SISEP eNotes: Decision Support Data Systems: An Organization Driver

Date Published:

November 14, 2016

November, 2016

Decision Support Data Systems: An Organization Driver

A Decision Support Data System (DSDS) is a system for identifying, collecting, and analyzing data for decision-making by program administrators and practitioners. These systems enable organizations to make informed and intentional decisions about critical implementation supports.

Program impact, successes, and challenges are communicated to administration, staff, stakeholders, and funders by Implementation Teams. The use of a DSDS allows teams to detect, prevent, and address barriers. The result is that practitioners have the implementation supports required to produce notable improved outcomes.

DSDS gather three types of data: fidelity, outcome, and programmatic. Programs need all of these types of data to celebrate successes and diagnose issues that emerge during implementation. This helps an organization understand if there is a process or performance issue, or an issue with the program and its fit.

Three types of DSDS data

Fidelity Data
Fidelity data are process data. Fidelity data measure content, competence and context.

Outcome Data
Outcome data are results data. They are measures of impacts of the innovation.

Programmatic Data
Programmatic data are relevant to administration and of the program.

DSDS are used and communicated across multiple levels of an organization to create an enabling context for practice. This hospitable environment is provided through the development of policy that supports and sustains all implementation efforts over time. Therefore, DSDS data and information must be reliable, valid, and timely so it can be used for problem solving and action planning in a Plan, Do, Study, Act Cycle.

Get ready to use DSDS by asking:
• How does your organization currently use data to drive decision-making?
• What type of data do you review? Who reviews this data and how often?

Get Started

AI Hub Drivers Ed: Decision Support Data System
This short lesson includes a DSDS simulation and three simple steps to make your DSDS useful and effective.
Handout 28: Decision Support Data System
AI Hub Lesson 6: The PDSA (Improvement) Cycle
This online lesson describes the key components in each PDSA Cycle phase and identifies why and when PDSA Cycles are used for rapid cycle problem solving in active implementation. After the lesson, you should be able to apply a PDSA Cycle to engage in rapid cycle problem solving as a Team.

Get Better

AI Hub Module 2: Implementation Drivers
This module provides an overview of the Implementation Drivers, one of the Active Implementation Frameworks.

Drivers Ed Lesson Series

The Drivers Ed series provides a quick overview of each Active Implementation Driver along with best practices and an opportunity to take the driver for a test drive. We now have 3 lessons in the series.

Decision Support Data Systems (13 min. 9 sec.)
In this short lesson we’ll provide an overview of Decision Support Data Systems. And then give you an opportunity to apply the concepts presented
Fidelity (17 min. 52 sec.)
The Fidelity Driver is key to effective implementation. In this lesson you will learn how to inventory your system for existing fidelity data & begin using fidelity data as a system diagnostic tool.
Selection (9 min. 13 sec.)
Selection refers to the purposeful process of recruiting, interviewing, and hiring ‘with the end in mind’. This lesson will introduce you to the Selection Driver, review best practices for Selection, and provide an opportunity to apply these concepts.

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