Disambiguating Between Privacy and Security in the Context of Health Care: New Insights on the Determinants of Health Technologies Use

Abstract

Health care providers are increasingly providing technologies for patient care; however, patients are still loath to use such technologies consistently. This research examines the impediments to patients' use of e-health portals. Our analysis of 836 data records showed that while privacy and security concerns have a negative impact on attitudes toward e-health portals, increasing the awareness of privacy and security controls alleviates such concerns. Our findings also suggest that individuals worry more about who possesses the right to access their health data (i.e., who, what, when, and why) than the mechanisms used to safeguard data from unauthorized access. We found that perceived benefits and support (i.e., emotional and technical support) positively influenced the determinants of use intentions. The implications of these findings for health care providers and policy makers are discussed.

Introduction

The future of health care in society is strongly tied to technology as sensors, wearable devices, and telemedicine continue to shift the health care paradigm. One such technology is the electronic (e-health) health portal. E-health portals are online tools that provide patients with electronic access to their own medical information,¹ allowing them to view their medical records and to interact with their doctors. There are several documented benefits of portal use to both patients and physicians^2,3 but the adoption continues to lag in comparison with other technologies.⁴ The lack of adoption of e-health technologies in any segment of the population (e.g., elderly, immigrants, or uneducated) can create wide health care disparities and needs to be addressed. In this research, we attempt to understand the critical success factors and barriers for e-health portal use; we investigate the role of information privacy and security, and identify factors that influence the known antecedents of adoption intentions.

Scholars have started addressing this need by studying how personality, cognition, health status (such as having a chronic ailment), and behavioral attributes (such as health habits) affect patients' adoption intentions.^5,6 Despite these attempts, solutions for reducing privacy concerns in the context of health information technologies adoption are yet to be identified. Moreover, the influence of security concerns is not well understood. In addition, privacy research in the field of Information Systems has taken a general approach of studying privacy and security, with little work on domain-specific problems such as health care technology adoption. We address these limitations by augmenting the Theory of Planned Behavior (TPB)⁷ to include variables that influence the determinants of e-health portal adoption intentions.

Theoretical development

TPB posits that the intention to engage in a specific behavior is guided by three determinants, namely, attitude toward the behavior, subjective norms, and perceived behavioral control.^7,8 We hypothesize and test the antecedents of the intention to adopt e-health portals (Fig. 1), namely, privacy awareness, security awareness, perceived benefits, and perceived support. We hypothesize based on previous findings that attitude, subjective norms, and perceived behavioral control will have a positive relationship with intention to adopt patient portals as given in Figure 1.^7,8

FIG. 1.

The hypothesized research model for e-health portal adoption intentions.

Privacy concerns

Privacy concerns relate to a state of worry concerning the confidentiality of personal information. Privacy concerns negatively influence attitudes toward health information technologies.^4,9,10 A major concern is that personally identifiable medical information is likely to be revealed and/or accessed by unauthorized individuals without the patient's consent. A person who is concerned about the privacy of their health information in the portal may develop negative attitudes toward e-health portals. We argue that in the context of e-health portals, privacy concerns will be linked with unfavorable attitudes toward e-health portals. Thus:

H4: Privacy concerns will be negatively related to attitudes toward e-health portals.

Security concerns

Security concerns relate to a lack of assurance in the effectiveness of information safeguards. Security concerns generally represent a fear of violation of the confidentiality, integrity, and availability of information. With health information being easily accessible through mobile devices, there are new threats of security breaches that could compromise health data in storage on the health provider's network, or in transit between devices. We argue that just like privacy concerns, individuals who are worried about the protection of their health information may develop negative attitudes toward e-health portals. Thus:

H5: Security concerns will be negatively related to attitudes toward e-health portals.

Awareness of privacy and security controls

Awareness of privacy controls refers to an individual's knowledge of how the privacy of personally identifiable health information is maintained, used by health providers, shared among providers, and overall, what controls are employed to maintain information confidentiality. Similarly, awareness of security controls refers to one's knowledge of the safeguards used to protect the confidentiality, integrity, and availability of personally identifiable information. Knowledge of privacy and security controls may be engendered by an individual's perceived understanding of the corresponding privacy and security issues inherent in the use of e-health portals. Privacy concerns and security concerns may stem from a lack of awareness of how information is utilized and protected. Individuals are willing to use an application if there are sufficient controls to mitigate privacy risks¹¹; however, the user must have knowledge of such controls to induce favorable perceptions of adoption. Creating this level of transparency in how information is handled may enhance the likelihood of technology use.¹² A portal user with significant knowledge of how information is shared, and the privacy and security policies related to e-health portals, is likely to worry less because of the assurance that personal information is not at risk. Thus:

H6: The awareness of privacy controls will reduce privacy concerns.

H7: The awareness of security controls will reduce security concerns.

Perceived benefits

Perceived benefit is defined as the extent to which one believes that the use of an e-health portal will result in positive outcomes for oneself. Portal use has been linked to several benefits such as increased patient adherence to treatment guidelines^2,13 and improved health outcomes.³ In addition, e-health portals allow individuals to have immediate, ‘anytime, anywhere’ access to their electronic medical records; a benefit that overcomes many problems related to the long and arduous process of requesting access to nonelectronic medical records. Considerable research has determined that an individual's perception of benefits positively influences their attitudes and behaviors related to the technology.^14–17 Thus, we hypothesize the following.

H8: Perceived benefits will positively influence attitudes toward e-health portal adoption.

Support

We define support as a form of assistance offered to individuals to facilitate e-health portal adoption. Support has been described as a multidimensional variable operationalized in terms of formal support, which relates to tangible assistance for accomplishing a specific task, and informal support, which often comes from peers.¹⁸ The type of formal support relevant to this research is technical support, which is assistance offered to individuals who experience technical problems when using an e-health portal. For informal support, we focus on emotional support, which pertains to expressing empathy and compassion. Through support, individuals are able to receive help and resources for dealing with new technologies, consequently, increasing the likelihood of adoption.¹⁹ Support may also strengthen subjective norms.¹⁹ This suggests that assistance may significantly lessen doubts and elicit positive perceptions about new technologies. We believe that in the context of e-health portals, perception of support (both formal and informal support) will enhance an individual's subjective norms. We hypothesize that:

H9a: Perceived support will positively influence subjective norms.

Scholars in psychology and health sciences have also widely recommended support as an intervention for bringing about behavioral change and commitment to positive behavior, and individual contribution toward achieving an organization's welfare.^20–23 These studies show that access to support encourages individuals to engage in supportive behaviors, consequently supporting the goals of both the individual and the health service provider. We argue that supportive behavior may be manifested through the development of favorable attitudes of e-health portals, and enhanced confidence in the ability to use portals. We therefore propose the following:

H9b: Perceived support will positively influence attitudes toward e-health portal adoption.

H9c: Perceived support will positively influence self-efficacy.

Methods

Sample and data collection

The research model was tested using a survey instrument. The survey instrument was reviewed and approved by the Institutional Review Board before data collection. We collected data using a cloud sourcing data collection platform called Amazon Mechanical Turk (mTurk). A total of 1218 respondents completed the survey. After removing missing data, duplicate entries, and invalid responses, 836 responses remained and were used in the analysis. Table 1 summarizes the demographic characteristics of the sample.

Table 1.

Descriptive Statistics for the Study Sample (N = 836)

Variable	Sample
Gender
Women	466
Men	367
Chronic illness
Respondents with a chronic illness	412
Respondents with no chronic illness	420
Age	Counts
18–24 years old	55
25–34 years old	324
35–44 years old	161
45–54 years old	78
55–64 years old	144
65–74 years old	68
75 years old	2
Race
White	583
Asian	141
Black	64
Hispanic/Latino	31

Construct operationalization

We adopted existing scales in a majority of cases, and created new scales as needed. Unless otherwise noted, all items were measured using a 7-point Likert scale (1 = strongly disagree; 7 = strongly agree). The details of the scales, including survey items, are provided in Supplementary Appendix A1.

Data analysis

We analyzed the reliability and validity of the measures. The values for composite reliability and Cronbach's alpha were above the recommended threshold of 0.7 (Table 2).²⁴ Convergent validity was also established; the values for the average variance extracted were all above the 0.50 threshold (Table 2).

Table 2.

Validity and Reliability Indicators for Reflective Constructs

Constructs	Items	Item loading	Cronbach's α	Composite reliability	Average variance extracted
Intention	Intention1	0.813	0.808	0.874	0.635
	Intention2	0.804
	Intention3	0.794
	Intention4	0.776
Attitude			0.834	0.901	0.753
	Attitude1	0.901
	Attitude2	0.878
	Attitude3	0.823
Subjective norms			0.909	0.937	0.788
	Subjective norms1	0.916
	Subjective norms2	0.913
	Subjective norms3	0.917
	Subjective norms4	0.799
Perceived behavioral control			0.920	0.943	0.806
	Perceived behavioral control1	0.899
	Perceived behavioral control2	0.875
	Perceived behavioral control3	0.901
	Perceived behavioral control4	0.915
Security concerns			0.797	0.908	0.832
		0.911
		0.914
Awareness of security controls			0.822	0.866	0.690
	Awareness of security controls1	0.946
	Awareness of security controls2	0.873

Awareness of privacy controls			0.870	0.921	0.795
	Awareness of privacy controls1	0.927
	Awareness of privacy controls2	0.914
Perceived benefit
Information accuracy			0.707	0.869	0.768
	Information accuracy1	0.830
	Information accuracy2	0.921
Support
Emotional support			0.867	0.910	0.716
	Emotional support 1	0.780
	Emotional support 2	0.845
	Emotional support 3	0.870
	Emotional support 4	0.887
Technical support			0.946	0.959	0.822
	Technical support 1	0.920
	Technical support 2	0.934
	Technical support 3	0.904
	Technical support 4	0.869
	Technical support 5	0.906

We tested the reliability of the formative measures by conducting a multicollinearity assessment.^25–27 The variance inflation factor values were all below the recommended threshold of 3.3, demonstrating the reliability of the formative variables and absence of multicollinearity^27,28 (Table 3). In addition, there was no evidence of systematic bias in the model.

Table 3.

Multicollinearity Assessment

Constructs	Variance inflation factor
Attitude	1.533
Subjective norm	1.299
Self-efficacy	1.246
Privacy concern	2.164
Security concern	2.22
Privacy awareness	1.00
Security awareness	1.00
Perceived benefit	1.826
Convenience	1.382
Information accuracy	1.702
Information availability	1.093
Information security	1.550
Perceived support	1.347
Emotional support	1.166
Technical support	1.166

We tested the hypothesized model using partial least squares structural equation modeling. We used SmartPLS 3.2 software to estimate the model using the bootstrapping method.²⁹

Results

The results of the path estimates are summarized (Fig. 2). All the hypothesized relationships were statistically significant at p < 0.05. The strongest predictor of intention was attitude (b = 0.578, p < 0.00), followed by subjective norms (b = 0.27, p < 0.00), and finally, perceived behavioral control (b = 0.05, p < 0.05). As was hypothesized, privacy concerns (b = −0.083, p < 0.01) and security concerns (b = −0.060, p < 0.05) lower favorable attitudes toward e-health portals. The suggested solutions for reducing privacy concerns and security concerns—awareness of privacy controls and awareness of security controls—were also statistically significant with b = −0.22, p < 0.00 and b = −0.17, p < 0.00, respectively. The results also reveal that perceived benefits (b = 0.50, p < 0.00) and perceived support (b = 0.07, p < 0.05) strengthen attitude toward e-health portals. Perceived support also had significant positive relationships with subjective norms (b = 0.32, p < 0.00), and perceived behavioral control (b = 0.27, p < 0.00). Finally, the model predicted 24.1 percent of the variance in e-health portal adoption, 52.5 percent of the variance in intention, 34.3 percent of the variance in attitude, 10.1 percent of the variance in subjective, and 21.6 percent of the variance in perceived behavioral control.

FIG. 2.

PLM SEM results for the hypothesized model of e-health portal adoption intentions. PLM, partial linear model; SEM, structural equation modeling.

Discussion

This research determined that privacy and security concerns influence portal adoption negatively through shaping user attitudes. And to reduce such concerns, knowledge of privacy controls used to protect information, such as the regulations that govern the sharing of medical information, produce favorable attitudes toward e-health portals, consequently increasing adoption intentions. Likewise, educating individuals about the safeguards used to protecting e-health portals from cybersecurity attacks reduces security concerns, consequently increasing favorable attitudes toward e-health portals. Although privacy and security are inherently interlinked, these two factors as defined in this research are distinct in the minds of the consumers. We found that privacy concerns had a stronger negative relation to attitude toward e-health portals than security concerns. This finding suggests that health care consumers are slightly more concerned about willful use of their medical information without consent than about security breaches. In other words, individuals worry more about who possesses the right to access their health data (i.e., who, what, when, and why) than the mechanisms used to safeguard data from unauthorized access.

Overall, this research provides insights for solutions alleviating privacy and security concerns, consequently improving e-health portal adoption and usage. We find that attitude toward e-health portals can be strengthened through support (both emotional support and technical support), reducing security and privacy concerns, and educating individuals about the benefits of using e-health portals. The significance of perceived benefit in increasing e-health portal adoption suggest that as greater benefits are realized through the use of e-health portals, more people will automatically adopt e-health portals. The benefits identified in this research include convenience afforded by the portal, a high level of information accuracy, information availability (i.e., anytime, anywhere), and information security. Second, awareness campaigns that highlight the benefits of adoption would perhaps be most influential in increasing adoption of e-health portals and health technologies in general. In addition, because adherence to subjective norms increases adoption intentions, solutions that strengthen such social beliefs would increase e-health portal adoption. With regard to support, both emotional support and technical support positively influence perceived support and can be used as interventions in increasing adoption of e-health portals. The influence of support was strongest for subjective norms, and weakest for attitude as would be expected.

Our findings have several practical implications. First, focusing solely on protecting e-health portals against potential cyberattacks is not sufficient for eliciting increased acceptance. However, developing privacy and security awareness training programs that detail how health providers mitigate privacy and security risks, will ensure acceptance of health care portals. Knowledge of portal privacy protection programs increases transparency of information practices, which may increase users' trust and confidence in using new health technologies, such as portals. Second, health care providers must adequately explain the benefits of e-health portals to individuals to increase their acceptance of e-health portals. Third, it is common for users to experience challenges (e.g., anxiety and technical difficulties) when adopting new technologies. We establish that offering both emotional support and technical support are essential in helping individuals overcome the challenges (e.g., anxiety and technical difficulties) of adopting new technologies.

This research has some limitations. First, research data were collected using a survey instrument at a single point in time and as such, the data do not reflect potential changes in users' behaviors and opinions about e-health portals. Second, survey data are inherently susceptible to response bias, which may lead to misleading data. Third, certain populations such as older adults generally lag behind in technology use and our findings may not be generalizable to these population groups. In addition, there may be differences in e-health portal adoption between high-income and low-income respondents, as well as differences owing to education background that this study does not account for. Future research is needed to identify the unique challenges to e-health portal use among underrepresented population and other demographic groups.

Footnotes

Author Disclosure Statement

No competing financial interests exist.

Funding Information

No funding was received for this study.

Supplementary Material

References

HealthIT. (2015) What is a patient portal? https://www.healthit.gov/providers-professionals/faqs/what-patient-portal (accessed Aug. 18, 2017).

Sarkar

, Lyles

, Parker

, et al. Use of the refill function through an online patient portal is associated with improved adherence to statins in an integrated health system. Medical Care, 2014; 52:194.

Devkota

, Salas

, Sayavong

, et al. Use of an online patient portal and glucose control in primary care patients with diabetes. Population Health Management, 2016; 19:125–131.

Ben-Zion

, Pliskin

, Fink

. Critical success factors for adoption of electronic health record systems: literature review and prescriptive analysis. Information Systems Management, 2014; 31:296–312.

Tavares

, Oliveira

. Electronic health record patient portal adoption by health care consumers: an acceptance model and survey. Journal of Medical Internet Research, 2016; 18:e49.

Archer

, Fevrier-Thomas

, Lokker

, et al. Personal health records: a scoping review. Journal of the American Medical Informatics Association, 2011; 18:515–522.

Ajzen

. The theory of planned behavior. Organizational Behavior and Human Decision Processes, 1991; 50:179–211.

Ajzen

. (1985) From intentions to actions: A theory of planned behavior. In Kuhl

, Beckmann

, eds. Action Control: From Cognition to Behavior. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 11–39.

Angst

, Agarwal

. Adoption of electronic health records in the presence of privacy concerns: the elaboration likelihood model and individual persuasion. MIS Quarterly, 2009; 33:339–370.

10.

Kisekka

, Giboney

. The Effectiveness of Health Care Information Technologies: Evaluation of Trust, Security Beliefs, and Privacy as Determinants of Health Care Outcomes. Journal of Medical Internet Research, 2018; 20:e107.

11.

Roback

, Wakefield

. Privacy risk versus socialness in the decision to use mobile location-based applications. SIGMIS Database, 2013; 44:19–38.

12.

Zhou

, Wang

, Xu

, et al. Perceived information transparency in B2C e-commerce: an empirical investigation. Information & Management, 2018; 55:912–927.

13.

Goldzweig

, Orshansky

, Paige

, et al. Electronic patient portals: evidence on health outcomes, satisfaction, efficiency, and attitudes: a systematic review. Annals of Internal Medicine, 2013; 159:677–687.

14.

Lee M−C. Factors influencing the adoption of internet banking: an integration of TAM and TPB with perceived risk and perceived benefit. Electronic Commerce Research and Applications, 2009; 8:130–141.

15.

Mitzner

, Boron

, Fausset

, et al. Older adults talk technology: Technology usage and attitudes. Computers in Human Behavior, 2010; 26:1710–1721.

16.

Dwivedi

, Rana

, Jeyaraj

, et al. Re-examining the unified theory of acceptance and use of technology (UTAUT): Towards a revised theoretical model. Information Systems Frontiers, 2017; 21:1–16.

17.

Zhang

, Guo

, Wu

, et al. Exploring the inhibitors of online health service use intention: A status quo bias perspective. Information & Management, 2017; 54:987–997.

18.

Lee

, Lee

, Olson

, et al. The effect of organizational support on ERP implementation. Industrial Management & Data Systems, 2010; 110:269–283.

19.

Venkatesh

, Bala

. Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 2008; 39:273–315.

20.

Hsu

Y-W

, Chou

C-P

, Nguyen-Rodriguez

, et al. Influences of social support, perceived barriers, and negative meanings of physical activity on physical activity in middle school students. Journal of Physical Activity and Health, 2011; 8:210–219.

21.

Mendonça

, Cheng

, Mélo

, et al. Physical activity and social support in adolescents: a systematic review. Health Education Research, 2014; 29:822–839.

22.

Muscott

, Mann

, LeBrun

. Positive behavioral interventions and supports in New Hampshire: effects of large-scale implementation of schoolwide positive behavior support on student discipline and academic achievement. Journal of Positive Behavior Interventions, 2008; 10:190–205.

23.

Eisenberger

, Armeli

, Rexwinkel

, et al. Reciprocation of perceived organizational support. Journal of Applied Psychology, 2001; 86:42.

24.

Podsakoff

, MacKenzie

, Lee

J-Y

, et al. Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 2003; 88:879.

25.

Andreev

, Heart

, Maoz

, et al. (2009) Validating formative partial least squares (PLS) models: Methodological review and empirical illustration. In ICIS 2009 Proceedings. 30th International Conference on Information Systems 2009 (ICIS 2009). Phoenix, AZ: Association for Information Systems.

26.

Diamantopoulos

, Riefler

, Roth

. Advancing formative measurement models. Journal of Business Research, 2008; 61:1203–1218.

27.

Kock

, Lynn

. Lateral collinearity and misleading results in variance-based SEM: an illustration and recommendations. Journal of the Association for Information Systems, 2012; 13:546–580.

28.

Diamantopoulos

. The error term in formative measurement models: interpretation and modeling implications. Journal of Modelling in Management, 2006; 1:7–17.

29.

Ringle

, Wende

, Becker

J-M

. (2014) SmartPLS 3. Hamburg: SmartPLS. www.smartpls.com (accessed Mar. 26, 2018).

Supplementary Material

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