Lindvall, Charlotta

Importance Many older adults with advanced cancer never communicate goals of care or treatment preferences to their clinicians, raising the risk that care received will not match their values. Scalable models of care may help surmount this barrier. Objective To test whether a combined patient and clinician intervention increased the rate of advance care planning (ACP) documentation in large health care systems. Design, Setting, and Participants This stepped-wedge cluster randomized clinical trial using an open cohort design included patients aged 65 years or older with advanced cancer seen at oncology clinics in 3 health care systems located in the US South, Midwest, and Mid-Atlantic regions from April 1, 2020, to November 30, 2022. Data collection ended in 2024. Intervention The intervention involved delivering brief evidence-based patient-facing video decision aids available in 25 languages as well as goals-of-care communication training to oncology clinicians. Patients in the control period received usual care. Main Outcomes and Measures The primary outcome was ACP documentation, which included any electronic health record documentation of a goals-of-care conversation, palliative care, hospice, or limitation of life-sustaining treatments, identified via a validated natural language processing program. Analysis was performed on an intention-to-treat basis. Results Twenty-nine practices, comprising 13 800 unique eligible patients with a total of 29 357 repeated measurements, were included (mean [SD] age, 74.5 [6.6] years; 52.3% men [15 344 of 29 357 measurements]). The proportion of patients with ACP documentation was greater in the intervention phase compared with the usual care phase (adjusted rate difference, 6.8% [95% CI, 2.8%-10.8%]; P < .001). ACP documentation in the intervention phase occurred among 3980 of 15 754 patients (25.3%) (goals-of-care conversation, 21.4% [3377 of 15 754]; palliative care, 9.6% [1517 of 15 754]; hospice, 5.4% [847 of 15 754]; and limitation of life-sustaining treatments, 7.2% [1128 of 15 754]). In comparison, ACP documentation in the usual care phase occurred among 2834 of 13 603 patients (20.8%) (goals-of-care conversation, 16.8% [2281 of 13 603]; palliative care, 9.5% [1287 of 13 603]; hospice, 5.3% [724 of 13 603]; and limitation of life-sustaining treatments, 8.4% [1149 of 13 603]). Conclusions and Relevance In this stepped-wedge cluster randomized clinical trial for older adults with advanced cancer, a bundled evidence-based decision aid and communication training intervention increased the proportion of older patients with ACP documentation. This approach offers an innovative paradigm with a clinically meaningful increase in ACP documentation, a widely used quality metric that reflects high-quality patient-centered care delivery. Trial Registration ClinicalTrials.gov Identifier: NCT03609177

Background: Electronic health records (EHRs) may help enable reliable, rapid data management for many uses, such as facilitating communication of advance care planning (ACP). However, issues with validity and accuracy of EHRs hinder the use of ACP information for practical applications. Design: We present a cross-sectional pilot study of 433 older adults with cancer from three large health care systems, participating in an ongoing multisite pragmatic trial (4UH3AG060626-02). We compared data extracted from dedicated structured EHR fields for ACP to a chart review of corresponding ACP documentation contained in the medical chart. Results: Structured ACP data existed for 43.2% of patients and varied by site (25.7% −48.9%). Of the identified structured ACP data elements, 59.2% of recorded elements were correct, 23.7% were incorrect, and 17.1% were duplicates with heterogeneity across sites. Conclusion: Structured ACP data in EHRs were frequently incorrect. This represents a problem for patients and their families, as well as quality improvement and research efforts. Clinical Trials Registration: NCT03609177.

Abstract Objective Electronic health record documentation by intensive care unit (ICU) clinicians may predict patient outcomes. However, it is unclear whether physician and nursing notes differ in their ability to predict short-term ICU prognosis. We aimed to investigate and compare the ability of physician and nursing notes, written in the first 48 hours of admission, to predict ICU length of stay and mortality using 3 analytical methods. Materials and Methods This was a retrospective cohort study with split sampling for model training and testing. We included patients ≥18 years of age admitted to the ICU at Beth Israel Deaconess Medical Center in Boston, Massachusetts, from 2008 to 2012. Physician or nursing notes generated within the first 48 hours of admission were used with standard machine learning methods to predict outcomes. Results For the primary outcome of composite score of ICU length of stay ≥7 days or in-hospital mortality, the gradient boosting model had better performance than the logistic regression and random forest models. Nursing and physician notes achieved area under the curves (AUCs) of 0.826 and 0.796, respectively, with even better predictive power when combined (AUC, 0.839). Discussion Models using only nursing notes more accurately predicted short-term prognosis than did models using only physician notes, but in combination, the models achieved the greatest accuracy in prediction. Conclusions Our findings demonstrate that statistical models derived from text analysis in the first 48 hours of ICU admission can predict patient outcomes. Physicians’ and nurses’ notes are both uniquely important in mortality prediction and combining these notes can produce a better predictive model.