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Integrating data sources across multiple organizations: benefits, barriers, and lessons

Efforts involving data sources integration across multiple organizations are examined in the cases of five states' cancer prevention and control planning model, seven states' statewide cancer control plan, the business process reengineering project at Clark County Recorder's Office, varied databases linking in MassCHIP (Massachusetts Community Health Information Profile), the cancer control intervention in New York State Department of Health, low-income family support at the Child Care Bureau of the U.S. Department of Health and Human Services, assessing hospital performance in QMAS (Quality Measurement Advisory Service) in Washington State, the efforts of the Health Care Data Governing Board in Kansas, and Kentucky's KIDS COUNT Program.

These efforts also tend to lead to the following benefits: increased customers service quality, increased existing personnel efficiency, improved quality, timeliness, and utilization of information, increased accessibility and analysis of information, and the elimination of redundant data and tasks.

While there are clearly advantages, using an integrated approach across multiple organizations presents a number of challenges. Obtaining data from other agencies is often difficult, and in many cases will be impossible. Legal restrictions often prevent access to a particular data set. It is also difficult to obtain the cooperation of agency heads, who will often decide whether to participate in data sharing. Data sharing often requires compatibility between different computer systems as well as the availability of information system personnel. Data integration also requires the concurrence of system administrators, directors of programs, and services consumers. In addition, more cost and time, few data standards, and information overload are also barriers to data integration across multiple organizations.

Based on the experiences in the cases where organizations are either in the same or multiple sectors of the economy, the following important lessons regarding the implementation of a comprehensive data integration project are identified: The purpose of data integration should be very clear. Data integration projects require a significant time commitment. Barriers to participation must be identified and addressed. Early financial commitment is a key to ensuring ongoing political commitment. Management information systems staff should be involved from the start.

It is worth noting that several cases in this paper are in health care field, it probably indicates that health care is a leader in data integration efforts. It also seems to us that organization-wide data integration is done for operational reasons, while data integration across multiple organizations (at least in the cases) is done for research and evaluation purposes.