case study on the healthcare industry
An analysis of the healthcare industry sheds light on how hospital systems might reorganize if intelligent machines become as ubiquitous as smartphones in the next five years. With medical knowledge doubling every 3.5 years, finding new ways to make evidence-based decisions has never been more critical. Watson™ is a powerful cognitive technology developed by IBM that processes information more like a human than a computer, by understanding natural language, generating hypotheses based on evidence, and learning along the way. In 2012, Memorial Sloan Kettering Cancer Center (MSKCC) and IBM collaborated to develop a powerful tool—Watson for Oncology. It combined the computational power of Watson with MSKCC’s decades of longitudinal data, clinical knowledge, molecular and genomic data, cancer case histories, and the latest research to create an evidence-based decision support system for oncologists. Watson for Oncology was launched in 2014 and free access was provided to hospitals worldwide. Participating hospitals had a mandate to share their data fully, which would make the tool more intelligent. Taking the US as an example, Watson for Oncology has great potential in the highly regulated healthcare industry (a classic problem solver and an asset builder). There are three tiers of hospitals: Tier 1—world-class, with top doctors; Tier 2—regional; and Tier 3—community, often with limited resources. Hospitals in each tier have the opportunity to reconfigure their business model to compete better. For example, Watson for Oncology could lead to a decoupling of diagnosis and treatment, which are typically vertically integrated and housed under one roof. It is also possible to save on expensive doctors and invest in areas that have more impact for cancer patients, such as nursing and after-treatment services. By specializing in a disease class, a new provider could do the job—at scale, more efficiently, and at a lower cost. Specialization involves streamlining the type of patient, changing the workflow, investing in specific equipment, standardizing procedures, and becoming outcome driven. Expertise and human intuition are expensive and time-consuming and the outcome is not predictable. The codification of human knowledge to enable data-based decision making is the way forward. Watson for Oncology has gained traction in countries such as Thailand, Malaysia, Korea, and India, many of which lack oncologists and specialist centers. The problem-solving industry will eventually become a network, with the ability to move data around as its fundamental gel. Although privacy concerns surrounding patient data will need to be addressed by regulation and technology—such as blockchains and encryption—Watson for Oncology is poised to change the architecture of healthcare.