Patient–provider communication data: linking process and outcomes in oncology care

Lisa Kennedy Sheldon^1,2, Fangxin Hong^3,4, Donna Berry^4,5
1University of Massachusetts Boston, Boston, MA, USA; ²St Joseph Hospital, Nashua, NH, USA; ³Dana-Farber Cancer Institute, Department of Biostatistics and Computational Biology, Boston, MA, USA; ⁴Dana-Farber Cancer Institute, Phyllis F Cantor Center for Research in Nursing and Patient Care Services, Boston, MA, USA; ⁵Harvard Medical School, Boston, MA, USA

Overview: Patient–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.

Keywords: communication, analysis, distress, cancer, outcomes