RESUMEN
The active involvement of citizen scientists in setting research agendas, partnering with academic investigators to conduct research, analyzing and disseminating results, and implementing learnings from research can improve both processes and outcomes. Adopting a citizen science approach to the practice of precision medicine in clinical care and research will require healthcare providers, researchers, and institutions to address a number of technical, organizational, and citizen scientist collaboration issues. Some changes can be made with relative ease, while others will necessitate cultural shifts, redistribution of power, recommitment to shared goals, and improved communication. This perspective, based on a workshop held at the 2018 AMIA Annual Symposium, identifies current barriers and needed changes to facilitate broad adoption of a citizen science-based approach in healthcare.
RESUMEN
The health outcomes of high-need patients can be substantially influenced by the degree of patient engagement in their own care. The role of care managers (CMs) includes enrolling patients and keeping them sufficiently engaged in care programs, so that patients complete assigned goals leading to improvement in their health outcomes. Here, we present a data-driven behavioral engagement scoring (BES) pipeline that can compute the patients' engagement level with regards to their interest in: (1) enrolling into a relevant care program, and (2) completing program goals. This score is leveraged to predict a patient's propensity to respond to CMs' actions. Using real-world care management data, we show that the BES pipeline successfully predicts patient engagement and provides interpretable insights to CMs, using prototypical patient cases as a point of reference, without sacrificing prediction performance.
Asunto(s)
Aprendizaje , Participación del Paciente , HumanosRESUMEN
Recent studies documented the importance of individuality and heterogeneity in care planning. In practice, varying behavioral responses are revealed in patients' care management (CM) records. However, today's care programs are structured around population-level evidence. What if care managers can take advantage of the revealed behavioral response for personalization? The goal of this study is thus to quantify behavioral response from CM records for informing individual-level intervention decisions. We present a Behavioral Response Inference Framework (BRIeF) for understanding differential behavioral responses that are key to effective care planning. We analyze CM records from a healthcare network over a 14-month period and obtain a set of 2,416 intervention-goal attainment records. Promising results demonstrate that the individual-level care planning strategies that are learned from practice by BRIeF, outperform population-level strategies, yielding significantly more accurate intervention recommendations for goal attainment. To our knowledge, this is the first study of learning practice-based evidence from CM records for care planning, suggesting that increased patient behavioral understanding could potentially benefit augmented intelligence for care management decision support.
Asunto(s)
Aprendizaje Automático , Manejo de Atención al Paciente/métodos , Medicina de Precisión , Conducta , Conjuntos de Datos como Asunto , Toma de Decisiones , Humanos , Registros Médicos , Planificación de Atención al Paciente , Atención Dirigida al PacienteRESUMEN
Psychological stress is a major contributor to the adoption of unhealthy behaviors, which in turn accounts for 41% of global cardiovascular disease burden. While the proliferation of mobile health apps has offered promise to stress management, these apps do not provide micro-level feedback with regard to how to adjust one's behaviors to achieve a desired health outcome. In this paper, we formulate the task of multi-stage stress management as a sequential decision-making problem and explore the application of reinforcement learning to provide micro-level feedback for stress reduction. Specifically, we incorporate a multi-stage threshold selection into Q-learning to derive an interpretable form of a recommendation policy for behavioral coaching. We apply this method on an observational dataset that contains Fitbit ActiGraph measurements and self-reported stress levels. The estimated policy is then used to understand how exercise patterns may affect users' psychological stress levels and to perform coaching more effectively.
Asunto(s)
Algoritmos , Terapia Conductista , Monitores de Ejercicio , Estrés Psicológico/terapia , Actigrafía , Conjuntos de Datos como Asunto , Ejercicio Físico , Retroalimentación , Humanos , Aprendizaje , Estudios Longitudinales , Aplicaciones Móviles , AutoinformeRESUMEN
Patient engagement is important to help patients become more informed and active in managing their health. Effective patient engagement demands short, yet valid instruments for measuring self-efficacy in various care dimensions. However, the static instruments are often too lengthy to be effective for assessment purposes. Furthermore, these tests could neither account for the dynamicity of measurements over time, nor differentiate care dimensions that are more critical to certain sub-populations. To remedy these disadvantages, we devise a dynamic instrument composition approach that can model the measurement of patient self-efficacy over time and iteratively select critical care dimensions and appropriate assessment questions based on dynamic user categorization. The dynamically composed instruments are expected to guide patients through self-management reinforcement cycles within or across care dimensions, while tightly integrated into clinical workflow and standard care processes.