ArticleJournal of visualized experiments : JoVE2020
Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack.
Article in Journal of visualized experiments : JoVE, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it, 33 citations in OpenAlex.
- Implementing Precision Psychiatry: A Systematic Review of Individualized Prediction Models for Clinical Practice.Schizophrenia bulletin · 2021Pooled it
- Establishing a clinical service to prevent psychosis: What, how and when? Systematic review.Translational psychiatry · 2021Pooled it
- A Conversational Agent for Providing Personalized Preexposure Prophylaxis (PrEP) Support: Protocol for Chatbot Implementation and Evaluation.JMIR research protocols · 2026Article
- Predicting long-term poor outcomes in individuals at clinical high risk for psychosis using real-world clinical data: the OASIS1000 prospective study.World psychiatry : official journal of the World Psychiatric Association (WPA) · 2026Article
- Transforming Opioid Poisoning Surveillance Through Novel Technologies: Rationale and Methodological Protocol for Applying Natural Language Processing to Emergency Department Data.Drug and alcohol review · 2026Article
- New horizons in prediction modelling using machine learning in older people's healthcare research.Age and ageing · 2024Article
- Clinical Information Retrieval: A Literature Review.Journal of healthcare informatics research · 2024Article
- Machine learning outcome prediction using stress perfusion cardiac magnetic resonance reports and natural language processing of electronic health records.Informatics in medicine unlocked · 2024Article
- Identifying features of risk periods for suicide attempts using document frequency and language use in electronic health records.Frontiers in psychiatry · 2023Article
- A survey on clinical natural language processing in the United Kingdom from 2007 to 2022.NPJ digital medicine · 2022Review
- Patient and public involvement to build trust in artificial intelligence: A framework, tools, and case studies.Patterns (New York, N.Y.) · 2022Review
- Building a Precision Medicine Delivery Platform for Clinics: The University of California, San Francisco, BRIDGE Experience.Journal of medical Internet research · 2022Article
- A class-contrastive human-interpretable machine learning approach to predict mortality in severe mental illness.NPJ schizophrenia · 2021Article
- Developing and Validating an Individualized Clinical Prediction Model to Forecast Psychotic Recurrence in Acute and Transient Psychotic Disorders: Electronic Health Record Cohort Study.Schizophrenia bulletin · 2021Article
- Using Natural Language Processing on Electronic Health Records to Enhance Detection and Prediction of Psychosis Risk.Schizophrenia bulletin · 2021Article
- Antipsychotic use in dementia: the relationship between neuropsychiatric symptom profiles and adverse outcomes.European journal of epidemiology · 2021Article
- Real-world implementation of precision psychiatry: Transdiagnostic risk calculator for the automatic detection of individuals at-risk of psychosis.Schizophrenia research · 2021Article
Corrections and comments
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Authors and funding
11 authors at 3 institutions in 2 countries.
Funding
Abstract
Recent studies have shown that an automated, lifespan-inclusive, transdiagnostic, and clinically based, individualized risk calculator provides a powerful system for supporting the early detection of individuals at-risk of psychosis at a large scale, by leveraging electronic health records (EHRs). This risk calculator has been externally validated twice and is undergoing feasibility testing for clinical implementation. Integration of this risk calculator in clinical routine should be facilitated by prospective feasibility studies, which are required to address pragmatic challenges, such as missing data, and the usability of this risk calculator in a real-world and routine clinical setting. Here, we present an approach for a prospective implementation of a real-time psychosis risk detection and alerting service in a real-world EHR system. This method leverages the CogStack platform, which is an open-source, lightweight, and distributed information retrieval and text extraction system. The CogStack platform incorporates a set of services that allow for full-text search of clinical data, lifespan-inclusive, real-time calculation of psychosis risk, early risk-alerting to clinicians, and the visual monitoring of patients over time. Our method includes: 1) ingestion and synchronization of data from multiple sources into the CogStack platform, 2) implementation of a risk calculator, whose algorithm was previously developed and validated, for timely computation of a patient's risk of psychosis, 3) creation of interactive visualizations and dashboards to monitor patients' health status over time, and 4) building automated alerting systems to ensure that clinicians are notified of patients at-risk, so that appropriate actions can be pursued. This is the first ever study that has developed and implemented a similar detection and alerting system in clinical routine for early detection of psychosis.
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Registered trials
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.