by Pamela Spence
Ernst & Young Global Limited is an IMA B2B Partner…
The health care model is in crisis: wasted or inappropriate care costs health systems billions of dollars, while life science companies’ R&D costs continue to rise, driven by payer skepticism and the need to demonstrate real world value.
As artificial intelligence (AI) grows more powerful, it can enable new solutions to these systemic issues. It’s not surprising, then, that health care stakeholders around the ecosystem see building AI capabilities as a business imperative in 2018. The recent increase in partnering between AI developers and traditional life sciences companies further demonstrates the sense of urgency. The question is, which organizations will play a leading role in shaping AI’s transformation of health care?
Better described as augmented intelligence, AI uses machine learning to rapidly analyze a range of environmental, behavioral and clinical data to generate insights. By using computational neural networks, it will someday be possible to use big data to quickly identify causal linkages between specific evidence-based treatments and improved patient outcomes. Nearer term, AI has a critical role to play in improving the efficiency of health care delivery and drug development.
Some merchants have already incorporated AI capabilities into their retail platforms via chatbots and apps to create natural, personalized customer experiences. Those same tools could be adapted for use in the health setting to automate lower-value (but time-consuming) tasks related to clinical documentation, allowing physicians to spend more time on high-touch services directly related to patient care. In addition, by mining large volumes of clinical data quickly, AI can also help physicians make better evidence-based treatment decisions, especially in therapy areas where the standard of care is changing rapidly.
Meanwhile, for the life sciences industry AI’s main draw is its potential to tackle ongoing R&D productivity challenges. AI analytics and predictive simulations can improve the drug development failure rate by offering an in silico screen to better understand which targeted interventions are likely to succeed or fail. By narrowing the funnel of drug candidates earlier, companies can begin to streamline research costs, while biasing human studies for success. AI can also improve the efficiency of clinical trials, enabling more targeted patient recruitment to reduce enrolment times, and, through adaptive behavioral analytics, increasing both patient compliance and retention.
The urgency to adopt AI is only increasing as the variety, volume and velocity of data generation increases. The digitization of health care records is only the first wave of a coming deluge of personal health care data streaming from a range of devices that includes mobile phones, wearables and implants. The reality is humans won’t be able to interpret the coming deluge of data easily or in a timely way; AI will be required to process the data and identify hidden patterns.
With the power of AI to mine these deep sources of patient-specific data, health care will eventually become precise and personalized, optimizing therapeutic outcomes for each individual. Physicians can begin to build real-time pictures of patient health status, anticipating emergent pathological trends and recommending targeted interventions before symptoms of disease manifest. Financial logic will compel payers and policymakers to embrace this shift toward proactive health management: upstream medical intervention is ultimately far more effective than long-term care for patients with multiple chronic conditions.
Meanwhile, the synergies between AI and life sciences are only going to grow more important. Patient data will allow far more effective fine-tuning of treatment regimens, and will inform the development of new treatments for identified patient subsets. New breakthrough techniques such as gene-editing have huge potential, but to use them safely and effectively will require deeper knowledge of the patient, at genomic and all other levels.
These impressive possibilities should not blind us to the fact that there are still major challenges to overcome. AI is still primarily used to analyse structured datasets; however, most health care data, from physician notes to scan images to patients’ self-descriptions of symptoms, are unstructured and heterogeneous, making analytics harder to program. There are other broad challenges to address: health care data remains locked in discrete silos, and is difficult to share with different health stakeholders due to the lack of interoperability of the existing infrastructure. In addition, due to its sensitive nature, health data is heavily regulated to maintain patient privacy. Building smart systems that allow the safe and appropriate sharing of data remains critical.
But all stakeholders have incentives to break down these barriers, and there are many signs that the process is already underway. The companies that can supply the AI capabilities to drive the reinvention of health care will have a significant head start in the emerging life sciences 4.0 revolution.
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