To improve health systems outcomes such as cost, efficiency, and access to patient information [1], there is national support for the widespread implementation and adoption of electronic health record systems. However, little research has evaluated the effects of these technologies on interactions between the patient and physician using dynamic methods. Several studies found that computers and health information technologies do affect physicians’ communication quality [2], cognitive functioning [3] and the ability of patients and physicians to build rapport and [4] establish emotional common ground. However, while the results from these studies indicate that there may be negative consequences that are the result of electronic health records, EHRs, the methods used to study the interactions do not provide enough evidence to describe exactly how the EHRs affect patient–physician interaction or what design guidelines for EHRs should look like. The study described in this paper illustrates the use of a dynamic analysis of patient and provider interaction with EHRs to answer key research questions: (1) what is the nature of patient/physician communication when electronic health records are used, and (2) what is the influence of EHR use on patient–physician communication dynamically? To study these questions gaze, or where an individual looks or focuses the direction of their eyes, is used to understand communication between patients and providers, and their attention to the EHR.
Gaze is used to understand communication for two purposes, first gaze provides a more objective and measurable indication of attention and communication, and second gaze is an attribute that can inform design guidelines. Many health communication studies focus on verbal communication to understand interactions between patients and physicians. These studies often focus on information flow [5] and comprehension, and emotional variables such as empathy [6]. Nonverbal communication is more recently being explored as an important aspect of communication. From a system design perspective, nonverbal communication is very useful for understanding important variables related to designing user or human-centered systems. For example, posture and body language can provide indications of comfort or satisfaction, facial expressions can provide more objective assessments of satisfaction or emotional state, and eye gaze can illustrate attention to people or other artifacts. Another benefits of exploring nonverbal communication is that findings related to some nonverbal behaviors can inform design guidelines. For example, technologies can be designed to afford pre-described levels of visual attention that might be necessary for optimal human performance or communication outcomes. If we find that physicians should make eye contact with patients for at least 50% of a clinical visit to influence care outcomes such as patient satisfaction, perception of physician empathy or comprehension of information, we should be able to design technologies that do not inhibit the physician's ability to make eye contact at that level.
Many studies focus on how the clinician's nonverbal behavior affects the patient's perspective, such as patient satisfaction [7]. However, there is a growing awareness of the value of nonverbal communication; more and more studies have focused on quantitatively evaluating nonverbal behavior. Coding system for nonverbal interaction have been developed, such as Nonverbal Communication in Doctor-Elderly Patient Transactions (NDEPT) [8], Nonverbal Accommodation Analysis System (NAAS) [9], Relational Communication Scale for Observational Measurement (RCS-O) [10], and instruments used in studies of the effects of physician gender on nonverbal behavior [11]. Although several coding system have been made developing a validated methods and reliable analysis tools are still needed to evaluate nonverbal communication in clinician–patient relationship [12].
While understanding the threshold of visual interaction needs is important, it may be that nonverbal communication is better understood dynamically rather than in terms of thresholds. Using the example above, it may be that patient's need to receive physician eye contact at certain intervals or in response to certain behaviors rather than simply 50% of the encounter. By understanding the important interactions, redesign may be in the form of design requirement for technology and/or in the form of training. With an observable state such as emotion, it is less common for an individual to have a single emotion for an entire encounter; also individuals can often influence another individuals’ emotional state either positively or negatively by expressing emotion during moments when the other individual would likely be most receptive. In these cases, to understand emotion, you would want to understand when the emotional state changes and the events that trigger the change, the events that influence the emotion are also the points that can lead to redesign or training. The study described in this paper focuses on gaze, but similar approaches can be used to understand the effects of other interactions that contribute to patient outcomes such as emotion.
Eye gaze patterns have not been dynamically evaluated in primary care settings where health information technologies are used. Lag sequential analysis is an appropriate method to assess eye gaze patterns dynamically, this approach has been validated in previous studies with patients and care providers [13]. In these studies doctor–patient eye gaze patterns in paper-based clinical settings were evaluated [13]. The results showed that doctor's gaze patterns are followed significantly by patient gaze patterns, for instance if doctor gazed at the patient, the patient gazed back at the doctor. However, the study also showed that there were no significant associations between patient gaze patterns and doctor gaze patterns when gaze was initiated by the patient. In other words, when the patient gazed at the doctor, it was unlikely that the doctor would gaze back to the patient. In the referenced study, the encounters were all new patient visits – there was no prior relationship between patients and doctors – and there was no computer (EHR) in the clinic room. In the present study, we will evaluate doctor–patient eye gaze patterns in computerized settings, where EHRs are used, to understand the effects of the EHR on doctor–patient eye gaze patterns.
The purpose of this study was to better understand the effects of health information technology (HIT) use on physician–technology, patient–physician, and patient–technology interactions. This study used field research methods with quantified ethnographic techniques, where observational data from video were reduced to codes of behaviors. Data from codes were used to build sequential models where hypotheses could be tested. The results are in the form of sequential models of patient, physician, and HIT interaction. The primary research questions for this study were: (1) how is the doctor's gaze related to the patient's gaze in computer mediated health encounters? and (2) how is the patient's gaze related to the doctor's gaze in computer-mediated encounters?