Readability, Contracts of Recurring Use, nd the Problem of Ex Post Judicial Governance of Health Insurance Policies

While the rhetoric surrounding the passage of the Patient Protection and Affordable Care Act focused on core issues such as cost, quality, and access to care, the dialog rarely acknowledged a key problem-the fact that most Americans do not understand their health insurance. Simply put, consumers do not fully grasp their health insurance coverage because the jargon found in many health insurance contracts is impenetrable to most Americans. This is disconcerting because consumer-oriented information is central to our increasingly consumer-directed health care system. Consumers are expected to make cost-effective choices among the array of health insurance plans that may be available to them, utilize health care services in a cost-effective manner, navigate provider networks, minimize their out-of-pocket expenses, and effectively appeal denials of coverage. Furthermore, unlike other types of insurance agreements, health insurance policies are contracts of recurring use. That is, health insurance policies are routinely and repeatedly invoked by consumers to finance their health care. Yet, such contracts are written at a level that is beyond the reading skills of most Americans. As such, insureds not only have difficultly understanding the details of their coverage, they do not fully comprehend the benefits and rights afforded by the policy. Consequently, the traditional approach of ex post judicial governance of insurance agreements (as adhesion contracts) by interpreting ambiguities in favor of insureds provides inadequate protection for health insurance consumers. If consumers do not understand their coverage rights and benefits, they cannot reasonably be expected to know when those benefits have been wrongly denied. The better, ex ante solution is to make health insurance contracts readable in the first instance by requiring that health insurance contracts meet an eighth grade readability standard as a condition of state approval

Computational Approaches to Assisting Patients\u27 Medical Comprehension from Electronic Health Records

Patient-centered care has been established as a fundamental approach to improve the quality of health care in a seminal report by the Institute of Medicine published at the start of the century. Improved access to health information and demand for greater transparency contributed to its move into the mainstream. Research has also demonstrated that actively involving patients in the management of their own health can lead to better outcomes, and potentially lower costs. However, despite the efforts in many areas of medicine to embrace patient-centered care, engaging patients is still considered a challenge. One of the barriers is the lack of effective tools to help patients understand their health conditions, options and their consequences. Patient portals are now widely adopted by hospitals and other healthcare practices to provide patients with the capabilities to view their own Electronic Health Records. They are a rich resource of information for patients. However, the language in the records are generally difficult for patients without training in medicine to understand. Furthermore, the amount of information can often be overwhelming as well. In this work, we propose computational approaches to foster patient engagement from three aspects by exploiting the rich information in the medical records. First, we design a framework to automatically generate health literacy instruments to measure a patient\u27s literacy levels. This framework exploits readily available large scale corpora to generate instruments in a commonly used test format. Second, we investigate methods that can determine the readability of complex documents such as health records. We propose to rank document readability, instead of assigning a grade level or a pre-defined difficulty category. Lastly, we examine the problem of finding targeted educational materials to facilitate patient comprehension of medical notes. We study methods to formulate effective queries from specialized and long clinical narratives. In addition, we propose a neural network based method to identify medical concepts that are important to patients. The three aspects of this work address the issues of the overabundance and technical complexity of medical language in health records. We demonstrate that our approaches are effective with various experiments and evaluation metric

Improving Patients\u27 Understanding of their Electronic Medical Record Data in Order to Improve Self-Management - A Quality Improvement Project

Background: Patients are increasingly given access to their electronic medical records (EMRs) to help them keep track of their care, but many may have a difficult time understanding what is in them. Programs such as NoteAid assist in translating medical records and may increase the number of patients who actively use their EMRs, a development which may improve the management of chronic diseases. Purpose: To work on a translation system developed by the University of Massachusetts Informatics group to make outpatient records more understandable for adult patients with chronic disease by using and testing a machine-learning database (NoteAid). Patients’ self-management of chronic disease may improve, as they increase their understanding of medical terminology. Methods: A test version of NoteAid was used with volunteer adult patients during face-to-face sessions in an outpatient office at a health system in Southeastern Pennsylvania. These sessions were used to test NoteAid’s effectiveness as a tool to improve patients’ understanding of their EMRs. Patients read their own office note from a recent visit without the use of NoteAid, and then interpreted the same note using it. Results: 13 participants participated over a two-month period with 85% reporting they would use the system from a patient portal and 100% answering strongly agree or agree when asked if the NoteAid system helped them comprehend their clinical EMR notes. Conclusions: Machine-learning databases like NoteAid have the potential to improve the management of chronic diseases. By integrating these systems into an informative and user-friendly portal, patients are afforded the opportunity to improve understanding of their EMRs. Keywords: medical terms, patient understanding, health literacy, chronic disease, and electronic health record usabilit

Readability of Online Hearing-Based Early Intervention Materials

Purpose: A quantitative readability assessment of currently accessible online materials for parents of children who are D/deaf and hard of hearing. Design: Consistent with current recommendations discussing “grade-level” of materials, Flesch-Kincaid Grade Level (FKGL) analysis, along with five other related measures, was conducted for each website. These analyses provide a readability score for each of the websites analyzed. Study sample: The first five pages of results from a Google search of “early intervention deaf” and “early intervention hear” were compiled for readability assessment. Results: Sixty-three websites were included in the analysis. Following article modification, inter- and intra-rater reliability were excellent (p th-grade reading level (m=12.62, SD=2.65). There was no significant impact of the search page, intended audience, or producer on FKGL (p \u3e.1). Conclusion: Currently accessible online resources for parents looking at early intervention for children who are D/deaf and hard of hearing are written at a level that may not be accessible. Materials may benefit from being revised and edited with readability and health literacy recommendations in mind

Methods to Facilitate the Capture, Use, and Reuse of Structured and Unstructured Clinical Data.

Electronic health records (EHRs) have great potential to improve quality of care and to support clinical and translational research. While EHRs are being increasingly implemented in U.S. hospitals and clinics, their anticipated benefits have been largely unachieved or underachieved. Among many factors, tedious documentation requirements and the lack of effective information retrieval tools to access and reuse data are two key reasons accounting for this deficiency. In this dissertation, I describe my research on developing novel methods to facilitate the capture, use, and reuse of both structured and unstructured clinical data. Specifically, I develop a framework to investigate potential issues in this research topic, with a focus on three significant challenges. The first challenge is structured data entry (SDE), which can be facilitated by four effective strategies based on my systematic review. I further propose a multi-strategy model to guide the development of future SDE applications. In the follow-up study, I focus on workflow integration and evaluate the feasibility of using EHR audit trail logs for clinical workflow analysis. The second challenge is the use of clinical narratives, which can be supported by my innovative information retrieval (IR) technique called “semantically-based query recommendation (SBQR)”. My user experiment shows that SBQR can help improve the perceived performance of a medical IR system, and may work better on search tasks with average difficulty. The third challenge involves reusing EHR data as a reference standard to benchmark the quality of other health-related information. My study assesses the readability of trial descriptions on ClinicalTrials.gov and found that trial descriptions are very hard to read, even harder than clinical notes. My dissertation has several contributions. First, it conducts pioneer studies with innovative methods to improve the capture, use, and reuse of clinical data. Second, my dissertation provides successful examples for investigators who would like to conduct interdisciplinary research in the field of health informatics. Third, the framework of my research can be a great tool to generate future research agenda in clinical documentation and EHRs. I will continue exploring innovative and effective methods to maximize the value of EHRs.PHDInformationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/135845/1/tzuyu_1.pd

Factors affecting engagement in web-based health care patient information: narrative review of the literature

BACKGROUND: Web-based content is rapidly becoming the primary source of health care information. There is a pressing need for web-based health care content to not only be accurate but also be engaging. Improved engagement of people with web-based health care content has the potential to inform as well as influence behavioral change to enable people to make better health care choices. The factors associated with better engagement with web-based health care content have previously not been considered. OBJECTIVE: The aims of this study are to identify the factors that affect engagement with web-based health care content and develop a framework to be considered when creating such content. METHODS: A comprehensive search of the PubMed and MEDLINE database was performed from January 1, 1946, to January 5, 2020. The reference lists of all included studies were also searched. The Medical Subject Headings database was used to derive the following keywords: "patient information," "online," "internet," "web," and "content." All studies in English pertaining to the factors affecting engagement in web-based health care patient information were included. No restrictions were set on the study type. Analysis of the themes arising from the results was performed using inductive content analysis. RESULTS: The search yielded 814 articles, of which 56 (6.9%) met our inclusion criteria. The studies ranged from observational and noncontrolled studies to quasi-experimental studies. Overall, there was significant heterogeneity in the types of interventions and outcome assessments, which made quantitative assessment difficult. Consensus among all authors of this study resulted in six categories that formed the basis of a framework to assess the factors affecting engagement in web-based health care content: easy to understand, support, adaptability, accessibility, visuals and content, and credibility and completeness. CONCLUSIONS: There is a paucity of high-quality data relating to the factors that improve the quality of engagement with web-based health care content. Our framework summarizes the reported studies, which may be useful to health care content creators. An evaluation of the utility of web-based content to engage users is of significant importance and may be accessible through tools such as the Net Promoter score. Web 3.0 technology and development of the field of psychographics for health care offer further potential for development. Future work may also involve improvement of the framework through a co-design process

Information Visualisation Practices for Improving Patient Readability of Blood Pressure, Health Data, and Health Literacy

Personal health data obtained through self-monitoring is often presented through standardised representations with little intrinsic meaning for those who may need it the most since low health literacy is associated with poor health. By failing to inform users about their health status, these representations can be dangerous, leaving patients feeling lost, confused, anxious, or even depressed. Information Visualisation can play an important role in aiding patients making sense of their health data and health status, as long as it's aligned with their needs, motivations, and goals. Following Human Centred Design practices, user research methods were applied in order to understand the context of self-monitorisation, as well as identifying which metrics differed the most from participants' mental models. Thanks to quantitative data obtained from a survey, Blood Pressure was identified as the most problematic health variable. A series of interviews allowed patients of chronic conditions to vocalize the challenges they faced in the management of their conditions. Taking into account information obtained from previous steps, multiple ways to map blood pressure data onto design elements were explored and different visualisations were designed. Finally, said visualisations were tested through guided interviews with patients with blood pressure problems. Results showed that participants prefered different visualisations for different goals, and enjoyed being able to choose freely from them; participants with lower literacy but who were deeply invested in monitoring their health found tables to be the most informative visualizations; finally, participants identified colour scales as the most intuitive method to represent health status and health risk

Self-management in heart failure using mHealth: A content validation.

AIM: To describe the development of a mobile health application -mICardiApp- designed by a multidisciplinary professional team and patients with heart failure and to evaluate its content validity. METHODS: Critical reviews of the literature, semi-structured interviews with patients, and user stories guided the development of the content of the mobile application. These contents were refined and validated through a modified Delphi process. An expert panel of healthcare and social care professionals together with patients and academics evaluated the content through two content validity indicators, relevance, and adequacy, and provided narrative feedback. The content validity of the app and each screen was determined by calculating the Content Validity Index (CVI). Similarly, the Adequacy Index (AI) was analyzed. RESULTS: The developed app is composed by 8 topics: (1) available resources, (2) cardiac rehabilitation, (3) control of signs and symptoms, (4) emotional support, (5) learning and having fun, (6) medication, (7) nutrition, and (8) physical activity. The results demonstrated high CVI of the screens and the full app. 57 of the 59 screens in the app reached an excellent CVI≥0.70 for both relevance and adequacy, except for 2 screens. The CVI Average Method of the app was 0.851. CONCLUSIONS: mICardiApp is presented as an application to improve health literacy and self-management of patients with multimorbidity and heart failure, with proven validation