment. D Cube leveraged previous analogs of the similar drugs in the market space for defining early adopters and creating a training dataset. The product brings along various prebuilt features which can be directly utilized for making the solution. It comes with all advanced AI/ML prebuilt models which would help accelerating modeling process. The deployment process would comprise of setting up data pipelines to input data files in the modelling engine. The engine would have the write back mechanism of sending back the results into the database. Attuning of the model would require human intervention in terms of refining model results and giving business intelligence to the output. Without attuning model will pick up variables based on statistical cutoffs and would not have any business intelligence.
Finally, the early adopter physician list would be available for Target Planning & Strategies. Finalization of KPIs, physician profiling and sales pattern migration are some extended segments of this exercise. Get in touch with us to find out how we can help you in identifying the early adopters and start getting market coverage right from the start of the drug launch.