Using Predictive Analytics to Improve Healthcare Outcomes. Группа авторов

Using Predictive Analytics to Improve Healthcare Outcomes - Группа авторов


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of Acronyms

      Acknowledgments

      Secondly, we would like to acknowledge all the analysts and mathematicians from other disciplines who have enthusiastically and humbly shared their knowledge of mathematics and how it is applied in science. We have been inspired by the depth and breadth of what you know and by your eagerness to learn from others. The lead editor would also like to ask the indulgence of all of the mathematicians, analysts, and scientists who will read this book, as you encounter moments in this book where brevity and simplicity have taken precedence over thorough scientific explanations. In an effort to make this book accessible to a lay audience, much of the technical talk has been truncated or eliminated.

      Thirdly, we acknowledge the visionary leaders who had the courage to step out and measure what matters—behavior and context. Without your understanding that data beyond frequencies was needed, the ability to use predictive analytics to improve healthcare outcomes would still be an elusive dream.

      Finally, the editors of this book acknowledge all the staff members who took part in these studies. Every one of you made each model of measurement better, and you played a vital part in producing the groundbreaking findings in this book. Without you, this book would not exist.

Section One Data, Theory, Operations, and Leadership

       John W. Nelson

      For predictive analytics to be useful in your quest to improve healthcare outcomes, models for measurement must reflect the exact context in which you seek to make improvements. Data must resonate with the staff members closest to the work, so that action plans premised on the data are specific, engaging, and instantly seen as relevant. This chapter provides 16 steps the author has used in healthcare settings to engage staff members in outcomes improvement. Models created using these steps have proven effective in improving outcomes and saving millions of dollars because the process engages the entire healthcare team to provide input into (a) the design of measurement instruments, (b) interpretation of results, and (c) application of interventions, based on the data, to improve outcomes. Analysts and staff members build models of measurement that tell the story of the organization empirically, which makes the data not only actionable but relatable.

       The presentation of data in healthcare should be interesting and engaging because it reflects empirically what people are experiencing operationally.

      With the advent of big data, machine learning, and artificial intelligence, we now too often turn one of our oldest, most cherished human traditions—storytelling—over to machines. The stories machines tell reveal


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