Prompts to Encourage Appointment Attendance for People With Serious Mental Illness

Rowett, Matthew; Reda, Sawsan; Makhoul, Samer

doi:10.1093/schbul/sbq050

Cited by 25 publications

(5 citation statements)

References 40 publications

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“…Less intensive strategies include delivering automated prompts and appointment reminders via telephone, text, or postal mail. In Reda and colleagues' (10,11) review of the use of appointment reminders among patients with serious mental illness, the authors concluded that using a simple prompt to attend a clinic visit is associated with increased attendance rates and offers a cost-effective and practical method of encouraging treatment engagement. Furthermore, the authors demonstrated that an orientation letter delivered within a few days prior to the scheduled appointment may be more effective than a telephone prompt.…”

Section: Methodsmentioning

confidence: 99%

Message Framing and Engagement in Specialty Mental Health Care

Mavandadi

Wright

Klaus

et al. 2018

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Section: Methodsmentioning

confidence: 99%

Message Framing and Engagement in Specialty Mental Health Care

Mavandadi

Wright

Klaus

et al. 2018

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“…As contributors to clinical research across various settings and populations, we are confident that these efforts are seen as critical to the success of such studies, yet we know of no standard reporting requirements in the primary trials literature. In the absence of empirical data, the value of these interventions remains largely unknown; notably, research on the impact of reminder calls and texts has yielded mixed findings (Rowett, Reda, & Makhoul, 2010; Sims et al, 2012), challenging assumptions of beneficial effects. There is clearly a need to better understand these factors, a feat that cannot be accomplished until such information is consistently reported alongside other study characteristics in a standardized fashion.…”

Section: Recommendationsmentioning

confidence: 99%

Rethinking Research on Prediction and Prevention of Psychotherapy Dropout: A Mechanism-Oriented Approach

et al. 2018

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Dropout is a ubiquitous psychotherapy outcome in clinical practice and treatment research alike, yet it remains a poorly understood problem. Contemporary dropout research is dominated by models of prediction that lack a strong theoretical foundation, often drawing on data from clinical trials that report on dropout in an inconsistent and incomplete fashion. In this article, we assert that dropout is a critical treatment outcome that is worthy of investigation as a mechanistic process. After briefly describing the scope of the dropout problem, we discuss the many factors that limit the field's present understanding of dropout. We then articulate and illustrate a transdiagnostic conceptual framework for examining psychotherapy dropout in contemporary research, concluding with recommendations for future research. With a more comprehensive understanding of the factors affecting retention, research efforts can shift toward investigating key processes underlying treatment dropout, thus, boosting prediction and informing strategies to mitigate dropout in clinical practice.

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“…While very few researchers consider the prediction of cancellation [10], numerous studies focus on no-show [18][19][20][21][22]. The probability of no-show in the literature fluctuates considerably for different diagnoses and demographics, stretching from almost zero up to 64 % [3,[23][24][25].…”

Section: Relevant Background and Problem Formulationmentioning

confidence: 99%

A Multi-way Multi-task Learning Approach for Multinomial Logistic Regression

Alaeddini

Hong

2017

Methods Inf Med

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Summary Objectives Whether they have been engineered for it or not, most healthcare systems experience a variety of unexpected events such as appointment miss-opportunities that can have significant impact on their revenue, cost and resource utilization. In this paper, a multi-way multi-task learning model based on multinomial logistic regression is proposed to jointly predict the occurrence of different types of miss-opportunities at multiple clinics. Methods An extension of L1/L2 regularization is proposed to enable transfer of information among various types of miss-opportunities as well as different clinics. A proximal algorithm is developed to transform the convex but non-smooth likelihood function of the multi-way multi-task learning model into a convex and smooth optimization problem solvable using gradient descent algorithm. Results A dataset of real attendance records of patients at four different clinics of a VA medical center is used to verify the performance of the proposed multi-task learning approach. Additionally, a simulation study, investigating more general data situations is provided to highlight the specific aspects of the proposed approach. Various individual and integrated multinomial logistic regression models with/without LASSO penalty along with a number of other common classification algorithms are fitted and compared against the proposed multi-way multi-task learning approach. Fivefold cross validation is used to estimate comparing models parameters and their predictive accuracy. The multi-way multi-task learning framework enables the proposed approach to achieve a considerable rate of parameter shrinkage and superior prediction accuracy across various types of miss-opportunities and clinics. Conclusions The proposed approach provides an integrated structure to effectively transfer knowledge among different miss-opportunities and clinics to reduce model size, increase estimation efficacy, and more importantly improve predictions results. The proposed framework can be effectively applied to medical centers with multiple clinics, especially those suffering from information scarcity on some type of disruptions and/or clinics.

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Prompts to Encourage Appointment Attendance for People With Serious Mental Illness

Cited by 25 publications

References 40 publications

Message Framing and Engagement in Specialty Mental Health Care

Message Framing and Engagement in Specialty Mental Health Care

Rethinking Research on Prediction and Prevention of Psychotherapy Dropout: A Mechanism-Oriented Approach

A Multi-way Multi-task Learning Approach for Multinomial Logistic Regression

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