Managing your Patients' Data in the Neonatal and Pediatric ICU An Introduction to Databases and Statistical Analysis

by Schulman, Joseph

• ISBN13: 9780727918703
• ISBN10: 0727918702
• Edition: 1st
• Format: Paperback
• Copyright: 2006-09-01
• Publisher: BMJ Books

Summary

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.

Author Biography

Joseph Schulman,MD, MS Division of Newborn Medicine, Weill Medical College of Cornell University, New York-Presbyterian Hospital, New York, USA

Table of Contents

eNICU installation and administration instructions,	ix
Acknowledgments,	x
Chapter 1 Introduction,	1	(8)
Part I Managing data and routine reporting
Section 1 The process of managing clinical data
Chapter 2 Paper-based patient records,	9	(4)
Chapter 3 Computer-based patient records,	13	(4)
Chapter 4 Aims of a patient data management process,	17	(3)
Section 2 Modeling data: Accurately representing our work and storing the data so we may reliably retrieve them
Chapter 5 Data, information, and knowledge,	20	(5)
Chapter 6 Single tables and their limitations,	25	(4)
Chapter 7 Multiple tables: where to put the data, relationships among tables, and creating a database,	29	(13)
Chapter 8 Relational database management systems: normalization (Codd's rules),	42	(6)
Section 3 Database software
Chapter 9 From data model to database software,	48	(12)
Chapter 10 Integrity: anticipating and preventing data accuracy problems,	60	(7)
Chapter 11 Queries, forms, and reports,	67	(27)
Chapter 12 Programming for greater software control,	94	(19)
Chapter 13 Turning ideas into a useful tool: eNICU, point of care database software for the NICU,	113	(20)
Chapter 14 Making eNICU serve your own needs,	133	(13)
Section 4 Database administration
Chapter 15 Single versus multiple users,	146	(5)
Chapter 16 Backup and recovery: assuring your data persists,	151	(6)
Chapter 17 Security: controlling access and protecting patient confidentiality,	157	(12)
Conclusion Part I: Maintaining focus on a moving target,	169	(6)
Part II Learning from aggregate experience: exploring and analyzing data sets
Section 5 Interrogating data
Chapter 18 Asking questions of a data set: crafting a conceptual framework and testable hypothesis,	175	(8)
Chapter 19 Stata: a software tool to analyze data and produce graphical displays,	183	(2)
Chapter 20 Preparing to analyze data,	185	(10)
Section 6 Analytical concepts and methods
Chapter 21 Variable types,	195	(3)
Chapter 22 Measurement values vary: describing their distribution and summarizing them quantitatively,	198	(22)
Chapter 23 Data from all versus some: populations and samples,	220	(8)
Chapter 24 Estimating population parameters: confidence intervals, 224 Chapter 25 Comparing two sample means and testing a hypothesis,	228	(10)
Chapter 26 Type I and type II error in a hypothesis test, power, and sample size,	238	(6)
Chapter 27 Comparing proportions: introduction to rates and odds,	244	(14)
Chapter 28 Stratifying the analysis of dichotomous outcomes: confounders and effect modifiers; the Mantel—Haenszel method,	258	(11)
Chapter 29 Ways to measure and compare the frequency of outcomes, and standardization to compare rates,	269	(9)
Chapter 30 Comparing the means of more than two samples,	278	(9)
Chapter 31 Assuming little about the data: nonparametric methods of hypothesis testing,	287	(3)
Chapter 32 Correlation: measuring the relationship between two continuous variables,	290	(4)
Chapter 33 Predicting continuous outcomes: univariate and multivariate linear regression,	294	(17)
Chapter 34 Predicting dichotomous outcomes: logistic regression, and receiver operating characteristic,	311	(14)
Chapter 35 Predicting outcomes over time: survival analysis,	325	(21)
Chapter 36 Choosing variables and hypotheses: practical considerations,	346	(5)
Conclusion The challenge of transforming data and information to shared knowledge: tools that make us smart,	351	(4)
References,	355	(6)
Index,	361