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.

Healthcare Analytics: Creating a Prioritized Improvement System with Performance Benchmarking.

The importance of healthcare improvement is difficult to overstate. This article describes our collaborative work with experts at Seattle Children's to create a prioritized improvement system using performance benchmarking. We applied analytics and modeling approaches to compare and assess performance metrics derived from U.S. News and World Report benchmarking data. We then compared a wide range of departmental performance metrics, including patient outcomes, structural and process metrics, survival rates, clinical practices, and subspecialist quality. By applying empirically simulated transformations and imputation methods, we built a predictive model that achieves departments' average rank correlation of 0.98 and average score correlation of 0.99. The results are then translated into prioritized departmental and enterprise-wide improvements, following a data to knowledge to outcomes paradigm. These approaches, which translate data into sustainable outcomes, are essential to solving a wide array of healthcare issues, improving patient care, and reducing costs.

Communication and data-intensive science in the beginning of the 21st century.

The advent of data-intensive science has sharpened our need for better communication within and between the fields of science and technology, to name a few. No one mind can encompass all that is necessary to be successful in controlling and analyzing the data deluge we are experiencing. Therefore, we must bring together diverse fields, communicate clearly, and build crossdisciplinary methods and tools to realize its potential. This article is a summary of the communication issues and challenges as discussed in the Data-Intensive Science (DIS) workshop in Seattle, September 19-20, 2010.