Understanding the EMR-Related Experiences of Pregnant Japanese Women to Redesign Antenatal Care EMR Systems

Helou, Samar; Abou-Khalil, Victoria; Yamamoto, Go; Kondoh, Eiji; Tamura, Hiroshi; Hiragi, Shusuke; Sugiyama, Osamu; Okamoto, Kazuya; Nambu, Masayuki; Kuroda, Tomohiro

doi:10.3390/informatics6020015

Cited by 5 publications

(5 citation statements)

References 57 publications

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“…It is important to note that in Japan, one in every 20 women may experience domestic violence (DV) during pregnancy [39]. Furthermore, pregnant Japanese women want their psychosocial information to be documented in detail in their EMRs [40]. Therefore, clearly documenting sensitive information is needed to (1) respect the preferences of the pregnant women and (2) address psychosocial issues, such as DV, that are not easily disclosed or discussed [41].…”

Section: Discussionmentioning

confidence: 99%

Understanding the Situated Roles of Electronic Medical Record Systems to Enable Redesign: Mixed Methods Study

Helou¹,

Abou-Khalil²,

Yamamoto³

et al. 2019

JMIR Hum Factors

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Background Redesigning electronic medical record (EMR) systems is needed to improve their usability and usefulness. Similar to other artifacts, EMR systems can evolve with time and exhibit situated roles. Situated roles refer to the ways in which a system is appropriated by its users, that is, the unintended ways the users engage with, relate to, and perceive the system in its context of use. These situated roles are usually unknown to the designers as they emerge and evolve as a response by the users to a contextual need or constraint. Understanding the system’s situated roles can expose the unarticulated needs of the users and enable redesign opportunities. Objective This study aimed to find EMR redesign opportunities by understanding the situated roles of EMR systems in prenatal care settings. Methods We conducted a field-based observational study at a Japanese prenatal care clinic. We observed 3 obstetricians and 6 midwives providing prenatal care to 37 pregnant women. We looked at how the EMR system is used during the checkups. We analyzed the observational data following a thematic analysis approach and identified the situated roles of the EMR system. Finally, we administered a survey to 5 obstetricians and 10 midwives to validate our results and understand the attitudes of the prenatal care staff regarding the situated roles of the EMR system. Results We identified 10 distinct situated roles that EMR systems play in prenatal care settings. Among them, 4 roles were regarded as favorable as most users wanted to experience them more frequently, and 4 roles were regarded as unfavorable as most users wanted to experience them less frequently; 2 ambivalent roles highlighted the providers’ reluctance to document sensitive psychosocial information in the EMR and their use of the EMR system as an accomplice to pause communication during the checkups. To improve the usability and usefulness of EMR systems, designers can amplify the favorable roles and minimize the unfavorable roles. Our results also showed that obstetricians and midwives may have different experiences, wants, and priorities regarding the use of the EMR system. Conclusions Currently, EMR systems are mainly viewed as tools that support the clinical workflow. Redesigning EMR systems is needed to amplify their roles as communication support tools. Our results provided multiple EMR redesign opportunities to improve the usability and usefulness of EMR systems in prenatal care. Designers can use the results to guide their EMR redesign activities and align them with the users’ wants and priorities. The biggest challenge is to redesign EMR systems in a way that amplifies their favorable roles for all the stakeholders concurrently.

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Section: Discussionmentioning

confidence: 99%

Understanding the Situated Roles of Electronic Medical Record Systems to Enable Redesign: Mixed Methods Study

Helou¹,

Abou-Khalil²,

Yamamoto³

et al. 2019

JMIR Hum Factors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Moreover, the classifier is built and evaluated around the premise that the only people in the clinic are the doctor and the patient and that the only screen is the doctor's computer screen. However, it is possible that patients are accompanied by their family members, friends, or partners [75] and that multiple health care staff are involved in the care of 1 patient and present during the consultation. In addition, extra screens might be installed inside clinics to engage the patient in their care and offer them an easy and clear view of their data.…”

Section: Limitations and Future Workmentioning

confidence: 99%

Automatic Classification of Screen Gaze and Dialogue in Doctor-Patient-Computer Interactions: Computational Ethnography Algorithm Development and Validation

et al. 2021

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Background The study of doctor-patient-computer interactions is a key research area for examining doctor-patient relationships; however, studying these interactions is costly and obtrusive as researchers usually set up complex mechanisms or intrude on consultations to collect, then manually analyze the data. Objective We aimed to facilitate human-computer and human-human interaction research in clinics by providing a computational ethnography tool: an unobtrusive automatic classifier of screen gaze and dialogue combinations in doctor-patient-computer interactions. Methods The classifier’s input is video taken by doctors using their computers' internal camera and microphone. By estimating the key points of the doctor's face and the presence of voice activity, we estimate the type of interaction that is taking place. The classification output of each video segment is 1 of 4 interaction classes: (1) screen gaze and dialogue, wherein the doctor is gazing at the computer screen while conversing with the patient; (2) dialogue, wherein the doctor is gazing away from the computer screen while conversing with the patient; (3) screen gaze, wherein the doctor is gazing at the computer screen without conversing with the patient; and (4) other, wherein no screen gaze or dialogue are detected. We evaluated the classifier using 30 minutes of video provided by 5 doctors simulating consultations in their clinics both in semi- and fully inclusive layouts. Results The classifier achieved an overall accuracy of 0.83, a performance similar to that of a human coder. Similar to the human coder, the classifier was more accurate in fully inclusive layouts than in semi-inclusive layouts. Conclusions The proposed classifier can be used by researchers, care providers, designers, medical educators, and others who are interested in exploring and answering questions related to screen gaze and dialogue in doctor-patient-computer interactions.

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“…Out of these registered 1.2 million, approximately 700,000 patients only got access to their EHR data. To the best of the authors' knowledge, there is limited research regarding sharing of electronic medical and health records with patients in Japan (15)(16)(17). None was done on experiences of actual users of patient open-EHR systems.…”

Section: Introductionmentioning

confidence: 99%

Investigation of users' experiences for online access to their electronic health records in Japan

Alami

Nemoto

Nakata

2021

GHM

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Understanding the EMR-Related Experiences of Pregnant Japanese Women to Redesign Antenatal Care EMR Systems

Cited by 5 publications

References 57 publications

Understanding the Situated Roles of Electronic Medical Record Systems to Enable Redesign: Mixed Methods Study

Understanding the Situated Roles of Electronic Medical Record Systems to Enable Redesign: Mixed Methods Study

Automatic Classification of Screen Gaze and Dialogue in Doctor-Patient-Computer Interactions: Computational Ethnography Algorithm Development and Validation

Investigation of users' experiences for online access to their electronic health records in Japan

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