Healthcare transformation requires us to continually look at new and better ways to manage insights - both within and outside the organization. Increasingly, the ability to glean and operationalize new insights efficiently as a byproduct of an organization's day-to-day operations is becoming vital for hospitals and health systems to survive and prosper. One of the long-standing challenges in healthcare informatics has been the ability to deal with the sheer variety and volume of disparate healthcare data and the increasing need to derive veracity and value out of it.
This book addresses several topics important to the understanding and use of data in healthcare. First, it provides a formal explanation based on epistemology (theory of knowledge) of what data actually is, what we can know about it, and how we can reason with it. The culture of data is also covered and where it fits into healthcare. Then, data quality is addressed, with a historical appreciation, as well as new concepts and insights derived from the author's 35 years of experience in technology.
The author provides a description of what healthcare data analysis is and how it is changing in the era of abundant data. Just as important is the topic of infrastructure and how it provides capability for data use. The book also describes how healthcare information infrastructure needs to change in order to meet current and future needs. The topics of artificial intelligence (AI) and machine learning in healthcare are also addressed. The author concludes with thoughts on the evolution of the role and use of data and information going into the future.
David Hartzband is currently the Director of Technology Research at the RCHN Community Health Foundation. In his role at the Foundation, he spearheads the organization's continued evaluation, assessment and findings related to health information technology. Recent projects include deployment of a contemporary (Hadoop-based) analytic stack into community health centers and working with the executive and operations staff to understand and use this resource, design and execution of population health projects at community health centers and Primary Care Associations, the redesign and deployment of updated health information technology infrastructure for a large Primary Care Association and assessment of data quality in electronic health records (EHRs) of healthcare practices.
He brings more than two decades of diverse experience in the private and public sectors as a consultant, executive and technology industry leader. He is founder and principal of PostTechnical Research, a trend analysis and technology strategy consulting firm. Previously he was Technology Vice President of the Collaboration in the Content Management Software Group of the EMC Corporation. He also served as the Chief Technology Officer for several technology companies including Documentum, eRoom Technology, Agile Software, Upstream Consulting and Riverton Software.
He also was a Consulting Software Engineer and senior technologist at the Digital Equipment Corporation from 1983-1995, where he held a variety of positions there including Architect for Digital's relational database (Rdb) at V1 and V2, architect for ObjectBroker, a distributed object-based development and execution environment, Technical Director for manufacturing software and Chief Scientist for the Artificial Intelligence Technology Group.
He is the author of numerous technical reports and journal articles in mathematics, artificial intelligence, concurrent engineering and cultural anthropology. He has served as an adjunct faculty member at both Stanford University (Computer Science Department and Knowledge Systems Laboratory) and the Massachusetts Institute of Technology (Leaders for Manufacturing Program). He is presently a Research Scholar at the Institute for Data Systems and Society at the Massachusetts Institute of Technology.
Preface. About the Author. 1. Introduction - Data Is Essential. 2. What is Data? 3. Data and Culture. 4. Data Quality. 5. What Is Data Analysis? 6. The Infrastructure and Applications Required for Current and Near-Future HIT? 7. Machine Intelligence in Healthcare. 8. Evolution of Data and Analysis in Healthcare. 9. Summary. Index.