ArticleCancer management and research2011
Patient-provider communication data: linking process and outcomes in oncology care.
Article in Cancer management and research, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Intensity of treatment in Swiss cancer patients at the end-of-life.Cancer management and research · 2018Article
- Effects of interdisciplinary teamwork on patient-reported experience of cancer care.BMC health services research · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
overviewPatient-provider communication is vital to quality patient care in oncology settings and impacts health outcomes. Newer communication datasets contain patient symptom reports, real-time audiofiles of visits, coded communication data, and visit outcomes. The purpose of this paper is to: (1) review the complex communication processes during patient-provider interaction during oncology care; (2) describe methods of gathering and coding communication data; (3) suggest logical approaches to analyses; and (4) describe one new dataset that allows linking of patient symptoms and communication processes with visit outcomes. CHALLENGES: Patient-provider communication research is complex due to numerous issues, including human subjects' concerns, methods of data collection, numerous coding schemes, and varying analytic techniques. DATA COLLECTION AND CODING: Coding of communication data is determined by the research question(s) and variables of interest. Subsequent coding and timestamping the behaviors provides categorical data and determines the interval between and patterns of behaviors. ANALYTIC APPROACHES: Sequential analyses move from descriptive statistics to explanatory analyses to direct analyses and conditional probabilities. In the final stage, explanatory modeling is used to predict outcomes from communication elements. Examples of patient and provider communication in the ambulatory oncology setting are provided from the new Electronic Self Report Assessment-Cancer II dataset. SUMMARY: More complex communication data sets provide opportunities to link elements of patient-provider communication with visit outcomes. Given more complex datasets, a step-wise approach is necessary to analyze and identify predictive variables. Sequential analyses move from descriptive results to predictive models with communication data, creating links between patient symptoms and concerns, real-time audiotaped communication, and visit outcomes. The results of these analyses will be useful in developing evidence-based interventions to enhance communication and improve psychosocial outcomes in oncology settings.
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Registered trials
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