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Waiting time variation in Early Intervention Psychosis services: longitudinal evidence from the SEPEA naturalistic cohort study.

ABSTRACT: Early Intervention Psychosis [EIP] services have gained traction internationally, but are currently undergoing various forms of reconfiguration. In England, such services are now mandated to ensure 50% of accepted referrals commence care within 14 days, but no empirical evidence exists. We sought to estimate waiting times to EIP services in a large, representative epidemiological cohort in England, and investigate possible reasons for any variation.We estimated median waiting time from referral to acceptance by EIP services and investigated whether this varied by clinical, demographic or neighbourhood-level factors, amongst 798 participants, 16-35 years old, presenting to six EIP services over 3.5 years in a defined catchment area serving 2.5 million people. We used parametric survival analysis to inspect variation in waiting times (in days).Median waiting time was 15 days (interquartile range 7-30), although this varied across services (p?

SUBMITTER: Kirkbride JB

PROVIDER: S-EPMC5423995 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature

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Waiting time variation in Early Intervention Psychosis services: longitudinal evidence from the SEPEA naturalistic cohort study.

Kirkbride James B JB Hameed Y Y Wright L L Russell K K Knight C C Perez J J Jones P B PB

Social psychiatry and psychiatric epidemiology 20170218 5

<h4>Purpose</h4>Early Intervention Psychosis [EIP] services have gained traction internationally, but are currently undergoing various forms of reconfiguration. In England, such services are now mandated to ensure 50% of accepted referrals commence care within 14 days, but no empirical evidence exists. We sought to estimate waiting times to EIP services in a large, representative epidemiological cohort in England, and investigate possible reasons for any variation.<h4>Methods</h4>We estimated me ...[more]

PMID: 28213813

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Project description:BackgroundTimely and comprehensive treatment in the form of early psychosis intervention (EPI) has become the standard of care for youth with psychosis. While EPI services were designed to be delivered in person, the COVID-19 pandemic required many EPI programs to rapidly transition to virtual delivery, with little evidence to guide intervention adaptations or to support the effectiveness and satisfaction with virtual EPI services.ObjectiveThis study aims to explore the adaptations required to deliver NAVIGATE, a model of coordinated specialty care used in EPI, in a virtual format. This study will evaluate implementation of the NAVIGATE model delivered virtually by describing the nature of the adaptations to the intervention, assessing fidelity to the EPI model and the satisfaction of clients, family members, and care providers. We will investigate barriers and facilitators to virtual NAVIGATE implementation, service engagement, and health equity impacts of this work.MethodsThe Centre for Addiction and Mental Health (Toronto, Ontario, Canada) transitioned to delivering NAVIGATE virtually early in the COVID-19 pandemic. The Framework for Reporting Adaptations and Modifications for Evidence-Based Interventions will be used to describe the adaptations required to deliver NAVIGATE virtually. Fidelity to the EPI model will be measured using the First Episode Psychosis Services Fidelity Scale and fidelity to NAVIGATE will be assessed by investigating adherence to its core components. Implementation facilitators and barriers will be explored using semistructured interviews with providers informed by the Consolidated Framework for Implementation Research. Satisfaction with virtually delivered NAVIGATE will be assessed with virtual client and provider experience surveys and qualitative interviews with clients, family members, and providers. Service engagement data will be collected through review of medical records, and potential impacts of virtually delivered NAVIGATE on different population groups will be assessed with the Health Equity Impact Assessment.ResultsVirtual clinical delivery of NAVIGATE started in March 2020 with additional adaptations and data collection is ongoing. Data will be analyzed using descriptive statistics and survival analysis for quantitative data. Qualitative data will be analyzed using thematic content analysis. Integration of qualitative and quantitative data will occur at the data collection, interpretation, and reporting levels following a convergent design.ConclusionsThis study will provide information regarding the type of intervention adaptations required for virtual delivery of NAVIGATE for youth with early psychosis, ensuring access to high-quality care for this population during the pandemic and beyond by guiding future implementation in similar contexts.International registered report identifier (irrid)DERR1-10.2196/34591.

Project description:Value added services (VAS) are an innovative dispensing system created to provide an alternative means of collecting partial drug supply from our hospital. This in turn was projected to reduce the necessity for patient to visit pharmacy counter and thus reduce the burden of prescription handling.To evaluate the impact of increased VAS uptake following promotional campaign towards patient waiting time and to explore factors that may affect patient waiting time at the Ambulatory Pharmacy, Queen Elizabeth Hospital.A quasi experimental study design was conducted from September 2014 till June 2015 at the Ambulatory Pharmacy. During pre-intervention phase, baseline parameters were collected retrospectively. Then, VAS promotional campaign was carried out for six months and whilst this was done, the primary outcome of patient waiting time was measured by percentage of prescription served less than 30 minutes. A linear regression analysis was used to determine the impact of increased VAS uptake towards patient waiting time.An increased in percentage of VAS registration (20.9% vs 35.7%, p<0.001) was observed after the promotional campaign. The mean percentage of prescription served less than 30 minutes increased from 83.2% SD=15.9 to 90.3% SD=11.5, p=0.001. After controlling for covariates, it was found that patient waiting time was affected by number of pharmacy technicians (b=-0.0349, 95%CI-0.0548 : -0.0150, p=0.001), number of pharmacy counters (b=0.1125, 95%CI 0.0631 : 0.1620, p<0.001), number of prescriptions (b=0.0008, 95%CI 0.0004 : 0.0011, p<0.001), and number of refill prescriptions (b=0.0004, 95%CI 0.0002 : 0.0007, p<0.001). The increased in percentage of VAS registration was associated with reduction in number of refill prescription (b=-2.9838, 95%CI -4.2289 : -1.7388, p<0.001).Patient waiting time at the Ambulatory Pharmacy improved with the increased in VAS registration. The impact of increased VAS uptake on patient waiting time resulted from reduction in refill prescriptions. Patient waiting time is influenced by number of pharmacy technicians, number of pharmacy counters, number of prescriptions and number of refill prescriptions.

Dataset Information

Waiting time variation in Early Intervention Psychosis services: longitudinal evidence from the SEPEA naturalistic cohort study.

Publications

Waiting time variation in Early Intervention Psychosis services: longitudinal evidence from the SEPEA naturalistic cohort study.

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