The Life Sciences Industry: Decision Making from Batch to Continuum


Decision Making from Batch to Continuum The life science industry is close to a significant transformation. Regulations are tightening considerably. In developed markets, governments are restricting the freedom to price new drugs. For many therapeutic areas in developed nations, mortality rates have drastically come down. This makes it difficult to demonstrate significant improvement in health outcomes over standard of care. Drug development dollars have been shifting to Oncology and Neurology which still provide a significant headroom and upside for breakthroughs. However, more R&D investment does not mean higher number of blockbusters. Research and drug development capabilities cannot increase manifold overnight and statistically, in fact, even with a higher number of candidates in the innovation funnel, they have not shown to improve number of innovations significantly.

Technology is also playing spoilsport to the breakthrough party. Digital capabilities and big data is transforming everything from discovery to commercialization. Be it identifying the right drugs or how to launch a drug – digital is omniscient. PWC in its 18th global CEO survey reports that 50% of Pharma CEOs were concerned about the speed of technological change, up from 32% in the previous year. In the same survey they are reported to be more worried than CEOs of other sectors about disruption to their Industry.

However, with this accelerated pace of change, decision making remains a batch process that is predominantly manual. Thousands of analysts manually curate data and sell it at a high premium to companies and clinicians to make decisions. The McKinsey Global Institute estimates that applying big data strategies to decision making could generate up to $100 billion in value annually across the US health-care 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. Public institutions and Governments are also yet to take the important baby steps to align data standards and governance in order to simplify curation process. One exception to this are the Nordic countries. In their book “New Horizons for a Data-Driven Economy”, Cavanillas, Curry & Wahlster argue the case for Sweden which, since 1970 continuously invested in public health analytics initiatives leading to 90 registries that covers more than 90 % of all Swedish patient data with selected characteristics even longitudinal data. A study mentioned in this book from PricewaterhouseCoopers (2009) showed that Sweden had the best healthcare outcomes in Europe with respect to average healthcare costs. Many other countries are starting registry initiatives – but these registries cannot ‘speak’ with registries in other countries!

IBM estimated in 2011 that of the 161 billion GB data that the healthcare Industry needs to analyze to make decisions, about one fourths of the total cost of curation is attributable to human effort, workflow tools and metadata. We believe that the costs of human efforts in data curation are substantially higher. Between 2006 and 2013, there was a seven-fold increase in data curation jobs (job trends from Peter Karp in Database (2016) estimates cost of curation of one bioinformatics article to be 219 USD. This cost excludes all other costs such as database costs, software license, outreach, preparation of publications etc. With increasing data volumes, velocity, veracity and variability, it is impossible to imagine a scenario of ever increasing armies of analysts driving curation work without cost explosion. We have seen the rise of a lot of Analytics as a Service companies in addition to the traditional consulting companies. Many of the use cases that call for a continuous decision support still follow the traditional batch process: ‘specs – analysts – presentations’. To keep a close watch on the competitive landscape, analyze potential for drug repurposing, tweaking field force deployment for niche indications based on responsiveness data or discovering and managing Key Opinion Leaders; decision makers need continuous analytics. Analytics that can triangulate enterprise data with external data and provide decision support as and when required. A holistic approach that combines collection, extraction, analysis and visualization is necessary as with piecemeal approaches manual effort would always remain high.

Innoplexus started a journey in 2011 to use AI to completely automate this process. Innoplexus’s Data as a Service platform iPlexusTM crawls terabytes of data using its universal crawler, aggregates data normalizing ambiguous entities, analyses important information critical for a specific context and visualizes the same in real time. An ecosystem of applications native and third party are built on top of iPlexusTM for various use cases providing continuous analytics. In the new P4 medicine paradigm where medicine has to be predictive, preventive, personalized and participatory – iPlexusTM would provide more power to clinicians and patients as well. Increasingly healthcare business models are going to be tied to healthcare outcomes with targeted personalized therapies embedded in real world evidence. Through iPlexusTM clinicians, researchers, decision makers in large and small pharmaceutical and biotech companies and even patients can get real time insights across the entire pharma value chain from discovery till commercialization.

The life science industry needs more collaboration in the data and continuous analytics realms. Access to health starts with awareness for patients and capabilities of healthcare providers at the last mile of healthcare delivery. IPlexusTM is a revolution in that direction, democratizing and empowering decision support to improve awareness, insights and ultimately patient outcomes.

Today’s post comes from guest blogger, Dr. Gunjan Bhardwaj (CEO, Innoplexus). Dr. Bhardwaj has also written for in the past.

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