As patient satisfaction is heavily linked to their choice of provider and medical outcomes, hospital administrations routinely consider a bevy of factors to improve patient satisfaction. These considerations are complex, so targeting the most important areas for improvement is challenging. However, consumers' online reviews of their hospital experience provide a vital lens into the factors associated with their satisfaction. In this study, we use a large dataset of Facebook reviews to construct a taxonomy of potential service attributes that consumers discuss online. We find partial overlap between this taxonomy and prior works and more traditional survey measures; the specific mix of service attributes found in these reviews is unique. Next, we utilize regression modeling to determine which service attributes are most closely associated with star ratings, which we use to measure overall satisfaction. This study demonstrates that mentions of waiting times, treatment effectiveness, communication, diagnostic quality, environmental sanitation, and cost considerations tend to be most associated with patients' overall ratings. Finally, we construct text analyses to rapidly detect consumers' mentions of these service attributes in an automated manner. We derive a set of "smoke terms," or terms especially prevalent in posts that mention specific service attributes. We find that these are generally non-emotive terms, indicating limited utility of traditional sentiment analysis. Managerially, this information helps to prioritize the areas in greatest need of improvement. Additionally, generating smoke terms for each service attribute aids health care policy makers and providers in rapidly monitoring concerns and adjusting

Food contamination and food poisoning pose enormous risks to consumers across the world. As discussions of consumer experiences have spread through online media, we propose the use of text mining to rapidly screen online media for mentions of food safety hazards. We compile a large data set of labeled consumer posts spanning two major websites. Utilizing text mining and supervised machine learning, we identify unique words and phrases in online posts that identify consumers' interactions with hazardous food products. We compare our methods to traditional sentiment-based text mining. We assess performance in a high-volume setting, utilizing a data set of over 4 million online reviews. Our methods were 77-90% accurate in top-ranking reviews, while sentiment analysis was just 11-26% accurate. Moreover, we aggregate review-level results to make product-level risk assessments. A panel of 21 food safety experts assessed our model's hazard-flagged products to exhibit substantially higher risk than baseline products. We suggest the use of these tools to profile food items and assess risk, building a postmarket decision support system to identify hazardous food products. Our research contributes to the literature and practice by providing practical and inexpensive means for rapidly monitoring food safety in real time.

Electronic documentation systems have been widely implemented in the healthcare field. These systems have become a critical part of the nursing profession. This research examines how nurses’ general computer skills, training, and self-efficacy affect their perceptions of using these systems. A sample of 248 nurses was surveyed to examine their general computer skills, self-efficacy, and training in electronic documentation systems in nursing programs. We propose a model to investigate the extent to which nurses’ computer skills, self-efficacy, and training in electronic documentation influence perceptions of using electronic documentation systems in hospitals. The data supports a mediated model in which general computer skills, self-efficacy, and training influence perceived usefulness through perceived ease of use. The significance of these findings was confirmed through structural equation modeling. As the electronic documentation systems are customized for every organization, our findings suggest value in nurses receiving training to learn these specific systems in the workplace or during their internships. Doing so may improve patient outcomes by ensuring that nurses use the systems consistently and effectively.

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