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Clinicians are faced with increasingly complex data management responsibilities while simultaneously attending to verbal and nonverbal cues to maintain some moments of patient-centered care (PCC) within triadic patient-doctor-computer communication (PDCC). The improved quantity and quality of the data provided by electronic health records (EHRs) has led to decreased errors, improved coordination of care, and greater patient satisfaction concerning accuracy of information retrieval. Increasing demands on clinicians to contribute to and access big data in real time during patient visits suggest that our performance metrics should consider workload, cognition, interruption, and multitasking as decision makers have in aviation, combat, and policing. However, the existing studies and our own data presented later in this chapter suggest that the interaction between clinician and computer detracts from the communication behaviors and outcomes that comprise PCC. These competing tasks challenge clinicians to speak to patients while recording data; health systems to balance tradeoffs between communication quality and data improvement; EHR builders to minimize clinician workload while maximizing data entry; investigators to elucidate mechanisms through which computer-centered care (CCC) correlates with PCC and health outcomes; and educators to align specific competencies with best practice. This chapter will focus specifically on the challenges the researchers face in elucidating the effects of CCC on PCC and healthcare quality.

Objective Silences in doctor-patient communication can be “connectional” and communicative, in contrast to silences that indicate awkwardness or distraction. Musical and lexical analyses can identify and characterize connectional silences in consultations between oncologists and patients. Methods Two medical students and a professor of voice screened all 1211 silences over 2 s in length from 124 oncology office visits. We developed a “strength of connection” taxonomy and examined ten connectional silences for lexical and musical features including pitch, volume, and speaker turn-taking rhythm. Results We identified connectional silences with good reliability. Typical dialog rhythms surrounding connectional silences are characterized by relatively equal turn lengths and frequent short vocalizations. We found no pattern of volume and pitch variability around these silences. Connectional silences occurred in a wide variety of lexical contexts. Conclusion Particular patterns of dialog rhythm mark connectional silences. Exploring structures of connectional silence extends our understanding of the audio-linguistic conditions that mark patient-clinician connection. Practice implications Communicating with an awareness of pitch, rhythm, and silence – in addition to lexical content – can facilitate shared understanding and emotional connection.