BMC Psychiatry (Nov 2024)

The California collaborative network to promote data driven care and improve outcomes in early psychosis (EPI-CAL) project: rationale, background, design and methodology

  • Valerie L. Tryon,
  • Kathleen E. Nye,
  • Mark Savill,
  • Rachel Loewy,
  • Madison J. Miles,
  • Laura M. Tully,
  • Andrew J. Padovani,
  • Daniel J. Tancredi,
  • Joy Melnikow,
  • Sabrina Ereshefsky,
  • Nitasha Sharma,
  • Amanda P. McNamara,
  • Merissa Kado-Walton,
  • Christopher Komei Hakusui,
  • Chelyah Miller,
  • Khanh Linh H. Nguyen,
  • Maliha Safdar,
  • Viviana E. Padilla,
  • Leigh Smith,
  • Adam B. Wilcox,
  • Lindsay M. Banks,
  • Stephania L. Hayes,
  • Katherine M. Pierce,
  • Karina Muro,
  • Daniel I. Shapiro,
  • Khalima A. Bolden-Thompson,
  • Renata M. Botello,
  • Rebecca E. Grattan,
  • Yi Zhang,
  • Bonita Hotz,
  • Lisa Dixon,
  • Cameron S. Carter,
  • Tara A. Niendam

DOI
https://doi.org/10.1186/s12888-024-06245-6
Journal volume & issue
Vol. 24, no. 1
pp. 1 – 16

Abstract

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Abstract Background A prolonged first episode of psychosis (FEP) without adequate treatment is a predictor of poor clinical, functional, and health outcomes and significant economic burden. Team-based “coordinated specialty care” (CSC) for early psychosis (EP) has established effectiveness in promoting clinical and functional recovery. However, California’s CSC program implementation has been unsystematic and could benefit from standardizing its processes and data collection infrastructure. To address this, we established a consortium of EP clinics across the state via a Learning Health Care Network (LHCN) framework to develop the Early Psychosis Intervention Network of California (EPI-CAL). EPI-CAL’s LHCN developed a core battery of evidence-based measures for service users and family members and linked them together using a unique data collection and visualization application, Beehive. Methods and objectives EPI-CAL’s LHCN collects, visualizes, and aggregates data at the individual and clinic level for EP programs across California via Beehive. Beehive was designed to: (1) collect outcomes data from service users receiving care at EP programs and their support persons, (2) provide the data to providers on a secure web-based dashboard to support measurement-based care, and (3) allow data to be used for program or research analysis. We will (1) determine the feasibility of implementing an LHCN across a diverse, decentralized network of early psychosis programs, (2) determine if the implementation of an LHCN increases the delivery of measurement-based care, and (3) determine if the implementation of measurement-based care is associated with significant improvements in key service user outcomes. EPI-CAL’s network will contribute data to the Early Psychosis Intervention Network (EPINET) program. Discussion The current study aims to establish an LHCN of EP clinics in California that implements harmonized data collection using Beehive and assesses the feasibility of establishing such a network. Our goal is for this harmonized data collection approach to be used to inform decisions and develop learning opportunities for service users, staff, and administrators, and to improve outcomes for service users and their supporters in CSC care. Further, the data will enable programs and research teams to examine what elements of care lead to program success and improved treatment outcomes for service users. Clinical trials registration www.ClinicalTrials.gov , identifier NCT04007510; registered 07/05/2019.

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