Subjecteducation of healthcare executives
health management education
health administration education
health management & policy
global health systems
Permanent link to this recordhttp://hdl.handle.net/20.500.12613/4596
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Has partFrontiers in Public Health
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Examining the relationship between choice, therapeutic alliance and outcomes in mental health servicesStanhope, V; Barrenger, SL; Salzer, MS; Marcus, SC (2013-01-01)Background: Self-determination within mental health services is increasingly recognized as an ethical imperative, but we still know little about the impact of choice on outcomes among people with severe mental illnesses. This study examines whether choice predicts outcomes and whether this relationship is mediated by therapeutic alliance. Method: The study sample of 396 participants completed a survey measuring choice, therapeutic alliance, recovery, quality of life and functioning. Multivariate analyses examined choice as a predictor of outcomes, and Sobel tests assessed alliance as a mediator. Results: Choice variables predicted recovery, quality of life and perceived outcomes. Sobel tests indicated that the relationship between choice and outcome variables was mediated by therapeutic alliance. Implications: The study demonstrates that providing more choice and opportunities for collaboration within services does improve consumer outcomes. The results also show that collaboration is dependent on the quality of the relationship between the provider and consumer. © 2013 by the authors; licensee MDPI, Basel, Switzerland.
Exploiting social influence to magnify population-level behaviour change in maternal and child health: Study protocol for a randomised controlled trial of network targeting algorithms in rural HondurasShakya, HB; Stafford, D; Hughes, DA; Keegan, T; Negron, R; Broome, J; McKnight, M; Nicoll, L; Nelson, J; Iriarte, E; Ordonez, M; Airoldi, E; Fowler, JH; Christakis, NA; Airoldi, Edoardo|0000-0002-3512-0542 (2017-03-01)© 2017 Published by the BMJ Publishing Group Limited. Introduction: Despite global progress on many measures of child health, rates of neonatal mortality remain high in the developing world. Evidence suggests that substantial improvements can be achieved with simple, low-cost interventions within family and community settings, particularly those designed to change knowledge and behaviour at the community level. Using social network analysis to identify structurally influential community members and then targeting them for intervention shows promise for the implementation of sustainable community-wide behaviour change. Methods and analysis: We will use a detailed understanding of social network structure and function to identify novel ways of targeting influential individuals to foster cascades of behavioural change at a population level. Our work will involve experimental and observational analyses. We will map face-to-face social networks of 30 000 people in 176 villages in Western Honduras, and then conduct a randomised controlled trial of a friendship-based network-targeting algorithm with a set of well-established care interventions. We will also test whether the proportion of the population targeted affects the degree to which the intervention spreads throughout the network. We will test scalable methods of network targeting that would not, in the future, require the actual mapping of social networks but would still offer the prospect of rapidly identifying influential targets for public health interventions. Ethics and dissemination: The Yale IRB and the Honduran Ministry of Health approved all data collection procedures (Protocol number 1506016012) and all participants will provide informed consent before enrolment. We will publish our findings in peer-reviewed journals as well as engage non-governmental organisations and other actors through venues for exchanging practical methods for behavioural health interventions, such as global health conferences. We will also develop a 'toolkit' for practitioners to use in network-based intervention efforts, including public release of our network mapping software.
Data Analytics and Modeling for Appointment No-show in Community Health CentersMohammadi, I; Wu, H; Turkcan, A; Toscos, T; Doebbeling, BN; Wu, Huanmei|0000-0003-0346-6044 (2018-11-01)© The Author(s) 2018. Objectives: Using predictive modeling techniques, we developed and compared appointment no-show prediction models to better understand appointment adherence in underserved populations. Methods and Materials: We collected electronic health record (EHR) data and appointment data including patient, provider and clinical visit characteristics over a 3-year period. All patient data came from an urban system of community health centers (CHCs) with 10 facilities. We sought to identify critical variables through logistic regression, artificial neural network, and naïve Bayes classifier models to predict missed appointments. We used 10-fold cross-validation to assess the models’ ability to identify patients missing their appointments. Results: Following data preprocessing and cleaning, the final dataset included 73811 unique appointments with 12,392 missed appointments. Predictors of missed appointments versus attended appointments included lead time (time between scheduling and the appointment), patient prior missed appointments, cell phone ownership, tobacco use and the number of days since last appointment. Models had a relatively high area under the curve for all 3 models (e.g., 0.86 for naïve Bayes classifier). Discussion: Patient appointment adherence varies across clinics within a healthcare system. Data analytics results demonstrate the value of existing clinical and operational data to address important operational and management issues. Conclusion: EHR data including patient and scheduling information predicted the missed appointments of underserved populations in urban CHCs. Our application of predictive modeling techniques helped prioritize the design and implementation of interventions that may improve efficiency in community health centers for more timely access to care. CHCs would benefit from investing in the technical resources needed to make these data readily available as a means to inform important operational and policy questions.