Managing your Patients' Data in the Neonatal and Pediatric ICU: An Introduction to Databases and Statistical Analysis
April 2008, BMJ Books
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
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
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.
Part I Managing data and routine reporting.
Section 1 The process of managing clinical data.
2 Paper-based patient records.
3 Computer-based patient records.
4 Aims of a patient data management process.
Section 2 Modeling data: Accurately representing our work and storing the data so we may reliably retrieve them.
5 Data, information, and knowledge.
6 Single tables and their limitations.
7 Multiple tables: where to put the data, relationships among tables, and creating a database.
8 Relational database management systems: normalization (Codd’s rules).
Section 3 Database software.
9 From data model to database software.
10 Integrity: anticipating and preventing data accuracy problems.
11 Queries, forms, and reports.
12 Programming for greater software control.
13 Turning ideas into a useful tool: eNICU, point of care database software for the NICU.
14 Making eNICU serve your own needs.
Section 4 Database administration.
15 Single versus multiple users.
16 Backup and recovery: assuring your data persists.
17 Security: controlling access and protecting patient confidentiality.
Conclusion - Part I: Maintaining focus on a moving target.
Part II Learning from aggregate experience: exploring and analyzing data sets.
Section 5 Interrogating data.
18 Asking questions of a data set: crafting a conceptual framework.
and testable hypothesis.
19 Stata: a software tool to analyze data and produce graphical.
20 Preparing to analyze data.
Section 6 Analytical concepts and methods.
21 Variable types.
22 Measurement values vary: describing their distribution and summarizing them quantitatively.
23 Data from all versus some: populations and samples.
24 Estimating population parameters: confidence intervals.
25 Comparing two sample means and testing a hypothesis.
26 Type I and type II error in a hypothesis test, power, and sample size.
27 Comparing proportions: introduction to rates and odds.
28 Stratifying the analysis of dichotomous outcomes: confounders and effect modifiers; the Mantel–Haenszel method.
29 Ways to measure and compare the frequency of outcomes, and standardization to compare rates.
30 Comparing the means of more than two samples.
31 Assuming little about the data: nonparametric methods of hypothesis testing.
32 Correlation: measuring the relationship between two continuous variables.
33 Predicting continuous outcomes: univariate and multivariate linear regression.
34 Predicting dichotomous outcomes: logistic regression, and receiver operating characteristic.
35 Predicting outcomes over time: survival analysis.
36 Choosing variables and hypotheses: practical considerations.
Conclusion The challenge of transforming data and information to shared knowledge: tools that make us smart.
CD ROM: eNICU files; practice data sets back cover.
“Microsoft, Access, SQL Server, andWindows are either registered trademarks or trademarks of Microsoft Corporation in the United States and/or other countries.”
- Improves treatments and time - and cost-effectiveness
- Includes relevant point-of-care downloadable software
application on CD-ROM; put the information straight into
- Tailored to the neonatal intensive care unit (NICU) and
- Transferable to other healthcare settings
Flash - with CD-ROM to put the information straight into practice!