mHealth, the use of mobile technologies for healthcare management and delivery, offers great promise to promote health and improve care. However, to date, most mHealth treatments have failed to demonstrate a significant impact on clinical outcomes, and there is surprisingly little knowledge of factors that drive its efficacy. This study examines mHealth effectiveness by investigating both mHealth design and social support. To do so, we leverage one of the world’s largest field experiments on improving the health of expectant mothers and reducing the rate of cesarean sections. We hypothesize that 1) the combination of both self-directed mHealth and provider-directed mHealth ensures the highest mHealth effectiveness; 2) the husband, as one of the most significant social supports for expectant women, can be an important moderator of mHealth effectiveness. Our analyses show that the combined mHealth design achieves significant reduction in cesarean section use. In addition, a husband’s healthy behavior is pivotal in enabling mHealth interventions to be effective we find that the cesarean section reduction rate of women whose husbands engage in healthy behavior is four times higher than it is for those whose husbands do not fully engage in healthy behavior. Further analyses reveal that the husband’s healthy behavior has a stronger influence on mHealth effectiveness when the wife has higher status in the marriage. Our findings represent the first study to examine the effectiveness of these two mHealth designs (self-directed and provider-directed) and the critical role of social support in determining mHealth effectiveness. The study has important implications for both academic research and the practice of mHealth.
Digital health solutions targeting diabetes self-care are popular and promising, but important questions remain about how these tools can most effectively help patients. Consistent with evidence of the salutary effects of note-taking in education, features that enable annotation of structured data entry might enhance the meaningfulness of the interaction, thereby promoting persistent use and benefits of a digital health solution.To examine the potential benefits of note-taking, we explored how patients with type 2 diabetes used annotation features of a digital health solution and assessed the relationship between annotation and persistence in engagement as well as improvements in glycated hemoglobin (A1C). Secondary data from 3142 users of the BlueStar digital health solution collected between December 2013 and June 2017 were analyzed, with a subgroup of 372 reporting A1C lab values.About a third of patients recorded annotations while using the platform. Annotation themes largely reflected self-management behaviors (diet, physical activity, medication adherence) and well-being (mood, health status). Early use of contextual annotations was associated with greater engagement over time and with greater improvements in A1C.Our research provides preliminary evidence of the benefits of annotation features in a digital health solution. Future research is needed to assess the causal impact of note-taking and the moderating role of thematic content reflected in notes.
This poster presents an innovative model for patient disease identification from clinical notes. CLSTM-Attention leverages the rich context information and learn the features automatically to extract the disease information of patients. Preliminary evaluation verified the effectiveness of the approach.
Adverse drug events (ADEs) are a serious health problem that can be life-threatening. While a lot of work on detecting correlation between a drug and an ADE, limited studies have been conducted on personalized ADE risk prediction. Avoiding the drugs with high likelihood of causing severe ADEs helps physicians to provide safer treatments to patients. The goal of this study is to assess personalized ADE risks that a target drug may induce on a target patient, based on patient medical history recorded in claim codes, which provide information about diagnosis, drugs taken, related medical supplies besides billing information. We developed a HTNNR model (Hierarchical Time-aware Neural Network for ADE Risk) that captures characteristics of claim codes and their relationship. Eempirical evaluation shows that the proposed HTNNR model substantially outperforms the comparison methods.
Fake online reviews are becoming more prevalent and are a significant concern for consumer protection groups and regulatory authorities. However, identifying fake reviews has been a challenge in IS, marketing, and computer science. In this study, we design a deep learning approach to capture the linguistic traits that differentiate between genuine and fake reviews. Our deep learning model is evaluated on a dataset of 181,951 doctor reviews, 8% of which are fake. Since a natural honeypot existed at one point on the platform that hosted these reviews, we are able to label the reviews that exploited the natural honeypot as fraudulent, thus overcoming the major challenge in constructing the ground truth for training the model. Our model shows a significant improvement in accuracy when compared to traditional machine learning algorithms such as logistic regression and random forest. Interestingly, we also find that human evaluators perform much worse than machine learning approaches. Compared to 200 human evaluators, our deep learning approach has a true positive rate (14.29% vs. 8.70%) that is twice as high, and it also achieves a much lower false positive rate (0.63% vs. 11.68%). We also observe that these evaluators are susceptible to human bias, as they are more likely to label fake reviews as genuine than they are to label genuine reviews as genuine. Our study offers further explanations for the advantages of deep learning and is the first to construct a deep learning model to detect fraudulent online reviews, an approach that can help curb fake reviews and increase information quality and market efficiency.
As AI applications become more pervasive, it is critical to understand how knowledge workers with different levels and types of experience can team with AI for productivity gains. We focus on the influence of two major types of human work experience, narrow experience based on the specific task volume and broad experience based on seniority, on the human-AI team dynamics. We developed an AI solution for medical chart coding in a publicly traded company and conducted a field study among the knowledge workers. Based on a detailed analysis performed at the medical chart level, we find evidence that AI benefits workers with greater task-based experience, but senior workers gain less from AI than their junior colleagues. Further investigation reveals that the relatively lower productivity lift from AI is not a result of seniority per se, but lower trust in AI, likely triggered by the senior workers’ broader job responsibilities. This study provides new empirical insights into the differential roles of worker experience in the collaborative dynamics between AI and knowledge workers, which have important societal and business implications.
This paper presents the first study that utilizes detailed clickstream data on online Word-of-Mouth to uncover mechanisms underlying its influence on consumer decision making. We leverage a feature launch on a major doctor appointment booking platform to study the impacts of online WOM on three dimensions of a consumer’s choice process: the consideration set size, the time taken to consider alternatives (web session duration), and the geographic dispersion among the choices considered. We further investigate the effects of online WOM on demand. We have several novel findings. First, the impacts of WOM on the decision-making processes are not monotonic, but rather are contingent on the density of WOM (number of rated doctors) in a market. When the density of WOM is low, the availability of WOM makes patients consider more doctors, browse for a longer duration, and consider doctors that are geographically more dispersed. In contrast, when the density of WOM is high, the availability of WOM makes patients consider fewer doctors, browse for a shorter duration, and focus on doctors that are geographically more proximate. Finally, our results show that the presence of WOM can have a cannibalization effect: when the ratings are published, the highly rated doctors reap the benefits (in the form of increased demand) at the expense of unrated doctors. Our study contributes to extant literature on online WOM by providing new insights through which WOM influences consumer decision making at a new level of granularity..
Proceedings of the 9th Conference on Health IT & Analytics (CHITA 2018). CHITA is an annual health IT and analytics research summit, including a doctoral consortium that each year gathers prominent scholars from more than 40 research institutes, and leading policy and practitioner attendees in a vibrant setting to discuss opportunities and challenges in the design, implementation and management of health information technology and analytics. Its goal is to deepen our understanding of strategy, policy and systems fostering health IT and analytics effective use and to stimulate new research ideas with both policy and business implications. This forum provides a productive venue to facilitate interaction and collaboration among academia, government, and industry. Now in its ninth year, each year CHITA draws over 100 participants. Convened by the Center for Health Information & Decision Systems (CHIDS), support for CHITA is provided by the Robert H. Smith School of Business, the University of Michigan School of Public Health, and the U.S. Agency for Healthcare Research and Quality.
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