The healthcare field is crucial to people’s lives, expensive, constantly increasing in complexity and cost, and dependent on a wide range of disciplines. It is one of the most challenging areas in which to apply advanced analytics, yet one of the most important. As the editors of this book say, “The adoption of [big data and analytic] approaches should lead to the development of next-generation innovations in healthcare and technology and serve as a guide to policy making.” This volume contains chapters on a wide range of current work in the area. In fact, one of the noteworthy things about this book is its breadth. Chapters cover topics ranging from a methodological design of a care delivery system to a retrospective analysis of pharmacy inventory management.

One notable chapter describes a study of the treatment of type 2 diabetes patients, with the goal of understanding the occurrence of and circumstances surrounding two types of treatment errors in response to progression of the disease: failure to change medication therapy and inappropriate change in therapy. The authors identify physicians as following either a feedback or a feedforward approach, and apply models to understand their response to changes in blood glucose levels. Their models suggest that physicians “would benefit by having decision support to indicate time-dependent dose effects.” The approach is creative, sound, and well supported.

Another chapter examines history data for multiple sclerosis (MS) patients to understand factors driving various lifestyle decisions. This is an example of an important class of applications: understanding the course and impact of chronic diseases. The authors apply latent growth modeling (LGM) and suggest that this is a suitable way to model changes over time, whether in the disease itself or in external factors. By looking at data for 3774 patients over six years, they found that there are controllable factors that can slow the progression of the disease.

An interesting chapter describes the application of natural language processing and decision trees to clinical notes from the VA system to uncover insights related to the use of contraceptives. One paper examines the attitudes of patients in the US and in Italy to the increased use of electronic health records (EHRs); in part, they conclude that trust in both regulatory mechanisms and in the technology itself are connected to increased comfort with the use of EHRs. Another author examined the impact of information technology integration on integrated delivery systems, and found that both costs and outcomes were improved. Other chapters cover the opportunities for the application of virtual worlds in healthcare, gesture recognition with applications to healthcare, and other topics related to the assessment of care mechanisms.

Analysis of healthcare is such a wide field that any volume will have some areas of underemphasis. This book has more material on the evaluation of care and delivery mechanisms, and modeling of provider behavior, than on disease progression, analysis of medication therapy outcomes, and predictive modeling. For readers interested in the application of big data techniques to healthcare data, other books may offer more material. Still, this book contains several important papers that can aid in increasing the effectiveness and the efficiency of healthcare.