Usability of Health Care Price Transparency Data in the United States: Mixed Methods Study.

BACKGROUND: Increasing health care expenditure in the United States has put policy makers under enormous pressure to find ways to curtail costs. Starting January 1, 2021, hospitals operating in the United States were mandated to publish transparent, accessible pricing information online about the items and services in a consumer-friendly format within comprehensive machine-readable files on their websites. OBJECTIVE: The aims of this study are to analyze the available files on hospitals' websites, answering the question-is price transparency (PT) information as provided usable for patients or for machines?-and to provide a solution. METHODS: We analyzed 39 main hospitals in Florida that have published machine-readable files on their website, including commercial carriers. We created an Excel (Microsoft) file that included those 39 hospitals along with the 4 most popular services-Current Procedural Terminology (CPT) 45380, 29827, and 70553 and Diagnosis-Related Group (DRG) 807-for the 4 most popular commercial carriers (Health Maintenance Organization [HMO] or Preferred Provider Organization [PPO] plans)-Aetna, Florida Blue, Cigna, and UnitedHealthcare. We conducted an A/B test using 67 MTurkers (randomly selected from US residents), investigating the level of awareness about PT legislation and the usability of available files. We also suggested format standardization, such as master field names using schema integration, to make machine-readable files consistent and usable for machines. RESULTS: The poor usability and inconsistent formats of the current PT information yielded no evidence of its usefulness for patients or its quality for machines. This indicates that the information does not meet the requirements for being consumer-friendly or machine readable as mandated by legislation. Based on the responses to the first part of the experiment (PT awareness), it was evident that participants need to be made aware of the PT legislation. However, they believe it is important to know the service price before receiving it. Based on the responses to the second part of the experiment (human usability of PT information), the average number of correct responses was not equal between the 2 groups, that is, the treatment group (mean 1.23, SD 1.30) found more correct answers than the control group (mean 2.76, SD 0.58; t65=6.46; P<.001; d=1.52). CONCLUSIONS: Consistent machine-readable files across all health systems facilitate the development of tools for estimating customer out-of-pocket costs, aligning with the PT rule's main objective-providing patients with valuable information and reducing health care expenditures.

The Effect of Monetary Incentives on Health Care Social Media Content: Study Based on Topic Modeling and Sentiment Analysis.

BACKGROUND: While there is high-quality online health information, a lot of recent work has unfortunately highlighted significant issues with the health content on social media platforms (eg, fake news and misinformation), the consequences of which are severe in health care. One solution is to investigate methods that encourage users to post high-quality content. OBJECTIVE: Incentives have been shown to work in many domains, but until recently, there was no method to provide financial incentives easily on social media for users to generate high-quality content. This study investigates the following question: What effect does the provision of incentives have on the creation of social media health care content? METHODS: We analyzed 8328 health-related posts from an incentive-based platform (Steemit) and 1682 health-related posts from a traditional platform (Reddit). Using topic modeling and sentiment analysis-based methods in machine learning, we analyzed these posts across the following 3 dimensions: (1) emotion and language style using the IBM Watson Tone Analyzer service, (2) topic similarity and difference from contrastive topic modeling, and (3) the extent to which posts resemble clickbait. We also conducted a survey using 276 Amazon Mechanical Turk (MTurk) users and asked them to score the quality of Steemit and Reddit posts. RESULTS: Using the Watson Tone Analyzer in a sample of 2000 posts from Steemit and Reddit, we found that more than double the number of Steemit posts had a confident language style compared with Reddit posts (77 vs 30). Moreover, 50% more Steemit posts had analytical content and 33% less Steemit posts had a tentative language style compared with Reddit posts (619 vs 430 and 416 vs 627, respectively). Furthermore, more than double the number of Steemit posts were considered joyful compared with Reddit posts (435 vs 200), whereas negative posts (eg, sadness, fear, and anger) were 33% less on Steemit than on Reddit (384 vs 569). Contrastive topic discovery showed that only 20% (2/10) of topics were common, and Steemit had more unique topics than Reddit (5 vs 3). Qualitatively, Steemit topics were more informational, while Reddit topics involved discussions, which may explain some of the quantitative differences. Manual labeling marked more Steemit headlines as clickbait than Reddit headlines (66 vs 26), and machine learning model labeling consistently identified a higher percentage of Steemit headlines as clickbait than Reddit headlines. In the survey, MTurk users said that at least 57% of Steemit posts had better quality than Reddit posts, and they were at least 52% more likely to like and comment on Steemit posts than Reddit posts. CONCLUSIONS: It is becoming increasingly important to ensure high-quality health content on social media; therefore, incentive-based social media could be important in the design of next-generation social platforms for health information.

Pattern discovery, validation, and online experiments: a methodology for discovering television shows for public health announcements.

OBJECTIVE: Public Health Announcements (PHAs) on television are a means of raising awareness about risk behaviors and chronic conditions. PHAs' scarce airtime puts stress on their target audience reach. We seek to help health campaigns select television shows for their PHAs about smoking, binge drinking, drug overdose, obesity, diabetes, STDs, and other conditions using available statistics. MATERIALS AND METHODS: Using Nielsen's TV viewership database for the entire US panel, we presented a novel show discovery methodology for PHAs that combined (i) pattern discovery from high-dimensional data (ii) nonparametric tests for validation, and (iii) online experiments on Facebook. RESULTS: The nonparametric tests verified the robustness of the discovered associations between the popularity of certain shows and health conditions. Findings from fifty (independent) online experiments (where our awareness messages were seen by nearly 1.5 million American adults) empirically demonstrated the value of the methodology. DISCUSSION: For 2016, the methodology identified several shows whose popularities were genuinely associated with certain health conditions, opening up the possibility of health agencies embracing both big data and large-scale experimentation to address an old problem in a new way. CONCLUSION: Policy makers can repeatedly apply the methodology as new data streams in, with perhaps different feature sets, pattern discovery techniques, and online experiments running over longer periods. The comparatively lower initial investment in the methodology can pay off by identifying several shows for a potentially national television campaign. As simply a by-product, the initial investment also results in awareness messages that might reach millions of individuals.

A complex systems perspective of news recommender systems: Guiding emergent outcomes with feedback models.

Algorithms are increasingly making decisions regarding what news articles should be shown to online users. In recent times, unhealthy outcomes from these systems have been highlighted including their vulnerability to amplifying small differences and offering less choice to readers. In this paper we present and study a new class of feedback models that exhibit a variety of self-organizing behaviors. In addition to showing important emergent properties, our model generalizes the popular "top-N news recommender systems" in a manner that provides media managers a mechanism to guide the emergent outcomes to mitigate potentially unhealthy outcomes driven by the self-organizing dynamics. We use complex adaptive systems framework to model the popularity evolution of news articles. In particular, we use agent-based simulation to model a reader's behavior at the microscopic level and study the impact of various simulation hyperparameters on overall emergent phenomena. This simulation exercise enables us to show how the feedback model can be used as an alternative recommender to conventional top-N systems. Finally, we present a design framework for multi-objective evolutionary optimization that enables recommendation systems to co-evolve with the changing online news readership landscape.

Machine learning for psychiatric patient triaging: an investigation of cascading classifiers.

Objective: Develop an approach, One-class-at-a-time, for triaging psychiatric patients using machine learning on textual patient records. Our approach aims to automate the triaging process and reduce expert effort while providing high classification reliability. Materials and Methods: The One-class-at-a-time approach is a multistage cascading classification technique that achieves higher triage classification accuracy compared to traditional multiclass classifiers through 1) classifying one class at a time (or stage), and 2) identification and application of the highest accuracy classifier at each stage. The approach was evaluated using a unique dataset of 433 psychiatric patient records with a triage class label provided by "I2B2 challenge," a recent competition in the medical informatics community. Results: The One-class-at-a-time cascading classifier outperformed state-of-the-art classification techniques with overall classification accuracy of 77% among 4 classes, exceeding accuracies of existing multiclass classifiers. The approach also enabled highly accurate classification of individual classes-the severe and mild with 85% accuracy, moderate with 64% accuracy, and absent with 60% accuracy. Discussion: The triaging of psychiatric cases is a challenging problem due to the lack of clear guidelines and protocols. Our work presents a machine learning approach using psychiatric records for triaging patients based on their severity condition. Conclusion: The One-class-at-a-time cascading classifier can be used as a decision aid to reduce triaging effort of physicians and nurses, while providing a unique opportunity to involve experts at each stage to reduce false positive and further improve the system's accuracy.

Patient Health Record Systems Scope and Functionalities: Literature Review and Future Directions.

BACKGROUND: A new generation of user-centric information systems is emerging in health care as patient health record (PHR) systems. These systems create a platform supporting the new vision of health services that empowers patients and enables patient-provider communication, with the goal of improving health outcomes and reducing costs. This evolution has generated new sets of data and capabilities, providing opportunities and challenges at the user, system, and industry levels. OBJECTIVE: The objective of our study was to assess PHR data types and functionalities through a review of the literature to inform the health care informatics community, and to provide recommendations for PHR design, research, and practice. METHODS: We conducted a review of the literature to assess PHR data types and functionalities. We searched PubMed, Embase, and MEDLINE databases from 1966 to 2015 for studies of PHRs, resulting in 1822 articles, from which we selected a total of 106 articles for a detailed review of PHR data content. RESULTS: We present several key findings related to the scope and functionalities in PHR systems. We also present a functional taxonomy and chronological analysis of PHR data types and functionalities, to improve understanding and provide insights for future directions. Functional taxonomy analysis of the extracted data revealed the presence of new PHR data sources such as tracking devices and data types such as time-series data. Chronological data analysis showed an evolution of PHR system functionalities over time, from simple data access to data modification and, more recently, automated assessment, prediction, and recommendation. CONCLUSIONS: Efforts are needed to improve (1) PHR data quality through patient-centered user interface design and standardized patient-generated data guidelines, (2) data integrity through consolidation of various types and sources, (3) PHR functionality through application of new data analytics methods, and (4) metrics to evaluate clinical outcomes associated with automated PHR system use, and costs associated with PHR data storage and analytics.

Predicting Presidential Election Outcomes from What People Watch.

In a recent article by Barfar and Padmanabhan (2015), we demonstrated how television viewership data could predict presidential election outcomes in the United States. In this article, we examine predictive models using a snapshot of Nielsen's national data on television viewership. The study is conducted with high-dimensional low sample size (HDLSS) data, whereby we conduct a comparative analysis with and without feature reduction on the data from the 2012 elections. We find that simple "single-show models" often provided more insights and predictive accuracies than models from feature reduction. Second, beyond the state and county levels of analysis, we show that the results continue to hold at the designated market area (DMA) level, crucial for television broadcasting because programs are often targeted at the DMA level. Finally, we examine the performance of the single-show models in the 2016 election season by applying them to the viewership information during the U.S. presidential primaries. We discuss implications of our findings for research and practice.

Does Television Viewership Predict Presidential Election Outcomes?

The days of surprise about actual election outcomes in the big data world are likely to be fewer in the years ahead, at least to those who may have access to such data. In this paper we highlight the potential for forecasting the Unites States presidential election outcomes at the state and county levels based solely on the data about viewership of television programs. A key consideration for relevance is that given the infrequent nature of elections, such models are useful only if they can be trained using recent data on viewership. However, the target variable (election outcome) is usually not known until the election is over. Related to this, we show here that such models may be trained with the television viewership data in the "safe" states (the ones where the outcome can be assumed even in the days preceding elections) to potentially forecast the outcomes in the swing states. In addition to their potential to forecast, these models could also help campaigns target programs for advertisements. Nearly two billion dollars were spent on television advertising in the 2012 presidential race, suggesting potential for big data-driven optimization of campaign spending.