CDS

Clinical Decision Support -- CDS

Overview

Clinical Decision Support (CDS) refers broadly to providing clinicians or patients with clinical knowledge and patient-related information, intelligently filtered or presented at appropriate times, to enhance patient care. Clinical knowledge of interest could range from simple facts and relationships to best practices for managing patients with specific disease states, new medical knowledge from clinical research and other types of information.

For an overview of the process that healthcare organizations can use to begin, or improve, a clinical decision support (CDS) initiative interested parties can follow the guidelines described in Improving Outcomes with Clinical Decision Suppport: An Implementer's Guide to measurably improve key healthcare outcomes such as the quality, safety, and cost-effectiveness of care delivery.

• History of decision support
• General system features associated with improvements in clinical practice
• Support Decisions with Diagnostic Aids

Modes of Interaction

• Interpretation
• Consultation
• Monitoring
• Critiquing
• Teaching

Information Resources

• Alerts and Reminders
• Drug-drug Interactions
• Drug-Allergy Interactions
• Alert Fatigue?
• Alert placement in clinical workflow
• Initial Selection of What to Alert on...

Examples available on the web

• GIDEON (Global Infectious Diseases and Epidemiology Network)

All articles related to clinical decision support

Business Intelligence and Data Warehousing - Healthcare Applications

• BI & DW for Healthcare

Healthcare organizations have historically struggled to find the elusive link between the investment in information technology and improved organizational performance. At least a portion of this gap has been driven by the focus on the implementation of information technology (IT) solutions to support transactional workflow with little to no attention paid to how people actually use the information contained in these solutions to make decisions. The strategic value of IT lies in its power to provide clinicians and leadership with direct visibility into the care delivery process. When little or no attention is given to the strategic use of information as part of an electronic health record (EHR)implementation, organizations are often disappointed by the return on investment and value received as a result of the significant investment.

Business intelligence was defined by David Losehin as the processes, technologies, and tools needed to turn data into information, information into knowledge, and knowledge into plans that drive profitable business applications. As such, business intelligence is a key discpline that needs to be adopted by healthcare organizations to transform the vast quantities of data contained in their transactional EHR systems into a format that enables improved strategic, tactical, and operational decision making. Examples of business intelligence applications include the following:

• Decision Support Systems
• Executive Information Systems
• Online Analytical Processing (OLAP)
• Query and Online Reporting
• Data Mining
• Business Process Monitoring
• Performance Scorecards and Dashboads

A healthcare organization must also develop a core competency in data warehousing to enable business intelligence applications. Wiliam Inmon defined data warehousing as a "subject-oriented, integrated, non-volatile, time vriant, collection of data organized to support management needs." Data warehousing includes both the process and the technologies required to achieve value from the data assets of an organization.

Components of a comprehensive business intelligence and data warehousing program include the following:

• A structured program to capture and manage meta data - "data about data"
• Defined business and clinical rules to cleanse data contained in transactional systems
• Tools to extract, translate, and load data from source systems into a format that enables analysis, reporting, and decision support
• A database management system that integrates and stores information from many source systems into a single solution
• Several different business views of data including population or disease specific data marts to support specific business or clinical questions
• An operational data store to manage information for real time and near real time monitoring applications

Several Business Intelligence and Data Warehousing technologies have demonstrated quantitative benefits and return on investment in non-healthcare industries. However, in the healthcare provider marker, Business Intelligence and Data Warehousing is in its infancy with less than 5% adoption of the technology.

Submitted by: Denise Johnson Sources: Gartner, Hype Cycle for Business Intelligence and Data Warehousing, 2005. Gartner, Hype Cycle for Healthcare Provider Applications, 2005. Loshin, David, Business Intelligence: The Savvy Manager's Guide, Addison Wesley, 2003. TDWI Business Intelligence Fundamentals, The Data Warehousing Institute, January 2005.