Hadi Kharrazi

Mobile personal health records: An evaluation of features and functionality

PURPOSE To evaluate stand-alone mobile personal health record (mPHR) applications for the three leading cellular phone platforms (iOS, BlackBerry, and Android), assessing each for content, function, security, and marketing characteristics. METHODS Nineteen stand-alone mPHR applications (8 for iOS, 5 for BlackBerry, and 6 for Android) were identified and evaluated. Main criteria used to include mPHRs were: operating standalone on a mobile platform; not requiring external connectivity; and covering a wide range of health topics. Selected mPHRs were analyzed considering product characteristics, data elements, and application features. We also reviewed additional features such as marketing tactics. RESULTS Within and between the different mobile platforms attributes for the mPHR were highly variable. None of the mPHRs contained all attributes included in our evaluation. The top four mPHRs contained 13 of the 14 features omitting only the in-case-of emergency feature. Surprisingly, seven mPHRs lacked basic security measures as important as password protection. The mPHRs were relatively inexpensive: ranging from no cost to

A Scoping Review of Health Game Research: Past, Present, and Future

9.99. The mPHR application cost varied in some instances based on whether it supported single or multiple users. Ten mPHRs supported multiple user profiles. Notably, eight mPHRs used scare tactics as marketing strategy. CONCLUSION mPHR is an emerging health care technology. The majority of existing mPHR apps is limited by at least one of the attributes considered for this study; however, as the mobile market continues to expand it is likely that more comprehensive mPHRs will be developed in the near future. New advancements in mobile technology can be utilized to enhance mPHRs by long-term patient empowerment features. Marketing strategies for mPHRs should target specific subpopulations and avoid scare tactics.

Facilitators and Barriers to Adopting Robotic-Assisted Surgery: Contextualizing the Unified Theory of Acceptance and Use of Technology

Health game research has flourished over the last decade. The number of peer-reviewed scientific publications has surged as the clinical application of health games has diversified. In response to this growth, several past literature reviews have assessed the effectiveness of health games in specific clinical subdomains. The past literature reviews, however, have not provided a general scope of health games independent of clinical context. The present systematic review identified 149 publications. All sources were published before 2011 in a peer-reviewed venue. To be included in this review, publications were required (1) to be an original research, (2) to focus on health, (3) to utilize a sound research design, (4) to report quantitative health outcomes, and (5) to target healthcare receivers. Initial findings showed certain trends in health game publications: Focus on younger male demographics, relatively low number of study participants, increased number of controlled trials, short duration of intervention periods, short duration and frequency of user-game interaction, dominance of exercise and rehab games, lack of underlying theoretical frameworks, and concentration on clinical contexts such as physical activity and nutrition. The review concludes that future research should (1) widen the demographics to include females and elderly, (2) increase the number of participants in controlled trials, (3) lengthen both the intervention period and user-game interaction duration, and (4) expand the application of health games in new clinical contexts.

Extending the Use of Games in Health Care

Robotic-assisted surgical techniques are not yet well established among surgeon practice groups beyond a few surgical subspecialties. To help identify the facilitators and barriers to their adoption, this belief-elicitation study contextualized and supplemented constructs of the unified theory of acceptance and use of technology (UTAUT) in robotic-assisted surgery. Semi-structured individual interviews were conducted with 21 surgeons comprising two groups: users and nonusers. The main facilitators to adoption were Perceived Usefulness and Facilitating Conditions among both users and nonusers, followed by Attitude Toward Using Technology among users and Extrinsic Motivation among nonusers. The three main barriers to adoption for both users and nonusers were Perceived Ease of Use and Complexity, Perceived Usefulness, and Perceived Behavioral Control. This studys findings can assist surgeons, hospital and medical school administrators, and other policy makers on the proper adoption of robotic-assisted surgery and can guide future research on the development of theories and framing of hypotheses.

Healthcare Game Design: Behavioral Modeling of Serious Gaming Design for Children with Chronic Diseases

Digital games have the ability to engage both children and adults alike. We are exploring the use of games for children with long term treatment regimes, where motivation for compliance is a key factor in the success of the treatment. In this paper, we describe the game framework we are building for this purpose. This framework is meant to support the long term use of a gaming world for children with three main goals: (a) provide easy and continual gaming access on a range of computing appliances including small screen devices; (b) offer games that can be personalized and are adaptable based on the child’s interests or specific illness; and (c) maintain novelty and interest in the treatment over time. This framework not only provides a benefit to the children involved, but also provides user data to the coaches, clinicians, and health researchers involved in the child’s treatment regime.

Characterizing diabetes, diet, exercise, and obesity comments on Twitter

This article introduces the design principles of serious games for chronic patients based on behavioral models. First, key features of the targeted chronic condition (Diabetes) are explained. Then, the role of psychological behavioral models in the management of chronic conditions is covered. After a short review of the existing health focused games, two recent health games that are developed based on behavioral models are overviewed in more detail. Furthermore, design principles and usability issues regarding the creation of these health games are discussed. Finally, the authors conclude that designing healthcare games based on behavioral models can increase the usability of the game in order to improve the effectiveness of the games desired healthcare outcomes.

Comparing population-based risk-stratification model performance using demographic, diagnosis and medication data extracted from outpatient electronic health records versus administrative claims

Social media provide a platform for users to express their opinions and share information. Understanding public health opinions on social media, such as Twitter, offers a unique approach to characterizing common health issues such as diabetes, diet, exercise, and obesity (DDEO), however, collecting and analyzing a large scale conversational public health data set is a challenging research task. The goal of this research is to analyze the characteristics of the general publics opinions in regard to diabetes, diet, exercise and obesity (DDEO) as expressed on Twitter. A multi-component semantic and linguistic framework was developed to collect Twitter data, discover topics of interest about DDEO, and analyze the topics. From the extracted 4.5 million tweets, 8% of tweets discussed diabetes, 23.7% diet, 16.6% exercise, and 51.7% obesity. The strongest correlation among the topics was determined between exercise and obesity. Other notable correlations were: diabetes and obesity, and diet and obesity DDEO terms were also identified as subtopics of each of the DDEO topics. The frequent subtopics discussed along with Diabetes, excluding the DDEO terms themselves, were blood pressure, heart attack, yoga, and Alzheimer. The non-DDEO subtopics for Diet included vegetarian, pregnancy, celebrities, weight loss, religious, and mental health, while subtopics for Exercise included computer games, brain, fitness, and daily plan. Non-DDEO subtopics for Obesity included Alzheimer, cancer, and children. With 2.67 billion social media users in 2016, publicly available data such as Twitter posts can be utilized to support clinical providers, public health experts, and social scientists in better understanding common public opinions in regard to diabetes, diet, exercise, and obesity.

What’s Past is Prologue: A Scoping Review of Recent Public and Global Health Informatics Literature

Background: There is an increasing demand for electronic health record (EHR)–based risk stratification and predictive modeling tools at the population level. This trend is partly due to increased value-based payment policies and the increasing availability of EHRs at the provider level. Risk stratification models, however, have been traditionally derived from claims or encounter systems. This study evaluates the challenges and opportunities of using EHR data instead of or in addition to administrative claims for risk stratification. Methods: This study used the structured EHR records and administrative claims of 85,581 patients receiving outpatient care at a large integrated provider system. Common data elements for risk stratification (ie, age, sex, diagnosis, and medication) were extracted from outpatient EHR records and administrative claims. The performance of a validated risk-stratification model was assessed using data extracted from claims alone, EHR alone, and claims and EHR combined. Results: EHR-derived metrics overlapped considerably with administrative claims (eg, number of chronic conditions). The accuracy of the model, when using EHR data alone, was acceptable with an area under the curve of ∼0.81 for hospitalization and ∼0.85 for identifying top 1% utilizers using the concurrent model. However, when using EHR data alone, the predictive model explained a lower amount of variation in utilization-based outcomes compared with administrative claims. Discussion: The results show a promising performance of models predicting cost and hospitalization using outpatient EHR’s diagnosis and medication data. More research is needed to evaluate the benefits of other EHR data types (eg, lab values and vital signs) for risk stratification.

Prospective EHR-Based Clinical Trials: The Challenge of Missing Data

Objective: To categorize and describe the public health informatics (PHI) and global health informatics (GHI) literature between 2012 and 2014. Methods: We conducted a semi-systematic review of articles published between January 2012 and September 2014 where information and communications technologies (ICT) was a primary subject of the study or a main component of the study methodology. Additional inclusion and exclusion criteria were used to filter PHI and GHI articles from the larger biomedical informatics domain. Articles were identified using MEDLINE as well as personal bibliographies from members of the American Medical Informatics Association PHI and GHI working groups. Results: A total of 85 PHI articles and 282 GHI articles were identified. While systems in PHI continue to support surveillance activities, we identified a shift towards support for prevention, environmental health, and public health care services. Furthermore, articles from the U.S. reveal a shift towards PHI applications at state and local levels. GHI articles focused on telemedicine, mHealth and eHealth applications. The development of adequate infrastructure to support ICT remains a challenge, although we identified a small but growing set of articles that measure the impact of ICT on clinical outcomes. Discussion: There is evidence of growth with respect to both implementation of information systems within the public health enterprise as well as a widening of scope within each informatics discipline. Yet the articles also illuminate the need for more primary research studies on what works and what does not as both searches yielded small numbers of primary, empirical articles. Conclusion: While the body of knowledge around PHI and GHI continues to mature, additional studies of higher quality are needed to generate the robust evidence base needed to support continued investment in ICT by governmental health agencies.

Fuzzy Approach Topic Discovery in Health and Medical Corpora

This discussion focuses on the challenges of using prospectively collected electronic health record (EHR) data as outcomes in clinical trials, with a particular emphasis on the issue of missing data. Our discussion is motivated by the article in this issue: ‘Translating the Hemoglobin A1C with More Easily Understood Feedback: A Randomized Controlled Trial’ by Gopalan et al.1 In the spirit of open science, the authors generously shared their study protocol, statistical analysis plan and analysis data set. Using their data set, we conducted analyses to help emphasize important statistical issues. This editorial should not be considered a criticism of their paper; rather, their study is used as a reference to expand on the challenges of missing data in EHRs and to provide suggestions for future studies.