Feb 062019

With accompanying software! Clinicians manage a lot of data – on assorted bits of paper and in their heads. This book is about better ways to manage and understand large amounts of clinical data. Following on from his ground breaking book, Evaluating the Processes of Neonatal Intensive Care, Joseph Schulman has produced this eminently readable guide to patient data analysis. He demystifies the technical methodology to make this crucial aspect of good clinical practice understandable and usable for all health care workers.Computer technology has been relatively slow to transform the daily work of health care, the way it has transformed other professions that work with large amounts of data. Each day, we do our work as we did it the day before, even though current technology offers much better ways. Here are much better ways to document and learn from the daily work of clinical care. Here are the principles of data management and analysis and detailed examples of how to implement them using computer technology. To show you that the knowledge is scalable and useful, and to get you off to a running start, the book includes a complete point of care database software application tailored to the neonatal intensive care unit (NICU). With examples from the NICU and the pediatric ward, this book is aimed specifically at the neonatal and pediatric teams. The accompanying software can be downloaded on to your system or PDA, so that continual record assessment becomes second nature – a skill that will immeasurably improve practice and outcomes for all your patients.Content: Chapter 1 Introduction (pages 1–6): Chapter 2 Paper?based Patient Records (pages 9–12): Chapter 3 Computer?based Patient Records (pages 13–16): Chapter 4 Aims of a Patient Data Management Process (pages 17–19): Chapter 5 Data, Information, and Knowledge (pages 20–24): Chapter 6 Single Tables and Their Limitations (pages 25–28): Chapter 7 Multiple Tables: Where to Put the Data, Relationships Among Tables, and Creating a Database (pages 29–41): Chapter 8 Relational Database Management Systems: Normalization (Codd’s Rules) (pages 42–47): Chapter 9 From Data Model to Database Software (pages 48–59): Chapter 10 Integrity: Anticipating and Preventing Data Accuracy Problems (pages 60–66): Chapter 11 Queries, Forms, and Reports (pages 67–93): Chapter 12 Programming for Greater Software Control (pages 94–112): Chapter 13 Turning Ideas into a Useful Tool: eNICU, Point of Care Database Software for the NICU (pages 113–132): Chapter 14 Making eNICU Serve Your Own Needs (pages 133–145): Chapter 15 Single Versus Multiple Users (pages 146–150): Chapter 16 Backup and Recovery: Assuring Your Data Persists (pages 151–156): Chapter 17 Security: Controlling Access and Protecting Patient Confidentiality (pages 157–171): Chapter 18 Asking Questions of a Data Set: Crafting a Conceptual Framework and Testable Hypothesis (pages 175–182): Chapter 19 Stata: A Software Tool to Analyze Data and Produce Graphical Displays (pages 183–184): Chapter 20 Preparing to Analyze Data (pages 175–194): Chapter 21 Variable Types (pages 195–197): Chapter 22 Measurement Values Vary: Describing Their Distribution and Summarizing Them Quantitatively (pages 198–219): Chapter 23 Data From All Versus Some: Populations and Samples (pages 220–223): Chapter 24 Estimating Population Parameters: Confidence Intervals (pages 224–227): Chapter 25 Comparing Two Sample Means and Testing a Hypothesis (pages 228–237): Chapter 26 Type I and Type II Error in a Hypothesis Test, Power, and Sample Size (pages 238–243): Chapter 27 Comparing Proportions: Introduction to Rates and Odds (pages 244–257): Chapter 28 Stratifying the Analysis of Dichotomous Outcomes: Confounders and Effect Modifiers; The Mantel–Haenszel Method (pages 258–268): Chapter 29 Ways to Measure and Compare the Frequency of Outcomes, and Standardization to Compare Rates (pages 269–277): Chapter 30 Comparing the Means of More Than Two Samples (pages 278–286): Chapter 31 Assuming Little About the Data: Nonparametric Methods of Hypothesis Testing (pages 287–289): Chapter 32 Correlation: Measuring the Relationship Between Two Continuous Variables (pages 290–293): Chapter 33 Predicting Continuous Outcomes: Univariate and Multivariate Linear Regression (pages 294–310): Chapter 34 Predicting Dichotomous Outcomes: Logistic Regression, and Receiver Operating Characteristic (pages 311–324): Chapter 35 Predicting Outcomes Over Time: Survival Analysis (pages 325–345): Chapter 36 Choosing Variables and Hypotheses: Practical Considerations (pages 346–353):
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