Innovation Management? Orienting Sepsis R&D and Technology Transfer Towards Stratified Medicine

How Healthcare Can Refocus on Its Super-Customers (Patients, n =1) and Customers (Doctors and Nurses) by Leveraging Lessons from Amazon, Uber, and Watson

Healthcare is transforming with data-intensive omics technologies and Big Data. The "revolution" has already happened in technology, but the bottlenecks have shifted to the social domain: Who can be empowered by Big Data? Who are the users and customers? In this review and innovation field analysis, we introduce the idea of a "super-customer" versus "customer" and relate both to 21st century healthcare. A "super-customer" in healthcare is the patient, sample size of n = 1, while "customers" are the providers of healthcare (e.g., doctors and nurses). The super-customers have been patients, enabled by unprecedented social practices, such as the ability to track one's physical activities, personal genomics, patient advocacy for greater autonomy, and self-governance, to name but a few. In contrast, the originally intended customers-providers, doctors, and nurses-have relatively lagged behind. With patients as super-customers, there are valuable lessons to be learned from industry examples, such as Amazon and Uber. To offer superior quality service, healthcare organizations have to refocus on the needs, pains, and aspirations of their super-customers by enabling the customers. We propose a strategic solution to this end: the PPT-DAM (People-Process-Technology empowered by Data, Analytics, and Metrics) approach. When applied together with the classic Experiment-Execute-Evaluate iterative methodology, we suggest PPT-DAM is an extremely powerful approach to deliver quality health services to super-customers and customers. As an example, we describe the PPT-DAM implementation by the Benchmarking Improvement Program at the Seattle Children's Hospital. Finally, we forecast that cognitive systems in general and IBM Watson in particular, if properly implemented, can bring transformative and sustainable capabilities in healthcare far beyond the current ones.

Pharmacogenomics and Global Precision Medicine in the Context of Adverse Drug Reactions: Top 10 Opportunities and Challenges for the Next Decade

In a move indicative of the enthusiastic support of precision medicine, the U.S. President Barack Obama announced the Precision Medicine Initiative in January 2015. The global precision medicine ecosystem is, thus, receiving generous support from the United States ($215 million), and numerous other governments have followed suit. In the context of precision medicine, drug treatment and prediction of its outcomes have been important for nearly six decades in the field of pharmacogenomics. The field offers an elegant solution for minimizing the effects and occurrence of adverse drug reactions (ADRs). The Clinical Pharmacogenetics Implementation Consortium (CPIC) plays an important role in this context, and it aims at specifically guiding the translation of clinically relevant and evidence-based pharmacogenomics research. In this forward-looking analysis, we make particular reference to several of the CPIC guidelines and their role in guiding the treatment of highly relevant diseases, namely cardiovascular disease, major depressive disorder, cancer, and human immunodeficiency virus, with a view to predicting and managing ADRs. In addition, we provide a list of the top 10 crosscutting opportunities and challenges facing the fields of precision medicine and pharmacogenomics, which have broad applicability independent of the drug class involved. Many of these opportunities and challenges pertain to infrastructure, study design, policy, and science culture in the early 21st century. Ultimately, rational pharmacogenomics study design and the acquisition of comprehensive phenotypic data that proportionately match the genomics data should be an imperative as we move forward toward global precision medicine.