Advance Care Planning: Promoting Effective and Aligned Communication in the Elderly (ACP-PEACE): the study protocol for a pragmatic stepped-wedge trial of older patients with cancer

Lakin, Joshua R.; Brannen, Elise N; Tulsky, James A.; Chang, Yuchiao; Gundersen, Daniel A.; El‐Jawahri, Areej; Volandes, Angelo E.

doi:10.1136/bmjopen-2020-040999

Cited by 16 publications

(9 citation statements)

References 82 publications

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“…Clustered care is a way of providing multiple elements of care in the same setting and, at the same time, is highly operational [16][17][18]. It is not just at the time of PICC placement, but throughout the entire care process after placement, so it needs to be applied with good rules of care, with clear goals and time characteristics [19].…”

Section: Discussionmentioning

confidence: 99%

Feasibility and Application of Cluster Nursing to the Care of Patients with Acute Oncology

Song

2022

Evidence-Based Complementary and Alternative Medicine

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Objective. To probe the utility of cluster nursing for the care of acute oncology clients. Methods. One hundred fourteen cases of acute oncology pioneers undergoing therapy in our clinic from April 2019 to February 2021 were randomly assigned into two consecutive arms, conventional care and cluster care, in accordance with the nursing modality. Complications, satisfaction, quality of survival, and negative emotions were compared across the two parties. Results. The comorbidity incidence rate of the subject matter in the research cohort was 7.02%, which was below the comorbidity rate of 17.54% in the reaction cohort ( P < 0.05 ); the percentage of satisfaction in the research cohort was 96.49%, which was higher than the satisfaction rate of 78.95% in the reaction cohort ( P < 0.05 ); aftercare, the quality of survival was significantly higher in both groups, and the SAS and SDS scores were significantly lower, with a more pronounced trend of change in the research cohort than in the reaction cohort ( P < 0.05 ). Conclusion. Bundled care for casualty oncology is of major value, with a marked reduction in the incidence of postoperative complications, high quality of survival, an excellent prognosis and negative mood, high patient morale and satisfaction and compliance with curative treatment, and good support for subsequent care.

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Section: Discussionmentioning

confidence: 99%

Feasibility and Application of Cluster Nursing to the Care of Patients with Acute Oncology

Song

2022

Evidence-Based Complementary and Alternative Medicine

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“…These studies reported having to collect primary data that were expected to be readily available, including patient-reported outcomes (PROs), previously completed questionnaires, and advance care planning conversations. 16 This led to time- and monetary-consuming adjustments as illustrated by a project using Epic when they said, “We would have appreciated greater ease and flexibility of automatically responding to patient-reported information, including PROs to match participants’ needs. Automatically assigning the intervention to specific patients using Epic EHR logic and based on EHR data was clunky, time consuming, and expensive.…”

Section: Resultsmentioning

confidence: 99%

Enhancing the use of EHR systems for pragmatic embedded research: lessons from the NIH Health Care Systems Research Collaboratory

Richesson

Marsolo

Douthit

et al. 2021

Journal of the American Medical Informatics Association

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Objective We identified challenges and solutions to using electronic health record (EHR) systems for the design and conduct of pragmatic research. Materials and Methods Since 2012, the Health Care Systems Research Collaboratory has served as the resource coordinating center for 21 pragmatic clinical trial demonstration projects. The EHR Core working group invited these demonstration projects to complete a written semistructured survey and used an inductive approach to review responses and identify EHR-related challenges and suggested EHR enhancements. Results We received survey responses from 20 projects and identified 21 challenges that fell into 6 broad themes: (1) inadequate collection of patient-reported outcome data, (2) lack of structured data collection, (3) data standardization, (4) resources to support customization of EHRs, (5) difficulties aggregating data across sites, and (6) accessing EHR data. Discussion Based on these findings, we formulated 6 prerequisites for PCTs that would enable the conduct of pragmatic research: (1) integrate the collection of patient-centered data into EHR systems, (2) facilitate structured research data collection by leveraging standard EHR functions, usable interfaces, and standard workflows, (3) support the creation of high-quality research data by using standards, (4) ensure adequate IT staff to support embedded research, (5) create aggregate, multidata type resources for multisite trials, and (6) create re-usable and automated queries. Conclusion We are hopeful our collection of specific EHR challenges and research needs will drive health system leaders, policymakers, and EHR designers to support these suggestions to improve our national capacity for generating real-world evidence.

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“…Natural language processing-screened human abstraction represents an efficient and useful approach for measuring EHR outcomes in large pragmatic studies and is increasingly used to measure palliative care outcomes similar to the one we examined. 34,[62][63][64][65] In the 2512-patient PICSI-H Trial 1, measuring the primary outcome using conventional manual abstraction would have required thousands of abstractor-hours, which is both costly and time consuming. In contrast, NLP-screened human abstraction allowed investigators to make up-front investments in developing NLP and collecting training and validation data and then measure the primary outcome during a smaller number of abstractor-hours with acceptable losses in sensitivity and power.…”

Section: Discussionmentioning

confidence: 99%

Assessment of Natural Language Processing of Electronic Health Records to Measure Goals-of-Care Discussions as a Clinical Trial Outcome

et al. 2023

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ImportanceMany clinical trial outcomes are documented in free-text electronic health records (EHRs), making manual data collection costly and infeasible at scale. Natural language processing (NLP) is a promising approach for measuring such outcomes efficiently, but ignoring NLP-related misclassification may lead to underpowered studies.ObjectiveTo evaluate the performance, feasibility, and power implications of using NLP to measure the primary outcome of EHR-documented goals-of-care discussions in a pragmatic randomized clinical trial of a communication intervention.Design, Setting, and ParticipantsThis diagnostic study compared the performance, feasibility, and power implications of measuring EHR-documented goals-of-care discussions using 3 approaches: (1) deep-learning NLP, (2) NLP-screened human abstraction (manual verification of NLP-positive records), and (3) conventional manual abstraction. The study included hospitalized patients aged 55 years or older with serious illness enrolled between April 23, 2020, and March 26, 2021, in a pragmatic randomized clinical trial of a communication intervention in a multihospital US academic health system.Main Outcomes and MeasuresMain outcomes were natural language processing performance characteristics, human abstractor-hours, and misclassification-adjusted statistical power of methods of measuring clinician-documented goals-of-care discussions. Performance of NLP was evaluated with receiver operating characteristic (ROC) curves and precision-recall (PR) analyses and examined the effects of misclassification on power using mathematical substitution and Monte Carlo simulation.ResultsA total of 2512 trial participants (mean [SD] age, 71.7 [10.8] years; 1456 [58%] female) amassed 44 324 clinical notes during 30-day follow-up. In a validation sample of 159 participants, deep-learning NLP trained on a separate training data set from identified patients with documented goals-of-care discussions with moderate accuracy (maximal F1 score, 0.82; area under the ROC curve, 0.924; area under the PR curve, 0.879). Manual abstraction of the outcome from the trial data set would require an estimated 2000 abstractor-hours and would power the trial to detect a risk difference of 5.4% (assuming 33.5% control-arm prevalence, 80% power, and 2-sided α = .05). Measuring the outcome by NLP alone would power the trial to detect a risk difference of 7.6%. Measuring the outcome by NLP-screened human abstraction would require 34.3 abstractor-hours to achieve estimated sensitivity of 92.6% and would power the trial to detect a risk difference of 5.7%. Monte Carlo simulations corroborated misclassification-adjusted power calculations.Conclusions and RelevanceIn this diagnostic study, deep-learning NLP and NLP-screened human abstraction had favorable characteristics for measuring an EHR outcome at scale. Adjusted power calculations accurately quantified power loss from NLP-related misclassification, suggesting that incorporation of this approach into the design of studies using NLP would be beneficial.

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Advance Care Planning: Promoting Effective and Aligned Communication in the Elderly (ACP-PEACE): the study protocol for a pragmatic stepped-wedge trial of older patients with cancer

Cited by 16 publications

References 82 publications

Feasibility and Application of Cluster Nursing to the Care of Patients with Acute Oncology

Feasibility and Application of Cluster Nursing to the Care of Patients with Acute Oncology

Enhancing the use of EHR systems for pragmatic embedded research: lessons from the NIH Health Care Systems Research Collaboratory

Assessment of Natural Language Processing of Electronic Health Records to Measure Goals-of-Care Discussions as a Clinical Trial Outcome

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