My last two blogs compared a hypothetical example of reactive and proactive pharmacovigilance (PV). My next installment in this series outlines how PV can work.
Rather than reacting to the data, in the second hypothetical scenario, Company A pivoted with ready-planned interventional pharmacovigilance practices at the level of the intake system and took a patient-centric approach using smart technology and dedicated patient advocates.
This guide analyzes how artificial intelligence – including machine learning – can be used by pharmaceutical and medical device companies to improve the clinical data review and cleansing process.
In the weeks following the implementation of patient adherence and patient support programs (e.g., reminding patients of their next dose, answering any questions or concerns), the rate of lack of drug effect and the rate of disease progression decreased to a level comparable with that reported in the pre-marketing clinical trials.
The patient assistance and adherence program is so successful that Company A suffers no financial impact, and payers and providers are now willing to cover the drug under their benefits. The novel treatment is so successful that Company A begins to look at other immune-mediated diseases in which its biologic could be as successful or other products where PSP or PAP are beneficial. The data from the patient disease journals was used as real-world data to change the device post-marketing to a smaller needle and less buffer to decrease patient injection site reactions.
The second scenario is an example of proactive PV. It is only successful if the data is digested and provided in real time. Are there technologies available that allow such views of the PV data? Unequivocally, yes!
To learn more about the history of pharmacovigilance, current challenges and ROI opportunities, and how to remain proactive in the future, you can download our guide here. Or, you can submit the form below.