US hospitals typically provide a set of code status options that includes Full Code and Do Not Resuscitate (DNR) but often includes additional options. Although US hospitals differ in the design of code status options, this variation and its impacts have not been empirically studied.Multi-institutional qualitative study at 7 US hospitals selected for variability in geographical location, type of institution and design of code status options. We triangulated across three data sources (policy documents, code status ordering menus and in-depth physician interviews) to characterise the code status options available at each hospital. Using inductive qualitative methods, we investigated design differences in hospital code status options and the perceived impacts of these differences.The code status options at each hospital varied widely with regard to the number of code status options, the names and definitions of code status options, and the formatting and capabilities of code status ordering menus. DNR orders were named and defined differently at each hospital studied. We identified five key design characteristics that impact the function of a code status order. Each hospital's code status options were unique with respect to these characteristics, indicating that code status plays differing roles in each hospital. Physician participants perceived that the design of code status options shapes communication and decision-making practices about resuscitation and life-sustaining treatments, especially at the end of life. We identified four potential mechanisms through which this may occur: framing conversations, prompting decisions, shaping inferences and creating categories.There are substantive differences in the design of hospital code status options that may contribute to known variability in end-of-life care and treatment intensity among US hospitals. Our framework can be used to design hospital code status options or evaluate their function.
The city homecare unit (CHU) of the Trivandrum Institute of Palliative Sciences was dissatisfied with the quality of care provided to their patient population.This study aims to improve the average satisfaction score of CHU during their daily homecare services.The improvement project for the CHU activities was conducted with a prospective plan-do-study-act design, with stepwise application of improvement tools.The A3 quality improvement (QI) methodology, which uses tools for (i) analysing contributors (process mapping, cause-effect diagram); (ii) to derive key drivers (Pareto chart) and (iii) for measuring impact of interventions and sustainability (annotated run chart) was applied. The project was conducted as a mentored activity of the PC-PAICE program. The team's weekly average satisfaction score was recorded prospectively as the outcome parameter, with 0 representing total dissatisfaction and 10 representing total satisfaction. Accuracy of triaging and appropriateness of registration process were the process parameters selected. These were recorded as run charts across the project period of 9 months.The cause-effect tool and the impact effort tool were used to analyse the mapped CHU processes. Even though we identified 22 contributors to the problem, eight of them were found to be significant. Key drivers were determined based on these eight and applied to the CHU processes. Over the project period, the satisfaction scores of the CHU improved significantly from 5.82 to 7.6 that is, satisfaction levels were high on most days. The triaging and registration goals were achieved. The team also built its own capacity for QI.The application of the A3 methodology simplified and streamlined efforts and achieved the quality goal for the CHU team.
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: End-of-life interventions should be predicated on consensus understanding of patient wishes. Written documents are not always understood; adding a video testimonial/message (VM) might improve clarity. Goals of this study were to (1) determine baseline rates of consensus in assigning code status and resuscitation decisions in critically ill scenarios and (2) determine whether adding a VM increases consensus.
Methods: We randomly assigned 2 web-based survey links to 1366 faculty and resident physicians at institutions with graduate medical education programs in emergency medicine, family practice, and internal medicine. Each survey asked for code status interpretation of stand-alone Physician Orders for Life-Sustaining Treatment (POLST) and living will (LW) documents in 9 scenarios. Respondents assigned code status and resuscitation decisions to each scenario. For 1 of 2 surveys, a VM was included to help clarify patient wishes.
Results: Response rate was 54%, and most were male emergency physicians who lacked formal advanced planning document interpretation training. Consensus was not achievable for stand-alone POLST or LW documents (68%–78% noted “DNR”). Two of 9 scenarios attained consensus for code status (97%–98% responses) and treatment decisions (96%–99%). Adding a VM significantly changed code status responses by 9% to 62% (P ≤ 0.026) in 7 of 9 scenarios with 4 achieving consensus. Resuscitation responses changed by 7% to 57% (P ≤ 0.005) with 4 of 9 achieving consensus with VMs.
Conclusions: 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.
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