Implementing Measurement-Based Care in a Youth Partial Hospital Setting: Leveraging Feedback for Sustainability.
Jill DonelanSusan DouglasAriane WillsonTyrena LesterStephanie DalyPublished in: Administration and policy in mental health (2024)
This paper describes the successful implementation of Measurement-Based Care (MBC) within a Partial Hospitalization Program (PHP) for children and adolescents. Measurement-based care (MBC), the practice of using patient-reported measures routinely to inform decision-making, is associated with improved clinical outcomes for behavioral health patients (Jong et al., Clinical Psychology Review 85, 2021; Fortney & Sladek, 2015). MBC holds great promise in partial hospital programs (PHP) to improve outcomes, yet implementation strategies are as complex as the setting itself. This paper provides a case study example of MBC implementation in a PHP for youth. Over the course of 18 months, an interdisciplinary staff of approximately 20 behavioral health professionals provided partial hospitalization level of care to 633 (39% in-person, 61% telehealth) youth from ages 5 to 18 years old. MBC in this setting incorporated daily patient self-report and weekly caregiver-reported measurements. This descriptive reconstruction, which includes examples of the data that were used during the implementation project, illustrates specific barriers and facilitators in a successful implementation in the local PHP setting. Implementation strategies to address workflow integration, leadership and supervision, and coaching are described, including evolution of these strategies over the course of implementation. Practical considerations for implementing MBC in youth PHP settings are discussed. Finally, the authors explore the potential relationships between the data-driven MBC model of decision making and the development of resilient organizations.
Keyphrases
- quality improvement
- healthcare
- primary care
- mental health
- physical activity
- decision making
- patient reported
- public health
- young adults
- palliative care
- end stage renal disease
- electronic health record
- type diabetes
- newly diagnosed
- chronic kidney disease
- health information
- machine learning
- prognostic factors
- patient reported outcomes
- artificial intelligence