HealthCare & Life Sciences
Artificial intelligence, automation, blockchain and other technologies driving digital transformation will fundamentally reshape how drug makers operate, from portfolio planning, drug development, direct-to-consumer marketing, to finance and other administrative functions.
Technology will also drive dramatic cultural changes at these organizations - the deliberative, scientifically driven nature of these companies is well-suited to take advantage of the power of applying analytics to a multitude of data points to uncover patterns that lead to better-defined, data-driven courses of action across the organization, for executives, managers and researchers.
KPMG’s 2018 CEO Outlook this year found that a quarter of life sciences executives are showing positive returns from their investments in digital transformation and artificial intelligence. Another third of these CEOs expect to see ROI within a year from digital transformation programs.1 However, U.S. and global life sciences CEOs diverge about the strategic value of technological investment. Global CEOs are almost equally divided about whether the nature of the technology investments are tactical or strategic, whereas U.S. CEOs see technology as a strategic play. Pharma Chief Information Officers, who are often tasked with carrying out digital transformation, seem to be more grounded about the limitations of technology, where only 11 percent say their digital strategies are “very effective.”2
However, the objectives behind digital transformation remain unchanged. Technologies provide pharmaceuticals executives a means to improve efficiency, uncover new business opportunities, and build better relationships with patients and prescribers. For example, emerging technologies, such as robotic process automation, are able to take complex and repetitive tasks and do them much more effectively than depending upon human interaction. With greater use of wearable technology, patients can provide a continuous datastream to help drug companies and providers better understand medical conditions, responses to treatment and potentially improve medical outcomes.
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The convergence of robotic process automation (RPA), machine learning, cognitive computing, artificial intelligence, and advanced analytics are driving unparalleled business model transformation.
Organizations need guidance to seamlessly integrate people and machines, while simultaneously harnessing the technological disruption into a competitive advantage. This creates a great deal of upheaval in organizations that will lead to difficult questions about how people will address new technology in the workplace. Life sciences organizations are streamlining their processes, modernizing enterprise systems and infrastructure to improve efficiency, flexibility, and speed-to-market. While these technologies will have their own respective adoption curves in the realm of healthcare and life sciences, they need to be evaluated for their utility and how they can best be deployed.
One of the areas where a considerable amount of traction has been obtained is the field of data & analytics (D&A). Figure 1 shows the evolution of Data & Analytics for enterprises. Life sciences companies presently fall between the realm of diagnostic and predictive use.
Some pilot programs are squarely in the predictive use area, but we anticipate widespread adoption of this approach to D&A in the future.
At the core, technology will touch every facet of the pharma business and will become greatly embedded into all facets of operations. In short, pharma companies are midway through their digital transformation.
Improving Patient Care
Connectivity among patients, devices and clinicians is growing at a rampant rate. Allied Market Research projected that the Internet of Things healthcare market is expected to grow to $136.8 billion by 2021.3 Connected devices create a multitude of touchpoints to monitor patients and their activity and the connectivity can amplify disease management efforts to help address how well the chronically ill are faring to help prevent medical emergencies and reduce unnecessary hospitalizations. Data & Analytics has a well-understood role in stratifying the patients that need the most attention, but an equally important opportunity is the use of analytics to track disease progression and alert a clinician when an intervention may be necessary.
Beyond remote monitoring, technology has been instrumental in guiding pharmacy safety to alerting patients and prescribers to drug or drug-disease interactions to improve safety and getting the right patients to use the right medications at the right dose. While safety is an essential part of the equation in pharmacy care, efficacy is not always assured. Not all drugs work for everyone. The FDA recently approved a DNA test for consumers about how patients may respond to 50 different commonly used medications.4 Some medicines plainly impair efficacy when they are used in combination.5 In the realm of behavioral medications, how many times do patients need to cycle through anti-depressants and antipsychotics for the patient to see a benefit or contend with side effects? The use of advanced analytics can help steer clinicians away from dead-ends when providing care and they also open a whole avenue for finding new treatments.
Drug Development and Discovery
It’s no coincidence that eff orts to map and analyze the human genome have followed the well-understood pattern of the power of Moore’s Law – a doubling of computing power every 18 months.
The exponential growth in this foundational capability has started to make its presence felt in the lab. At the core, the study of genetics is driven by analysis of the variety of permutations of combinations of sequenced proteins. The massive amounts of data in the genome are still being unlocked. There is still a lot to explore. The power of machine learning and robotic process automation can exert itself in the field of uncovering biomarkers and determining which patients may benefit the most from a drug under development, creating better targeting for drug indications. The technologies can be extremely powerful in the early stages of development.
While there are opportunities for new clinical trial designs and greater collaboration among partners, population health can help establish some benchmarks to target to show efficacy of a medicine being developed. By looking at entire populations of patient groups and taking finer cuts of data, the treatment programs can be much more tailored for patients and the possible indications for drugs can reflect more personalized care. Biomarkers will increasingly become a part of this refinement of care delivery. “Biomarkers can be used to individualize treatment by targeting populations more likely to respond leading to smaller and more efficient trials. Finally, using new trial design such as basket, umbrella or more broadly platform trials to assess a number of therapies simultaneously offers the potential to transform the drug development process,” researchers wrote in the October 2018 issue of Diabetes & Obesity Metabolism.6
October 2018 issue of Diabetes & Obesity Metabolism.6 Approximately 15% of medical products approved by the European Medicines Agency and 138 medicines approved by FDA contain pharmacogenomic labels.7 The world of pharmacogenomics has drawn connections between certain genes and drug metabolism, particularly around tolerability, efficacy of behavioral, cardiovascular and oncology medicines.8-10 These insights also can lead to more personalized diagnosis and treatment, as well as stratifying risk and predicting response to treatment.
Beyond that, the multitude of patients with complex health conditions creates a complex array of treatment programs and combinations of medications. Artificial intelligence can help caregivers work out a treatment program that addresses the complexity of medical conditions.
Pharmacoeconomics
One of the biggest issues confronting all of healthcare is “How do we pay for innovation?” Life sciences companies are under increased pressure to show how medications can improve outcomes, but also show their value when examining the total cost of care for patients.
These calculations can be complex, given the level of activities tied to patient care. On a different level, how do you calculate the value of a hospitalization that has been prevented? How do you calculate that cost for patients with chronic diseases over decades? Analytics can help with those calculations.
Drug makers would soon be able to bargain with payers from an improved position with this sort of information. It also raises important strategic issues about drug pricing and what treatments should be more aggressively pursued. This type of business intelligence and the ability to use predictive analytics can help executive teams map out different scenarios about the market for different therapeutic categories. From a competitive standpoint, the analytics can also help cut through questions about comparative effectiveness of medications under development versus current standards of care, which will also feed into pricing decisions.
Making Business Processes More Effective
Pharmacoeconomics is part of the equation in the policy debate over prescription drug prices. Drugs that are on the market pay for a lot of failed experimental treatments. Life sciences executives are being asked to do more with less as organizations deal with patent expirations, R&D productivity and an investment climate that rewards efficiency.
Life sciences executives are confronted with an opportunity to use technology to transform their business models. The use of artificial intelligence can help augment human judgment and RPA can automate physical tasks, creating opportunities in finance, procurement, HR, regulatory/legal, and other functions to cut costs and expand employee capacity for higher-value work. For example, AI can guide investment decisions on expected returns and managing risk in the finance function. Digital transformation can reshape the regulatory process. Regulatory compliance is a case where technology can take complex and repetitive tasks and fulfill a narrowly defined mission, such as certain FDA submissions. Blockchain can have a role in tracking and tracing drugs and raw materials throughout the supply chain, making compliance with the Drug Supply Chain Security Act much less burdensome.
These are some of the net benefits tied to digital transformation:
- Automation means moving some functions to bots or to web-based platforms.
- Dashboards can provide insights and analysis to concisely track operations, customer insights and workflow in a very clear format with a fraction of the labor.
- A greater flow of information can lead to simpler organizations with less hierarchy.
- Employees will need a more strategic mindset and their skills should be developed to focus more on outcomes and building the factors that will drive success and less on process, which can largely be automated.
While this can be revolutionary for a large organization, there is a bit of a paradox. Information technology will be so far embedded in business operations that an enterprise-wide IT function may not be necessary with many of the capabilities being automated or moved to cloud vendors to manage technology. Transformation of the IT department, drug development and other functions raises additional questions about talent, training and helping people migrate to changing roles.
The majority of life sciences CEOs surveyed by KPMG, however, say artificial intelligence and robotic technologies will be net job creators for their organizations.10
Conclusion
The future of pharma is becoming enmeshed with digital transformation that has been affecting nearly all industries, changing the nature of work, the markets and most significantly the pace of innovation. Drug makers need to embrace innovations to address patent expirations of existing products, promotional efforts, high costs, R&D productivity, and also measuring pharmacoeconomics to make the case for product pricing.
Given the data-intensive nature of genetics and pharmaceutical development, the pharmaceutical industry has a unique opportunity for digital transformation. While CEOs are seeing gains from digital transformation and artificial intelligence, CIOs see this as a work in progress with 28 percent of those surveyed acknowledging they are working on their digital strategy and only 19 percent said their organization doesn’t have a clear digital business vision and strategy.11
We have three approaches for pharmaceutical leaders to consider:
- Stabilize, streamline and protect the enterprise – Tech platforms need to be modernized, secure and aimed at improving the safety and effi cacy of prescription drugs or the consumer experience.
- Enhance and innovate – Embrace the Internet of Things, wearables, mobile technologies and draw from the power of big data.
- Prepare for the next generation of IT – Position the organization with a combination of retained and outsourced technology to get the most from blockchain, natural language processing and other disruptive technologies. (see Figure 2)
Ultimately, we envision a future with leaner drug makers that apply the innovations from computer sciences to merge with medical and biological sciences. The result will be more personalized medicine that leads to more effective and efficient care.
Author Biographies
Bharat Rao is National Leader, Data & Analytics for HealthCare & Life Sciences at KPMG LLP. Justin Hoss is KPMG’s National Technology Sector Leader for Life Sciences.
References
- 2018 KPMG CEO Outlook
- Harvey Nash KPMG CIO Survey
- Allied Market Research, “Internet of Things (IoT) Healthcare Market is Expected to Reach $136.8 Billion Worldwide, by 2021,”press release dated April 16, 2016. https://www.marketwatch.com/press-release/internet-of-things-iot-healthcare-market-is-expectedto-reach-1368-billion-worldwide-by-2021-2016-04-12-8203318
- U.S. Food and Drug Administration, “FDA authorizes fi rst direct-to-consumer test for detecting genetic variants that may be associated with medication metabolism,” Oct. 31, 2018 press release https://www.fda.gov/NewsEvents/Newsroom/PressAnnouncements/ucm624753.htm
- Inserro, Allison, “Ten Drug Combinations that show the Risks of Psychiatric Polypharmacy,” American Journal of Managed Care, Oct. 28, 2018 https://www.ajmc.com/conferences/psychcongress2018/ten-drug-combinations-that-show-the-risks-of-psychiatricpolypharmacy-
- Heerspink, NJL, J. List and V. Perkovic, “New clinical trial designs for establishing drugefficacy and safety in a precision medicine era,” Diabetes Obes Metab. 2018 Oct;20 Suppl3:14-18. doi: 10.1111/dom.13417
- Regulation of drug metabolism and toxicity by multiple factors of genetics, epigenetics, lncRNAs, gut microbiota, and diseases: a meeting report of the 21stInternational Symposium on Microsomes and Drug Oxidations (MDO)” Acta Pharmaceutica Sinica B, Volume 7, Issue 2, March 2017, Pages 241-248 https://www.sciencedirect.com/science/article/pii/S2211383516303999
- Jonathan F. Lister (2016) Pharmacogenomics: A focus on antidepressants and atypical antipsychotics. Mental Health Clinician: January 2016, Vol. 6, No. 1, pp. 48-53.http://mhc.cpnp.org/action/showFullPopup?id=i2168-9709-6-1-48-t01&doi=10.9740%2Fmhc.2016.01.048
- Depta, Jeremiah P and Sharon Cresci, “CYP450 pharmacogenomics: a cardiology perspective,” Personalized Medicine, Vol. 12, No. 2, March 23, 2015. https://www.futuremedicine.com/doi/abs/10.2217/pme.14.76?journalCode=pme
- 2018 KPMG CEO Outlook Table 116, Question 26A
- 2018 Harvey Nash KPMG CIO survey, life sciences industry cut.