Competing in Healthcare
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Digital Transformation
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the Age of AI

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Digital Maturity

Reputable industry studies seem to suggest that the Healthcare industry lags other industries in digital maturity by several years. Although regulatory requirements with respect to Patient information and health safety, are frequently cited as the primary reasons, massive consolidation in the US may have somewhat desensitized the industry to the importance of the customer experience, the main driver of digital transformation. According to Forrester, a research company, healthcare firms lag most other industries in adopting business technologies for improved customer engagement, although the picture is more nuanced when looking at the key healthcare subsectors: Medtech, Providers and Bio/Pharma (source: BCG, a consulting company). Medtech is understandably very much advanced with technology and software being the core of its business and its need to compete in a B2B marketplace. Bio/Pharma was much less advanced with 70% of firms being digital laggards (BCG) due in part to complex supply chains and protection in the US. Providers, which here includes hospitals, medical centers and insurers, were also very much behind. Overall, taking into account customer experience, operational excellence, and business innovation, the Healthcare Industry received a relative digital maturity score of around 58%.

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Case Studies

Digital Transformation in the Healthcare Industry - CASE STUDIES

Data Science makes development of cancer drugs faster, safer and cheaper
Clinical trials for cancer drugsBackground
Until recently, documenting pharmaceutical research was a painstaking, principally manual process of cross-referencing information in repositories of publicly available data and information published in scientific and medical journals with a company’s information from its own drug development and testing. The full drug development process took from ten to eighteen years with clinical tests costing $40,000 to $50,000 per patient.
Challenge
A major international pharmaceutical research company focused on a full range of cancer treatments including acute myeloid leukemia (AML), needed a method for more quickly and accurately processing the massive amounts of data emerging from their own trials, from available research, and from the Cancer Cell Line Encyclopedia .
Inputs
Cognizant’s AI & Analytics team applied its expertise in data science and analytics alongside their experience in the life sciences industry to build an automated process for analyzing data in pre-clinical trials research and during clinical trials specifically for one treatment for AML.
Output
Using an Agile development model, Cognizant designed and built an automated pipeline that intakes this vast range of disparate data, normalizes it, performs analytical processing at blinding speed, and delivers easily understood reports on outcomes
Outcome
The pharmaceutical company can now deliver to oncologists conducting trials for this specific treatment more accurate, informed recommendations on dosages cross-indexed to a staggering amount of information. This solution has trimmed up to four years from the process and offers savings of as much as 10% of total costs.

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