Toward Determining the Structure of Psychiatric Visit Nonadherence

Alafaireet, Patricia E.; Houghton, Howard; Petroski, Gregory F.; Gong, Yang; Savage, Grant T.

doi:10.1097/jac.0b013e3181d91649

Cited by 9 publications

(10 citation statements)

References 24 publications

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“…When a person experiencing homelessness enters the ED or hospital, they are at high risk of losing contact with community-based health care providers and case managers [ 22 ]. This is exacerbated in the people experiencing homelessness living with depression as it is more difficult to manage their chronic conditions, including attending appointments and taking medications as prescribed [ 23 ]. The loss of contact between homeless individuals and their community-based care team creates a time of high risk for the individual and represents missed opportunities to provide services and potentially decrease acute health care use.…”

Section: Introductionmentioning

confidence: 99%

GPS Mobile Health Intervention Among People Experiencing Homelessness: Pre-Post Study

Moczygemba¹,

Thurman²,

Tormey³

et al. 2021

JMIR Mhealth Uhealth

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Background People experiencing homelessness are at risk for gaps in care after an emergency department (ED) or hospital visit, which leads to increased use, poor health outcomes, and high health care costs. Most people experiencing homelessness have a mobile phone of some type, which makes mobile health (mHealth) interventions a feasible way to connect a person experiencing homelessness with providers. Objective This study aims to investigate the accuracy, acceptability, and preliminary outcomes of a GPS-enabled mHealth (GPS-mHealth) intervention designed to alert community health paramedics when people experiencing homelessness are in the ED or hospital. Methods This study was a pre-post design with baseline and 4-month postenrollment assessments. People experiencing homelessness, taking at least 2 medications for chronic conditions, scoring at least 10 on the Patient Health Questionnaire-9, and having at least 2 ED or hospital visits in the previous 6 months were eligible. Participants were issued a study smartphone with a GPS app programmed to alert a community health paramedic when a participant entered an ED or hospital. For each alert, community health paramedics followed up via telephone to assess care coordination needs. Participants also received a daily email to assess medication adherence. GPS alerts were compared with ED and hospital data from the local health information exchange (HIE) to assess accuracy. Paired t tests compared scores on the Patient Health Questionnaire-9, Medical Outcomes Study Social Support Survey, and Adherence Starts with Knowledge-12 adherence survey at baseline and exit. Semistructured exit interviews examined the perceptions and benefits of the intervention. Results In total, 30 participants were enrolled; the mean age was 44.1 (SD 9.7) years. Most participants were male (20/30, 67%), White (17/30, 57%), and not working (19/30, 63%). Only 19% (3/16) of the ED or hospital visit alerts aligned with HIE data, mainly because of patients not having the smartphone with them during the visit, the smartphone being off, and gaps in GPS technology. There was a significant difference in depressive symptoms between baseline (mean 16.9, SD 5.8) and exit (mean 12.7, SD 8.2; t19=2.9; P=.009) and a significant difference in adherence barriers between baseline (mean 2.4, SD 1.4) and exit (mean 1.5, SD 1.5; t17=2.47; P=.03). Participants agreed that the app was easy to use (mean 4.4/5, SD 1.0, with 5=strongly agree), and the email helped them remember to take their medications (mean 4.6/5, SD 0.6). Qualitative data indicated that unlimited smartphone access allowed participants to meet social needs and maintain contact with case managers, health care providers, family, and friends. Conclusions mHealth interventions are acceptable to people experiencing homelessness. HIE data provided more accurate ED and hospital visit information; however, unlimited access to reliable communication provided benefits to participants beyond the study purpose of improving care coordination.

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Section: Introductionmentioning

confidence: 99%

GPS Mobile Health Intervention Among People Experiencing Homelessness: Pre-Post Study

Moczygemba¹,

Thurman²,

Tormey³

et al. 2021

JMIR Mhealth Uhealth

View full text Add to dashboard Cite

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“…, Alafaireet et al . .) Poor compliance also impacts stability of disease and length of homelessness and is a barrier to obtaining stable housing (McNiel & Binder ).…”

Section: Discussionmentioning

confidence: 99%

“…, Alafaireet et al . ). Not surprisingly, poor visit adherence leads to negative outcomes and increased mortality (McNiel & Binder , McCarthy et al .…”

Section: Introductionmentioning

confidence: 97%

See 1 more Smart Citation

Adherence to behavioral therapy and psychiatry visits in a safety-net setting in Virginia, USA

Moczygemba

Osborn²,

Lapane

2014

Health Soc Care Community

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Little is known about predictors of adherence to outpatient behavioural therapy and psychiatry visits in those who experience homelessness. Yet, consistent receipt of services in the community is critical to preventing use of acute care psychiatric services, which cause a significant cost burden to the mental health system. This retrospective study examined sociodemographic, housing instability and health-related factors associated with adherence to behavioural therapy and psychiatry appointments among 1711 clients served by an urban healthcare for the homeless centre in Virginia, USA. Clients ≥18 years old with a behavioural health condition who had an intake assessment and at least one behavioural therapy or psychiatry appointment scheduled during October 2005-September 2009 were eligible for the study. Of those with scheduled behavioural therapy visits, 27.7% were high adherers and 19.3% did not attend any appointments, whereas of those with scheduled psychiatry visits, 13.6% were high adherers and 22.1% did not attend any appointments. African Americans, when compared with whites, and those with a primary diagnosis of bipolar disorder were less likely to be high adherers to behavioural therapy. Women and being ≥35 years old were associated with a decreased likelihood of failing to attend psychiatry appointments, whereas African Americans, when compared with whites, and those with co-occurring disorders were more likely to not attend any psychiatry appointments. Understanding factors related to adherence to behavioural health services can help homeless care providers tailor strategies for improving visit adherence.

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“…Various reasons are reported for no-show ranging from conflict with other appointment to lack of transportation to forgetting the appointments [10,[26][27][28][29][30]. Several contributing factors are also associated with nonattendance behavior [9,17,[31][32][33][34]. Some studies discuss the relationship between miss-opportunities and some specific health outcomes including primary care, psychiatric, and diabetes [35][36][37][38][39].…”

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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Toward Determining the Structure of Psychiatric Visit Nonadherence

Cited by 9 publications

References 24 publications

GPS Mobile Health Intervention Among People Experiencing Homelessness: Pre-Post Study

GPS Mobile Health Intervention Among People Experiencing Homelessness: Pre-Post Study

Adherence to behavioral therapy and psychiatry visits in a safety-net setting in Virginia, USA

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

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