A book chapter on ‘Design for improved workflow’

Mustafa, Blaine, Sunyoung, and I wrote a chapter on Design for Improved Workflow as part of Design for Health: Applications of Human Factors, published by Elsevier.

Abstract

Workflow is a commonly used term in human factors and informatics literature. We embraced a broad definition and used it as a concept to examine various work phenomena. This chapter aims to discuss how design can improve workflow in health settings and eventually make care delivery patient-centered and safer. In general, design studies can improve workflow in two different ways: (1) the overall workflow and (2) interventions that would improve workflow. We highlighted two design approaches: user-centered design and participatory. These are two closely related approaches that engage (to varying degrees) targeted users along a continuum of participation to improve usability. For each of the three informatics subfields (clinical, public health, and consumer health), we provide relevant examples from studies, where users were engaged in the design process. We have identified whether the notion of workflow was formally addressed and gave examples of how workflow methodology might complement user-centered and participatory design efforts in clinical, public health, and consumer health informatics research. Although user-centered design includes a variety of methods, we introduced contextual inquiry and participatory design. These designs demonstrated two key distinctive features of user-centered design: the importance of understanding the holistic context of users’ social, technical, and cultural environments; and engaging users as codesigners. We deepen our discussion through a case that focuses on supporting patient engagement for improved intra- and cross-institutional workflow in an emergency department. Design studies that involve multidisciplinary perspectives are necessary to improve workflow that contributes to safer, higher quality, and more accessible care delivery.

https://www.sciencedirect.com/science/article/pii/B9780128164273000130

Paper accepted at JMIR Formative Research

Our interview study of students’ mental wellbeing challenges and brainstorming design solutions has been accepted at JMIR Formative Research.

S. Park, N. Andalibi, Y. Zou, S. Ambulkar,  J. Huh-Yoo.  Understanding Students’ Mental Wellbeing Challenges on University Campus: An Interview Study. Journal of Medical Internet Research Formative Research (25% acceptance rate). (2020). In Press.

FamilyLog: Monitoring Family Mealtime Activities by Mobile Devices

Paper Presents empirical evaluation of using lightweight signal features from a set of complex activities during the meal (e.g., clattering sound, arm gestures of eating, human voice, TV sound) and fusing built-in sensor data of multiple mobile devices available in a family with a CRFs-based classifier.

Abstract. Monitoring the family mealtime activities enables the analysis of the previous daily routine, hence the positive changes can be made towards better relationships among family members and better physical/mental health. Moreover, the details of family mealtime activities provide important information for study in sociology and culture. This paper presents FamilyLog — a practical system to log family mealtime activities using smartphones and smartwatches. FamilyLog automatically detects and logs details of activities during the mealtime, including occurrence and duration of meal, conversations, participants, TV viewing etc., in an unobtrusive manner. Based on the sensor data collected from real families, we carefully design robust yet lightweight signal features from a set of complex activities during the meal, including clattering sound, arm gestures of eating, human voice, TV sound, etc. Moreover, FamilyLog opportunistically fuses data from built-in sensors of multiple mobile devices available in a family with a CRFs-based classifier. To evaluate the real-world performance of FamilyLog, we perform extensive experiments that consist of 77 days of sensor data from 37 subjects in 8 families with children. FamilyLog can detect those events with high accuracy across different families and home environments.

Bi C, Xing G, Hao T, Huh J, Peng W, Ma M, Chang X. FamilyLog: Monitoring Family Mealtime Activities by Mobile Devices. IEEE Transactions on Mobile Computing. 2019 May 14.

Consumer Health Informatics Adoption among Underserved Populations: Thinking beyond the Digital Divide

Paper Systematic review of facilitators and barriers to underserved populations’ consumer health technology adoption

Objectives: Underserved populations can benefit from consumer health informatics (CHI) that promotes self-management at a lower cost. However, prior literature suggested that the digital divide and low motivation constituted barriers to CHI adoption. Despite increased Internet use, underserved populations continue to show slow CHI uptake. The aim of the paper is to revisit barriers and facilitators that may impact CHI adoption among underserved populations.

Methods: We surveyed the past five years of literature. We searched PubMed for articles published between 2012 and 2017 that describe empirical evaluations involving CHI use by under- served populations. We abstracted and summarized data about facilitators and barriers impacting CHI adoption.

Results:From 645 search results, after abstract and full-text screening, 13 publications met the inclusion criteria of identify- ing barriers to and facilitators of underserved populations’ CHI adoption. Contrary to earlier literature, the studies suggested that the motivation to improve health literacy and adopt technology was high among studied populations. Beyond the digital divide, barriers included: low health and computer literacy, challenges in accepting the presented information, poor usability, and unclear content. Factors associated with increased use were: user needs for information, user-access mediated by a proxy person, and early user engagement in system design.

Conclusions: While the digital divide remains a barrier, newer studies show that high motivation for CHI use exists. However, simply gaining access to technology is not sufficient to improve adoption unless CHI technology is tailored to address user needs. Future interventions should consider building larger empirical evidence on identifying CHI barriers and facilitators.

Keywords Medical informatics applications, consumer health information, ethnic groups, socioeconomic factors, minority groups, health disparities

Rejoining MSU

As of 4/15/2018, I am excited to be rejoining the Department of Media and Information at Michigan State University. I will live in San Diego, CA and travel to Michigan.

Toward Predicting Social Support Needs in Online Health Social Networks

Identified the features critical for predicting social support needs in online health communities.

Background: While online health social networks (OHSNs) serve as an effective platform for patients to fulfill their various social support needs, predicting the needs of users and providing tailored information remains a challenge.

Objective: The objective of this study is to discriminate important features for identifying users’ social support needs based on knowledge gathered from survey data. This study also provides guidelines for a technical framework which can be used to predict users’ social support needs based on raw data collected from OHSNs.

Methods: We initially conducted an online survey with 184 OHSN users. From this survey data, we extracted 34 features based on five categories: (1) demographics, (2) reading behavior, (3) posting behavior, (4) perceived roles in OHSNs, and (5) values sought in OHSNs. Features from the first four categories were used as variables for binary classification. For the prediction outcomes, we used features from the last category: the needs for emotional support, experience-based information, unconventional information, and medical facts. We compared 5 binary classifier algorithms: Gradient Boosting Tree, Random Forest, Decision Tree, Support Vector Machines, and Logistic Regression. We then calculated the scores of the area under the ROC curve (AUC) to understand the comparative effectiveness of the used features.

Results: The best performance was AUC scores of 0.89 for predicting users seeking emotional support, 0.86 for experience-based information, 0.80 for unconventional information, and 0.83 for medical facts. With the gradient boosting tree as our best performing model, we analyzed the strength of individual features in predicting one’s social support need. Among other discoveries, we discovered that users seeking emotional support tend to post more in OHSNs compared to others.

Conclusions: We developed an initial framework for automatically predicting social support needs in OHSNs using survey data. Future work should involve non-survey data to evaluate the feasibility of the framework. Our study contributes to providing personalized social support in OHSNs.

Min-Je Choi, Sung-Hee Kim, Sukwon Lee, et al. 2017. Toward Predicting Social Support Needs in Online Health Social Networks. Journal of Medical Internet Research (forthcoming). http://doi.org/10.2196/jmir.7660