Q1: How can health care bridge the gap between those who are internet savvy and those who are not? The access gap between the internet savvy patients and those who are not is an information journey that begins with an online search activity, often using a commercial search engine, such as Google. As early as this stage, a gap already exists between the savvy and the non-savvy internet users, such as the digital divide and search strategy problems.
The first gap involves the access to computer or the internet itself. It is particularly existent among elderly adults, disabled, or low-income consumers (Laxman, Krishnan, & Dhillon, 2015). Making available free for general use broadband or Wi-Fi services will address this gap considerably among those able to use the computer or have access to it through various mobile devices. This problem is expectedly exacerbated by low literacy in computer use or reading, which requires no observable physical or cognitive barrier. Patients who knows nothing on how to switch on the computer will have obvious problems in access online health info.
The second gap involves problems pertaining to “ineffective search strategies” (Alpay et al., 2009), observed in the frequent use of short and highly general search words. To address this gap, enhanced search systems had been developed, such as the Health Information Query Assistant (HIQuA) (Zeng et al., 2006), which was designed to facilitate better retrieval of consumer health information through the internet. This system provides query recommendations, which provides faster and better query selections for people are not internet savvy at all. Evaluation outcomes indicated higher rates of query successes at statistically significant levels.
Another approach utilized a cognitive model of information retrieval exemplified by those used in the design and development of the website Senior Gezond (Alpay et al., 2009), which exclusively support older adults with concerns over fall prevention. This model followed the proposal made by Sutcliffe and Ennis in the late 1990s.
Q2: What demographic and socioeconomic factors affect eHealth access and literacy?
Demographic factors: Certain demographic characteristics show almost inevitable problems with eHealth access and literacy; thus, requiring the assistance of other people in accessing eHealth information on the internet. These factors include age, physical disability, and health literacy. People aging more than 65 years old will have potential problems with their cognitive functions, which both affect their access and literacy capabilities, and their computer and internet use literacy and skills (Laxman, Krishnan, & Dhillon, 2015). Moreover, people with physical disabilities may have physical barriers in the use of a computer and internet-capable devices. Blind patients will have serious issues with internet and regular literacy. Conversely, younger persons with unfettered physical mobility may have problem reading health information online within adequate level of health literacy (e.g. understanding medical terms that are frequently used in online health information and source archives).
Socioeconomic factors: Low-income individuals will have difficulty obtaining financial resources to use a computer or purchase a mobile device from which the internet-based health information may be accessed (Laxman, Krishnan, & Dhillon, 2015). This is also true with their capability to obtain broadband or wi-fi services unless in government-sponsored free use areas.
Common interventions to address the demographic and socioeconomic barriers: Strong research and development in the field had already provided measures to address these barriers through such technology as telehealth (through the telecommunication technology), mobile health (through the wireless mobile technology), and the traditional face-to-face physician consultation (Laxman, Krishnan, & Dhillon, 2015). Literacy issues had been attempted address through user friendly programs, such as the DISCERN project (UK), which provide user guidance tools (Alpay et al., 2009).
Q3: What steps do you think patients will likely take to correct their EHR data? What do you think the advantages and disadvantages of such a system would be?
Based on the PHR Plus functionality described in the case study, patients can have limited control over their personal data in the area of editing or correcting erroneous entries. However, the physicians had apparent control on the patient data; thus, allowing a message to the physician capable of requesting a correction of her electronic health records (HER) data.
One approach that the patient can take is to demand from the EHR service provider a role-based access (or ‘role-based security’) to her data to allow her to “create, read, update, and delete” (AHIMA Work Group, 2015) data that are erroneous, particularly in her demographic characteristics. The advantages of this approach include ability to correct erroneous demographic information and avoid risky medical errors. Conversely, it has an important disadvantage on the reliability and accuracy of the information patient’s put it. However, under threats from medical identity thefts, simple demographic matching is no longer sufficient today. Theft-free systems must be preferred.
Consequently, the patient must also seek to participate in EHR systems that contain notification features, such as alerts or prompts, in cases of potential incorrect association with other patients’ demographic data, such as identical names and date of births (even among multiple birth siblings. It is also recommended that uncommon identifiers, such as photograph, fingerprints, and palm vein scanning (AHIMA Work Group, 2015), must be utilized to enhance theft protection. This system is advantageous for its high portability and far quicker alarm system. However, it is disadvantaged by its potentially high costs of enrolment.
References
AHIMA Work Group. (2015, May). Assessing and improving EHR data quality (2015 update). Journal of AHIMA, 86(5), 58-64.
Alpay, L., Verhoef, J., Xie, B., Te’eni, D., & Zwetsloot-Schonk, J.H.M. (2009). Current challenge in consumer health informatics: Bridging the gap between access to information and information understanding. Biomedical Informatics Insights, 2(1), 1-10.
Blumenthal, D. & Squires, D. (2014, December 6). Giving patients control of their EHR data. Journal of General Internal Medicine, 30(1), 42.
Laxman, K., Krishnan, S.B., & Dhillon, J.S. (2015). Barriers to adoption of consumer health informatics applications for health self-management. Health Science Journal, 9(5), 7.
Zeng, Q.T., Crowell, J., Plovnick, R.M., Kim E., Ngo, L., & Dibble, E. (2006). Assisting consumer information retrieval with query recommendations. Journal of American Medical Informatics Association, 13(1), 80-90.
