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

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

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.

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"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