Data Sharing and Interoperability

Introduction

Local, state and national data systems contain data about individuals and services delivered to families receiving home visiting. These data can provide valuable information about program effectiveness and family well-being. However, there are significant barriers to fully utilizing this data due to a lack of common definitions in the field and data sharing considerations.

Data Sharing and Interoperability

An specific to home visiting provides the necessary framework and definitions to advance home visiting research. Developing a home visiting ontology will allow researchers to combine existing data more easily across models, states, territories, tribal entities, and localities. This will provide large sample sizes of families and programs providing enough power for multi-level modeling and other multi-variable analytic techniques. Further, the ontology can help index research papers and results using to speed the process of systematic reviews and increase knowledge about a particular home visiting concept or intervention using information gathered across many studies.

In , an ontology is an organizing framework made up of three layers: knowledge, information, and data. Knowledge or the upper layer, provides a high-level view of key concepts relevant to home visiting and their relationships to each other. The information layer explains how concepts from the knowledge layer are defined and connects those concepts from the knowledge layer with the data layer. The data layer specifies how data are defined, organized, and stored in databases. Modern solutions (e.g., National Information Exchange Model) are driven by the information layer which allows researchers to store data in-house, while allowing for data sharing and collaboration across centers. Often the emphasis is on sharing at the data layer, however the field can benefit from sharing at the information (e.g., surveys, screenings) and knowledge (e.g., publications) layers.

As an example, we cannot adequately study the intricacies of family engagement without a standard operational definition across the many home visiting models. Once we have a definition of family engagement, the information layer can be used as a tool to identify gaps between what data is needed to answer research questions of interest and the data currently available in home visiting models’ management information systems (MIS). It may be that the data needed to answer some of the questions around family engagement are not available within existing MIS. This finding may inform opportunities for modifications in data collection. Changes to MIS systems could be tested with small-scale projects that are low burden but high impact.

HARC plans to use the ontology layers to conceptualize common definitions, promote data sharing between agencies at all levels, and use linked data sets to produce high-quality home visiting research. Data linkage between models of home visiting, across states, and among state agencies will more fully encompass family’s’ experience and strengthen generalizability of findings across the field.

For More Information

Data Sharing in Cross-Sector Collaborations: Insights of Integrated Data Systems

From Allen et. al. (2021), this paper summarizes findings from their study examining established integrated data systems to learn about the facilitators and barriers to successful cross sector data-sharing efforts.

Data Sharing in Cross-Sector Collaborations

Identifying Home Visiting Data to Integrate with Other Early Childhood Data

The purpose of this resource from Child Trends is to provide a guide that states can use to develop a map of home visiting and other early childhood data. Specifically, this resource outlines how to 1) compile a list of home visiting programs in a state, 2) identify available home visiting data and linkages, and 3) create a data map.

Identifying Home Visiting Data

MIECHV Data and Continuous Quality Improvement: Data Exchange Standards

HRSA’s Data Exchange Standards can be used to support federal reporting and facilitate the electronic data exchange between the MIECHV state agency and other state agencies. By sharing data, MIECHV awardees can address key policy, programmatic, and research questions about the home visiting field that may not be answerable with current data infrastructures. The 5 resources under Data Exchange Standards are available to support MIECHV awardees interested in implementing data exchange standards in their state or territory.

MIECHV Data and Continuous Quality Improvement

One Step at a Time: Benefits of Incremental Integration of HV and Other EC Data

The purpose of this resource from Child Trends is to provide states with examples of various ways to integrate their home visiting data into their ECIDS over time. This resource will highlight five examples of how states can approach this incremental integration of home visiting data.

One Step at a Time

An ontology is a framework for organizing and defining concepts and how they are related to one another.

Machine learning is the process by which a computer is able to improve its own performance using algorithms that continuously incorporate new data to create a model that can be used to describe, predict, or prescribe actions.

Informatics is the science of processing data for storage and retrieval; information science.

Interoperability is the ability of a system to communicate with another system without human intervention.