Actionable Intelligence in Healthcare

This book shows healthcare professionals how to turn data points into meaningful knowledge upon which they can take effective action. Actionable intelligence can take many forms, from informing health policymakers on effective strategies for the population to providing direct and predictive insights on patients to healthcare providers so they can achieve positive outcomes. It can assist those performing clinical research where relevant statistical methods are applied to both identify the efficacy of treatments and improve clinical trial design. It also benefits healthcare data standards groups through which pertinent data governance policies are implemented to ensure quality data are obtained, measured, and evaluated for the benefit of all involved.

Although the obvious constant thread among all of these important healthcare use cases of actionable intelligence is the data at hand, such data in and of itself merely represents one element of the full structure of healthcare data analytics. This book examines the structure for turning data into actionable knowledge and discusses:

• The importance of establishing research questions
• Data collection policies and data governance
• Principle-centered data analytics to transform data into information
• Understanding the "why" of classified causes and effects
• Narratives and visualizations to inform all interested parties

Actionable Intelligence in Healthcare is an important examination of how proper healthcare-related questions should be formulated, how relevant data must be transformed to associated information, and how the processing of information relates to knowledge. It indicates to clinicians and researchers why this relative knowledge is meaningful and how best to apply such newfound understanding for the betterment of all.

這本書向醫療專業人士展示如何將數據轉化為有意義的知識，以便他們能夠采取有效的行動。可行的智能可以有多種形式，從向衛生政策制定者提供有關人口有效策略的信息，到向醫療提供者提供有關患者的直接和預測性見解，以便他們能夠取得積極的結果。它可以幫助那些進行臨床研究的人，在這些研究中，相關的統計方法被應用於確定治療的有效性並改進臨床試驗設計。它還有益於醫療數據標準組織，通過該組織實施相關的數據治理政策，以確保獲得質量數據，並為所有相關方提供利益。

儘管在所有這些重要的可行智能醫療應用案例中，明顯的共同點是手頭的數據，但這些數據本身僅僅代表醫療數據分析的完整結構的一個元素。本書探討了將數據轉化為可行知識的結構，並討論了以下內容：

- 建立研究問題的重要性
- 數據收集政策和數據治理
- 以原則為中心的數據分析，將數據轉化為信息
- 理解分類原因和效果的原因
- 敘事和可視化，以向所有感興趣的各方提供信息

《醫療可行智能》是對如何制定正確的與醫療相關的問題，如何將相關數據轉化為相關信息，以及信息處理與知識之間的關係的重要考察。它向臨床醫生和研究人員指出了為什麼這種相對知識是有意義的，以及如何最好地應用這種新發現的理解，以造福所有人。

Dr. Jay Liebowitz is the DiSanto Visiting Chair in Applied Business and Finance at Harrisburg University of Science and Technology. He previously served as the Orkand Endowed Chair of Management and Technology in the Graduate School at the University of Maryland University College (UMUC). Prior to UMUC, Dr. Liebowitz was a full professor in the Carey Business School at Johns Hopkins University. He was ranked one of the top 10 knowledge management researchers/practitioners out of 11,000 worldwide. Dr. Liebowitz is the founding editor-in-chief of Expert Systems with Applications: An International Journal, which is ranked as a top-tier journal worldwide.

Amanda Dawson holds a Ph.D. in Experimental Psychology and is the Director of Research at Select Medical where she oversees clinical research and quality improvement initiatives at more than 100 nation-wide long-term acute care hospitals. Prior to joining Select Medical, she was a research fellow in biomedicine at Albert Einstein Hospital's Moss Rehabilitation Research Institute and completed her post-doctoral training in Physical Medicine & Rehabilitation from the University of Pennsylvania Medical School. Over the past decade, she has published numerous articles on identifying patient subpopulations and characterizing clinical practice patterns and patient outcomes over time.

Dr. Jay Liebowitz是哈里斯堡科技大學應用商業與金融的DiSanto訪問講座教授。他曾在馬里蘭大學大學學院（UMUC）的經營管理和技術領域擔任Orkand榮譽講座教授。在加入UMUC之前，Liebowitz博士是約翰霍普金斯大學凱里商學院的正教授。他在全球11,000名知識管理研究者/實踐者中排名前十。Liebowitz博士是《Expert Systems with Applications: An International Journal》的創刊主編，該期刊在全球被評為頂級期刊。

Amanda Dawson擁有實驗心理學博士學位，是Select Medical的研究主任，負責監督100多家全國長期急性護理醫院的臨床研究和質量改進項目。在加入Select Medical之前，她曾在阿爾伯特愛因斯坦醫院的莫斯康復研究所擔任生物醫學研究員，並在賓夕法尼亞大學醫學院進行物理醫學和康復的博士後培訓。在過去十年中，她發表了許多關於識別患者亞群體和描述臨床實踐模式和患者結果的文章。