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You are here > Home > Reading Lists > Information Systems, Medical Records  & Technology Books > Managing Your Patients' Data in the Neonatal and Pediatric ICU: An Introduction to Databases and Statistical Analysis Managing Your Patients' Data in the Neonatal and Pediatric ICU: An Introduction to Databases and Statistical Analysis AKA: "Using Software for Data Management and Analysis in NICU" Joseph Schulman, Associate Professor of Pediatrics, Albany Medical College, Senior Scientist, Clinical Informatics and Outcomes Research, Children’s Hospital at Albany Medical Center, New York Softcover, 352 pages + bonus CD-ROM + software ISBN 0727918702 978-0727918703 BMJ Books / Blackwell Publishing May 2006 (click button below for the very best currently available price for this important resource) This book comes with accompanying software! 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. This book gives you: Better ways to document and learn from the daily work of clinical care. Principles of data management and analysis and detailed examples of how to implement them using computer technology. A complete point-of-care database software application tailored to the neonatal intensive care unit. To show you that the knowledge is scalable and useful, and to get you off to a running start, this 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. 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. This book fully covers both introductory and advanced topics for you: Managing data and routine reporting The process of managing clinical data Paper-based patient records Computer-based patient records Aims of a patient data management process Modeling data: accurately representing our work and storing the data so we may reliably retrieve them Data, information, and knowledge Single tables and their limitations Multiple tables: where to put the data; relationships among tables; creating a database Relational database management systems: normalization; Codd’s rules Database software From data model to database software Integrity: anticipating and preventing problems with data accuracy Queries, forms, and reports Programming for greater software control Turning ideas into a useful tool: eNICU – point of care database software for the NICU Making eNICU serve your own needs Database administration Single vs. multiple user Backup: assuring your data persists Security: controlling access and protecting patient confidentiality Learning from aggregate experience: exploring and analyzing datasets Interrogating data Crafting a conceptual framework and testable hypothesis Stata: a software tool to analyze data and produce graphical displays Preparing to analyze data Analytical concepts and methods Variable types Measurement values vary: describing their distribution and summarizing them quantitatively Data from all or some: populations and samples Estimating population parameters; confidence intervals Comparing two sample means; statistical significance and clinical significance Type I and type II error in a hypothesis test; power; sample size Comparing proportions; introduction to rates and odds Stratifying the analysis of dichotomous outcomes; confounders and effect modifiers; multiple 2 X 2 tables: the Mantel-Haenszel method Ways to measure and compare the frequency of outcomes; standardization Comparing the means of more than two samples Assuming little about the data: non-parametric methods of hypothesis testing Correlation: measuring the relationship between two continuous variables Predicting continuous outcomes: univariate and multivariate linear regression Predicting dichotomous outcomes: logistic regression; receiver operating characteristic (ROC) Predicting outcomes over time: survival analysis Choosing variables and hypotheses: practical considerations The challenge of transforming data and information to shared knowledge This book comes with accompanying software! 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