Data, Diagnostics, Drugs & Doctors How Digital Technology is Revolutionizing Healthcare

By Benjamin Seet, Deputy Group CEO, National Health Group

Benjamin Seet, Deputy Group CEO, National Health Group

In 2018, the US spent $3.65 trillion, or 16.9 percent of its GDP on healthcare. Despite spending more than all other countries, it ranked 37th in WHO’s ranking of overall health system performance. This discrepancy is as much an indictment of the inefficiencies of the US healthcare system, as a general warning that spending more may not mean getting better results. Singapore, which has one of the best healthcare systems globally, spends about 5 percent of its GDP on health. Needless to say, the growth in healthcare spending has been deemed to be unsustainable, with projections that this could triple by 2030.

In the same year, the number of persons aged 65 or above outnumbered children under five globally for the first time in human history. Lower fertility rates and increased longevity means the number of seniors is expected to double to 25 percent by 2030 in Singapore. An ageing population increases healthcare demands, particularly long-term care for chronic and degenerative conditions. As healthcare spending outstrips economic growth, critical questions emerge around its sustainability, efficiency, and value.

Advances in technology can disrupt healthcare delivery. Biotechnology has introduced new approaches to therapy, such as precision medicine, cell, and immune therapy and gene editing, giving doctors the potential to cure the “incurable”, but it has also raised new benchmarks for medical spending. Data and AI will redefine how doctors make decisions, how patients make choices, as well as how healthcare is delivered and paid for. What is less understood is whether a system as complex as health, with its layers of competing stakeholders, bureaucracies and rising concerns about patient rights and confidentiality, is fully able to capitalize on the opportunities and avoid the pitfalls. We highlight a few areas where digital innovations can affect and potentially disrupt healthcare and health spending.

Big Data in Medicine

Precision medicine accounts for a combination of genetic, lifestyle, and environmental factors to determine an individual’s disease risk and treatment options. This ongoing revolution is driven by our capacity to generate and to make sense of more and more data. Stanford Medicine’s Health Trends Report estimated that 2,314 exabytes of healthcare data will be produced by 2020, compared to 153 exabytes in 2013,driven by electronic medical records, wearable devices, and commercial DNA testing. Progress in analytics and machine learning have greatly improved what we can learn from these observational datasets, but implementation of precision medicine will be mired with policy, regulatory, funding and ethical considerations. Furthermore, the majority of the world’s populations are underrepresented in the research necessary to bring precision into the clinic. There are initiatives by various global and national organizations to tackle some of these thorny issues, but we will continue to see a lag in wider adoption.

Virtual Doc

In 2011, IBM Watson beat Jeopardy!’s two greatest (human) contestants, demonstrating the remarkable ability of a computing system to operate in the highly contextual realm of natural language, process huge amounts of data and solve problems successfully. The Watson team then set its sights on a bigger goal—revolutionizing healthcare, where 80 percent of medical information is unstructured and an estimated 10- 20 percent of diagnosis is inaccurate or incomplete.

IBM was willing to bet big on this moonshot, investing $1 billion to start the Watson Group in 2014, and making a series of acquisitions including Explorys, Phytel, and Truven Health Analytics, with the promise of giving the world its best diagnostician. By April 2019, Watson halted its drug discovery programme in the face of sluggish sales, to double down on Watson Health, where it faced problems contextualizing the medical literature into actionable decisions in a real clinic setting. Ping An Good Doctor, the one-stop online healthcare “ecosystem” backed by Ping An Insurance Group, has now signed up 300 million registered users (or one in every three Chinese internet users), with the goal to provide every person with an e-profile and healthcare management plan. Machine learning can also transform medical imaging, with new innovations to read x-rays and CT scans. Selena+ (Singapore Eye Lesion Analyser Plus), a deep learning system, has been trained to detect diabetic eye diseases in retinal images, and is set to be introduced in public clinics across Singapore.

Biotechnology Has Introduced New Approaches to Therapy, Such As Precision Medicine, Cell, and Immune Therapy and Gene Editing, Giving Doctors the Potential to Cure the “Incurable

Insilico Drug Development

Ex-FDA Commissioner Scott Gottlieb said drug development is on an “unsustainable path where the cost of drug development is growing enormously”. A study published in the Journal of Health Economics put post-approval R&D costs at $2.87B in 2013, up from $802 million in 2003, or a 145 percent increase. This can be attributed to the high attrition rate, with as many as 90 percent of potential lead compounds failing to reach clinical trial. Companies like Benevolent AI, Insilico Medicine and Engine Biosciences, have combined scientific and clinical data with deep learning to accelerate and improve the drug discovery process. For example, Insilico has successfully used generative adversarial networks and reinforcement learning (GENTRL) to discover 6 new lead molecules in just 21 days, a number which have since been validated in cell assays and with animal models. What is laudable was Insilico’s decision to make GENTRL open source and accessible.

Digital Animals

Animal testing has been a cornerstone for medical research for decades. Today, they remain vital to determine safety and efficacy of new drugs before starting human trials, although critics argue that lab animal models are unreliable predictors for human responses. While the European Union has issued a ban on animal testing for cosmetics since 2013, it rejected a subsequent call to expand the ban to their use in medical research. Nonetheless, both the FDA and EMA have advocated for the reduction of animals for medical research as well as for developing new test methods. The University of Oxford has now developed computational models of human heart cells, and claims that they have higher accuracy in predicting adverse drug reactions such as arrythmias. On a grander scale, the Comp BioMed project brings together academics, clinicians and industry to develop a “virtual human” that can be a predictive model for health and disease.

CyberDrugs

Substance abuse is a major cause of morbidity and death, and has in fact, contributed to a decline in life expectancy of the US population for the first time since World War I. It also costs the US an estimated $740 billion in healthcare, crime, and lost productivity. One barrier to individuals seeking help is the associated stigma and discrimination, as well as the long process for rehabilitation. One milestone is the recognition that substance abuse is as much a medical disorder, as it is a behavioral choice. In 2018, FDA approved the first software-only digital treatment, for Pear Therapeutics to provide its reset cognitive behavioral therapy for substance use disorder. This was subsequently extended to treatment of opioid use disorder. Akili integrates neuroscience and videogames to improve cognitive function in diseases like depression, autism and attention-deficit/ hyperactivity disorder. While this field remains nascent, its value is particularly prominent in conditions that benefit from continuous engagement to raise patient compliance.

Conclusion

The convergence of science, medicine and data provides new tools and technologies that could reinvent how healthcare is developed and delivered. This could change clinical outcomes and transform the patient’s healthcare journey. At the same time, such technologies currently come at high cost, widening the already huge divides in healthcare access and standards of care. There is a need for sensible, evidence-based and sustainable solutions that cut though the noise, hype and promises. When looking for a yardstick for success, answer a simple question. Does it make the patient’s life better?