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The value of coding provider–patient health interaction lies in the ability of this method to provide detailed understanding of the communication dynamics of the medical encounter. In this entry, verbal coding methods are explained; the history of coding health interaction, including a summary of published coding systems, is reviewed; instructions are given for applying this method to real-time communication between providers and patients, including calculating reliability coefficients; and various outcome measures are related to coding results.

Summary

Quantitative coding is an effective method to gain detailed information about how patients and health care providers communicate. Coding methods examining verbal exchange messages focus on the actual talk that occurs between two people—what is said, how it is said, and how these messages create perceptions of a relationship with one another. (There are coding methods that examine nonverbal behaviors.)

Most of the research using verbal (and nonverbal) coding methods has focused on physician-patient interactions. Physicians and patients follow a particular style when they interact—who says what and when. This style, combined with the tasks of the medical interaction such as gathering information and deciding on treatment goals, creates the nature of what to analyze. For instance, one might examine the communication of uncertainty during emergency department visits and whether it affects patient understanding of diagnosis. One could focus on how the emergency room doctor uses transitions or supportive statements to reduce a patient's uncertainty about her sudden illness. The interaction is recorded (e.g., a digital recorder) in real-time in the emergency room. The recording enables researchers to identify transitions and supportive statements, count the instances, identify how the other person responds, and relate that to patient understanding of her condition and satisfaction with the emergency room visit.

History of Coding Interaction

The history of how coding interaction emerged in health communication research is informative. Most agree that coding started with small group communication research in the 1950s. Robert Bales created a measure for how small groups accomplish their goals during communication. Bales identified 12 behaviors exhibited by group members with categories such as “gives information” and “seems friendly.” This system of coding utterances into categories is called interaction process analysis (IPA) and allows for quantification of communication behaviors.

Much of the credit for examining physician-patient communication goes to Barbara Korsch. Her work recording pediatrician-patient interactions set the stage for research conducted on physician-patient interaction. In the 1960s she identified questions, answers, empathy, and relationship-building utterances in order to understand their impact on parents' satisfaction and adherence. Her work emphasized how both parties influence one another and create a relationship through their communicative behaviors. This research was innovative at the time for providing practical suggestions to improve delivery of care by paying attention to communication style.

As the field of health communication began to take shape in the 1980s, researchers focused on the communicative tasks accomplished in the traditional medical interview. New models in the delivery of care emerged and researchers were interested in determining how providers exhibited a traditional biomedical approach to communication compared to a biopsychosocial approach (relating to a patient's environment, relationships, and psychological issues). Researchers used these models to identify broad tasks such as information seeking (questions), information giving (answers, explanations), coordination of utterances, and relationship-building (agreement, confirmation, humor). Because many of the early health communication researchers were trained in quantitative methods, they used hypothesis testing for which characteristics of communication were related to outcome measures. For example, following a relational control framework, researchers measured how controlling utterances (e.g., interruptions) by physicians and patients were related to patient adherence with the recommended treatment.

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