Secondary Analysis of Electronic Health Records [Internet]
- PMID: 31314219
- Bookshelf ID: NBK543630
- DOI: 10.1007/978-3-319-43742-2
Secondary Analysis of Electronic Health Records [Internet]
Excerpt
This open access book trains the next generation of scientists representing different disciplines to leverage the data generated during routine patient care. It formulates a more complete lexicon of evidence-based recommendations and support shared, ethical decision making by doctors with their patients.
Diagnostic and therapeutic technologies continue to evolve rapidly, and both individual practitioners and clinical teams face increasingly complex ethical decisions. Unfortunately, the current state of medical knowledge does not provide the guidance to make the majority of clinical decisions on the basis of evidence.
The present research infrastructure is inefficient and frequently produces unreliable results that cannot be replicated. Even randomized controlled trials (RCTs), the traditional gold standards of the research reliability hierarchy, are not without limitations. They can be costly, labor intensive, and slow, and can return results that are seldom generalizable to every patient population. Furthermore, many pertinent but unresolved clinical and medical systems issues do not seem to have attracted the interest of the research enterprise, which has come to focus instead on cellular and molecular investigations and single-agent (e.g., a drug or device) effects. For clinicians, the end result is a bit of a “data desert” when it comes to making decisions. The new research infrastructure proposed in this book will help the medical profession to make ethically sound and well informed decisions for their patients.
Copyright 2016, The Editor(s) (if applicable) and The Author(s). This book is published open access.
Sections
- Preface
- MIT Critical Data
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I. Setting the Stage: Rationale Behind and Challenges to Health Data Analysis
- Introduction
- 1. Objectives of the Secondary Analysis of Electronic Health Record Data
- 2. Review of Clinical Databases
- 3. Challenges and Opportunities in Secondary Analyses of Electronic Health Record Data
- 4. Pulling It All Together: Envisioning a Data-Driven, Ideal Care System
- 5. The Story of MIMIC
- 6. Integrating Non-clinical Data with EHRs
- 7. Using EHR to Conduct Outcome and Health Services Research
- 8. Residual Confounding Lurking in Big Data: A Source of Error
- II. A Cookbook: From Research Question Formulation to Validation of Findings
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III. Case Studies Using MIMIC
- Introduction
- 18. Trend Analysis: Evolution of Tidal Volume Over Time for Patients Receiving Invasive Mechanical Ventilation
- 19. Instrumental Variable Analysis of Electronic Health Records
- 20. Mortality Prediction in the ICU Based on MIMIC-II Results from the Super ICU Learner Algorithm (SICULA) Project
- 21. Mortality Prediction in the ICU
- 22. Data Fusion Techniques for Early Warning of Clinical Deterioration
- 23. Comparative Effectiveness: Propensity Score Analysis
- 24. Markov Models and Cost Effectiveness Analysis: Applications in Medical Research
- 25. Blood Pressure and the Risk of Acute Kidney Injury in the ICU: Case-Control Versus Case-Crossover Designs
- 26. Waveform Analysis to Estimate Respiratory Rate
- 27. Signal Processing: False Alarm Reduction
- 28. Improving Patient Cohort Identification Using Natural Language Processing
- 29. Hyperparameter Selection
- Erratum to: Secondary Analysis of Electronic Health Records
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