The Medical Authority of AI: A Study of AI-enabled Consumer-Facing Health Technology

You, You; Kou, Yubo; Ding, Xianghua; Gui, Xinning

doi:10.1145/3411764.3445657

Cited by 8 publications

(2 citation statements)

References 124 publications

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“…We found that most of the participants wanted some form of automatic pattern detection in their EHR data to support sensemaking and, similar to other studies, they needed well-crafted, pattern-based recommendations for establishing trust in the AI [51] and securing safety and actionability [27]. Previous work has put great emphasis on how to craft user-friendly presentations for explaining complex clinical topics [52,53] and how to deliver safe actions that patients should take based on data patterns [27,54].…”

Section: Xsl • Fosupporting

confidence: 65%

Alerts and Collections for Automating Patients’ Sensemaking and Organizing of Their Electronic Health Record Data for Reflection, Planning, and Clinical Visits: Qualitative Research-Through-Design Study

Nakikj¹,

Kreda²,

Gehlenborg³

2023

JMIR Hum Factors

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Background Electronic health record (EHR) data from multiple providers often exhibit important but convoluted and complex patterns that patients find hard and time-consuming to identify and interpret. However, existing patient-facing applications lack the capability to incorporate automatic pattern detection robustly and toward supporting making sense of the patient’s EHR data. In addition, there is no means to organize EHR data in an efficient way that suits the patient’s needs and makes them more actionable in real-life settings. These shortcomings often result in a skewed and incomplete picture of the patient’s health status, which may lead to suboptimal decision-making and actions that put the patient at risk. Objective Our main goal was to investigate patients’ attitudes, needs, and use scenarios with respect to automatic support for surfacing important patterns in their EHR data and providing means for organizing them that best suit patients’ needs. Methods We conducted an inquisitive research-through-design study with 14 participants. Presented in the context of a cutting-edge application with strong emphasis on independent EHR data sensemaking, called Discovery, we used high-level mock-ups for the new features that were supposed to support automatic identification of important data patterns and offer recommendations—Alerts—and means for organizing the medical records based on patients’ needs, much like photos in albums—Collections. The combined audio recording transcripts and in-study notes were analyzed using the reflexive thematic analysis approach. Results The Alerts and Collections can be used for raising awareness, reflection, planning, and especially evidence-based patient-provider communication. Moreover, patients desired carefully designed automatic pattern detection with safe and actionable recommendations, which produced a well-tailored and scoped landscape of alerts for both potential threats and positive progress. Furthermore, patients wanted to contribute their own data (eg, progress notes) and log feelings, daily observations, and measurements to enrich the meaning and enable easier sensemaking of the alerts and collections. On the basis of the findings, we renamed Alerts to Reports for a more neutral tone and offered design implications for contextualizing the reports more deeply for increased actionability; automatically generating the collections for more expedited and exhaustive organization of the EHR data; enabling patient-generated data input in various formats to support coarser organization, richer pattern detection, and learning from experience; and using the reports and collections for efficient, reliable, and common-ground patient-provider communication. Conclusions Patients need to have a flexible and rich way to organize and annotate their EHR data; be introduced to insights from these data—both positive and negative; and share these artifacts with their physicians in clinical visits or via messaging for establishing shared mental models for clear goals, agreed-upon priorities, and feasible actions.

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Section: Xsl • Fosupporting

confidence: 65%

Alerts and Collections for Automating Patients’ Sensemaking and Organizing of Their Electronic Health Record Data for Reflection, Planning, and Clinical Visits: Qualitative Research-Through-Design Study

Nakikj¹,

Kreda²,

Gehlenborg³

2023

JMIR Hum Factors

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show abstract

“…We found that most of the participants wanted some form of automatic pattern detection in their EHR data to support the sensemaking and, similar to other studies, they needed wellcrafted pattern-based recommendations for establishing trust in the AI [42] and securing safety and actionability [15]. Previous work put great emphasis on how to craft userfriendly presentations for explaining complex clinical topics [8,35] and how to deliver safe actions the patients should take based on data patterns [9,15].…”

Section: Summary Of Findings and Contributionssupporting

confidence: 60%

Alerts and Collections for Automating the Patients’ Sensemaking and Organizing Their Multi-provider EHR Data for Reflection, Planning and Clinical Visits: A Qualitative User-Centered Design Study

Nakikj¹,

Kreda²,

Gehlenborg³

2022

Preprint

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Impact of assignment completion assisted by Large Language Model-based chatbot on middle school students’ learning

Zhu,

et al. 2024

Educ Inf Technol

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The Medical Authority of AI: A Study of AI-enabled Consumer-Facing Health Technology

Cited by 8 publications

References 124 publications

Alerts and Collections for Automating Patients’ Sensemaking and Organizing of Their Electronic Health Record Data for Reflection, Planning, and Clinical Visits: Qualitative Research-Through-Design Study

Alerts and Collections for Automating Patients’ Sensemaking and Organizing of Their Electronic Health Record Data for Reflection, Planning, and Clinical Visits: Qualitative Research-Through-Design Study

Alerts and Collections for Automating the Patients’ Sensemaking and Organizing Their Multi-provider EHR Data for Reflection, Planning and Clinical Visits: A Qualitative User-Centered Design Study

Impact of assignment completion assisted by Large Language Model-based chatbot on middle school students’ learning

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