Epidemiologic Research on Real-World Medical Data in Japan: Volume 2 (SpringerBriefs for Data Scientists and Innovators, 2)

This book analyzes the development of medical big data projects in Japan. Japan is experiencing unprecedented population aging, and labor productivity has decreased accordingly. Big data analysis of the Japanese medical real-world database (RWD) has the potential to tackle this issue. To allow readers to gain an understanding of Japanese medical big data analysis, the book discusses the original Japanese system that generates medical RWDs in the hospital medical records system, the nationwide standardized health checkup system, and the public medical insurance system in Japan. After introducing four major big data projects in the healthcare–medical field in Japan, the book explains the importance of creating information standards to maintain data quality and to analyze medical big data. It enables readers to analyze which standards are installed in which RWDs, how the standards are maintained, and which issues are prevalent in Japan. This book also describes the ethical processes involved in big data projects involving medical RWDs in Japan Preface 6 Overview 8 Contents 19 Contributors 21 Abbreviations 23 List of Figures 26 List of Tables 28 Clinical Pathway 29 Real World Medical Data and Clinical Pathway in Japan 30 1 Clinical Pathway in Japan 30 2 Outcome-Oriented Clinical Pathways 31 3 How to Collect Data from CP 32 4 BOM (Basic Outcome Master) 34 5 Dynamic Template [2] 36 6 How to Store the Data 36 7 How to Produce Big Data 38 8 CP and Recording 39 9 How to Analyze the Data and Improve Quality of Medicine 41 10 Future Subject 43 References 44 Medical Process Analysis by Using All-Variance Type Outcome-Oriented Electronic Clinical Pathway Data-Exploratory Extracting Critical Indicator 46 1 Introduction 46 2 Critical Indicator 46 3 Analysis of Outcome Oriented CP Data-Exploratory Extracting Critical Indicator (CI) 47 4 Conclusion 49 References 50 Information and Data Standard Development for Clinical Pathways 51 1 Introduction 51 2 Overall System Design of the ePath Project 52 3 ePath Standards 53 3.1 ePath Message 53 3.2 ePath Data Repository 53 3.3 Standard Outcome-Oriented Pathway System 54 3.4 ePath Data Platform 55 4 ePath Project in the Future 55 References 56 Standard Code Mapping and Data Quality 57 Japan Laboratory Code (JLAC) 10 58 1 History 58 2 Structure of JLAC10Description of Activity and Work Performed 58 3 Operation and Maintenance 60 4 JLAC11 61 References 61 ICD-10 and ICD-11 in Japan 63 1 History of ICD and Usage in Japan 63 2 ICD-11 64 3 Application of ICD-11 to Japan and Future Prospects 65 Standard Codes for Prescribing Drugs Use Multiple Code Systems Depending on Their Purpose 66 References 67 Data Quality Governance Experience at the MID-NET Project 68 1 Development of Procedure for Unified Management of Standard Codes 68 2 Effect of Data Quality Management Through a Governance Center 69 3 Development of Real-Time Validation Tool for Central Governance 70 References 71 Phenotyping 73 Phenotyping in Japan 74 1 Real-World Data, Designed Data, and Role of Phenotyping 75 2 Three Stages that Put the Use of Clinical Data on Track 76 3 Methods of Phenotyping 76 4 Evaluation of Phenotyping 78 5 What is the Best Phenotyping Algorithm? 79 6 Outcome Validation in Pharmacoepidemiology and Phenotyping 80 Phenotyping of Administrative Claims Data 81 A Phenotyping Study Using MID-NET Database 84 1 Introduction 84 2 Background: Phenotyping Method Development Using MID-NET 84 3 Trial: Development of Phenotyping Algorithms to Identify Interstitial Pneumonia 85 4 Future Prospects of Phenotyping Studies Using MID-NET 86 References 87 Integration of Phenotyping Algorithms in Japan 88 1 Introduction 88 2 Short Report: Phenotyping Algorithm Collection in AMED Projects 88 2.1 Study Methods 89 2.2 Results 90 3 Future Prospects for Integration of Phenotyping Algorithms 90 References 92 Data Analysis on Real World Data 93 Analysis on Real-World Data: An Overview 94 1 What Can We Do with Real-World Data? 94 1.1 Real-World Medical Databases Compared to Conventional Study Designs 94 1.2 Distinguish the Purposes: Prediction Versus Causal Inference 95 2 Sources of and Measures Against Biases in RWD 96 2.1 Missing Data 96 2.2 Measurement Error 97 2.3 Nonrandom Sample Selection 98 2.4 Confounding 98 3 Statistical Methods to Handle the Confounding Bias 100 3.1 Regression and Propensity Score Modeling to Approximate Stratified Analysis 100 3.2 Methods for Time-Dependent Confounding in Sustained/Repeated Treatments 101 3.3 Applicability of Machine-Learning and Quasi-Experimental Techniques 102 4 Summary 103 References 103 Problems in Japanese Real-World Medical Data Analyses 106 1 A Mini Review of Studies Analyzing the Japanese Real-World Medical Data (RWMD) 106 1.1 The Method of Mini Review 106 1.2 The Results of the Review 107 2 The Problems in Japanese RWMD Analyses 112 3 Summary 115 Appendix 115 References 124 Ethical Issues of Data Secondary Use in Japan 126 Ethical, Legal, and Social Issues Pertaining to the Use of Real-World Health Data in Japan 127 1 Introduction 127 2 Protecting Privacy Through the Act on Personal Information Protection 128 3 Privacy in the Age of Big Data 128 4 Ethical, Legal, and Social Implications 129 The Next-Generation Medical Infrastructure Law 130 1 Japan’s Progress in Introducing IT in Healthcare 130 2 Operating Rules and Security 131 3 Japan’s Legal System for Protecting Personal Information 132 4 The Next-Generation Medical Infrastructure Law 133 5 Conclusion 135 References 135 This two volume set analyzes the development of medical big data projects in Japan. Japan is experiencing unprecedented population aging, and labor productivity has decreased accordingly. Big data analysis of the Japanese medical real-world database (RWD) has the potential to tackle this issue. To allow readers to gain an understanding of Japanese medical big data analysis, the book discusses the original Japanese system that generates medical RWDs in the hospital medical records system, the nationwide standardized health checkup system, and the public medical insurance system in Japan. After introducing four major big data projects in the healthcaremedical field in Japan, the book explains the importance of creating information standards to maintain data quality and to analyze medical big data. It enables readers to analyze which standards are installed in which RWDs, how the standards are maintained, and which issues are prevalent in Japan. This book also describes the ethical processes involved in big data projects involving medical RWDs in Japan