explaining the adoption of personal health records with health belief ...

EXPLAINING THE ADOPTION OF PERSONAL HEALTH RECORDS WITH HEALTH BELIEF MODEL: AN EXAMPLE OF STROKE

ABSTRACT The increasing prevalence of personal health records over decades, the government and private sectors have all encourage personal health records (PHRs) adoption. The purpose of this study was to analysis the individual perception factor based on belief theory, and provides the comment to bridge the gap between PHRs and patients. This paper will focus on the dimension of social cognition to explain the patient’s health behavior. Our findings including low “perceived seriousness”, low “perceived susceptibility” and “perceived barriers to act”. Design for such case which had a poor perception on seriousness and susceptibility of disease, the functional of risk prediction reminder may be the first priority.From social-cognition perspective, it may have contribution to look insight into behavioral research opportunities in accelerate the adoption of PHR.

KEYWORDS Health Belief Model, Personal health Record, Stroke

1. INTRODUCTION The increasing prevalence of personal health records over decades, the government and private sectors have all encourage personal health records (PHRs) adoption. Consumers with chronic conditions, “3C,” represent the first wave of 21st-century healthcare consumers and are very familiar with the healthcare system and are more likely to use PHRs (Leonard, Casselman, and Wiljer 2008). For patients, PHR have a wide variety of potential benefits. PHR offer the promise of improving disease management, empowering patients, reducing the overall costs of healthcare(Reti, Feldman, and Safran 2009). While the potential for using PHR to improve healthcare has been acknowledged, the enabling technology are still not always accepted by patients for variety of reason such as including individual factors (Or and Karsh 2009). There are gaps between PHRs and patients that lead to slow adoption of PHR. The purpose of this study was to analysis the individual perception factor based on belief theory, set an adopted scenario and provides the comment to develop PHR.

2. DEFINITION, BENEFITS, and BARRIERS OF PHR Personal health record referred to as consumer health information technology, has been upsurge for improving healthcare system, disease management. PHR is described as an electronic application through which individuals can access, manage and share their health information. It can be helpful in maintaining health and wellness as well as illness recovery (Chen et al. 2008). The electronic applications for PHR have various types range from smart card, CDs, websites to personal digital assistants. Those devices facilitate the users (1) collect, monitor, and organize daily health data; (2) gather knowledge; and (3) share and query health information or personal data (Lee, Kozar, and Larsen 2003). For patients, PHRs have a wide variety of potential benefits. Patients with chronic illnesses will be able to track their diseases in conjunction with their providers, promoting earlier interventions when they encounter a deviation or problem (Chen et al. 2008; Halamka, Mandl, and Tang 2008; Lee, Kozar, and Larsen 2003). Apparently the PHRs were well developed; such consumer health information technologies can not yet fulfill the need of patients to manage their own health and disease. Currently developed products of PHRs are being created by vendors or agencies, such as online Microsoft HealthVault, GoogleHealth, HealthSpace (Reti, Feldman, and Safran 2009), MyChart (Halamka, Mandl, and Tang 2008), IowaPHR (Lee, Kozar, and Larsen 2003). Large number questions were raised from researchers to investigate the challenge of PHR adoption. The influential factors and challenges for accelerate the acceptance rate from previously studies on utilization of PHR services, including individual factor, environmental barrier, human-technology interaction factors and task factor (Halamka, Mandl, and Tang 2008; Kahn, Aulakh, and Bosworth 2009; Chen et al. 2008). There is a gap between today’s PHRs and what they want and need from this electronic tool for managing their health (Kahn, Aulakh, and Bosworth 2009). PHRs help improve patient compliance and play a key role in health promotion. After wide adopted, PHRs will become even more valuable for patients once their functionality match patient’s desire and ideality.

3. THE INDIVIDUAL FACTOR IMPACT THE PHR ADOPTION The main control of PHR handle by patient owner, some reasons are influencing their desire to use the PHRs in the real world. One of the importance obstacles is the patient individual-level barriers that were proposed in a 2005 working symposium of the American College of Medical Informatics(Chen et al. 2008). The barriers to PHR adoption among patients include cost, concern that information is not protected, and inability to share information across organization (Kahn, Aulakh, and Bosworth 2009), sociodemographic factors, and voluntariness of use (Dasgupta et al. 2009; Or and Karsh 2009). However, there is very few studies measured health belief and voluntariness of use to date especially in healthcare information technology field. Widespread adoption and use of PHRs will occur till PHRs provide perceptible value to users, are easy to

learn and easy to use, and have justified costs related to the PHR’s perceived value. Most studies discuss technology acceptance’s predictors were perceived usefulness and perceived ease of use which were posited by Davis’ technology acceptance model but ignore the motivation to act. To initiate PHR adoption such a new health service, it would be helpful to understand patients’ health belief regarding perception on course of disease and then motivate them to act. Developing the PHR, research suggested to know what patients want and need for managing their disease (Kahn, Aulakh, and Bosworth 2009). Although PHR programmer designs the common function and the content can subsume the interface easy and simple following the existing guideline. A study recommend to design the PHR should deep understand individual’s daily lives related to health challenge, for its design to be driven by health behavior theories such as health belief model(Lee, Kozar, and Larsen 2003).

4. THE HEALTH BELIEF MODEL The Health Belief Model (HBM) is by far the most commonly used theory in health education and health promotion, using health service predition also focus on personal health behavior(Taylor et al. 2006; Rosenstock 1966). The Health Belief Model is a conceptual framework used to understand health behavior and possible reasons for non-compliance with recommended health action. Recently, the HBM was used to predict the acceptance of new technology (Davis, Bagozzi, and Warshaw 1989; van de Wijngaert and Bouwman 2009; Taylor et al. 2006; Huang and Lin 2009). The following four perceptions serve as the main constructs of the model: perceived seriousness, perceived susceptibility, perceived benefits, and perceived barriers. Theoretical constructs explanations as follows: y Perceived Seriousness：The construct of perceived seriousness speaks to an individual’s belief about the seriousness or severity of a disease. y Perceived Susceptibility：Personal risk or susceptibility is one of the more powerful perceptions in prompting people to adopt healthier behaviors. The greater the perceived risk, the greater the likelihood of engaging in behaviors to decrease the risk. y Perceived Benefits：The construct of perceived benefits is a person’s opinion of the value or usefulness of a new behavior in decreasing the risk of developing a disease. People tend to adopt healthier behaviors when they believe the new behavior will decrease their chances of developing a disease. y Perceived Barriers：This is an individual’s own evaluation of the obstacles in the way of him or her adopting a new behavior. Of all the constructs, perceived barriers are the most significant in determining behavior change. The HBM factors, susceptibility, severity, benefits, and barriers would predict the intention to undertake an action with the potential to reduce illness risks (Figure 1).

5. THE CASE STUDY AND ANALYSIS GUIDED BY HBM We use case study and take stroke disease as an example to analyze health behavior and develop the strategies to accept PHR technology. The analysis categorized into three levels: individual perception, assessments, and likelihood of action. Each level will be elucidated with the concept of HBM theory. The author proposed the following one case study from several stroke patients those were interviewed with regarding their illness experience and information need from personal health information technology.

5.1. The Case Mr. Huang was an obese 55 year-old married Taiwanese lived with daughter in a mall town on the north coast of Taiwan. His lifestyle was not healthy and irregular because he had to work in several cities between China with Taiwan. He said “Sometime I stayed in Shandong (China) in a daytime then flight to Shanghai (China) in night-time for business”. The poor habits such as alcohol drinking and smoking also had admitted. One day in May 2007, the first time stroke attacked, Mr. Huang suffered severe dizzy then went to hospital for examination but without abnormal result. The symptom disappeared after few days naturally. He did not take it seriously and kept the life as usual. The second time: One year after that sudden event, he suffered numbness sensation on the right limb and right side of the face. He was admitted at local hospital of Shandong (China) one week for examination but still unknown the etiology. A cerebrovascular disease was detected until he was referred to a Medical Center in Taiwan for advance examination. The physician told him that he had recurrence attacked of minor stroke according the result of magnetic resonance imaging (MRI) examination. He did not sure what to do and how the stroke will progress, and then he decided to obey the physician’s order somewhat. In fact, he still took medicine irregularly for prevent recurrence. The patient was unknown the serious outcome of stroke disease.

The third time: One day, the stroke attacked more seriously then before, he got tremor on both of limbs and was disabled in handling. After receiving some recommendations from the hospital, he decided to make a change. The question he asked ‘How to assure golden three hour for stroke treatment?” He expressed an interesting on the prevention method, such as finding the warning signs and adjusting lifestyle. He reported “I had no idea to access a technology tool for preventive purpose and maintain healthy life.” He hoped the personal health information technology would be helpful and safety for his health.

5.2. Case analysis applied with HBM theory This paper used the HBM conceptual frameworks guided the health behavior analysis. In the individual perception analysis, Mr. Huang perception on seriousness and susceptibility had changed slowly during these stroke attacks. y Perceived Seriousness and Perceived Susceptibility：The patient responded uncertainty about what to do because he did not consider the disease attack will progress a severe outcome. He has motivation to adherence the treatment since he perceived the severity symptom of stroke. Patient expressed optimism about the stroke outcome. He has no awareness that he was in high risk for stroke and even second recurrence happened to him due to unknown the warning signs. He was not compliant with clinical treatment. The constructs of perceived seriousness and perceived susceptibility had impact his health behavior and intention to receive healthcare.

y Perceived Barriers to act：In managing health, he was frustrated by himself willpower, a factor influenced by the perceived seriousness and perceived susceptibility. Although he was interested in health information technology, appropriate technology equipments could not available at that time for him to prevent attack. One barrier to evacuation was financial reason; he had to struggle for it for family and indirect impacted his lifestyle.

6. THE SCENARIO OF THE STROKE PATIENT USING A PHR Scenario-based approaches are attracting more and more interest in requirements engineering research and practice (Weidenhaupt et al. 1998). The following scenario is a summary from the case study to depict PHR used in a stroke patient.

6.1. The scenario Mr. Huang equipped with a personal digital assistant as an active PHR and other physiological measuring device. He collects, saves his daily symptom, blood pressure and pulse and other physiological data. This health information via RSS feed send to Mr. Huang’s PHR to increase his awareness and knowledge, after

that he could adjust his activities. The personal health data stored in the PHR provide the important value is the risk prediction. The risk was calculated by the algorithm installed in PHR based on Framingham Coronary Heart Disease prediction score. According his gender, age, blood pressure at180/110 mimHg, prior stroke history and smoking habit. The probability of his suffering a stroke attack estimates 20% in five years. When Mr. Huang had dizzy symptom and high blood pressure, PHR alarm him to seek medical care as soon as possible. The list of stroke center units nearby his location was provided in the PHR. After come back to Taiwan, the healthcare system can provide continuity of care according the referral information related to the process of medical treatment located at China was stored in the PHR. …A PHR could be helpful and useful for patient in other situations. Limited to space, the scenario does not describe in detail about the interaction with a PHR.

7. DISCUSSION AND CONCLUSION The main aim of this study was to examine beliefs about a stroke patient that form a scenario of using PHR. We began by assessing participants’ receptivity towards and motivation to engage in a consumer health information technology program. Our findings relate to 3 categories relative to the HBM. The first category is low “perceived seriousness” that he did not believe the bad outcome due to past experience with minor stroke attack. The second category is low “perceived susceptibility” to a risk probability attributed in part to lack of risk awareness, which attenuated their sense of vulnerability. The third category is “perceived barriers to action.” The barriers were willpower to strengthen him adhere to treatment and recommendation. Other barriers related to technology equipments access. Findings suggest that patient was generally negative about his disease course and the unmet need with technology-assisted tools. In the scenario, the authors depicted how to use a PHR in such the stroke case to clarify our thoughts on the possible future advances in consumer health information field. The successful adoption of PHRs will not only focus on the functionality and friendly interface but also the willingness and barriers. Design for such case which had a poor perception on seriousness and susceptibility of disease, the functional of risk prediction reminder may be the first priority. There has been no standard model of the PHR developed to date, few studies provide the guidelines to build general desired functionalities(Sun 2001; Kim and Johnson 2002; Ball, Carla Smith, and Bakalar). Consumers and patients’ behavior change is a challenge contributing to slow PHR adoption. This study suggested that support individual’s effort to change health behavior by sophisticate technology tool such as PHR. There are several study limitations that should be noted, some of which are specific to a stroke case study. The generalization was limited to specific disease situation and can not reflect the other chronic condition. The relative utility of the belief model as a basis for developing consumer health information technology to be demonstrated empirically in future investigations. In summary, the individual’s needs of people at risk of stroke indicate there is demand for appropriately

designed technology tool. Despite more controls by patients themselves are reaching a common view, we still need to pay more attention on patients’ involvement and notion to enhance patients’ motivation and then change their behavior.

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