Exploring Patients’ Use Intention of Personal Health Record Systems: Implications for Design

Abstract

Personal health record (PHR) systems offer a technology for personal health information management (PHIM) activities. Despite efforts to increase the use of PHR systems as a mechanism to support better patient-centered care and improve information management across the continuum of care, PHR adoption remains low. The purpose of this study was to explore how to design a PHR system that can adequately support personal health information management activities. Using a mixed-methods approach (questionnaires and interviews), we identified the factors affecting a person’s intention to use PHRs and also described the personal health information management activities among people from a wide age range in the United States. Results indicated that the intention to use PHR systems was affected by system-related factors, such as perceived usefulness, health information understandability, personalization, and patient–clinician communication support, and user-related factors, such as social influence, self-efficacy, and willingness to share. Furthermore, five types of personal health information management activities were found, including storage, organization, maintenance, retrieval, and sharing. Informed by the study findings, we developed seven design recommendations to improve PHR systems. Future studies can focus on further validating these findings using other methods and be based on larger and more representative PHR users.     

Hedaquin: A Reputation-based Health Data Quality Indicator

A number of applications based on personal health records (PHRs) are emerging in the field of health care and wellness. PHRs empower patients by giving them control over their health data. Health data for PHRs can be supplied by patients, wellness providers and health care providers. Health care providers may use the PHRs to provide medical care. Unfortunately, the quality of the health data cannot be guaranteed in all cases (e.g. consider cases where non-professionals such as patients and wellness providers supplied the data).To address this problem, we present in this paper Hedaquin, a system that provides health care professionals with an indication of the quality of health data in a PHR. This indication is based on the reputation of the supplier and on metadata provided by measurement devices. The proposed reputation system mimics the way trust in health data and their suppliers is built in the real world.Hedaquin uses the Beta reputation system as a starting point and extends it in several directions to cover specific PHR requirements. Firstly, Hedaquin supports the automatic calculation of a rating based on a repeated measurement. Secondly, certificates for the user such as diplomas are taken into account. Thirdly, Hedaquin calculates reputation for different scopes in order to discriminate among different tasks the suppliers of health data can perform. Finally, the time difference between the ratings and the calculation of the reputation influences the weight that is given to a rating.     

Understanding the evaluation of mHealth app features based on a cross-country Kano analysis

While mobile health (mHealth) apps play an increasingly important role in digitalized health care, little is known regarding the effects of specific mHealth app features on user satisfaction across different healthcare system contexts. Using personal health record (PHR) apps as an example, this study identifies how potential users in Germany and Denmark evaluate a set of 26 app features, and whether evaluation differences can be explained by the differences in four pertinent user characteristics, namely privacy concerns, mHealth literacy, mHealth self-efficacy, and adult playfulness. Based on survey data from both countries, we employed the Kano method to evaluate PHR features and applied a quartile-based sample-split approach to understand the underlying relationships between user characteristics and their perceptions of features. Our results not only reveal significant differences in 14 of the features between Germans and Danes, they also demonstrate which of the user characteristics best explain each of these differences. Our two key contributions are, first, to explain the evaluation of specific PHR app features on user satisfaction in two different healthcare contexts and, second, to demonstrate how to extend the Kano method in terms of explaining subgroup differences through user characteristic antecedents. The implications for app providers and policymakers are discussed.

     

User preference mining techniques for personalized applications

Advanced personalized e-applications require comprehensive knowledge about their users’ likes and dislikes in order to provide individual product recommendations, personal customer advice, and custom-tailored product offers. In our approach we model such preferences as strict partial orders with “A is better than B” semantics, which has been proven to be very suitable in various e-applications. In this paper we present preference mining techniques for detecting strict partial order preferences in user log data. Real-life e-applications like online shops or financial services usually have large log data sets containing the transactions of their customers. Since the preference miner uses sophisticated SQL operations to execute all data intensive operations on database layer, our algorithms scale well even for such large log data sets. With preference mining personalized e-applications can gain valuable knowledge about their customers’ preferences, which can be applied for personalized product recommendations, individual customer service, or one-to-one marketing.     

A cognitive approach for agent-based personalized recommendation

There is an increasing need for various e-service, e-commerce and e-business sites to provide personalized recommendations to on-line customers. This paper proposes a new type of personalized recommendation agents called fuzzy cognitive agents. Fuzzy cognitive agents are designed to give personalized suggestions based on the user’s current personal preferences, other user’s common preferences, and expert’s domain knowledge. Fuzzy cognitive agents are able to represent knowledge via extended fuzzy cognitive maps, to learn users’ preferences from most recent cases and to help customers make inferences and decisions through numeric computation instead of symbolic and logic deduction. A case study is included to illustrate how personalized recommendations are made by fuzzy cognitive agents in e-commerce sites. The case study demonstrates that the fuzzy cognitive agent is both flexible and effective in supporting e-commerce applications.     

On the making of service recommendations: An action theory based on utility,reputation, and risk attitude

In the current service-oriented economy, professional workforce and service personnel have to make not only reasonable but also personalized recommendations in response to individual customer’s query. These actions affect not only the likelihood that the customer takes the recommendations in a short-term but also the service provider’s reputation in a long run because often more risky recommendations may provide more utility but failures have a negative impact to reputation. As different customers have different risk attitudes, they have different trade-off between the service providers’ reputation and the recommendations’ utilities. Therefore, the classical decision model considering only the utility is inadequate. We reconsider the problem of making recommendations from multiple perspectives, including also reputation and risk attitude. Based on this model, we propose an action theory to explain how service providers can act effectively at the strategic, tactical, and operations level, particularly through personalized recommendations.     

A semantic-expansion approach to personalized knowledge recommendation

The rapid propagation of the Internet and information technologies has changed the nature of many industries. Fast response and personalized recommendations have become natural trends for all businesses. This is particularly important for content-related products and services, such as consulting, news, and knowledge management in an organization. The digital nature of their products allows for more customized delivery over the Internet. To provide personalized services, however, a complete understanding of user profile and accurate recommendation are essential.In this paper, an Internet recommendation system that allows customized content to be suggested based on the user's browsing profile is developed. The method adopts a semantic-expansion approach to build the user profile by analyzing documents previously read by the person. Once the customer profile is constructed, personalized contents can be provided by the system. An empirical study using master theses in the National Central library in Taiwan shows that the semantic-expansion approach outperforms the traditional keyword approach in catching user interests. The proper usage of this technology can increase customer satisfaction.     

Older adults’ perceptions of usefulness of personal health records

Electronic personal health records (PHRs) have the potential to both make health information more accessible to patients and function as a decision-support system for patients managing chronic conditions. Age-related changes in cognition may make traditional strategies of integrating and understanding existing (i.e., paper-based) health information more difficult for older adults. The centralized and integrated nature of health information, as well as the long-term tracking capabilities present in many PHRs, may be especially beneficial for older patients’ management of health. However, older adults tend to be late adopters of technology and may be hesitant to adopt a PHR if the benefits are not made clear (perceived usefulness). Toward the design of a useful PHR, a needs analysis was conducted to determine how people currently manage their health information, what they perceive as useful, and to identify any unmet needs. This paper describes two qualitative studies examining the health information needs of both younger and older adults. The first study used a 2-week diary methodology to examine everyday health questions or concerns, while the second study examined maintenance of health information and perceptions of PHRs through the use of a three-part interview. User’s perceptions of the usefulness of PHRs are provided as recommendations for the design of e-health technology, especially those targeted for older adult healthcare consumers. The results suggest that both older and younger adults would deem a PHR useful if it provides memory support in the form of reminders, provides tools to aid in comprehension of one’s health concerns, is interactive and provides automatic functions, and is highly accessible to authorized users, yet one’s information is kept secure and private.     

Mining health-risk factors using PHR similarity in a hybrid P2P network

In an era of many diseases and increased longevity, more attention has been paid to chronic diseases that require constant health care. Under this circumstance, the development of research and development (R&D) for smart-device-based constant health care has drawn great attention. With the emergence of wearable devices, personal health devices (PHDs), and smartphones, various contents for constant health care have been developed. By using these devices, the users are able to collect personal health records (PHRs) that include data such as activity amount, heart rate, stress, and blood sugar. The range of the collected PHRs can be limited depending on the equipment or the surrounding environment. To overcome this problem, it is necessary to make a comparison with similar users in a cluster. Also, it is necessary to provide a service that can analyze and visually display the collected personal-health information. In this paper, we propose the mining of health-risk factors using the PHR similarity in a hybrid P2P network. This is a method of predicting a user’s health status using similarity-based data mining, where the PHRs are employed in a hybrid P2P environment consisting of a peer, a server, and a gateway. In a hybrid P2P environment, a user receives feedback on the result of a structured-data analysis. A peer searches for a different peer and gateway through a server and exchanges information. Depending on the data type, the PHR is divided into medical health examination, self-diagnosis, and personal-health data. The medical health examination contains the personal-health data that are generated regularly by a medical institution. Self-diagnosis represents the data of mental health, pains, and fatigue that can be changed often but cannot be collected by devices. Personal-health data mean the data that can be collected by individuals in everyday life. For the PHR-data analysis, an index is given to each attribute, and preprocessing is performed after a binary-code conversion. To predict a user’s health status, the PHR data are clustered on the basis of similarity in a hybrid P2P environment. The similarity between a user’s PHR and a PHR that is searched for in the network is measured. After the measurement, an index is given to the PHR that meets the minimum similarity and the PHR is incorporated into a Similarity PHR Group. The Similarity PHR Group flexibly changes depending on a user’s PHR status and the statuses of the users who have accessed the hybrid P2P network. A representative value of the Similarity PHR Group is extracted and is then compared with the user’s PHR to judge the user’s health status. The proposed method is suitable for a smart health service for chronic diseases requiring constant care, elderly health, and aftercare. This is a user-oriented health-care and promotion service wherein a user’s health status can be predicted through the mining of the health-risk factors of PHRs.     

Living Profiles: an example of user-centered design in developing a teen-oriented personal health record

Living Profiles is a personal health record (PHR) designed for and by teens with chronic diseases transitioning from the world of pediatric care to the adult system of medical care. It incorporates typical teen behaviors and attitudes about health and wellness while promoting independence, empowerment, and self-care. Our multi-disciplinary team of designers, medical providers, and engineers employed a user-centered design approach to create a PHR prototype based on teen-specific needs, behaviors, and personal experiences. We advocate a human-centered design approach, especially in the collection of data that adolescents find important and insightful, such as peer interactions, short- and long-term aspirations, and goals. These data can be leveraged to be a part of a successful clinical encounter and promote better self-awareness and chronic disease management. Our collaboration that resulted in a semi-working prototype populated by data important both to teen and medical provider became a launch point for more meaningful patient-healthcare provider exchanges.     

A user-centric evaluation of context-aware recommendations for a mobile news service

Traditional recommender systems provide personal suggestions based on the user’s preferences, without taking into account any additional contextual information, such as time or device type. The added value of contextual information for the recommendation process is highly dependent on the application domain, the type of contextual information, and variations in users’ usage behavior in different contextual situations. This paper investigates whether users utilize a mobile news service in different contextual situations and whether the context has an influence on their consumption behavior. Furthermore, the importance of context for the recommendation process is investigated by comparing the user satisfaction with recommendations based on an explicit static profile, content-based recommendations using the actual user behavior but ignoring the context, and context-aware content-based recommendations incorporating user behavior as well as context. Considering the recommendations based on the static profile as a reference condition, the results indicate a significant improvement for recommendations that are based on the actual user behavior. This improvement is due to the discrepancy between explicitly stated preferences (initial profile) and the actual consumption behavior of the user. The context-aware content-based recommendations did not significantly outperform the content-based recommendations in our user study. Context-aware content-based recommendations may induce a higher user satisfaction after a longer period of service operation, enabling the recommender to overcome the cold-start problem and distinguish user preferences in various contextual situations.     

Impact of design methods on decision support systems success: An empirical assessment

Many of the Decision Support Systems (DSS) developed and implemented in the last decade or so, have not been used or are only used for mundane report generation rather than for direct support of strategic planning. These failures can be attributed to the fact that existing DSS design methods (representation-based, evolutive, and adaptive) have never been evaluated with respect to their success. Thus, the designer had no way of knowing what design method to employ in developing an effective DSS.This paper investigated the relationship between the selection of a DSS design method and its ultimate success. DSS success was measured through DSS usage, user satisfaction, and user attitudes and perceptions criteria. The data utilized was collected from individuals responsible for designing and evaluating DSS, specifically the managers, intermediaries, and designers. In terms of the DSS usage and user satisfaction criteria, all three groups rated the evolutive design method superior, followed by the representation-based and adaptive methods. Using the DSS attitudes and perceptions criteria, the designer group rated the evolutive method best, followed by the representation-based, and adaptive methods. The ratings by the managers and intermediaries were rather mixed. Each design method did better in some success criteria than in others.     

Understanding continuance usage intention of mobile internet sites

Due to the high acquisition costs and low switching costs, retaining users and facilitating their continuance usage are crucial for mobile service providers. Integrating both perspectives of perceived utility and flow experience, this research identifies the factors affecting continuance usage intention of mobile internet sites. Data were collected through a survey, and data analysis was then conducted with structural equation modeling. The results indicated that system quality and information quality affect perceived usefulness, satisfaction and flow. And these three factors determine continuance usage intention. Among them, flow has the largest effect on continuance usage intention. The results imply that service providers need to improve users’ experience in order to facilitate their continuance usage of mobile internet sites.     

A web content manipulation technique based on page Fragmentation

Web portals today offer a variety of content and services to their users. This content can be split into various categories and usually content semantically related is placed in the same area. In this paper, a software technique is presented that allows the viewers of web sites to build their own personalized portals, using specific areas of their preferred sites. This technique saves users’ time and reduces the cost of browsing the web by minimizing the volume of data that has to be downloaded. It is based on an algorithm, which fragments a web page in discrete fragments using the page's internal structure. Users utilize a web interface to define which parts of selected web pages they desire to appear in their personalized portal. No additional software needs to be installed on the users’ personal computers, since this technique is designed to function centrally as a data source for a Web Server. In addition, usage of this technique reduces user perceived latency during browsing sessions, since less data must be transferred to users’ personal computers.     

A laboratory study of user characteristics and decision-making performance in end-user computing

A laboratory study using a simulated business setting was conducted to examine relationships among user characteristics, DSS usage patterns, and decision-making performance. MBA-student subjects self-selected into one of two categories: (1) “end-user programmers” who chose to use the DSS modeling language to develop personalized decision models, or (2) “command level users” who used only “canned” models and query language retrievals. The results show that early development of personalized decision models was positively related to the end-user's previous computer experience. The findings also support the contention that end-user programming improves decision-making performance. End-user programmers consistently outperformed the command level users in terms of their firm's stock price, market share, and return on assets.     

An empirical examination of patient-physician portal acceptance

Healthcare providers have recently begun deploying Internet-based patient–physician portals. These applications allow patients to both communicate with their providers and access personal medical information, such as laboratory results. Research within medical informatics explores, and debates, the utility of such functions from the patient and physician perspectives. However, work does not examine factors influencing patient use, assuming that once built and deployed patients will come. Hence, the current research empirically investigates patient behavioral intentions (BI) with respect to (a) use of electronic communications functions and (b) accessing of individual medical information through an Internet-based patient–physician portal. Surveying 294 patients, this work incorporates individual factors, namely computer self-efficacy and personal innovativeness in the domain of information technology, with technology-based factors identified in the Technology Acceptance Model. The analysis finds that usefulness and innovativeness have a positive direct effect on BI with respect to both functions, namely communications and information access. Additionally, patients with greater healthcare needs foresee increased use of portals to access their personal medical information. Finally, patients in primary care, as opposed to specialist, provider settings intend to engage in electronic communications.     

Understanding Service Quality and System Quality Success Factors in Cloud Archiving From an End-User Perspective

This study seeks to explain the adoption of cloud storage services as a means of personal archiving thereby focusing on users’ service and system quality perceptions and their drivers. The authors derive and empirically validate a model that incorporates users’ perceptions of service/system quality as well as behavioral factors to explain usage. Finally, the authors highlight important determinants of system/service quality perceptions that cloud providers should pay attention to in their attempts to increase marketshare.     

Combining personal diaries with territorial intelligence to empower diabetic patients

Information is today recognized as a major source of benefit, for those who are able to properly create and manage it. With the advent of new computing, storing and networking technologies, transforming data into useful, ‘marketable’ information has become a major goal for companies, organizations and governments. The healthcare domain makes no exception. Governments and healthcare companies are paying increasing attention to patient-centered care and to its positive effects on business metrics, such as finances, quality, safety, satisfaction and market share. Appropriate information sharing and communication are then recognized to be one of the key factors for patient-centered care. In this paper, we propose an infrastructure for defining new processes to support a fruitful exchange of strategic information at the local level, which could encourage local dynamics and improve the relationships between ultimate users and the territory and vice versa. This is particularly relevant in the healthcare domain, so we have developed a mobile application to provide diabetic patients with personalized services based on a technology with a low level invasive impact, through the experimentation of new process models meant to share information and integrate software components. Such a special-purpose application is meant to improve care experience of diabetic patients while creating public value for services. This is achieved by a profitable combination of territorial knowledge with personal data and events available and processed on smartphones.