Exploratory study of personal health information management using health literacy model
| DOI | https://doi.org/10.1108/AJIM-03-2017-0062 |
| Pages | 104-122 |
| Date | 15 January 2018 |
| Published date | 15 January 2018 |
| Author | Sujin Kim,Sue Yeon Syn,Donghee Sinn |
| Subject Matter | Library & information science,Information behaviour & retrieval,Information & knowledge management,Information management & governance,Information management |
Exploratory study of personal
health information management
using health literacy model
Sujin Kim
Department of Internal Medicine,
University of Kentucky, Lexington, Kentucky, USA
Sue Yeon Syn
Department of Library and Information Science,
The Catholic University of America,
Washington, District of Columbia, USA, and
Donghee Sinn
University at Albany –State University of New York, Albany, New York, USA
Abstract
Purpose –The purpose of this paper is to empirically test whether individuals’internal factors
(prior knowledge, resources, and capability) and environmental factors(stimuli, limitation) have any influence
on the development of personal health information management (PHIM) literacy skills and which constructs
are statistically associated with general health-related outcomes.
Design/methodology/app roach –Survey responses were collected from Amazon’s Mec hanical
Turk (mTurk), a crowdsourcing internet service, in December 2013. A total of 578 responses were
analyzed using partial-least squares structural equation modeling technique.
Findings –The model as a whole exhibited 62.8 percent of variance in health-related outcomes. The findings
suggest that prior knowledge has a direct effect on health literacy (HL) skills (H3:β¼0.212, po0.001).
The PHIM stimuli (H4:β¼0.475, po0.001) have a direct impact on HL skills, and they have an indirect
effect on the comprehension of stimuli (H6:β¼0.526, po0.001) through the mediator of stimuli and the
knowledge variable.
Research limitations/implications –One possible limitation of this study is that the study may include a
highly technology literate group, as survey respondents were recruited from the online service mTurk.
Practical implications –The study posesimplications for further researchand practice. This researchwas
an exploratory work for further model development so future studies should investigate deeper into real
personal health record (PHR) user groups (e.g. patients and caregivers). For example, studies by White and
Horvitz (2009a, b) conducted real-time user studies that the authors could apply to the authors’future PHR
studies. Sincethe findings cannot be generalizableto these specific groups, similarresearch may be conducted.
Using caregiver groups of PHR users in comparison to patient groups could determine the similarities and
differences of theirPHIM activities and related outcomes for optimaldesign of self-care management.
Social implications –Further, it is suggested to conduct large scale, real-time-based studies using a PHR
transaction log analysis to achieve conclusiveness and generalizability. Additionally, future studies should
addressnot only diverse real-time usergroups, but also various PHR data sourcesand their presentation issues.
Originality/value –This study model offers an important perspective on PHIM and its causal pathway for
use not only by patient educators and healthcare providers but also information providers, personal health
record (PHR) system developers, and PHR users.
Keywords Personal health records, Health literacy model, Health literacy skills framework,
MTurk survey, Partial-least squares structural equation modeling,Personal health information management
Paper type Research paper
Introduction
The vision to improve and develop patient-engaged care plans or tools that facilitate active
engagement has emerged and gained great popularity. Sharing healthcare documents with
patients is suggested as a key enabler of patient engagement in the current care continuum.
In addition to care documents, numerous sources of health information are readily available
Aslib Journal of Information
Management
Vol. 70 No. 1, 2018
pp. 104-122
© Emerald PublishingLimited
2050-3806
DOI 10.1108/AJIM-03-2017-0062
Received 8 March 2017
Revised 25 September 2017
Accepted 13 November 2017
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/2050-3806.htm
104
AJIM
70,1
to health consumers via diverse channels, such as health professionals, hospitals, health
departments, health web portals, mass media, and family and friends, to name a few.
As such development of the patient-centered healthcare evolves, “the vision for informed,
self-managing patients as active partners in healthcare represents a major cultural shift”
(Greenhalgh et al., 2010). However, the reality of the current healthcare community and
practice in this vision comes with a price in terms of technological support and human
endeavors to utilize the developed tools.
As a technological solution, personal health records (PHRs) have become a key
informatics tool by facilitating health record collection and exchange of personal health
information. PHRs are defined as an electronic application managed by patients to keep
up-to-date care documents such as diet plans or data from home monitoring systems, as well
as patient contact information, diagnosis lists, medication lists, allergy lists, immunization
histories, and much more (Greenhalgh et al., 2010; Fetter, 2009). Unlike their counterpart
products –electronic medical records –which are managed by healthcare providers, PHRs
are designed for use by any health consumer (Fetter, 2009; Marshall, 2009). Care documents
and health (or wellness) information stored and distributed through PHRs have become a
critical part of health communication for patients discussing their health matters with care
providers. Although critical issues like privacy, security, and technical or cultural barriers
are raised, diverse healthcare sectors are increasingly engaged in the development and
distribution of PHR tools and applications (Roberts, 2009; Wynia and Dunn, 2010). Scientific
endeavors have reported PHR implementation focusing on diverse features by major
healthcare sectors. My Health Manager, an early example of PHRs by Kaiser Permanente,
offers patients access to appointment information, insurance benefits, medical billing, and
basic clinical information such as treatments, follow-ups, and communication features with
care providers. In a recent study, Ford et al. (2016) reported that PHRs are widely adopted
as they “exceed what are required for patient engagement measures for Meaningful
Use stage 2 (MU-2)”. The use of PHRs as a tool to manage personal care documents is
predicted to increase in usage 75 percent by 2020 (Ford et al., 2016).
Despite the wide adoption of PHRs, there has been little attention given to an
individual’s capabilities in managing and understanding his or her own health documents.
Notably, PHRs are supposed to combine multiple healthcare documents from multiple
sources such as hospitals, web search engines, patient-generated inputs, health devices,
insurance companies, etc. In addition to levels of difficulty on care document context,
various issues in relation to accessibility of documents and keeping or sharing decisions
are quite cumbersome. Increasingly, patients are nowadays asked to provide their own
data such as medical history or regimen uptake or fitness/nutrition progress through
PHRs. All are newly demanded health literacy (HL) skills that were never asked before.
Most importantly, some tethered PHRs serve as a reference for patients to review care
documents while discussing lab results or medication updates. Again, these tasks
addressing their health conditions using PHRs or decisions about keeping or discarding
their own health records are definitely new and challenging tasks. Whether individuals
utilize hospital tethered PHRs or commercial PHRs to manage their documents, it is
difficult to measure how well they can perform information management activities that
are conventionally performed by professionals.
As a popular measure of individuals’capabilities, HL is used as a degree or a set of skills
that is essential to access, process, comprehend, and apply health information to assist in
making informedmedical decisions (Kindig et al., 2004).Numerous studies have reported that
high HL improves chronic care management ( Johnson and Case, 2012), while low literacy
brings less desirable health outcomes. A wide variety of skills such as reading, verifying,
and comprehending health implications or medical instructions of health information are
needed to be health literate (Berkman et al., 2010; DeWalt et al., 2004; Mancuso, 2008).
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Exploratory
study of PHIM
using HL
model
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