Statistical Methods for Hospital Monitoring With R

Hospitals monitoring is becoming more complex and is increasing both because staff want their data analysed and because of increasing mandated surveillance.  This book provides a suite of functions in R, enabling scientists and data analysts working in infection management and quality improvement departments in hospitals, to analyse their often non-independent data which is frequently in the form of trended, over-dispersed and sometimes auto-correlated time series; this is often difficult to analyse using standard office software. 

This book provides much-needed guidance on data analysis using R for the growing number of scientists in hospital departments who are responsible for producing reports, and who may have limited statistical expertise.

This book explores data analysis using R and is aimed at scientists in hospital departments who are responsible for producing reports, and who are involved in improving safety. Professionals working in the healthcare quality and safety community will also find this book of interest

Statistical Methods for Hospital Monitoring with R:

• Provides functions to perform quality improvement and infection management data analysis.
• Explores the characteristics of complex systems, such as self-organisation and emergent behaviour, along with their implications for such activities as root-cause analysis and the Pareto principle that seek few key causes of adverse events.
• Provides a summary of key non-statistical aspects of hospital safety and easy to use functions.
• Provides R scripts in an accompanying web site enabling analyses to be performed by the reader http://www.wiley.com/go/hospital_monitoring
• Covers issues that will be of increasing importance in the future, such as, generalised additive models, and complex systems, networks and power laws.

Anthony Morton and Geoffrey Playford, Princess Alexandra Hospital, Brisbane, Australia

Kerrie Mengersen, Science and Engineering Faculty, Queensland University of Technology, Australia

Michael Whitby, Greenslopes Specialist Centre, Queensland, Australia

Introduction.

Overview and rationale for this book

Motivation for this book

Why R?

Other reading for R

What methods are covered in this book?

Structure of his book

Using R

Entering data

Dates

Dates, important note and chron()

Exporting data

Further notes

Charts introduction

When there is no date column

Control Chart Menu

Chapter 1.

Proportion

Confidence interval for

Significance test for

Likelihood ratio

Confidence interval for series of proportions

Difference between two proportions

Confidence intervals for

Significance test for

More than two proportions

Summary average of a series of proportions, Newcombe’s method

Stratified proportion data, differences between rates, Newcombe’s method

Mantel-Haenszel analysis

DerSimonian-Laird analysis

Chapter 2.

Risk adjustment

Stratification

Logistic regression

Discrimination

Calibration

Re-calibration

Displaying and analyzing data from multiple institutions

Tabulations

Tables in wide format

Z-scores

Multiple confidence intervals

Funnel plot

Indirect standardisation

False discovery

SMR

Random effects, shrinkage

Openbugs Gamma-Poisson Hierarchical Model

Empirical Bayes

Bootstrap

Variation in predicted values

Complex surgical site infections (low rate data)

Funnel plot analysis of low rate data

Shrinkage analysis of low rate data with Openbugs Gamma-Poisson Hierarchical Model

Appendix 1, further tabulations

Appendix 2, risk scores for a hospital derived from data with its data removed

Chapter 3.

CUSUM and related charts for binary data

Cumulative Observed-Expected (O-E) chart and combined CUSUM and O-E chart

Cumulative Funnel plot and combined CUSUM and Funnel plot

Example

Including Risk Adjustment

CUSUM chart

Cumulative observed minus expected (O-E) chart

Funnel plot

Discrimination and Calibration of Risk Adjustment

Shewhart P chart and EWMA chart

Run-Sum chart

The EWMA chart

Expected values

Spline or generalized additive model (GAM) chart

Few time periods

Quarterly data and data without a first date column

Composite measures

Additional tabulations

Underreporting

New CUSUM and EWMA charts, predicting the current value for low-rate data

Risk-adjusted Bernoulli CUSUM

Single observation data and EWMA charts

Quarterly rates for current rate values in low rate data

Intervals between uncommon binary adverse events

Appendix single observation EWMA limits, a proposal

Chapter 4.

Introduction

Rate and count data

Single count or rate

Confidence Interval for single count or rate

Significance Test for single count or rate

Confidence limits for columns of counts and rates

Two independent rates

Confidence interval for two independent rates

Hypothesis test for two independent rates

Bayesian approach

Chi-squared and trend tests for count and rate data

Stratified count and rate data

Summary rate

Stratified count and rate data two sets of rates

Indirect standardisation

Direct standardisation

Mantel-Haenszel, Homogeneity and Trend Tests

Count data variation

Complex Systems, Networks and Variation

Chapter 5

Introduction, data, limitations of aggregated count data analysis

Confidence intervals for the hospitals’ Staphylococcus aureus data

Funnel plots for the Staphylococcus aureus data

Tabulations and Z-scores

Overdispersion, false discovery, very small expected

Proposal for false discovery modified funnel plot

Bayesian shrinkage

Openbugs gamma-Poisson hierarchical model

Empirical Bayes

Further tabulations, device-related and MRSA bacteraemias

Rearranging hospital levels for MRSA

Bacteraemia Risk Adjustment demonstration

Chapter 6.

Arranging data by weeks, months, quarters

Means and variances

Predictability

Tabulations

Denominators

Shewhart, EWMA and GAM control charts without denominators

Shewhart/EWMA charts

Shewhart, EWMA and GAM control charts with denominators

Overdispersion

Charts for quarterly data and data without a first date column

When there are few time periods

Cross-tabulation in wide format

Uncommon count data AEs.

Additional scripts for tabulations and charts

Intervals between uncommon count data events

Negative binomial parameters for control charts when denominators vary

Weighted variance

Linear approximation (Bissell)

Comparisons of simple weighted variance and linear approximation

Chapter 7

Multiple antibiotic-resistant organism (MRO) Prevalence

Antibiotic Usage

Spurious proportions

RIDIT charts

Numerical data

Length of stay (LOS) data

Change point

Assessing agreement

Numerical data agreement

Making Decisions (Decision Analysis)

Investigating Outbreaks, analysis of stratified data

Reviewing stratified data analysis

Outbreak investigation example

Chapter 8.

Overview of Hospital Quality Improvement

Five pillars

Customer

Practitioner

Evidence based system

Manifestations of poor systems

Malpractice

Criminal activity

Substandard performance

Medical error

Bundles and checklists

Discipline and accountability

Analyzing & implementing evidence-based systems

Change management

The Feedback loop

Implementation of the Quality Improvement Process

Obtaining data

Hospital as a network

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