BackgroundAccess to palliative care is a key quality metric which most healthcare organizations strive to improve. The primary challenges to increasing palliative care access are a combination of physicians over-estimating patient prognoses, and a shortage of palliative staff in general. This, in combination with treatment inertia can result in a mismatch between patient wishes, and their actual care towards the end of life.MethodsIn this work, we address this problem, with Institutional Review Board approval, using machine learning and Electronic Health Record (EHR) data of patients. We train a Deep Neural Network model on the EHR data of patients from previous years, to predict mortality of patients within the next 3-12 month period. This prediction is used as a proxy decision for identifying patients who could benefit from palliative care.ResultsThe EHR data of all admitted patients are evaluated every night by this algorithm, and the palliative care team is automatically notified of the list of patients with a positive prediction. In addition, we present a novel technique for decision interpretation, using which we provide explanations for the model’s predictions.ConclusionThe automatic screening and notification saves the palliative care team the burden of time consuming chart reviews of all patients, and allows them to take a proactive approach in reaching out to such patients rather then relying on referrals from the treating physicians.

Abstract-Improving the quality of end-of-life care for hospitalized patients is a priority for healthcare organizations. Studies have shown that physicians tend to over-estimate prognoses, which in combination with treatment inertia results in a mismatch between patients wishes and actual care at the end of life . We describe a method to address this problem using Deep Learning and Electronic Health Record (EHR) data, which is currently being piloted, with Institutional Review Board approval, at an academic medical center. The EHR data of admitted patients are automatically evaluated by an algorithm, which brings patients who are likely to benefit from palliative care services to the attention of the Palliative Care team. The algorithm is a Deep Neural Network trained on the EHR data from previous years, to predict all-cause 3-12 month mortality of patients as a proxy for patients that could benefit from palliative care. Our predictions enable the Palliative Care team to take a proactive approach in reaching out to such patients, rather than relying on referrals from treating physicians, or conduct time consuming chart reviews of all patients. We also present a novel interpretation technique which we use to provide explanations of the model's predictions.

Objective To analyze the impact of factors in healthcare delivery on the net benefit of triggering an Advanced Care Planning (ACP) workflow based on predictions of 12-month mortality. Materials and Methods We built a predictive model of 12-month mortality using electronic health record data and evaluated the impact of healthcare delivery factors on the net benefit of triggering an ACP workflow based on the models’ predictions. Factors included nonclinical reasons that make ACP inappropriate: limited capacity for ACP, inability to follow up due to patient discharge, and availability of an outpatient workflow to follow up on missed cases. We also quantified the relative benefits of increasing capacity for inpatient ACP versus outpatient ACP. Results Work capacity constraints and discharge timing can significantly reduce the net benefit of triggering the ACP workflow based on a model’s predictions. However, the reduction can be mitigated by creating an outpatient ACP workflow. Given limited resources to either add capacity for inpatient ACP versus developing outpatient ACP capability, the latter is likely to provide more benefit to patient care. Discussion The benefit of using a predictive model for identifying patients for interventions is highly dependent on the capacity to execute the workflow triggered by the model. We provide a framework for quantifying the impact of healthcare delivery factors and work capacity constraints on achieved benefit. Conclusion An analysis of the sensitivity of the net benefit realized by a predictive model triggered clinical workflow to various healthcare delivery factors is necessary for making predictive models useful in practice.

Recent innovations in palliative care education for medical students represent varied settings, learner levels, instructors, educational modalities, and palliative care topics. Future curricula should continue to incorporate interdisciplinary faculty. Studies could be improved by integrating longitudinal curricula and longer-term outcomes; collaborating across institutions; using validated measures; and assessing higher-level outcomes including skills, behaviors, and impact on patient care.

Our study suggests that late referrals may have a marked negative impact on health outcomes, which argues for the design and implementation of hospital policies that encourage early referral to palliative care for advanced cancer patients.

For most scenarios, consensus was not attained for code status and resuscitation decisions with stand-alone LW and POLST documents. Adding VMs produced significant impacts toward achieving interpretive consensus.

Patient engagement through shared decision-making (SDM) is increasingly seen as a key component for patient safety, patient satisfaction, and quality of care. Current SDM models do not adequately account for medical and environmental contexts, which may influence medical decisions in the hospital. We identified leading SDM models and reviews to inductively construct a novel SDM model appropriate for the inpatient setting. A team of medicine and pediatric hospitalists reviewed the literature to integrate core SDM concepts and processes and iteratively constructed a synthesized draft model. We then solicited broad SDM expert feedback on the draft model for validation and further refinement. The SDM 3 Circle Model identifies 3 core categories of variables that dynamically interact within an “environmental frame.” The resulting Venn diagram includes overlapping circles for 1)patient/family, 2)provider/team, and 3)medical context. The environmental frame includes all external, contextual factors that may influence any of the 3 circles. Existing multistep SDM process models were then re-articulated and contextualized to illustrate how a shared decision might be made. The SDM 3 Circle Model accounts for important environmental and contextual characteristics that vary across settings. The visual emphasis generated by each “circle” and by the environmental frame direct attention to often overlooked interactive forces and has the potential to more precisely define, promote, and improve SDM. This model provides a framework to develop interventions to improve quality and patient safety through SDM and patient engagement for hospitalists.

Important opportunities exist to improve inpatient SDM. Team size, number of learners, patient census, and type of decision being made did not affect SDM, suggesting that even large, busy services can perform SDM if properly trained.