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Linking data to decision-making: applying qualitative data analysis methods and software to identify mechanisms for

Vaishali N Patel1, Anne W Riley

  • 1Clinical and Health Informatics Research Group, Faculty of Medicine, Department of Psychiatry, McGill University, Montreal, Canada. vpatel@aya.yale.edu

The Journal of Behavioral Health Services & Research
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Summary

Child out-of-home care staff rarely used the Outcomes Management System (OMS) but relied on other data sources for decision-making. Improving OMS utility requires staff engagement and system design aligned with decision-making processes.

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Area of Science:

  • Child welfare research
  • Healthcare management
  • Information systems in social services

Background:

  • Child out-of-home care programs generate significant data.
  • Effective decision-making is crucial for program quality and client outcomes.
  • Outcomes Management Systems (OMS) are designed to support data-driven decisions.

Purpose of the Study:

  • To investigate how staff in child out-of-home care programs utilize data from an OMS and other sources for decision-making.
  • To identify barriers and facilitators to the use of OMS data in practice.
  • To provide recommendations for enhancing the utility of OMS in child welfare settings.

Main Methods:

  • Multiple case study design involving two treatment foster care programs and two residential treatment centers.
  • Semi-structured interviews with 37 clinicians, managers, directors, and OMS developers.
  • Observations of clinical and quality management meetings.
  • Grounded theory methodology and qualitative data analysis software were employed.

Main Results:

  • Staff rarely utilized data directly from the Outcomes Management System (OMS).
  • Staff actively used other sources of systematically collected information for clinical, quality management, and program decisions.
  • Data use was influenced by the perceived relevance and accessibility of information.

Conclusions:

  • Improving the utility of OMS in child out-of-home care requires active staff participation in data-based decision-making.
  • OMS design and implementation should better reflect existing decision-making workflows and processes.
  • Systematic data collection from diverse sources is currently informing practice, highlighting potential for OMS integration.