Managing your Patients' Data in the Neonatal and Pediatric ICU (eBook)

An Introduction to Databases and Statistical Analysis

Joseph Schulman (Autor)

eBook Download: PDF

2008 | 1. Auflage
376 Seiten
John Wiley & Sons (Verlag)
978-0-470-75741-3 (ISBN)

Infos

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.

Autor

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

Inhalt

1 Introduction.

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.

displays.

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.

References.

Index.

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."

Rezensionen

"A detailed and practical guide how to manage the large amount of clinical data accummulated in ICU's with special orientation to neonatal intensive care units. .For neonatologists who want to learn from what they do" Pediatric Endocrinology Reviews December 2007

Produkt-Details

• Erscheint lt. Verlag: 8.5.2008
• Sprache: englisch
• Themenwelt: Medizin / Pharmazie ► Medizinische Fachgebiete ► Pädiatrie
• Schlagworte: Medical Science • Medizin • Pädiatrie • Pädiatrie • Pediatrics
• ISBN-10: 0-470-75741-8 / 0470757418
• ISBN-13: 978-0-470-75741-3 / 9780470757413