Ryan Kennedy – författare

The rapid growth of software applications within healthcare organizations has made it essential to have defined methodologies and formal processes for the management of the entire Information Technology (IT) portfolio. Utilizing a portfolio management framework throughout an application’s lifecycle will provide the necessary structure to ensure that all new applications are properly evaluated, and, once implemented, remain relevant while continuing to meet organizational requirements.While an organization may have a few large "organization-wide" systems such as the Electronic Health Record (EHR), lab or radiology systems, they also have a large quantity of other clinical, administrative, and research systems. Some larger organizations now have hundreds of software applications to support and manage. The IT staff must be able to implement new requests while still maintaining the current application portfolio. Utilizing a standard repeatable process will help to manage these large portfolios of software applications.This book reviews the management of applications throughout their lifecycle, from initial request through disposition. Best practices dictate that every newly requested application undergoes analysis followed by an approval decision from the organization’s governance committee. The initial implementation project must include activities to prepare for ongoing support while ensuring the application is compliant with all security, privacy, and architecture requirements. An application spends years in operations and maintenance where changes occur regularly through configuration and release management, or additional projects. The cycle continues until disposition. Understanding when to dispose of an application is just as important as deciding when to implement a new one. A defined process for disposing of an application ensures all parts are properly removed or destroyed.

The rapid growth of software applications within healthcare organizations has made it essential to have defined methodologies and formal processes for the management of the entire Information Technology (IT) portfolio. Utilizing a portfolio management framework throughout an application’s lifecycle will provide the necessary structure to ensure that all new applications are properly evaluated, and, once implemented, remain relevant while continuing to meet organizational requirements.While an organization may have a few large "organization-wide" systems such as the Electronic Health Record (EHR), lab or radiology systems, they also have a large quantity of other clinical, administrative, and research systems. Some larger organizations now have hundreds of software applications to support and manage. The IT staff must be able to implement new requests while still maintaining the current application portfolio. Utilizing a standard repeatable process will help to manage these large portfolios of software applications.This book reviews the management of applications throughout their lifecycle, from initial request through disposition. Best practices dictate that every newly requested application undergoes analysis followed by an approval decision from the organization’s governance committee. The initial implementation project must include activities to prepare for ongoing support while ensuring the application is compliant with all security, privacy, and architecture requirements. An application spends years in operations and maintenance where changes occur regularly through configuration and release management, or additional projects. The cycle continues until disposition. Understanding when to dispose of an application is just as important as deciding when to implement a new one. A defined process for disposing of an application ensures all parts are properly removed or destroyed.

Introduction to R for Social Scientists: A Tidy Programming Approach introduces the Tidy approach to programming in R for social science research to help quantitative researchers develop a modern technical toolbox. The Tidy approach is built around consistent syntax, common grammar, and stacked code, which contribute to clear, efficient programming. The authors include hundreds of lines of code to demonstrate a suite of techniques for developing and debugging an efficient social science research workflow. To deepen the dedication to teaching Tidy best practices for conducting social science research in R, the authors include numerous examples using real world data including the American National Election Study and the World Indicators Data. While no prior experience in R is assumed, readers are expected to be acquainted with common social science research designs and terminology.Whether used as a reference manual or read from cover to cover, readers will be equipped with a deeper understanding of R and the Tidyverse, as well as a framework for how best to leverage these powerful tools to write tidy, efficient code for solving problems. To this end, the authors provide many suggestions for additional readings and tools to build on the concepts covered. They use all covered techniques in their own work as scholars and practitioners.

Introduction to R for Social Scientists: A Tidy Programming Approach introduces the Tidy approach to programming in R for social science research to help quantitative researchers develop a modern technical toolbox. The Tidy approach is built around consistent syntax, common grammar, and stacked code, which contribute to clear, efficient programming. The authors include hundreds of lines of code to demonstrate a suite of techniques for developing and debugging an efficient social science research workflow. To deepen the dedication to teaching Tidy best practices for conducting social science research in R, the authors include numerous examples using real world data including the American National Election Study and the World Indicators Data. While no prior experience in R is assumed, readers are expected to be acquainted with common social science research designs and terminology.Whether used as a reference manual or read from cover to cover, readers will be equipped with a deeper understanding of R and the Tidyverse, as well as a framework for how best to leverage these powerful tools to write tidy, efficient code for solving problems. To this end, the authors provide many suggestions for additional readings and tools to build on the concepts covered. They use all covered techniques in their own work as scholars and practitioners.