Examining how physician factors influence patient satisfaction during clinical consultations about cancer prognosis and pain

Lou, Zhouyang; Vivas-Valencia, Carolina; Shields, Cleveland G.; Kong, Nan

doi:10.1016/j.pecinn.2022.100017

Cited by 5 publications

(6 citation statements)

References 70 publications

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“…DiMartino et al (2022) [58] used NLP to evaluate uncontrolled symptoms (i.e., pain), showing promise of AI in pain prediction (accuracy 61%). Lou et al (2022) [39] delved into patient satisfaction in cancer pain consultation, using LR and RF, revealing associations with physician factors but facing limitations in data collection and ML metric reporting. Lu et al (2021) [59] tested ML algorithms on young cancer survivors, with Bidirectional Encoder Representations from Transformers (BERT) showing higher accuracy in identifying pain interference.…”

Section: Resultsmentioning

confidence: 99%

“…• Cancer pain prediction • Post cancer treatment pain prediction • Cancer pain analgesics requirement Random Forest (RF) [24], [32], [33], [34], [26], [35], [27], [36], [37], [16], [10], [38], [39], [40], [41] An ensemble approach that combines the output of multiple decision trees to reach a single high accurate result. It provides a good predictive performance, low overfitting, and easy interpretability.…”

Section: Classificationmentioning

confidence: 99%

“…Generalized linear mixed models (GLMMs), least absolute shrinkage and selection operator (Lasso), Linear regression (LR), Logistic regression (LR), Bayesian regression [42], [32], [43], [33], [34], [35], [27], [44], [45], [46], [39], [41], [31] Model that estimates the relationship between one dependent variable and one or more independent variables using a line.…”

Section: Regressionmentioning

confidence: 99%

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Artificial Intelligence and Machine Learning in Cancer Related Pain: A Systematic Review

Salama,

Godinich,

Geng

et al. 2023

Preprint

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Background/objectivePain is a challenging multifaceted symptom reported by most cancer patients, resulting in a substantial burden on both patients and healthcare systems. This systematic review aims to explore applications of artificial intelligence/machine learning (AI/ML) in predicting pain-related outcomes and supporting decision-making processes in pain management in cancer.MethodsA comprehensive search of Ovid MEDLINE, EMBASE and Web of Science databases was conducted using terms including “Cancer”, “Pain”, “Pain Management”, “Analgesics”, “Opioids”, “Artificial Intelligence”, “Machine Learning”, “Deep Learning”, and “Neural Networks” published up to September 7, 2023. The screening process was performed using the Covidence screening tool. Only original studies conducted in human cohorts were included. AI/ML models, their validation and performance and adherence to TRIPOD guidelines were summarized from the final included studies.ResultsThis systematic review included 44 studies from 2006-2023. Most studies were prospective and uni-institutional. There was an increase in the trend of AI/ML studies in cancer pain in the last 4 years. Nineteen studies used AI/ML for classifying cancer patients’ pain development after cancer therapy, with median AUC 0.80 (range 0.76-0.94). Eighteen studies focused on cancer pain research with median AUC 0.86 (range 0.50-0.99), and 7 focused on applying AI/ML for cancer pain management decisions with median AUC 0.71 (range 0.47-0.89). Multiple ML models were investigated with. median AUC across all models in all studies (0.77). Random forest models demonstrated the highest performance (median AUC 0.81), lasso models had the highest median sensitivity (1), while Support Vector Machine had the highest median specificity (0.74). Overall adherence of included studies to TRIPOD guidelines was 70.7%. Lack of external validation (14%) and clinical application (23%) of most included studies was detected. Reporting of model calibration was also missing in the majority of studies (5%).ConclusionImplementation of various novel AI/ML tools promises significant advances in the classification, risk stratification, and management decisions for cancer pain. These advanced tools will integrate big health-related data for personalized pain management in cancer patients. Further research focusing on model calibration and rigorous external clinical validation in real healthcare settings is imperative for ensuring its practical and reliable application in clinical practice.

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Section: Resultsmentioning

confidence: 99%

Section: Classificationmentioning

confidence: 99%

Section: Regressionmentioning

confidence: 99%

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Artificial Intelligence and Machine Learning in Cancer Related Pain: A Systematic Review

Salama,

Godinich,

Geng

et al. 2023

Preprint

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“…Patient relationships improve when the time for consultations is longer [ 99 ]. In the same context, the time pressure that physicians are often under is recognized as being a potential barrier to evidence-based care [ 100 ].…”

Section: Informationmentioning

confidence: 99%

“…This is apparent in doctors with poor relationship skills. The author of the report described the behaviour as providing ‘information to the patients without showing enough empathy or thinking about the patient’s perspective’ [ 99 ].…”

Section: Concordancementioning

confidence: 99%

When can cancer patient treatment nonadherence be considered intentional or unintentional? A scoping review

2023

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Background Treatment nonadherence in cancer patients remains high with most interventions having had limited success. Most studies omit the multi-factorial aspects of treatment adherence and refer to medication adherence. The behaviour is rarely defined as intentional or unintentional. Aim The aim of this Scoping Review is to increase understanding of modifiable factors in treatment nonadherence through the relationships that physicians have with their patients. This knowledge can help define when treatment nonadherence is intentional or unintentional and can assist in predicting cancer patients at risk of nonadherence and in intervention design. The scoping review provides the basis for method triangulation in two subsequent qualitative studies: 1. Sentiment analysis of online cancer support groups in relation to treatment nonadherence; 2. A qualitative validation survey to refute / or validate claims from this scoping review. Thereafter, framework development for a future (cancer patient) online peer support intervention. Methods A Scoping Review was performed to identify peer reviewed studies that concern treatment / medication nonadherence in cancer patients—published between 2000 to 2021 (and partial 2022). The review was registered in the Prospero database CRD42020210340 and follows the PRISMA-S: an extension to the PRISMA Statement for Reporting Literature Searches in Systematic Searches. The principles of meta-ethnography are used in a synthesis of qualitative findings that preserve the context of primary data. An aim of meta-ethnography is to identify common and refuted themes across studies. This is not a mixed methods study, but due to a limited qualitativevidence base and to broaden findings, the qualitative elements (author interpretations) found within relevant quantitative studies have been included. Results Of 7510 articles identified, 240 full texts were reviewed with 35 included. These comprise 15 qualitative and 20 quantitative studies. One major theme, that embraces 6 sub themes has emerged: ‘Physician factors can influence patient factors in treatment nonadherence’. The six (6) subthemes are: 1. Suboptimal Communication; 2. The concept of Information differs between Patient and Physician; 3.Inadequate time. 4. The need for Treatment Concordance is vague or missing from concepts; 5. The importance of Trust in the physician / patient relationship is understated in papers; 6. Treatment concordance as a concept is rarely defined and largely missing from studies. Line of argument was drawn Treatment (or medication) nonadherence that is intentional or unintentional is often attributed to patient factors—with far less attention to the potential influence of physician communication factors. The differentation between intentional or unintentional nonadherence is missing from most qualitative and quantitative studies. The holistic inter-dimensional / multi-factorial concept of ‘treatment adherence’ receives scant attention. The main focus is on medication adherence / nonadherence in the singular context. Nonadherence that is unintentional is not necessarily passive behaviour and may overlap with intentional nonadherence. The absence of treatment concordance is a barrier to treatment adherence and is rarely articulated or defined in studies. Conclusion This review demonstrates how cancer patient treatment nonadherence is often a shared outcome. An equal focus on physican and patient factors can increase understanding of the two main types of nonadherence (intentional or unintentional). This differentation should help improve the fundamentals of intervention design.

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Culturally Sensitive Communication in Health Care: The Ways Forward

Claramita,

Pratidina Susilo

2024

Bridging the Communication Gap in Health Care

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Examining how physician factors influence patient satisfaction during clinical consultations about cancer prognosis and pain

Cited by 5 publications

References 70 publications

Artificial Intelligence and Machine Learning in Cancer Related Pain: A Systematic Review

Artificial Intelligence and Machine Learning in Cancer Related Pain: A Systematic Review

When can cancer patient treatment nonadherence be considered intentional or unintentional? A scoping review

Culturally Sensitive Communication in Health Care: The Ways Forward

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