Evidence map›Paper›PMID 39892249›Full record

ReviewSchizophrenia research2025

A mixed-methods study exploring the benefits, drawbacks, and utilization of data in care: Findings from the EPI-CAL early psychosis learning health care network.

Sabrina Ereshefsky, Regina Gemignani, Mark Savill, Katherine C Sanford, Lindsay M Banks, Valerie L Tryon, Kathleen E Nye, Katherine M Pierce, Madison J Miles, Chelyah Miller and 13 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Schizophrenia research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

23 authors.

Sabrina EreshefskyUniversity of California Davis Medical Center, Sacramento, CA, USA.
Regina GemignaniUniversity of California Davis Medical Center, Sacramento, CA, USA. Electronic address: rgemignani@ucdavis.edu.
Mark SavillUniversity of California Davis Medical Center, Sacramento, CA, USA.
Katherine C SanfordUniversity of California Davis Medical Center, Sacramento, CA, USA.
Lindsay M BanksUniversity of California Davis Medical Center, Sacramento, CA, USA.
Valerie L TryonUniversity of California Davis Medical Center, Sacramento, CA, USA.
Kathleen E NyeUniversity of California Davis Medical Center, Sacramento, CA, USA.
Katherine M PierceUniversity of California Davis Medical Center, Sacramento, CA, USA.
Madison J MilesUniversity of California Davis Medical Center, Sacramento, CA, USA.
Chelyah MillerUniversity of California Davis Medical Center, Sacramento, CA, USA.
Khanh Linh H NguyenUniversity of California Davis Medical Center, Sacramento, CA, USA.
Nitasha SharmaUniversity of California Davis Medical Center, Sacramento, CA, USA.
Merissa Kado-WaltonUniversity of California, San Diego, CA, USA.
Christopher K HakusuiUniversity of California Davis Medical Center, Sacramento, CA, USA.
Leigh SmithUniversity of California Davis Medical Center, Sacramento, CA, USA.
Viviana E PadillaUniversity of California Davis Medical Center, Sacramento, CA, USA.
Amanda P McNamaraUniversity of California, San Diego, CA, USA.
Maliha SafdarUniversity of California, San Diego, CA, USA.
Andrew J PadovaniUniversity of California Davis Medical Center, Sacramento, CA, USA.
Rachel L LoewyUniversity of California, San Francisco, CA, USA.
Adam B WilcoxWashington University in St. Louis, St. Louis, MO, USA.
Laura M TullyUniversity of California Davis Medical Center, Sacramento, CA, USA.
Tara A NiendamUniversity of California Davis Medical Center, Sacramento, CA, USA.

Funding

California Collaborative Network to Promote Data Driven Care and Improve Outcomes in Early Psychosis (EPI-CAL)R01MH120555 · NIMH · UNIVERSITY OF CALIFORNIA AT DAVIS · PI NIENDAM, TARA ANN · 2019 to 2024
$7.9M
NIMH NIH HHS R01 MH120555
6 · The paper itself

Abstract

Using data collected in routine care delivery to inform treatment is a key feature of a learning health system (LHS). In this study, we explored the experiences of service users and providers adopting measurement-based care (MBC) in early psychosis (EP) specialty care settings. Qualitative interviews were conducted with 32 providers and 12 service users across 18 programs in the Early Psychosis Intervention Network of California (EPI-CAL). These findings were compared with quantitative data from Beehive, EPI-CAL's data collection and review application. Regarding the clinical benefits of MBC in EP, three broad themes were identified - supporting safety monitoring and response, the assessment process, and delivery of psychotherapy. Outside of direct clinical care, Beehive was considered to support clinical supervision and external reporting, while service users reported data collection facilitated self-reflection. In the quantitative Beehive application data collected from 23 EP programs, high utilization of the safety alert system was evident (349 alerts in total, of which 338 [96.85 %] were resolved at a median of 2.03 days). However, service users' key survey data was only reviewed by assigned providers in 32.22 % (142 of 441) of cases. While providers and service users saw many benefits to Beehive, utilization was highly inconsistent outside of the alert system. Going forward, further consideration of how best to support EP providers to consistently use data in care is necessary to maximize the utility of the LHS approach and positively impact outcomes.

Indexed as

Mental Health ServicesPsychotic DisordersAdultCaliforniaFemaleHealth PersonnelHumansMaleQualitative ResearchYoung AdultCoordinated specialty careEarly psychosiseHealthEPINETLearning health systemsMeasurement-based careQualitative data

Identifiers

PMID39892249
PMCPMC13348623

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.