Expert Insights Exploring quantum computing use cases for healthcare Accelerate diagnoses, personalize medicine, and optimize pricing Experts on this topic Dr. Frederik Flöther Dr. Frederik Flöther is an IBM Quantum Industry Consultant and globally leads efforts for the Life Sciences Life Sciences & Healthcare Lead and Healthcare sector. Frederik is an IBM Academy of IBM Quantum Industry Consulting Technology member and Senior Inventor. He has deep IBM Services expertise in quantum computing and artificial intelligence linkedin.com/in/frederikfloether/ and works with clients to create value through these next- [email protected] generation technologies. Frederik has authored more than 20 patents, peer-reviewed publications, and white papers. Judy Murphy, RN, Judy Murphy is Chief Nursing Officer at IBM Global Healthcare. Previously, she was Deputy National FACMI, FAAN Coordinator at ONC in Washington DC and Vice President, Chief Nursing Officer Applications at Aurora Health Care in Wisconsin. She IBM Global Healthcare has more than 30 years of health informatics experience linkedin.com/in/judy-murphy-rn- and is a fellow in the American College of Medical facmi-fhimss-faan-4066442/ Informatics and the American Academy of Nursing. [email protected] Judy has published and lectured internationally and has received numerous Health IT awards. John Murtha John Murtha sets the strategic direction for IBM’s Global Health Plan Segment and works with priority accounts Health Plan Industry Segment envisioning next generation health plans. A twenty year Leader health plan industry veteran, John has led several large IBM Industry Platforms scale transformation initiatives that extensively used linkedin.com/in/john-g-murtha/ technology, and reengineered processes. Prior to joining [email protected] IBM, he was the Chief Operating Officer of VNS CHOICE Health Plans and during his tenure their HIV Special Needs Plan achieved the highest HIV Suppression Rate in the country. Dr. Daby Sow Dr. Daby Sow manages the Biomedical Analytics and Modeling group in the Center for Computational Health. Principal Research Staff Member He drives various efforts developing and applying novel Center for Computational Health AI techniques to disease progression and intervention IBM Research modeling. Daby holds more than a dozen patents and linkedin.com/in/daby-sow- has authored more than 50 scientific articles and book 2a53b31 chapters in areas such as computational health, [email protected] information theory, knowledge discovery and data mining, middleware, and pervasive computing. It has been proven that quantum computing can have an advantage over classical approaches. Talking points Data for improved healthcare Disruptive healthcare use cases experiences and results In the healthcare industry, quantum Healthcare data—such as information from clinical trials, computing could enable a range of disease registries, electronic health records (EHRs), and medical devices—is growing at a compound annual growth disruptive use cases for providers and rate of 36 percent.1 Increasingly, this data helps address health plans by accelerating diagnoses, challenges associated with the “quadruple aim” of personalizing medicine, and optimizing healthcare: better health, lower cost, enhanced patient experiences, and improved healthcare practitioner work pricing. Quantum-enhanced machine lives.2 At the same time, healthcare consumers are making learning algorithms are particularly more decisions and have to navigate an increasingly relevant to the sector. complex system. Significant investments are being made to deliver the right Benefit from multiple data sources data and powerful insights at the point of care. Industry As access to health-relevant data sources incumbents and new entrants alike are trying to create continues to grow, the potential for the digital experiences that reinforce healthy, preventive behaviors. Despite that, accounting for the exponential combination of quantum computing and possibilities from this diversity of new data is stretching classical modeling to save lives and the capabilities of classical computing systems. reduce costs increases. Enter quantum computing. Time to act is now A century after the birth of quantum mechanics, it has Healthcare is likely to benefit significantly been proven that quantum computing can have an 3 from quantum computing. However, much advantage over classical approaches. Quantum computing does not merely provide an incremental of the early intellectual property in quantum speedup. It is the only known technology that can be computing may be proprietary, raising the exponentially faster than classical computers for certain urgency to get started and engage with tasks, potentially reducing calculation times from years to minutes.4 partners and ecosystems today. Quantum computing necessitates a different way of thinking, a new and highly sought-after set of skills, distinct IT architectures, and novel corporate strategies. The technology also has immediate implications for security.5 Security is an area of particular relevance for healthcare, given the sector’s data privacy responsibilities and challenges. 1 Insight: Bits and qubits In healthcare, as in other industries, using quantum computers in concert with classical computers is likely to Quantum computers process information in a fundamen- bestow substantial advantages that classical computing tally different way from traditional computers. Previous alone cannot deliver. As a result, there is now a race toward computer technology advancements—such as integrated quantum applications. Following are three key potential circuits—enabled faster computing, but were still based quantum use cases that are central to the healthcare on classical information processing. Quantum computers industry’s ongoing transformation (see Figure 1): manipulate quantum bits (qubits). 1. Diagnostic assistance: Diagnose patients early, These are unlike classical bits, which store information accurately, and efficiently as either a 0 or 1, and they can display uniquely quantum 2. Precision medicine: Keep people healthy based on properties, such as entanglement. As a result, it becomes personalized interventions/treatments possible to construct quantum algorithms that can outper- form their classical counterparts which are not able to 3. Pricing: Optimize insurance premiums and pricing. leverage quantum phenomena. Quantum computers could be particularly useful in tackling problems that involve: – Chemistry, machine learning/artificial intelligence (AI), Figure 1 optimization, or simulation tasks. In fact, machine Quantum computers may enable three key healthcare use learning has shown potential to be enhanced by quantum cases that reinforce each other in a virtuous cycle. For computing and is symbiotically helping drive quantum instance, accurate diagnoses enable precise treatments, advances6 as well as a better reflection of patient risks in pricing – Complex correlations and interdependencies among models. many highly interconnected elements, such as molecular structures in which many electrons interact – Inherent scaling limits of relevant classical algorithms. Diagnostic For instance, the resource requirements of classical assistance algorithms may increase exponentially with problem size, as is the case when simulating the time evolution of Precision 7 quantum systems. medicine Pricing 2 Quantum computing has the potential to improve the analysis of medical images, including processing steps, such as edge detection and image matching. Together, these use cases significantly help advance One challenge is the classification of cells based on their healthcare’s quadruple aim. Diagnostic assistance could many physical and biochemical characteristics. These improve health, cost, experience, and jobs, while precision cause the feature space, that is, the abstract space in medicine should enable better patient outcomes and which the predictor variables live, to be large (high- experiences, and pricing is expected to help reduce costs. dimensional). Such classification is important, for example, in distinguishing cancerous from normal cells. Quantum- Use Case 1: Diagnostic assistance enhanced machine learning approaches, such as quantum support vector machines, appear poised to enhance Early, accurate, and efficient diagnoses usually engender classification and could boost single-cell diagnostic better outcomes and lower treatment costs. For example, methods. survival rates increase by a factor of 9 and treatment costs Moreover, discovering and characterizing biomarkers decrease by a factor of 4 when colon cancer is diagnosed may necessitate analysis of complex “-omics” datasets, early.8 At the same time, for a wide range of conditions, such as genomics, transcriptomics, proteomics, and current diagnostics are complex and costly.9 Even once a metabolomics.13 These can entail a large feature space, diagnosis has been established, estimates suggest that as well as many interacting features leading to it is wrong in 5–20 percent of cases.10 interdependencies, correlations, and patterns that Medical imaging techniques, such as CT, MRI, and are challenging to find with traditional computational X-ray scans, have become a crucial diagnostic tool for methods.14 Further extending biomarker insights down to practitioners over the last century. Computer-aided the level of the individual naturally requires even more detection and diagnosis methods for medical images have advanced modeling.