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Child welfare informatics: a new definition for an established practice.

Child welfare, one of the most important arenas of social work, has benefited from advances in information technology in the past two decades (Weaver, Moses, Furman, & Lindsey, 2003). This whole discipline of social work can be captured best through the term "child welfare informatics." Although the term "informatics" may be new to the field of child welfare, it has served the health care field well for many years. The first federal funding of health informatics began in the late 1960s through the precursor of the Agency for Healthcare Research and Quality at the U.S. Department of Health and Human Services (Fitzmaurice, Adams, & Eisenberg, 2002). There has been an extensive examination of the different fields of health informatics, medical informatics, public health informatics, and nursing informatics (Staggers & Thompson, 2002). In fact, the basis of many social work practices that have combined information technology, social work research, and best practices through areas such as evidence-based practice have their origins in the health sciences fields (Gilgun, 2005).

What is informatics? Staggers and Thompson (2002) provided the following definition, which is specific to nursing informatics but can be generalized to any of the health informatics fields:
 Nursing informatics is a specialty that integrates nursing science,
 computer science, and information science to manage and communicate
 data, information, and knowledge in nursing practice. Nursing
 informatics facilitates the integration of data, information, and
 knowledge to support patients, nurses, and other providers in their
 decision-making in all roles and settings. (p. 260)


The field of informatics developed because of the recognition of two significant factors (Rosenbaum Burke, Benevelli, Borzi, & Repasch, 2005): First, a report by the Institute of Medicine noted that thousands of patient deaths a year could have been avoided through more advanced and detailed use of health information (Rosenbaum et al.). Second, there is strong evidence to suggest significant socioeconomic disparities in the outcomes of health care, thereby making information on aspects such as race, ethnicity, language, and other socioeconomic factors a critical dimension of health care itself (Rosenbaum et al.).

More recently, the different fields of health informatics have aggressively moved away from analyzing information after the fact (for example, collecting longitudinal data to conduct epidemiologic studies) to computer-aided decision making to support physician decisions in direct patient health care (Fitzmaurice et al., 2002). This has been done by developing the availability of real-time computer-based medical records across different departments within a health agency, across different health agencies, and even across jurisdictions. Kaiser Permanente has spent $3.3 billion in the past decade to integrate medical records with registration, billing, and other information to enhance patient care and service ("Making Healthy Connections," 2006). This integration allows health care practitioners to access, through locally available technologies, databases that along with decision-support tools minimize potential adverse health outcomes while maximizing patient care.

The most recent push for a nationwide health information network was a concept paper that recommended an integrated system to provide a secure exchange of health information across health care agencies in different jurisdictions through multiple centralized databases (National Committee on Vital and Health Care Statistics, 2006). The authors argued that there would be a number of uses that could provide real-time data as well as reducing potential adverse health effects. For example, if a person is on vacation in a different state and has a medical emergency, the emergency room staff would be able to link into a national centralized database and, on the basis of the information found there, able to avoid certain medications that would cause life-threatening allergic reactions.

The use of computer technology in social work practice has existed for several years (Weaver et al. 2003), but there has been no collective discussion to define a field of child welfare informatics. There have been critical advances using research-based methods that are consistent with a part of what should be child welfare informatics. For example, the use of structured decision making has revolutionized risk assessments using research-based methodologies (Johnson, 2004). The application of structured decision making has been a valuable tool in child welfare practice, especially in terms of providing social work practitioners with a practical approach to assessing risk.

Furthermore, of all the fields of social work, child welfare has the richest history of researchers and practitioners developing systems to capture large amounts of data to better understand ways to deliver services to clients. There are national and state databases that offer child welfare information that can be examined over time. National data on foster care and adoptions are available through the Adoptions and Foster Care Reporting and Analysis System (U.S. Department of Health and Human Services, 2007), and more detailed state comparisons and other information from the same system can be used to create meaningful data analyses through the National Data Analysis System (Child Welfare League of America, 2007). Furthermore, data processing systems to analyze data have been developed for specific states, such as the Child Welfare Services/Case Management System, California's child welfare data system (Needell et al., 2007). Although the data are very useful, such data usually have to be rigorously processed by data analysts before presentation to policymakers and use by frontline staff. Unfortunately, there are few knowledgeable analysts who can process this information quickly, and even fewer who are able to translate this information to an understandable form for the line workers (Webster, Needell, & Wildfire, 2002).

At the local level, the use of information technologies has reduced reliance on manually reconciling caseloads and ameliorated the need for resource-draining approaches to collecting data. In the summer of 1999, 24 social work staff in the Los Angeles County Department of Children and Family Services were assigned to a special project in the Adoptions Division, the largest public adoptions agency in the nation. Using a form consisting of milestone dates relevant to an adoption case, the staff examined the physical file of every child to manually record the dates and provide management with an overview of where each case was. The process took two weeks, consuming 1,920 staff hours (24 staff for 40 hours per week for two weeks). It was a necessary but one-time effort for only that year. Currently, departmental staff have developed a system that uses Child Welfare Services/Case Management System data to develop a reporting system for management. It currently takes only eight staff hours to accomplish what took two weeks in 1999. Furthermore, the process can be updated semimonthly as opposed to yearly.

Unfortunately, the major drawback of all of these systems is that the information still has to be processed in such a way that there is a significant time lag between the information received and the feasibility of its use by a line worker. For example, we have information on the median length of stay of certain populations of children entering the system in the past several years (Needell et al., 2007). However, imagine trying to explain to the line worker that the median length of stay for a child in Los Angeles County entering foster care from January 1, 2001, to December 31, 2003, was 619 days. How does this immediately apply to the line worker, and how much time would it take to translate this into information that would readily be useful to the line worker?

This is not to say that these types of information are not important or even critical to modifying policy and identifying best practices in social services agencies. However, there are subsequent questions that have to be asked and answered. Are there socioeconomic differences in the data while controlling for other data? Can these differences be mitigated if we maximize resources and services to specific populations at certain points in the case? How can this information be processed in real-time terms so that the line worker can use a decision-making model to efficiently provide services to a child while reducing risk, increasing the child's quality of life while the child is in the system, and ultimately minimizing the child's stay in foster care?

Using an integrated social services information system validated through child welfare informatics, one can envision very practical approaches to areas such as child welfare. Imagine an emergency response social worker being able to use a laptop in the field to tap into a centralized database containing child welfare and other data from other agencies. This database would already contain vital demographic information available to authorized users, as well as other pertinent social and health information. Using research-based statistics, the program would have the ability to identify the most likely outcome for the child if the child ends up in foster care. Also, the system could identify what resources would be able to reduce the child's stay in the system even before a detention hearing. The emergency worker would add information, and through the use of informatics, possible case plans and service approaches would already have been analyzed and made available to the next worker.

As such, and refining a composite of the many definitions of informatics as noted by Staggers and Thomson (2002), one can perhaps define child welfare informatics as follows:
 a specialty that integrates child welfare practice, information
 technology, and computer sciences to process and synthesize data in
 an effort to develop, support, and enhance social work practice,
 policy, and administration at all levels of child welfare. Child
 welfare informatics facilitates the integration of such data,
 information, and knowledge to support clients, social work
 practitioners, and other providers in their decision making in all
 roles and settings.


Child welfare informatics will not replace clinical practice and the skills of a child welfare worker in providing best social work practices. However, child welfare informatics may develop more effective tools for those who are providing services to one of the most vulnerable populations.

Original manuscript received February 15, 2007

Accepted March 14, 2007

REFERENCES

Child Welfare League of America. (2007). The National Data Analysis System. Retrieved February 1,2007, from http://www.cwla.org/ndas.htm

Fitzmaurice, J. M., Adams, I., & Eisenberg, J. M. (2002). Three decades of research on computer applications in health care: Medical informatics support at the Agency for Health Care Research and Quality. Journal of the American Medical informatics Association, 9, 144-160.

Gilgun, J. F. (2005). The four cornerstones of evidence-based practice in social work. Research on Social Work Practice, 15, 52-61.

Johnson, W. (2004). Effectiveness of California's child welfare structured decision-making (SDM) model: A prospective study of the validity of the California Family Risk Assessment. Madison, WI: Children's Research Center. Retrieved February 3, 2007, from http://www.nccdcrc.org/crc/pubs/ca_sdm model feb04.pdf

Making healthy connections: Unleashing the power of information technology systems. (2006, November). UCLA Public Health, pp. 14-17. Retrieved June 4, 2007, from http://www.ph.ucla.edu/magazine/ sphmag_nov06_informatics.pdf (Available from UCLA School of Public Health, 16-035 CHS, Box 951772, Los Angeles, CA 90095-1772)

National Committee on Vital and Health Care Statistics. (2006). Report to the secretary of the U S. Department of Health and Human Services on functional requirements needed for the initial definition of a Nationwide Health Information Network (NHIN). Washington, DC: U.S. Department of Health and Human Services. Retrieved February 3, 2007, from http://www. ncvhs.hhs.gov/0610301t.pdf

Needell, B., Webster, D., Armijo, M., Lee, S., Cuccaro-Alamin, S., Shaw, T, Dawson, W, Piccus, W., Magruder, J., Exel, M., Smith, J., Dunn, A., Frerer, K., Putnam Hornstein, E., Ataie, Y., Atkinson, L., & Lee, S. H. (2007). Child welfare services (CWS/CMS) reports. Retrieved January 31,2007, from http://cssr. berkeley.edu/CWSCMSreports/

Rosenbaum, S., Burke, T, Benevelli, J., Borzi, P. C., & Repasch, L. (2005). Law and the public's health. Public Health Reports, 120, 350-352.

Staggers, N., & Thompson, C. B. (2002). The evolution of definitions for nursing informatics: A critical analysis and revised definition. Journal of the American Medical Informatics Association, 9, 255-261.

U.S. Department of Health and Human Services, Administration for Children and Families, Children's Bureau. (2007). Adoption and foster care statistics. Retrieved February 3, 2005, from http://www. acf.hhs.gov/programs/cb/stats_research/index. htm#afcars

Weaver, D., Moses, T, Furman, W, & Lindsey, D. (2003). The effects of computerization on public child welfare practice. Journal of Social Service Research, 29, 67-80.

Webster, D., Needell, B., & Wildfire, J. (2002). Data are your friends: Child welfare agency self-evaluation in Los Angeles County with the Family to Family Initiative. Children and Youth Services Review, 24, 471-484.

Loc H. Nguyen, DrPH, MSW, is supervising children's social worker, Adoption and Permanency Resources Division, Department of Children and Family Services, County of Los Angeles, 532 East Colorado Boulevard, Pasadena, CA 91101; e-mail: nguyelc@dcfs.lacounty.gov. He is also the lead analyst for the division in addressing statistical analyses of child welfare data and development of management control tools from such data.
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Author:Nguyen, Loc H.
Publication:Social Work
Geographic Code:1USA
Date:Oct 1, 2007
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