Oncologist Perceptions of Algorithm-Based Nudges to Prompt Early Serious Illness Communication: A Qualitative Study

Parikh, Ravi B.; Manz, Christopher R; Nelson, Michael N.; Ferrell, William; Belardo, Zoe; Temel, Jennifer S.; Patel, Mitesh S.; Shea, Judy A.

doi:10.1089/jpm.2022.0095

Cited by 8 publications

(11 citation statements)

References 15 publications

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“…We found that the most common use of AI for predictive modeling in SPC was focused on mortality. Many studies in Table 1 and approximately half of all studies included in total focused on predicting mortality as a clinical outcome, which includes predicting short-term mortality risk 14▪,15,18▪,24▪ and survival over a longer horizon 19–21,22▪▪,23▪▪,25,29. Mortality risk and survival time were both usually predicted using machine learning (ML) models that analyze various patient factors such as clinical parameters, changes during treatment, and symptoms 14▪,15,18▪,20,21,22▪▪,23▪▪,24▪,25.…”

Section: Discussionmentioning

confidence: 99%

“…Many studies in Table 1 and approximately half of all studies included in total focused on predicting mortality as a clinical outcome, which includes predicting short-term mortality risk 14▪,15,18▪,24▪ and survival over a longer horizon 19–21,22▪▪,23▪▪,25,29. Mortality risk and survival time were both usually predicted using machine learning (ML) models that analyze various patient factors such as clinical parameters, changes during treatment, and symptoms 14▪,15,18▪,20,21,22▪▪,23▪▪,24▪,25. The accuracy of ML and deep learning (DL) models is typically evaluated by their area under the curve (AUC) value, which measures the accuracy of predictions and a model’s discriminative ability where 1.0 represents the highest possible AUC score indicating perfect discrimination 11,39.…”

Section: Discussionmentioning

confidence: 99%

“…One potential application of these models is to identify high-risk patients and hence prioritize those who should receive increased services such as SPC or hospice care 16,19–21,22▪▪,23▪▪,24▪,25–27, and require documentation like advance care planning, and serious illness communication 22▪▪,23▪▪. Moreover, the results could assist clinicians and patients in their decision making regarding advance care planning and end-of-life preferences 19,20.…”

Section: Discussionmentioning

confidence: 99%

“…While most studies in the last 2 years have focused on building predictive models, there is also emerging prospective data to support the potential benefit of using these tools. Parikh et al 22▪▪ investigated the utility of the 180-day ML mortality model developed by Manz et al . 20 in nudging oncologists to have serious illness communication discussions with the patients at highest risk of acute mortality (NCT03984773).…”

Section: Discussionmentioning

confidence: 99%

See 3 more Smart Citations

Recent advances in artificial intelligence applications for supportive and palliative care in cancer patients

Reddy

Nafees

Raman

2023

Current Opinion in Supportive &Amp; Palliative Care

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Purpose of review Artificial intelligence (AI) is a transformative technology that has the potential to improve and augment the clinical workflow in supportive and palliative care (SPC). The objective of this study was to provide an overview of the recent studies applying AI to SPC in cancer patients. Recent findings Between 2020 and 2022, 29 relevant studies were identified and categorized into two applications: predictive modeling and text screening. Predictive modeling uses machine learning and/or deep learning algorithms to make predictions regarding clinical outcomes. Most studies focused on predicting short-term mortality risk or survival within 6 months, while others used models to predict complications in patients receiving treatment and forecast the need for SPC services. Text screening typically uses natural language processing (NLP) to identify specific keywords, phrases, or documents from patient notes. Various applications of NLP were found, including the classification of symptom severity, identifying patients without documentation related to advance care planning, and monitoring online support group chat data. Summary This literature review indicates that AI tools can be used to support SPC clinicians in decision-making and reduce manual workload, leading to potentially improved care and outcomes for cancer patients. Emerging data from prospective studies supports the clinical benefit of these tools; however, more rigorous clinical validation is required before AI is routinely adopted in the SPC clinical workflow.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Recent advances in artificial intelligence applications for supportive and palliative care in cancer patients

Reddy

Nafees

Raman

2023

Current Opinion in Supportive &Amp; Palliative Care

View full text Add to dashboard Cite

show abstract

“…Globally, there is increased demand for palliative and hospice care, which comprehensively evaluates and manages the physical, psychosocial, and spiritual domains, including pain and symptom relief, for patients at the end of life and their families 2 . Discussions regarding end of life are not always welcome in a death-averse social context, but when hospice caregivers initiate these conversations and conduct them appropriately, they have the potential to drastically change a dying person's illness experiences 3 . Patient-centered communication in end-of-life care can increase treatment satisfaction, reduce the use of aggressive and costly medical services, alleviate the experience of bereavement, and ultimately improve patients' quality of life 4 …”

mentioning

confidence: 99%

Nurses' Subjectivity in Patient-Centered Communication for End-of-Life Patients

Jeong,

Han

2023

J Hosp Palliat Nurs

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Effective patient-centered communication is essential in providing quality care for patients at the end of life. This study aimed to explore the subjectivity of nurses' communication approaches using Q methodology. In this study, 33 Q-samples were constructed based on in-depth interviews with 12 hospice nurses. The Q classification was performed by a total of 38 hospice nurses who had more than 3 years of experience in hospice settings. The collected data were analyzed through the principal component analysis method and the varimax rotation process using the PQ method program. As a result, a total of 4 distinct types emerged: “sincere listener,” “family-centered caregiver,” “resource utilization facilitator,” and “sufficient time devoter.” The factors accounted for 64% of the variance: 19%, 15%, 16%, and 14% respectively. By identifying distinct viewpoints, valuable insights into the diverse perspectives held by nurses in patient-centered communication were gained. Understanding these viewpoints will contribute to the development of tailored communication strategies and training programs. In addition, the study underscores the need for training, resources, and organizational support to enhance nurses' communication skills.

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Default Palliative Care Consultation for Seriously Ill Hospitalized Patients

Courtright,

Madden,

Bayes

et al. 2024

JAMA

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ImportanceIncreasing inpatient palliative care delivery is prioritized, but large-scale, experimental evidence of its effectiveness is lacking.ObjectiveTo determine whether ordering palliative care consultation by default for seriously ill hospitalized patients without requiring greater palliative care staffing increased consultations and improved outcomes.Design, Setting, and ParticipantsA pragmatic, stepped-wedge, cluster randomized trial was conducted among patients 65 years or older with advanced chronic obstructive pulmonary disease, dementia, or kidney failure admitted from March 21, 2016, through November 14, 2018, to 11 US hospitals. Outcome data collection ended on January 31, 2019.InterventionOrdering palliative care consultation by default for eligible patients, while allowing clinicians to opt-out, was compared with usual care, in which clinicians could choose to order palliative care.Main Outcomes and MeasuresThe primary outcome was hospital length of stay, with deaths coded as the longest length of stay, and secondary end points included palliative care consult rate, discharge to hospice, do-not-resuscitate orders, and in-hospital mortality.ResultsOf 34 239 patients enrolled, 24 065 had lengths of stay of at least 72 hours and were included in the primary analytic sample (10 313 in the default order group and 13 752 in the usual care group; 13 338 [55.4%] women; mean age, 77.9 years). A higher percentage of patients in the default order group received palliative care consultation than in the standard care group (43.9% vs 16.6%; adjusted odds ratio [aOR], 5.17 [95% CI, 4.59-5.81]) and received consultation earlier (mean [SD] of 3.4 [2.6] days after admission vs 4.6 [4.8] days; P &lt; .001). Length of stay did not differ between the default order and usual care groups (percent difference in median length of stay, −0.53% [95% CI, −3.51% to 2.53%]). Patients in the default order group had higher rates of do-not-resuscitate orders at discharge (aOR, 1.40 [95% CI, 1.21-1.63]) and discharge to hospice (aOR, 1.30 [95% CI, 1.07-1.57]) than the usual care group, and similar in-hospital mortality (4.7% vs 4.2%; aOR, 0.86 [95% CI, 0.68-1.08]).Conclusions and RelevanceDefault palliative care consult orders did not reduce length of stay for older, hospitalized patients with advanced chronic illnesses, but did improve the rate and timing of consultation and some end-of-life care processes.Trial RegistrationClinicalTrials.gov Identifier: NCT02505035

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Oncologist Perceptions of Algorithm-Based Nudges to Prompt Early Serious Illness Communication: A Qualitative Study

Cited by 8 publications

References 15 publications

Recent advances in artificial intelligence applications for supportive and palliative care in cancer patients

Recent advances in artificial intelligence applications for supportive and palliative care in cancer patients

Nurses' Subjectivity in Patient-Centered Communication for End-of-Life Patients

Default Palliative Care Consultation for Seriously Ill Hospitalized Patients

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