Indegene: AI is transforming the pharma industry
A leader from the healthcare solutions firm shares perspective on how application of advanced technologies has driven the evolution of the entire industry.
14 JULY, 2020
Artificial intelligence (AI), machine learning and other advanced computing technologies have changed the way professionals in the life science industries do business. These solutions can accelerate drug discovery, streamline clinical trials, facilitate process improvements, and more.
Outsourcing-Pharma (OSP) spoke with Manish Gupta (MG), CEO of Indegene, about how companies all along the supply chain are making use of such technologies, and how intelligent use of data can lead to a range of benefits.
OSP: Could you please tell us about Indegene?
MG: Indegene was founded on the premise of bringing medical expertise and technology together to drive effectiveness in healthcare; this is a core part of the company DNA and has therefore been embedded in all our solutions, ever since we started. The first monetization of this happened to be in pharma marketing and medical communications in India two decades ago.
Since then, we have evolved into a global healthcare solutions company that is enabling life sciences organizations bring products to the market using modern medical and commercial capabilities and operations. A manifestation of this evolution has been the leveraging of our early success in delivering content solutions for pharma marketing in India to create a global powerhouse which combines medical expertise with AI/neurolinguistic programming (NLP) to drive modernization, automation, excellence and efficiency in the entire pharma content supply chain.
OSP: How has use of healthcare data evolved in the life sciences in recent years, especially the use of AI?
The healthcare industry is becoming increasingly integrated with life sciences as both industries have realized that this collaboration can great potential to improve patient care. One key consequence of this desire for collaboration is the evolution of a marketplace for healthcare data and its corresponding analytics.
In the R&D side, this data propels novel use cases in research such as translational research or understanding of disease trajectories and in clinical development such as designing appropriate protocols or using data to accelerate patient recruitment. On the other hand, examples of novel use cases in the commercial are generating evidence for market access and product pricing to improving coordination of care for patients to achieve superior health outcomes.
Pharma companies have also pivoted from using these data-based approaches as an alternative way of doing things to trying to make these the primary way of doing things. However, there are challenges.
Healthcare data is neither harmonized nor properly structured; likewise, the millions of data records may help us understand what happened but not what is the best course of action for a given situation. This is where NLP technologies and data sciences are playing a key role; NLP reads through unstructured data and make sense of it while data sciences techniques can be used to identify insights that may be humanly impossible to discern.
With these technologies becoming mainstream, competitive advantage of pharma companies is evolving from “who does science best” or “who has the best marketing muscle” to “who can create best patient outcomes by harvesting data to make effective R&D or commercial decisions” which requires human in combination with technology.
OSP: Could you please go into detail about how clinical trial teams are making use of AI?
MG: AI in healthcare and pharma has tremendous applicability; it will alter existing processes, transform how goals are achieved, probably make some of the established processes redundant. We see the pharma industry using AI in every area critical to its existence such as drug discovery, clinical trials, supply chain and delivery, regulatory submissions, etc.
One of the key areas where Indegene uses AI is sales and marketing. Pharma companies spend about 25% of their revenue on sales and marketing activities, this is where we believe AI can make a significant impact.
In India, we benefitted from life science companies outsourcing pharmacovigilance or drug safety processes, which was fairly standardized with IT companies. We have a significant solution to automate pharmacovigilance processes; these include voice recognition from customer calls, automated voice-text conversion, processing of crucial data from emails and faxes using NLP, and the like.
We are even more excited about using AI in the drug regulatory process with significantly higher impact. It is especially helpful for drugs that need to be expedited—for instance, the COVID-19 situation.
The use of AI in crucial processes of drug regulation could include increasing probability of first-time success upon submission of documents and consequently shortening the cycle time to getting regulatory approvals. Some of our AI-based solutions currently deployed in pharma companies as pilots are showing extremely promising results.
The other big area we are partnering with the industry is medical content. While the industry is organized into clinical, regulatory, safety, medical affairs and commercial silos, the reality is that drug and disease content are fundamentally the same.
This content is assembled and presented in different ways to achieve different objectives—for example, seeking regulatory approvals from FDA, getting a drug listed on a formulary, or convincing a physician of the therapeutic use and superiority of a drug etc. Life sciences industry sells content, not the drug itself. The solution we are developing leverages AI to ultimately accelerate time to agency and market, increase velocity and drive personalization of communication to various stakeholders.
One of the other areas we use advanced technology is in managing drug labels in global markets. We use a combination of AI, NLP and computer vision in these areas for ensuring regulatory compliance. We also use AI to enable faster responses to physician queries for overall improved patient care.
We have highly experienced in-house technology and medical teams and we also partner with tech majors like Microsoft, Google and Amazon for synergies.
OSP: What benefits can the selection and use of the right datasets mean for clinical trials, and for pharmaceutical companies?
MG: Healthcare data is indeed extremely vast and voluminous; the availability of these datasets has thrown up a significant number of use cases and opportunities to deliver more value for patients. Indegene is already helping the global life sciences industry to unlock value from data in the following domains:
Using real world healthcare data to drive efficiency and effectiveness of clinical trials. We are using EMR, claims, safety, trials, genomics data, etc. to design clinical trials protocols more sharply, find the most appropriate patients and investigators for recruitment in trials, auto- authoring of various clinical trials documents.
Driving more targeted, individualized content messaging to physicians and patients for digital sales and marketing. By using the same data sets to better understand patient journeys, and develop more granular profiles with a deeper understanding of physician knowledge and networks, we are able to deliver more targeted messaging and hence bring down S&M costs from 20%+ by almost half.
OSP: What steps can be taken to at the same time provide robust, accurate data, and protect patient privacy?
MG: In our businesses with healthcare/life sciences, we handle two crucial types of personal information that includes sensitive personal information related to healthcare analytics (such as patient data, disease profile, etc.); and low risk personal information for commercialization purposes (such as first and last names, contact information, etc.)
Our data privacy management framework is designed to comply with most data privacy laws like GDPR, PIPEDA, PDPA, CCPA etc. Our privacy management framework has a modular structure, i.e. all facets of personal data privacy management at the time of personally identifiable information identification, collection, processing, transmission, storage and destruction can be controlled to comply with various regulatory requirements depending on the operations associated with personal data handling.
Our data privacy management framework, based on the British Privacy Standard BS 10012:2017 A1 2018, allows data privacy management both as a data controller and a data processor.
OSP: How can advanced data management help lead to a better patient experience in clinical trials?
MG: One of the challenges we see with clinical trials is that the trials involve large patient pools, the chances of failure are high. With smaller and targeted patient pools, advanced data helps in managing cohorts of patients better, enhancing trial success.
The traditional waterfall approach of clinical data was to collect patient data largely from a site, curate it, perform analytics on a data lake downstream and run bio statistics off the data. With modern technologies being available, this function is no longer siloed but is converging into one unified data infrastructure to enable performing all these activities in one data workbench.
This evolution of advanced data management now allows for collecting lots more data from myriad different sources like wearables or the patient’s smart phone, performing automated real-time validation on it, running advanced analytics on it to perform functions like fraud detection to detect anomalies in data, and alerting care givers if and when their intervention is needed. This data backbone massively increases the ability to serve far greater number of patients and monitor their data more continuously than ever before.
This means patients can now benefit from the flexibility offered in trials to collect data from their homes, the convenience of a truly connected health ecosystem to coordinate care between various parties involved in the study, and the ability to have their health monitored by machines 24 x 7 with alerts going to humans when there are interventions needed.
Without the power of data analytics, fraud detection, advanced statistical analytics and automation, this would never be possible.
These capabilities like personalized longitudinal tracking of patients and patient convenience are critical if the industry is to succeed in its endeavors like better precision medicine, faster trials, and improvement of patient experience. In the long run, these technologies and its corresponding advanced data management capabilities will become the standard of care as cheaper and patient centric way to achieve better health outcomes.
OSP: The pandemic obviously has had negative impacts on the industry—could you please tell us about the opportunities the health crisis might offer to clinical trial professionals?
MG: We believe that technology is just an enabler though a powerful one to drive better patient outcomes. Technology, by itself, will not play a role in fast tracking the regulatory process. Instead, the pressures on the healthcare system driven by an ageing population, chronic diseases and the need to increase access to healthcare are the factors that are pushing the system to fast track processes.
Organizations and initiatives that can move the needle on efficiency, effectiveness and reach while mitigating risk will be the ones that would be able to move fast through the regulatory process. We are seeing regulators especially the USFDA being very open to ideas using technology.
Clinical trials have been experimenting with the idea of virtual/hybrid trials, RWD/RWE, remote audits etc. The adoption of these was still not significant because the risk of experimenting is too high in spite of maturity in the technology and capabilities. The current crisis has accelerated the shift towards these technologies; this offers tremendous opportunities to pharma companies to design a new normal for patient centric trials, to create a future where patient burden is significantly lowered while at the same time this benefits pharma immensely as it increases the patient pool willing to participate in such trials.
The creation of the infrastructure and processes associated with modern trials, digital patient recruitment and remote monitoring is the opportunity for clinical trial professionals.
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