Foreword |
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xv | |
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Foreword |
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xvii | |
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Foreword |
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xix | |
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Preface |
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xxi | |
About the Authors |
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xxiii | |
Acknowledgments |
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xxvii | |
Guest Authors |
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xxix | |
Software Instructions |
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xxxi | |
Introduction |
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xxxii | |
Prologue to Part |
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1 | (4) |
Part 1 Historical Perspective and the Issues of Concern for Healthcare Delivery in the 21st Century |
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1 History of Predictive Analytics in Medicine and Health Care |
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5 | (1) |
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5 | (1) |
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6 | (1) |
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Part 1: Development of Bodies of Medical Knowledge |
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Earliest Medical Records in Ancient Cultures |
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Classification of Medical Practices IN Ancient and Modern Cultures |
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8 | (1) |
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Medical Practice Documents in Major Ancient World Cultures of Europe and the Middle East |
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9 | (1) |
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10 | (2) |
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12 | (2) |
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14 | (1) |
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Summary of Royal Decrees Of Medical Documentation in Ancient Cultures |
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Effects of the Middle Ages on Medical Documentation |
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Rebirth of Interest in Medical Documentation During the Renaissance |
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The Protestant Reformation |
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17 | (1) |
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Andreas Vesalius (1514-1564) |
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William Harvey (1578-1657) |
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Medical Documentation Since the Enlightenment |
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Medical Case Documentation |
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The Development of the US National Library of Medicine |
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Part 2: Analytical and Decision Systems in Medicine and Health Care |
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19 | (1) |
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Computers and Medical Databases |
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19 | (1) |
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20 | (1) |
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Guidelines of the American Academy of Neurology |
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21 | (1) |
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21 | (1) |
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2 Why did We Write This Book? |
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23 | (1) |
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23 | (1) |
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Reason 1: Current Problems in Medical Research |
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Inaccuracies in Published Research Papers |
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24 | (1) |
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Design Problems in Research Studies |
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A "Framework" for Determining Research Gaps |
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26 | (1) |
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Reason 2: Practical Assistance is Needed to Insure Success for the New Initiatives and Accreditation Standards |
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27 | (2) |
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Other Standards Organizations in Health Care |
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28 | (1) |
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Reason 3: To Meet The Standards, Healthcare Organizations Need Practical Assistance and Tools With Implementing Lean Systems |
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29 | (4) |
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Examples of Problems that Highlight the Need for "Lean" and Predictive Tools |
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29 | (2) |
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Six Sigma and Biomedical Informatics |
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31 | (2) |
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Reason 4: Research into Technological/Organizational/Payment Changes Will be Necessary |
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33 | (3) |
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Push Back in the Face of Change |
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33 | (1) |
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Confusion in Coding and Payments Caused by Changes |
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34 | (1) |
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35 | (1) |
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35 | (1) |
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Population Studies Versus Patient-Focused Care |
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36 | (1) |
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Reason 5: Practical Real World Examples are Needed that Bridge into A Phenomenal Future |
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36 | (3) |
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Exploratory Statistics/Individualized Statistics/Predictive Statistics |
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36 | (1) |
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Quality Medical Care Examples |
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37 | (1) |
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Practical Predictive Analytics for the Lean Movement |
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37 | (1) |
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38 | (1) |
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39 | (1) |
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39 | (3) |
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42 | (1) |
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The Rise of Predictive Analytics in Health Care |
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42 | (1) |
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Moving From Reactive to Proactive Response in Health Care |
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43 | (1) |
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43 | (1) |
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An Approach to Predictive Analytics Projects |
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44 | (5) |
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The Predictive Analytics Process in Health Care |
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45 | (1) |
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45 | (4) |
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49 | (1) |
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Translational Bioinformatics |
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49 | (1) |
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Clinical Decision Support Systems |
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49 | (2) |
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50 | (1) |
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Consumer Health Informatics |
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51 | (1) |
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Direct-to-Consumer Genetic Testing |
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51 | (1) |
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Use of Predictive Analytics to Avoid an Undesirable Future |
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52 | (1) |
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52 | (1) |
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Patient Monitoring Systems |
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52 | (2) |
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Public Health Informatics |
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54 | (1) |
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55 | (1) |
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Clinical Research Informatics |
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56 | (1) |
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Intelligent Search Engines |
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56 | (1) |
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56 | (1) |
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57 | (1) |
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57 | (1) |
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58 | (1) |
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59 | (1) |
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59 | (1) |
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59 | (1) |
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4 HIMSS and Organizations That Develop HIT Standards |
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60 | (1) |
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60 | (2) |
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Introduction to the Strategic Partners |
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61 | (1) |
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Relationship Between ANSI, HIMSS, and ONC |
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62 | (1) |
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Organizations Connected to or Influenced By HIMSS |
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62 | (1) |
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Goals, Issues, and Ideals of HIMSS |
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62 | (1) |
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63 | (1) |
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64 | (1) |
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Standardization in Coding |
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65 | (1) |
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Care Continuum Alliance (Another CCA) and Health Outcome Data |
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65 | (1) |
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66 | (1) |
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67 | (2) |
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69 | (1) |
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69 | (1) |
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69 | (1) |
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Long-Range Problems and Opportunities |
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70 | (2) |
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72 | (1) |
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72 | (1) |
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72 | (1) |
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73 | (1) |
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5 Electronic Medical Records: Analytics' Best Hope |
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74 | (1) |
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74 | (1) |
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75 | (1) |
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A Bit (Of a "Byte") of History |
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76 | (1) |
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77 | (3) |
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78 | (1) |
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78 | (1) |
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79 | (1) |
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79 | (1) |
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Ferraris and Country Roads |
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80 | (5) |
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85 | (1) |
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85 | (1) |
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Bibliography of Additional References on the Topic of Medical Records |
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85 | (2) |
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6 Open-Source EMR and Decision Management Systems |
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87 | (1) |
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87 | (1) |
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Why Choose an Open-Source EMR Software Application? |
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88 | (1) |
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VistA The Veterans Administration System That Started it All |
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89 | (1) |
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Five of the Best Open-Source EMR Systems for Medical Practices |
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89 | (5) |
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89 | (1) |
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90 | (1) |
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OpenMRS by Partners in Health |
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91 | (2) |
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93 | (1) |
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93 | (1) |
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Global Open-Source EMR Systems and the Future of Analytics |
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94 | (1) |
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94 | (1) |
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95 | (1) |
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7 Evidence-Based Medicine |
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96 | (1) |
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96 | (1) |
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Geodemographic Elements of Medical Treatment |
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97 | (1) |
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How can we Define the Nature and Boundaries of EBM? |
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98 | (1) |
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General Problems with EBM |
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98 | (1) |
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Evidence-Based Medicine and Analytics |
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98 | (1) |
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99 | (1) |
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What is a Randomized Controlled Trial? |
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100 | (1) |
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If not Evidence Based, then What? |
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101 | (1) |
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102 | (1) |
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103 | (1) |
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103 | (1) |
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Evidence-Based Medicine Versus the Art of Medicine |
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104 | (1) |
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Predictive Analytics and EBM |
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104 | (1) |
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104 | (1) |
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104 | (2) |
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106 | (1) |
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106 | (1) |
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106 | (1) |
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107 | (1) |
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Elements Of ICD Documentation |
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107 | (1) |
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108 | (1) |
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Changes Ahead for ICD-10 Users |
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108 | (1) |
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Comparison of ICD-9 and ICD-10 |
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109 | (1) |
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Increased Ability to Describe and Justify Treatment |
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109 | (1) |
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The ICD-10 Descriptive Language is Much Richer |
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109 | (1) |
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Facilitation of Mortality and Morbidity Analyses |
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109 | (1) |
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Implications of ICD-10 Changes |
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109 | (1) |
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Greater Scalability and Extensibility Foster Information Sharing Among Institutions |
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109 | (1) |
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More Specific Categories and Codes |
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110 | (1) |
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110 | (1) |
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110 | (1) |
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ICD-10 Changes in Terminology |
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111 | (1) |
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Implementation Issues of Changing to ICD-10 |
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111 | (1) |
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What Lies Ahead for Payers and Providers? |
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112 | (1) |
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112 | (1) |
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112 | (1) |
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Transition is a Joint Effort |
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112 | (1) |
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113 | (1) |
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113 | (3) |
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9 "Meaningful Use" The New Buzzword in Medicine |
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116 | (1) |
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116 | (1) |
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Stage I of "Meaningful Use" |
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117 | (1) |
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Meaningful Use Goals for Hospitals |
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117 | (6) |
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The 14 Requirements (Hospitals Must Meet All of These) |
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117 | (3) |
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The 10 Choice Objectives (Hospitals Must Meet 5 of These) |
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120 | (3) |
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Meaningful Use Goals For Doctors |
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123 | (5) |
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The 15 Requirements (Doctors Must Meet All of These) |
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124 | (2) |
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The 10 Additional Choice Objectives for Individual Physicians (5 of These Must Be Met to Achieve Compliance) |
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126 | (2) |
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Meaningful Use Requirements Of Stage I, Stage II, and Stage III |
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128 | (1) |
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Requirements for Stage I of Meaningful Use |
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128 | (1) |
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129 | (1) |
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129 | (4) |
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10 The Joint Commission: Formerly the Joint Commission on Accreditation of Healthcare Organizations (JCAHO) |
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133 | (1) |
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History of the Joint Commission |
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133 | (1) |
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The Joint Commission International |
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134 | (1) |
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Joint Commission Accreditation |
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135 | (1) |
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136 | (1) |
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Other Regulatory Organizations |
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136 | (1) |
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Joint Commission Standards |
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137 | (1) |
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National Patient Safety Goals |
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138 | (2) |
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140 | (1) |
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140 | (4) |
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11 Root Cause Analysis, Six Sigma, and Overall Quality Control and Lean Concepts: The First Process to Bring Quality and Cost-Effectiveness to Medical Care Delivery |
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144 | (1) |
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144 | (1) |
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Part 1: Six Sigma and Quality Control, Root Cause Analysis, and Leapfrog as they Developed During the 1990's and Early 2000's: Learning from Medical Errors and Turning Them into Quality Improvements |
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144 | (1) |
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The Need For Quality: Medical Errors |
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144 | (1) |
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Epidemiology of Medical Errors |
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144 | (1) |
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144 | (1) |
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144 | (2) |
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Statistical Process Control |
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144 | (1) |
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145 | (1) |
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145 | (1) |
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145 | (1) |
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145 | (1) |
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146 | (1) |
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146 | (1) |
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146 | (1) |
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Methods to Improve Safety and Reduce Error |
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146 | (1) |
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146 | (1) |
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146 | (1) |
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147 | (1) |
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Systems for Ensuring Review |
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147 | (1) |
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History of Quality in Health Care |
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147 | (3) |
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Crossing the Quality Chasm: The IOM Health Care Quality Initiative |
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147 | (2) |
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Comprehensive Drug Safety |
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149 | (1) |
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150 | (2) |
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Organizational Goals of the Leapfrog Group |
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150 | (1) |
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150 | (1) |
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Leaps in Hospital Quality and Safety |
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151 | (1) |
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Four Primary Criteria for Purchasing |
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151 | (1) |
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152 | (1) |
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Part 2: Root Cause Analysis, Six Sigma and Quality Control, and Lean Concepts in Hospitals and Healthcare Facilities as They Exist in 2013-2014 |
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152 | (1) |
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152 | (1) |
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152 | (1) |
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153 | (1) |
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Examples of Using Six Sigma in Health Care |
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153 | (1) |
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Lean Concepts for Health Care: The Lean Hospital as a Methodology of Six Sigma |
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154 | (1) |
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155 | (3) |
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Part 3: Experiences of a Doctor who Implemented a Quality Control Department in a Hospital System During the 1990's An Era When Quality was Anything But the Norm |
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158 | (1) |
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158 | (4) |
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158 | (1) |
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Definition of Healthcare Quality |
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158 | (1) |
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The Quality Department in a Hospital |
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159 | (1) |
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160 | (2) |
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162 | (1) |
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162 | (1) |
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163 | (1) |
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163 | (1) |
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163 | (2) |
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12 Lean Hospital Examples |
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165 | (1) |
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165 | (1) |
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165 | (3) |
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168 | (1) |
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The Joint Commission Annual Report, 2013 |
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168 | (1) |
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Transparency Just Increased |
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168 | (1) |
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Kaiser Permanente Managed Care Organization |
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169 | (1) |
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Virginia Mason Hospital in Seattle |
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170 | (1) |
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Examples of Lean Projects |
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171 | (3) |
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Oncology: Infusion Therapy |
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171 | (1) |
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172 | (1) |
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172 | (1) |
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Reducing Catheter-Associated Urinary Tract Infections |
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173 | (1) |
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Intravenous (IV) Laboratory Lean Project |
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173 | (1) |
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Emergency Room Application of Lean |
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173 | (1) |
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174 | (1) |
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174 | (1) |
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174 | (3) |
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177 | (1) |
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What is Personalized Medicine? |
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177 | (1) |
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178 | (1) |
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178 | (1) |
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Personalized Medicine, Genomics, and Pharmacogenomics |
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178 | (2) |
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179 | (1) |
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Differences Go Beyond Our Body and Into Our Environment |
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179 | (1) |
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Changes from Birth to Death |
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179 | (1) |
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180 | (1) |
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It Is Not About Just Our Genome |
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180 | (1) |
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Changing the Definition of Diseases |
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180 | (1) |
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181 | (1) |
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Efficacy of Current Methods Why We Need Personalized Medicine |
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182 | (1) |
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Predictive Analytics in Personalized Medicine |
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183 | (1) |
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The Future: Predictive and Prescriptive Medicine |
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183 | (2) |
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Application of Predictive Analytics and Decisioning in Predictive and Prescriptive Medicine |
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183 | (2) |
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The Diversity of Available Healthcare Data |
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185 | (13) |
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Diversity of Data Types Available |
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185 | (13) |
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198 | (1) |
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198 | (4) |
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200 | (2) |
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202 | (1) |
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202 | (3) |
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14 Patient-Directed Health Care |
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205 | (1) |
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205 | (1) |
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206 | (1) |
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Concept 1: Empowerment and Involvement How can Patients be Empowered to Become More Involved with their Medical Care? |
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207 | (2) |
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207 | (1) |
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Hindrances to Patient Involvement |
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207 | (2) |
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Concept 2: Coordination of Care and Communication |
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209 | (4) |
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The Integrated Healthcare Delivery System Model |
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213 | (1) |
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Concept 3: Consumerism in Health Care |
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213 | (4) |
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Concept 4: Patient Payment Models |
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217 | (3) |
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Burden of Health Care upon the Future |
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218 | (1) |
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Mis-application of Treatment Increases Costs |
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218 | (1) |
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Many Insurance Plans Few Differences |
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218 | (2) |
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Concept 5: Patient Education and Patient Self-Education and Decisions |
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220 | (3) |
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Information Concerning Obesity |
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221 | (2) |
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223 | (1) |
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223 | (1) |
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Concept 6: Alternatives and New Models |
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223 | (8) |
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Insurance Companies Going International |
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223 | (1) |
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224 | (1) |
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Alternative Ways of Knowing about Ourselves |
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225 | (6) |
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231 | (1) |
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231 | (1) |
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231 | (3) |
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234 | (5) |
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Prologue to Part 1, Chapter 15 |
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15 Prediction in Medicine The Data Mining Algorithms of Predictive Analytics |
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239 | (1) |
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239 | (1) |
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The Use of Simple Descriptive Statistics, Graphics, and Visual Data Mining in Predictive Analytics |
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240 | (2) |
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The Insight of Simple Descriptive Statistics |
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240 | (1) |
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240 | (2) |
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Predictive Modeling: Using Data to Predict Important Outcomes |
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242 | (8) |
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The Difference Between Statistical Models and General Predictive Modeling |
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242 | (1) |
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The Algorithms of Predictive Modeling |
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243 | (6) |
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Choosing the Right Algorithm for the Right Analysis |
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249 | (1) |
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Clustering: Identifying Clusters of Similar Cases, and Outliers |
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250 | (5) |
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250 | (5) |
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255 | (1) |
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Dimension Reduction Techniques |
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255 | (3) |
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255 | (1) |
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256 | (1) |
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Feature Selection vs. Feature Extraction |
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257 | (1) |
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Detecting the Interrelationships and Structure of Data Through Association and Link Analysis |
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258 | (1) |
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The Support and Confidence Statistics |
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258 | (1) |
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258 | (1) |
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259 | (1) |
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259 | (8) |
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Part 2 Practical Step-by-Step Tutorials and Case Studies |
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A Case Study: Imputing Medical Specialty Using Data Mining Models |
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267 | (32) |
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B Case Study: Using Association Rules to Investigate Characteristics of Hospital Readmissions |
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299 | (17) |
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C Constructing Decision Trees for Medicare Claims Using R and Rattle |
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316 | (25) |
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D Predictive and Prescriptive Analytics for Optimal Decisioning: Hospital Readmission Risk Mitigation |
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341 | (18) |
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E Obesity Group: Predicting Medicine and Conditions That Achieved the Greatest Weight Loss in a Group of Obese/Morbidly Obese Patients |
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359 | (29) |
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H Obesity Individual: Predicting Best Treatment for an Individual from Portal Data at a Clinic |
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388 | (58) |
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F2 Obesity Individual: Automatic Binning of Continuous Variables and WoE to Produce a Better Model Than the "Hand Binned" Stepwise Regression Model of Tutorial F1 |
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446 | (16) |
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G Resiliency Study for First and Second Year Medical Residents |
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462 | (69) |
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H Medicare Enrollment Analysis Using Visual Data Mining |
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531 | (13) |
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I Case Study: Detection of Stress-Induced Ischemia in Patients with Chest Pain After "Rule-Out ACS" Protocol |
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544 | (14) |
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J1 Predicting Survival or Mortality for Patients with Disseminated Intravascular Coagulation (DIC) and/or Critical Illnesses |
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558 | (66) |
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J2 Decisioning for DIC 603 Predicting Allergy Symptoms |
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624 | (11) |
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C Exploring Discrete Database Networks of Tricare Health Data Using R and Shiny |
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635 | (24) |
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D Schistosomiasis Data from WHO |
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659 | (38) |
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E The Poland Medical Bundle |
|
|
697 | (29) |
|
O Medical Advice Acceptance Prediction |
|
|
726 | (19) |
|
P Using Neural Network Analysis to Assist in Classifying Neuropsychological Data |
|
|
745 | (12) |
|
Q Developing Interactive Decision Trees Using Inpatient Claims (with SAS Enterprise Miner) |
|
|
757 | (38) |
|
R Divining Healthcare Charges for Optimal Health Benefits Under the Affordable Care Act |
|
|
795 | (22) |
|
S Availability of Hospital Beds for Newly Admitted Patients: The Impact of Environmental Services on Hospital Throughput |
|
|
817 | (15) |
|
T Predicting Vascular Thrombosis: Comparing Predictive Analytic Models and Building an Ensemble Model for "Best Prediction" |
|
|
832 | (18) |
|
U Predicting Breast Cancer Diagnosis Using Support Vector Machines |
|
|
850 | (16) |
|
V Heart Disease: Evaluating Variables That Might Have an Effect on Cholesterol Level (Using Recode of Variables Function) |
|
|
866 | (10) |
|
W Blood Pressure Predictive Factors |
|
|
876 | (20) |
|
X Gene Search and the Related Risk Estimates: A Statistical Analysis of Prostate Cancer Data |
|
|
896 | (25) |
|
Y Ovarian Cancer Prediction via Proteomic Mass Spectrometry |
|
|
921 | (18) |
|
Z Influence of Stent Vendor Representatives in the Catheterization Lab |
|
|
939 | (30) |
|
|
Part 3 Practical Solutions and Advanced Topics in Administration and Delivery of Health Care Including Practical Predictive Analytics for Medicine |
|
|
16 Predictive Analytics in Nursing Informatics |
|
|
|
|
969 | (1) |
|
|
969 | (1) |
|
|
970 | (3) |
|
|
970 | (1) |
|
|
971 | (1) |
|
Predicting the Patient's Future Development |
|
|
971 | (1) |
|
|
971 | (1) |
|
Home Nursing and Nursing Homes |
|
|
971 | (1) |
|
|
972 | (1) |
|
|
972 | (1) |
|
Preventing Inpatient Morbidity |
|
|
972 | (1) |
|
Patient Comfort and Satisfaction |
|
|
972 | (1) |
|
|
973 | (1) |
|
|
973 | (1) |
|
|
973 | (1) |
|
|
973 | (1) |
|
|
973 | (3) |
|
17 The Predictive Potential of Connected Digital Health |
|
|
|
|
976 | (1) |
|
Why Don't Clinicians Embrace Digital Consumer Connections? |
|
|
976 | (6) |
|
1 What Do I Do with the Data? |
|
|
977 | (1) |
|
2 Who Says That the Data Are Valuable? |
|
|
977 | (1) |
|
3 What New Liabilities Emerge from Precision and Probabilistic Medicine? |
|
|
978 | (1) |
|
4 How Cumbersome and Difficult Are New Data Collection Solutions? |
|
|
979 | (1) |
|
5 How Do the Devices and Apps Integrate and Interoperate? |
|
|
980 | (1) |
|
6 How Do You Maintain Privacy and Security with Mobile Consumer Engagement? |
|
|
981 | (1) |
|
7 How and When Will Clinicians Get Paid for Participating in Mobile Health? |
|
|
982 | (1) |
|
Promise and Problems of Shifting to Mobile Health Technology |
|
|
982 | (1) |
|
What Can We Learn from the VA About the Potential of Predictions? |
|
|
983 | (2) |
|
What Can We Learn from Financial Services Regarding Digital Transformation? |
|
|
985 | (3) |
|
The Rise of Mobile Financial Services |
|
|
985 | (1) |
|
How Do These Five Insights from the Digitization of the Financial Services Industry Inform Our Views About the Digitization of Healthcare Information? |
|
|
986 | (2) |
|
Summary and Recommendations |
|
|
988 | (1) |
|
|
988 | (1) |
|
|
988 | (1) |
|
|
|
|
989 | (1) |
|
|
989 | (1) |
|
|
990 | (1) |
|
Definition of Fraud in the Healthcare Context |
|
|
990 | (2) |
|
Fraud Perpetrated by a Provider |
|
|
991 | (1) |
|
Fraud Perpetrated by a Patient Subscriber |
|
|
991 | (1) |
|
Fraud Perpetrated by Third Parties |
|
|
991 | (1) |
|
Fraud Perpetrated by Agents/Brokers |
|
|
991 | (1) |
|
Statutes and Regulations Intended to Prevent, Detect, and Prosecute Fraud |
|
|
992 | (1) |
|
Major Agencies Involved in Healthcare Anti-Fraud Efforts |
|
|
992 | (1) |
|
Challenges That Face Anti-Fraud Efforts |
|
|
992 | (2) |
|
|
993 | (1) |
|
|
993 | (1) |
|
Traditional Means of Detection |
|
|
994 | (1) |
|
Limitations of Traditional Means of Detection |
|
|
994 | (1) |
|
The Emergence of Big Data in Healthcare Investigations |
|
|
994 | (1) |
|
ACA Anti-Fraud Provisions |
|
|
994 | (1) |
|
Analytical Anti-Fraud Approaches |
|
|
995 | (1) |
|
|
995 | (1) |
|
|
995 | (1) |
|
|
995 | (1) |
|
Combined Analytical Techniques |
|
|
995 | (1) |
|
The Future of Healthcare Anti-Fraud Efforts |
|
|
995 | (1) |
|
|
996 | (1) |
|
|
996 | (1) |
|
|
996 | (2) |
|
19 Challenges for Healthcare Administration and Delivery: Integrating Predictive and Prescriptive Modeling into Personalized Health Care |
|
|
|
|
998 | (1) |
|
|
998 | (2) |
|
|
1000 | (1) |
|
|
1001 | (1) |
|
20 Challenges of Medical Research for the Remainder of the 21st Century |
|
|
|
|
1002 | (1) |
|
|
1002 | (1) |
|
|
1003 | (1) |
|
21 Introduction to the Cornerstone Chapters of this Book, Chapters 22-25: The "Three Processes" Quality Control, Predictive Analytics, and Decisioning |
|
|
|
|
1004 | (1) |
|
|
1004 | (1) |
|
Traditional Statistics vs Data Mining vs Predictive Analytics |
|
|
1005 | (2) |
|
|
1007 | (1) |
|
22 The Nature of Insight from Data and Implications for Automated Decisioning: Predictive and Prescriptive Models, Decisions, and Actions |
|
|
|
|
1008 | (1) |
|
|
1008 | (1) |
|
The Nature of Insight and Expertise |
|
|
1009 | (1) |
|
Procedural and Declarative Knowledge |
|
|
1009 | (1) |
|
Non-Conscious Acquisition of Knowledge |
|
|
1009 | (1) |
|
Conclusion: Expertise and the Application of Pattern Recognition Methods |
|
|
1010 | (1) |
|
Statistical Analysis vs Pattern Recognition |
|
|
1010 | (4) |
|
|
1011 | (1) |
|
Pattern Recognition: Data are the Model |
|
|
1011 | (2) |
|
Pattern Recognition and Declarative Knowledge: Interpretability of Results |
|
|
1013 | (1) |
|
Predictive Modeling and Prescriptive Models |
|
|
1014 | (4) |
|
Rules, Conditional Scoring Logic, Action Plans |
|
|
1015 | (1) |
|
An Example System: The STATISTICA Enterprise Decisioning Platform® |
|
|
1015 | (3) |
|
|
1018 | (1) |
|
|
1018 | (1) |
|
|
1018 | (1) |
|
23 Platform for Data Integration and Analysis, and Publishing Medical Knowledge as Done in a Large Hospital |
|
|
|
|
1019 | (1) |
|
|
1019 | (1) |
|
Functions and Applications of the Platform |
|
|
1020 | (1) |
|
Platform Components and Architecture |
|
|
1020 | (9) |
|
|
1021 | (1) |
|
|
1021 | (1) |
|
Medical Research Environment-Overview |
|
|
1022 | (3) |
|
|
1025 | (2) |
|
Reports for NHF Contract Monitoring and Clearance |
|
|
1027 | (2) |
|
|
1029 | (1) |
|
|
1029 | (1) |
|
|
1029 | (1) |
|
|
1029 | (1) |
|
24 Decisioning Systems (Platforms) Coupled With Predictive Analytics in a Real Hospital Setting A Model for the World |
|
|
|
|
1030 | (1) |
|
|
1030 | (1) |
|
Setting the Stage for a Decisioning Platform |
|
|
1031 | (3) |
|
Getting Support from Information Technology and Hospital Leadership |
|
|
1031 | (1) |
|
Creating an Analytical Culture (Or, Have You Ever Tried to Tell a Surgeon He's Doing Things Wrong?) |
|
|
1032 | (1) |
|
Defining the Outcomes Targets |
|
|
1033 | (1) |
|
Defining the Clinical Decisions |
|
|
1033 | (1) |
|
Define the Resources That Need to be Managed |
|
|
1033 | (1) |
|
Determine What Data You Have Access to |
|
|
1034 | (1) |
|
Deploying the Decision Management System |
|
|
1034 | (2) |
|
Decision Management System Tools |
|
|
1034 | (1) |
|
Decision Management Process |
|
|
1035 | (1) |
|
Decision Management System Workflow Example |
|
|
1035 | (1) |
|
|
1036 | (1) |
|
|
1037 | (1) |
|
|
1037 | (1) |
|
25 IBM Watson for Clinical Decision Support |
|
|
|
|
1038 | (1) |
|
|
1038 | (1) |
|
Personalized Health Care and Clinical Decision Support |
|
|
1038 | (1) |
|
IBM Watson and Medical Decision-Making |
|
|
1039 | (1) |
|
|
1040 | (1) |
|
|
1040 | (1) |
|
26 21st Century Health Care and Wellness: Getting the Health Care Delivery System That Meets Global Needs |
|
|
|
|
1041 | (1) |
|
|
1042 | (1) |
|
Background and Need for Change |
|
|
1042 | (1) |
|
|
1043 | (1) |
|
Trends Impacting Healthcare Industries |
|
|
1043 | (1) |
|
Existing and Emerging Healthcare Organizations |
|
|
1044 | (1) |
|
Health Start-Ups and Established Technology Firms Contributing to Health Care |
|
|
1045 | (4) |
|
|
1045 | (1) |
|
New Technology and 21st Century Health Care: Health Start-Up Firms |
|
|
1046 | (1) |
|
Building the Star Trek Tricorder |
|
|
1046 | (2) |
|
Wearable Computers for Doctors |
|
|
1048 | (1) |
|
|
1048 | (1) |
|
Technology Trends That Impact Health and Wellness |
|
|
1049 | (1) |
|
Current Trends Outside Healthcare Facilities |
|
|
1049 | (1) |
|
Trends and Expectations for the Future of Health It and Analytics |
|
|
1049 | (2) |
|
The Next 4 Years by-2018 Predictions |
|
|
1050 | (1) |
|
The Next 9 Years by-2023 Predictions |
|
|
1050 | (1) |
|
Conclusions and Summary of Important Concepts Presented in This Book |
|
|
1051 | (2) |
|
Technology for the Elderly |
|
|
1051 | (1) |
|
Technology for Rural Areas |
|
|
1052 | (1) |
|
Final Concluding Statements |
|
|
1052 | (1) |
|
|
1053 | (1) |
|
|
1054 | (1) |
Index |
|
1055 | |