Conducting Goals-of-Care Discussions Takes Less Time Than Imagined

Pintova, Sofya; Leibrandt, Ryan; Smith, Cardinale B.; Adelson, Kerin B.; Gonsky, Jason; Egorova, Natalia; Franco, Rebeca; Bickell, Nina A.

doi:10.1200/jop.19.00743

Cited by 4 publications

(3 citation statements)

References 16 publications

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“…Fifth, we did not survey clinicians' attitudes about our GOC documentation initiative, including perceived benefits or burdens. However, it meets proposed best practices 26 and clinicians' desire for systematic integration into clinical care, 45 and data suggest that high‐quality GOC discussions need not be lengthy 46 and do not affect oncologist productivity 47 . The templated note we implemented was designed by an interdisciplinary group of clinicians with the aim of being brief, user‐friendly, and accommodating of clinical team workflows.…”

Section: Discussionmentioning

confidence: 99%

Goals of care documentation by medical oncologists and oncology patient end‐of‐life care outcomes

Epstein

Riley

Nelson

et al. 2022

Cancer

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Background Goals of care (GOC) documentation is important but underused. We aimed to improve oncologist GOC documentation and end‐of‐life (EOL) care. Methods In April 2020, our cancer center launched a GOC note template, including optional fields for documenting discussion with the patient about: cancer natural history, goals, and/or EOL (resuscitation preferences, hospice receptivity). Associations between GOC notes and EOL care were evaluated. Results Among 1721 patients dying between June 1, 2020 and June 30, 2021, median days from first GOC note (± with documentation of EOL discussion) to death was 92, whereas a GOC note including EOL discussion (“GOC EOL note”), specifically, was 31. Patients with a first GOC note >60 days before death spent fewer days inpatient (6.7 vs 10.6 days, p < .001). Among patients with GOC EOL notes, those with such documentation >30 days before death had fewer inpatient (5 vs 11, p < .001) and intensive care unit days (0.5 vs 1.5, p < .001), more hospice referrals (57% vs 44%, p = .003), and less chemotherapy ≤14 days before death (6% vs 11%, p = .010). Of 925 admissions of patients dying within ≤30 days, those with GOC EOL notes were shorter (7 vs 9 days, p = .013) but not associated with more hospice discharge (30% vs 25%, p = .163). Oncologist (vs nononcologist) GOC documentation and earlier documentation of EOL discussion were associated in subset analyses with less inpatient care and more hospice referrals. Conclusions Documentation of GOC, including EOL discussions, is associated with favorable performance on accepted indicators of quality EOL care.

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Section: Discussionmentioning

confidence: 99%

Goals of care documentation by medical oncologists and oncology patient end‐of‐life care outcomes

Epstein

Riley

Nelson

et al. 2022

Cancer

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“…It is readily reproducible if practice patterns change over time and we believe it is insightful about clinical decision making, noting that physicians may be influenced by an inflated perception of GOC-related cost. 46 This technique could facilitate the clinical utility assessment of other models for improving good practices in time-constrained environments, in which utilities cannot be inferred from the desired risk threshold. We used CSOs because they were the only electronic indicator of GOC documentation at our institution, but the same technique could be applied for other standardized indicators of the EOL communication process, like Physician Orders for Life-Sustaining Treatment.…”

Section: Discussionmentioning

confidence: 99%

Expected clinical utility of automatable prediction models for improving palliative and end-of-life care outcomes: Toward routine decision analysis before implementation

Taseen

Éthier

2021

Journal of the American Medical Informatics Association

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Objective The study sought to evaluate the expected clinical utility of automatable prediction models for increasing goals-of-care discussions (GOCDs) among hospitalized patients at the end of life (EOL). Materials and Methods We built a decision model from the perspective of clinicians who aim to increase GOCDs at the EOL using an automated alert system. The alternative strategies were 4 prediction models—3 random forest models and the Modified Hospital One-year Mortality Risk model—to generate alerts for patients at a high risk of 1-year mortality. They were trained on admissions from 2011 to 2016 (70 788 patients) and tested with admissions from 2017-2018 (16 490 patients). GOCDs occurring in usual care were measured with code status orders. We calculated the expected risk difference (beneficial outcomes with alerts minus beneficial outcomes without alerts among those at the EOL), the number needed to benefit (number of alerts needed to increase benefit over usual care by 1 outcome), and the net benefit (benefit minus cost) of each strategy. Results Models had a C-statistic between 0.79 and 0.86. A code status order occurred during 2599 of 3773 (69%) hospitalizations at the EOL. At a risk threshold corresponding to an alert prevalence of 10%, the expected risk difference ranged from 5.4% to 10.7% and the number needed to benefit ranged from 5.4 to 10.9 alerts. Using revealed preferences, only 2 models improved net benefit over usual care. A random forest model with diagnostic predictors had the highest expected value, including in sensitivity analyses. Discussion Prediction models with acceptable predictive validity differed meaningfully in their ability to improve over usual decision making. Conclusions An evaluation of clinical utility, such as by using decision curve analysis, is recommended after validating a prediction model because metrics of model predictiveness, such as the C-statistic, are not informative of clinical value.

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“…It is readily reproducible if practice patterns change over time and we believe insightful about clinical decision-making, noting that physicians may be influenced by an inflated perception of GOC-related cost. [45] This technique could facilitate the clinical utility assessment of other models for improving good practices in time-constrained environments, where utilities cannot be inferred from the model threshold. We used CSOs because they were the only electronic indicator of GOC documentation at our institution, but the same technique could be applied for other standardized indicators of the EOL communication process, like Physician Orders for Life-Sustaining Treatment.…”

Section: Discussionmentioning

confidence: 99%

Clinical Utility of Automatable Prediction Models for Improving Palliative and End-Of-Life Care Outcomes: Towards Routine Decision Analysis Before Implementation

Taseen

Éthier

2021

Preprint

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Objective To evaluate the clinical utility of automatable prediction models for increasing goals-of-care discussions among hospitalized patients at the end-of-life. Materials and Methods We developed three Random Forest (RF) models and updated the Modified Hospital One-year Mortality Risk model: alternative models to predict one-year mortality (proxy for EOL status) using admission-time data. Admissions from July 2011-2016 were used for training and those from July 2017-2018 were used for temporal validation. We simulated alerts for admissions in the validation cohort and modelled alternative scenarios where alerts lead to code status orders (CSOs) in the EHR. We linked actual CSOs and calculated the expected risk difference (eRD), the number needed to benefit (NNB) and the net benefit (NB) of each model for the patient-centered outcome of a CSO among EOL hospitalizations. Results Models had a C-statistic of 0.79-0.86 among unique patients. A CSO was documented during 2599 of 3773 hospitalizations at the EOL (68.9%). At a threshold that identified 10% of eligible admissions, the eRD ranged from 5.4% to 10.7% (NNB 5.4-10.9 alerts). Under usual care, a CSO had a 34% PPV for EOL status. Using this to inform the relative cost of FPs, only two models improved NB over usual care. A RF model with diagnostic predictors had the highest clinical utility by either measure, including in sensitivity analyses. Discussion Automatable prediction models with acceptable temporal validity differed meaningfully in their expected ability to improve patient-centered outcomes over usual care. Conclusion Decision-analysis should precede implementation of automated prediction models for improving palliative and EOL care outcomes.

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Conducting Goals-of-Care Discussions Takes Less Time Than Imagined

Cited by 4 publications

References 16 publications

Goals of care documentation by medical oncologists and oncology patient end‐of‐life care outcomes

Goals of care documentation by medical oncologists and oncology patient end‐of‐life care outcomes

Expected clinical utility of automatable prediction models for improving palliative and end-of-life care outcomes: Toward routine decision analysis before implementation

Clinical Utility of Automatable Prediction Models for Improving Palliative and End-Of-Life Care Outcomes: Towards Routine Decision Analysis Before Implementation

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