Time of Clinic Appointment and Serious Illness Communication in Oncology.
Ontology highlight
ABSTRACT: Introduction
Serious illness communication in oncology increases goal concordant care. Factors associated with the frequency of serious illness conversations are not well understood. Given prior evidence of the association between suboptimal decision-making and clinic time, we aimed to investigate the relationship between appointment time and the likelihood of serious illness conversations in oncology.Methods
We conducted a retrospective study of electronic health record data from 55 367 patient encounters between June 2019 to April 2020, using generalized estimating equations to model the likelihood of a serious illness conversation across clinic time.Results
Documentation rate decreased from 2.1 to 1.5% in the morning clinic session (8am-12pm) and from 1.2% to .9% in the afternoon clinic session (1pm-4pm). Adjusted odds ratios for Serious illness conversations documentation rates were significantly lower for all hours of each session after the earliest hour (adjusted odds ratios .91 [95% CI, .84-.97], P = .006 for overall linear trend).Conclusions
Serious illness conversations between oncologists and patients decrease considerably through the clinic day, and proactive strategies to avoid missed conversations should be investigated.
SUBMITTER: Kolla L
PROVIDER: S-EPMC10126780 | biostudies-literature | 2023 Jan-Dec
REPOSITORIES: biostudies-literature
Publications
Time of Clinic Appointment and Serious Illness Communication in Oncology.
Kolla Likhitha L Chen Jinbo J Parikh Ravi B RB
Cancer control : journal of the Moffitt Cancer Center 20230101
<h4>Introduction</h4>Serious illness communication in oncology increases goal concordant care. Factors associated with the frequency of serious illness conversations are not well understood. Given prior evidence of the association between suboptimal decision-making and clinic time, we aimed to investigate the relationship between appointment time and the likelihood of serious illness conversations in oncology.<h4>Methods</h4>We conducted a retrospective study of electronic health record data fro ...[more]
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Project description:BackgroundTo investigate the impact of a serious illness communication skills training course on medical students' attitudes regarding clinical empathy, self-efficacy in empathic communication, and learning on different dimensions of empathy.MethodsA mixed-methods design was used. A blended learning Serious Illness Communication Skills Training (SI-CST) course was delivered to sixth-year medical students. Students (n=185) completed questionnaires with the 20-item Jefferson Scale of Empathy (JSE) and self-rated preparedness level for five empathic communication tasks at baseline (T1), six weeks (T2), and three-to-six months post-training (T3). Written reflections on key lessons learned were analyzed using inductive thematic analysis.ResultsTotal JSE scores significantly improved from T1 to T2 (111.4 vs 113.9, P=.01) and from T1 to T3 (111.4 vs 113.9, P=.01). There was an increase in Standing in Patient's Shoes subscale of the JSE with an effect size of 0.56 whereas the Perspective-Taking and Compassionate Care subscales showed no significant changes. Self-rated preparedness for all five empathic communication tasks significantly improved from T1 to T2 (P ≤ .001) and from T1 to T3 (P ≤ .001) with large effect sizes (1.09-1.41). Four key themes emerged from the qualitative analysis. They were appreciating the important role of empathy in clinical care (moral empathy), learning skills in detecting and understanding patient emotions (cognitive empathy), learning skills in responding to emotion with empathy (behavioral empathy), and appreciating that communication skills can be improved with continual practice and self-reflection.ConclusionsOur results revealed that SI-CST improved medical students' empathic attitudes and self-efficacy in empathic communication. Qualitative results found learning on the cognitive, behavioral and moral dimensions of empathy.
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Project description:Conversation has been a primary means for the exchange of information since ancient times. Understanding patterns of information flow in conversations is a critical step in assessing and improving communication quality. In this paper, we describe COnversational DYnamics Model (CODYM) analysis, a novel approach for studying patterns of information flow in conversations. CODYMs are Markov Models that capture sequential dependencies in the lengths of speaker turns. The proposed method is automated and scalable, and preserves the privacy of the conversational participants. The primary function of CODYM analysis is to quantify and visualize patterns of information flow, concisely summarized over sequential turns from one or more conversations. Our approach is general and complements existing methods, providing a new tool for use in the analysis of any type of conversation. As an important first application, we demonstrate the model on transcribed conversations between palliative care clinicians and seriously ill patients. These conversations are dynamic and complex, taking place amidst heavy emotions, and include difficult topics such as end-of-life preferences and patient values. We use CODYMs to identify normative patterns of information flow in serious illness conversations, show how these normative patterns change over the course of the conversations, and show how they differ in conversations where the patient does or doesn't audibly express anger or fear. Potential applications of CODYMs range from assessment and training of effective healthcare communication to comparing conversational dynamics across languages, cultures, and contexts with the prospect of identifying universal similarities and unique "fingerprints" of information flow.
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Project description:Background/objectiveOncology guidelines recommend earlier communication with patients about prognosis and goals-of-care in serious illness. However, current evidence leaves gaps in our understanding of the experience of these conversations. This analysis evaluates the patient and clinician experience of a conversation using a Serious Illness Conversation Guide (SICG).Design/settingSecondary analysis from a cluster-randomized clinical trial in a northeastern cancer center.ParticipantsPhysicians, advanced practice clinicians, and patients with advanced cancer who received the intervention.InterventionSICG, clinician training, systems-changes.Main outcomes and measuresThe patient questionnaire assessed perceptions of the conversation and impact on anxiety, hopefulness, peacefulness, sense of control over medical decisions, closeness with their clinician, and behaviors. The clinician questionnaire assessed feasibility, acceptability, and impact on satisfaction in their role.ResultsWe enrolled 54 clinicians and 163 patients; 41 clinicians and 118 patients had a SICG discussion. Most patients described the conversation as worthwhile (79%) and reported no change or improvement in their sense of peacefulness, hopefulness, and anxiety (on average 79%); 56% reported feeling closer with their clinician. Qualitative patient data described positive behavior changes, including enhanced planning for future care and increased focus on personal priorities. Nearly 90% of clinicians agreed that the SICG facilitated timely, effective conversations, and 70% reported increased satisfaction in their role.ConclusionConversations using a SICG were feasible, acceptable, and were associated with positive experiences for both patients and clinicians in oncology in ways that align with national recommendations for serious illness communication. This trial is registered at ClinicalTrials.gov: NCT01786811 https://clinicaltrials.gov/ct2/show/NCT01786811.
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