Big Data has consistently demonstrated the ability to improve customer-facing functions such as sales and marketing across many industries. As data engineers continue to explore the vast potential of Big Data Analytics and AI, healthcare organizations can begin to target and address more healthcare specific challenges. Big pharma has long been challenged with siloed data resulting from drug discovery information, clinical trial results and product marketing research stored separately in decade-old legacy systems.
Thus, the pharmaceutical industry is ripe for the actionable insights offered by these advances to offset the growing costs of drug discovery while still meeting the demands of a value-based care model. It is time for a connected approach in the pharmaceutical industry.
The current pharma environment is plagued with expensive and lengthy drug discovery cycles coupled with pricing pressures by both payers and consumers.
“The average cost to research and develop each a successful drug is estimated to be $2.6 billion. This number incorporates the cost of failures – of the thousands and sometimes millions of
compounds that may be screened and assessed early in the R&D process, only a few of which will
ultimately receive approval. The overall probability of clinical success (the likelihood that a drug entering clinical testing will eventually be approved) is estimated to be less than 12%.”1
“Pharmaceutical R&D suffers from declining success rates and a stagnant pipeline. Big data and the analytics that go with it could be a key element of the cure. The McKinsey Global Institute estimates that applying big-data strategies to better inform decision making could generate up to $100 billion in value annually across the US healthcare system, by optimizing innovation, improving the efficiency of research and clinical trials, and building new tools for physicians, consumers, insurers, and regulators to meet the promise of more individualized approaches.” 2
Pharmaceutical R&D is just the tip of the proverbial iceberg. pharmaceutical industry is faced with strong product pricing pressures, dwindling new product pipelines, increased operating expenses, stringent regulatory requirements, and rising stakeholder expectations. All these developments collectively are making the drug companies, both global and local, search for efficiencies in their processes in an efforts to achieve corporate financial goals more than ever before.
Despite the continuous change, most pharmaceutical business models fail to meet the increasing economic expectations of the shareholders, other investors, and the stock markets. Most
entities have yet to adopt a data management initiative to capitalize on the advances in Artificial
Intelligence “AI” to drive improvements within the organization. This includes even the most
critical interface between an organization and the consumers – pharma sales and marketing.
Currently, the general response to the much-hyped digital transformations is mostly reactive and
traditionally defensive in nature, rather than proactive, as the overall business environment around the industry keeps becoming increasingly demanding. Most pharma players understand the benefit of adopting new technologies but there remains a persistent and troubling gap between strategy and the organization’s ability to adapt and deploy a data analytics working solution.
It is not enough to simply analyze drug discovery data but to remain competitive, pharma must learn
from the analytics. This is accomplished through yet another disruptive technology – Artificial
Intelligence “AI”. The adoption of AI allows for learning from real-time data – identifying the right
candidates for clinical trials, processing real-time patient feedback, integrating data exchanges with
partners, distributors, and caregivers are just a few examples on how to improve drug discovery
outcomes while aligning operational efficiencies to deliver better care outcomes to the patients.
Often getting the right medication to the right patient at the right time is really about getting the right information in front of the healthcare provider. Armed with complete real-time drug insights, doctors are able to choose the right prescription for the best possible outcome.
Further, as patients become more engaged in their healthcare decisions, they will turn to the internet
to research possible medication options. Through target audience marketing, pharmaceutical
companies can further assure the right information is presented at the right time to facilitate informed
patient and provider discussions.
It is time for Connected Pharma!
Read the White Paper – ARTIFICIAL INTELLIGENCE: NEXT FRONTIER FOR CONNECTED PHARMA
1. Biopharmaceutical Research & Development – PhRMA
2. How big data can revolutionize pharmaceutical R&D – By Jamie Cattell, Sastry Chilukuri, and Michael Levy – McKinsey & Company
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