Trust Factors in Healthcare Technology: A Healthcare Professional

Perspective

Raja Manzar Abbas

, Noel Carroll

, Ita Richardson

1,3

and Sarah Beecham

Lero - the Irish Software Research Centre, University of Limerick, Limerick, Ireland

Lero - the Irish Software Research Centre, National University of Ireland Galway, Galway, Ireland

HRI- Health Research Institute, University of Limerick, Limerick, Ireland

Keywords: Trust, Healthcare Technology, Technology Adoption, Healthcare Professionals, Mapping Study.

Abstract: Being able to trust technology is of vital importance to its potential users. This is particularly true within the

healthcare sector where lives increasingly depend on the correct application of technology to support

clinical decision-making. Despite the risk posed by improper use of technology in the healthcare domain,

there is a lack of research that examines why healthcare professionals trust healthcare technology.

Therefore, there is little evidence regarding the key trust facilitators and barriers. In this paper, we

investigate the concept of trust within a healthcare technology context. We conducted a systematic mapping

study to identify relevant trust facilitators and barriers in published work in well-known bibliographic

databases. Our results present a synthesis of 47 studies that describe trust factors that healthcare

professionals associate with healthcare technology. Facilitators include compatibility and perceived systems

usefulness, while barriers include privacy concerns and lack of knowledge. We conclude that HCT trust is

complex, multi-dimensional, and influenced by a variety of factors at individual and organizational levels.

1 INTRODUCTION

Healthcare technology (HCT) is defined by the

World Health Organization as the “application of

organized knowledge and skills in the form of

devices, medicines, vaccines, procedures and

systems developed to solve a health problem and

improve quality of lives” (WHO, 2017). This

includes the pharmaceuticals, devices, procedures

and organizational systems used in healthcare. HCT

has the potential to address many of the challenges

that healthcare is currently confronting. For

example, HCT improves information management,

access to health services, quality and safety of care,

continuity of services, and costs containment (Miles

and Asbridge, 2014).

Due to the growth in population and shift in

demographics, there is considerable pressure on

global healthcare systems to provide an effective and

efficient service. Shojania et al. (2016) attribute

deaths of 251,454 people in US hospitals per year to

medical errors, the third-leading cause of death in

the USA. The Institute of Medicine study estimated

the cost of nonfatal medical errors is between $17

billion and $19 billion each year, and that between

2.9% and 3.7% of all patients admitted suffer some

type of injury as a result of medical mismanagement.

As a result, there is a growing focus on HCT support

for healthcare services which has given rise to a

comprehensive sociotechnical model for managing

healthcare through technology (Carroll, 2016).

Technological advances have encouraged the

development of new technologies that drive

connectivity across the healthcare sector, for example,

systems that manage care using just-in-time

information (Leroy et al., 2014).

Research suggests that patients also want

clinicians to use HCT (Car and Sheikh, 2004). With

increasing global computerisation, HCT is expected

to become part of healthcare professional practice.

Nevertheless, it appears that several HCT applications

remain underused by healthcare professionals (Berner

et al., 2005, Brooks and Menachemi, 2006).

Healthcare organizations, particularly physician

practices, are often used as examples for lagging

behind in trusting and adopting these technologies

(Yarbrough and Smith, 2007). Human and

organizational factors have frequently been identified

as the main causes of HCT implementation and usage

failure (Pagliari, 2005, Carroll et al., 2016).

454

Abbas, R., Carroll, N., Richardson, I. and Beecham, S.

Trust Factors in Healthcare Technology: A Healthcare Professional Perspective.

DOI: 10.5220/0006594204540462

In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - Volume 5: HEALTHINF, pages 454-462

ISBN: 978-989-758-281-3

1.1 Problem Statement

Although barriers and facilitators of trust in HCT

settings are described to a certain extent in the

literature, only a few studies have systematically

reviewed factors influencing trust in different types

of HCT (Yarbrough and Smith, 2007, Anderson,

2007, Kukafka et al., 2003, Yusof et al., 2007,

Gagnon et al., 2012). Furthermore, there is no

consensus on the categorisation of barriers and

facilitators related to trust in HCT since most of

these reviews have not been from a healthcare

professional prospective.

The study in this paper systematically maps the

key trust factors that are positively (facilitators) or

negatively (barriers) associated with HCT used in

clinical settings by healthcare professionals.

Furthermore, this mapping allows us to highlight the

differences and similarities of trust factors between

different HCT types. This study serves as an initial

basis for developing a fine-grained understanding of

what comprises ‘trust’ in HCT from a healthcare

professional’s view-point. Such information can be

crucial to design and implementation strategies that

take end-users’ concerns about trust into account and

thus, have a higher chance of being accepted or

implemented.

The remainder of the paper is organized as

follows: Section 2 provides a brief overview of trust

and different trust definitions in the healthcare and

computer science literature. Section 3 describes our

methodology adopted for this study. Section 4

describes our findings and results from the literature.

Section 5 presents the discussion from the findings

and results. Finally, Section 6 presents the

conclusion, limitations and future research

opportunities.

2 OVERVIEW OF TRUST

The ultimate goal of technology is to support end-

users in accomplishing their tasks in a convenient

and efficient manner. However, the literature

suggests a loss of productivity while using HCT and

this leads to a lack of trust in the HCT (Van Velsen

et al., 2016).

Trust is generally seen as an important

antecedent of the acceptance, use of, and loyalty

towards technology (Wu and Chen, 2005, Bélanger

and Carter, 2008, van Velsen et al., 2015). This is

also the case for HCT, where trust has been found to

be an important antecedent of patient acceptance

(Park et al., 2011), patients’ and healthy persons’

thoughts on the usefulness of a personal health

record (Cocosila and Archer, 2014), and physicians’

intention to use HCT for rehabilitation care (Wu et

al., 2008).

Trust is investigated in many research fields,

such as computer science, economics, politics,

sociology and philosophy (Grandison and Sloman,

2000, Jøsang et al., 2007, Misztal, 2013). However,

there is no agreement regarding the definition and

properties of trust (Gollmann, 2006, Massa, 2007,

Raya et al., 2008). According to the literature, trust

is difficult to define, convey, measure or specify.

Michael et al. (2002) explain that ‘trust is a term

with many meanings,’ and this is supported by a

large number of definitions proposed in the

literature. Almenárez et al. (2004) define trust as the

belief that an entity has about another entity, from

past experiences, knowledge about the entity's nature

and/or recommendations from trusted entities.

Similarly, Robinson (1996) indicates that trust is

one's expectations, assumptions or beliefs about the

likelihood that another's future actions will be

beneficial, favourable or at least not detrimental to

one's interests. A more ‘common sense’ form of trust

is derived from Alford (2004) who explains that to

trust someone is to be confident that in a situation

where you are vulnerable, one will be disposed to

act benignly towards you.

Trust is also defined in different ways in the

same research field, such as in computer science

(Jøsang et al., 2007, Raya et al., 2008). For instance,

Massa (2007) defines trust as the judgment

expressed by one user about another user, often

directly and explicitly, sometimes indirectly through

an evaluation of artefacts produced by that user or

their activity on the system. Reliability trust is

defined as the subjective probability by which an

individual expects that another individual perform a

given action on which its welfare depends (Jøsang et

al., 2007).

Trust is a key factor in the delivery of healthcare,

high levels of provider/patient trust is conducive to

more effective healthcare (Hall et al., 2002). Trust in

healthcare can be seen as a three-part relationship

between patient (truster), provider or organization

(trustee), and the specific context of delivering

healthcare (technology).

There are various definitions of trust in computer

science and healthcare that may lead to a confusion

about trust in the context of technology (Gollmann,

2006). Since, our focus is on HCT, we follow the

trust definition in McKnight et al. (2002) where trust

in the technology is defined as an individual’s belief

that using a specific technology is safe and secure.

Trust Factors in Healthcare Technology: A Healthcare Professional Perspective

455

In the same way, Krishna and Maarof (2002) explain

that trust is the firm belief in the competence of an

entity to act dependably, securely and reliably within

a specified context.

3 METHOD

In the research literature, we examined the concept

of trust in HCT, how the technology is accepted, and

what is the criteria for its use. We have employed

mapping study guidelines presented by Petersen et

al. (2015). Our motivation to undertake a mapping

study is to synthesize evidence, and bring about

some structure to this research area - HCT trust

factors demonstrated by healthcare professionals.

Considering the broad nature of technological use in

healthcare, we argue that stakeholders need to have a

set of criteria by which they can assess the level of

trustworthiness of a given technology. We present

evidence related to trust facilitators and barriers

based on the frequency of them occurring in the

literature. In this study, we acknowledge that trust is

often considered an elusive term. Therefore, in

order to ensure that we captured various nuances

related to trust in HCT, we have included terms

related to the adoption, usage and acceptance of

HCT. This builds on our knowledge that the

literature uses these terms interchangeably when

alluding to the concept of trust.

3.1 Eligibility Criteria

To account for the different types of studies on trust

factors for HCT by healthcare professionals, a mixed

study review was conducted. This can be

conceptualized as a mixed methods research study

where data consists of the text of papers reporting

primary qualitative and quantitative studies in

addition to mixed methods studies (Pluye et al.,

2009).

3.2 Search Strategy

We searched seven well-established digital

databases (CINAHL, Embase, IEEE Xplore, Science

Direct, Scopus, Springer Link and Web of Science)

for relevant publications. For expediency, we ran

one search using the following search string (or

variants of the search string to fit the various

databases):

("Health care" OR Healthcare) AND (Trust* OR

Accept* OR Adopt* OR Usage) AND (Software OR

“Information Technology” OR “Information

System”).

The search process and result is in Figure 1.

Figure 1: Study selection flow diagram.

3.3 Article Selection

Titles and abstracts were screened by one of the

authors [R1]. Out of the total 7,678 studies, 956

studies were removed by EndNote software as they

were duplicated. From 6,722 studies, 340 full

articles were selected by R1 through applying the

inclusion exclusion criteria shown in Table 1. For

validation purpose, a random 44 studies out of 6722

were selected and sent to two authors [R2] [R3].

Where there were conflicts with inclusion, this

discrepancy was resolved by arbitration and mutual

consent. In next step, inclusion exclusion criteria

were applied by R1 on the remaining 340 articles.

This resulted in 294 articles being excluded. For

validation of the excluded articles, a randomly

chosen 40 studies from these 294 articles were

reviewed by R2, and agreement was observed. Out

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of 46 included articles, four were found to be

replicated and were removed.

In addition, using the snowball method, references

from included articles were checked to ensure

inclusion of relevant studies which may have been

overlooked. Five articles were added resulting in a

total of 47 articles presented in this study.

Table 1: Inclusion (I) and Exclusion (E) criteria.

I: Original and peer-reviewed research written in English;

I: Qualitative, quantitative or mixed methods research;

I: Study on healthcare technology;

I: Study containing healthcare professional prospective;

I: Suggests/ recommends or contains/defines at least one trust

attribute for healthcare technology;

I: Describes factors that influence trust or the intention to use

technology in healthcare practice.

E: White or grey literature

E: Presents research noted in a prior/subsequent paper

3.4 Synthesis

A narrative synthesis, the process of synthesising

primary studies to explore heterogeneity

descriptively rather than statistically (Mays et al.,

2005), was performed to summarize the evidence.

We abstracted the trust factors into three categories:

HCT factors or characteristics of the HCT,

Individual factors or healthcare professional

characteristics and Organizational factors.

4 MAPPING RESULTS

This systematic mixed mapping study presents an

integrative and comprehensive structure of trust

factors and barriers associated with HCT for

healthcare professionals. Additionally, we present

their relative importance for specific types of HCT

used in healthcare. In this mapping study, 47 papers

presented 57 trust facilitators and 48 trust barriers in

HCT demonstrated by healthcare professionals.

Table 2 shows different types of HCT throughout

the literature and categorizes them into 8 specific

types of HCT based on their characteristics. Most

discussed HCT were Information Systems

(including; online databases, electronic guidelines,

information technology, electronic appointment

system and computer systems), Telemedicine

(including; smartphones, m-health, mobile health

systems, tele-health and e-health), Electronic

Records (including; medical/ health/ patient record,

health information exchange, electronically

mediated services and electronic logistics

information system), Wearable Devices, Evidence-

based Medicine, Adverse Event Reporting

System, Multi-agent System and Computerised

Medical Diagnosis Systems. Tables 3 and 4 present

trust facilitators and trust barriers most frequently

discussed in the literature. Studies are referenced in

our associated technical report (Abbas et al., 2017).

There are differences and similarities between trust

factors associated with each type of HCT. Perceived

system usefulness is a consistent factor across all

types of HCT, but its importance varied according to

the technology. Security issues and privacy concerns

are the most prominent trust barriers.

Table 2: Healthcare Technology within studies.

Type of Healthcare

Study reference number

Information System (IS)

S2,S3, S8, S11, S20, S25,

S33, S34, S35, S36, S37,

S39, S46, S47

Telemedicine (TM)

S7, S10, S12, S17, S19,

S26, S30, S31, S40, S43,

S45

Electronic Record (ER)

S2, S4, S6, S9, S13, S14,

S15, S18, S21, S22, S23,

S32, S41, S44

Wearable Devices (WD)

S24, S28

Evidence‐Based Medicine

(EBM)

S1, S16, S29

Adverse Event Reporting

System (AERS)

Multi-Agent Systems

(MAS)

S38

Computerised Medical

Diagnosing System

(CMDS)

S29

5 DISCUSSION

Various types of factors (technological, human, and

organizational) influence the level of HCT trust by

healthcare professionals. Factors facilitating HCT

trust tend to be mostly related to the perception of

the characteristics of the specific HCT and to

organizational aspects. Barriers are also related to

HCT characteristics, and are found in each of the

individual, professional, and organizational levels.

Some of the trust factors identified are ‘multilevel’

since they could affect more than one level (e.g. ease

Trust Factors in Healthcare Technology: A Healthcare Professional Perspective

457

Table 3: Trust facilitators.

Types of Trust

Factors

Trust Facilitators

EBM

AERS

MAS

CMDS

HCT factors or

characteristics

of the HCT

Compatibility

Security

Reliability

Functionality

Usability

Individual factors or

healthcare

professional

characteristics

Knowledge

Positive attitude towards usage

Perceived system usefulness

Organizational factors

Training and technical support

Table 4: Trust barriers.

Types of Trust

Factors

Trust Barriers

EBM

AERS

MAS

CMDS

HCT factors or

characteristics

of the HCT

Privacy concerns

Security issues

Lack of efficiency

Cost issues

Poor quality

Design & technical concerns

Individual factors

or healthcare

professional

characteristics

Lack of knowledge

Negative attitude towards usage

Perceived risks of usage

Task complexity

Organizational

factors

Poor training and technical

support

Governance/regulatory

compliance and policies

of use can be seen as a characteristic of the HCT but

is also related to familiarity with HCT at the

individual level). Interestingly, they are described as

a facilitator in one level, but a barrier in another

level indicating the importance of context.

5.2 HCT Factors

Compatibility is a trust facilitator within the

characteristics of HCT category which is discussed

six times in the literature. S37: Hung et al. (2014)

defined compatibility as the degree to which the

system is consistent with [nurses'] work practices or

preferences. Determining whether HCT is consistent

is an important trust factor because its function has

been specifically updated and modified to meet the

current needs. When the user sees that a particular

HCT is compatible with their work practice or style,

then they start to trust the technology or see the

relative advantage of using it.

Reliability is also discussed as a crucial trust

facilitator. S45: Van Velsen et al. (2016) discussed

trust in a rehabilitation portal technology, which was

mainly determined by its reliability. They defined

reliability for the rehabilitation portal technology as:

“That it works properly; is not constantly offline. But

also scientifically reliable.”

S14: Ross et al. (2010) expressed functionality as

a trust facilitator for HCT as Electronic Medical

Record (EMR) functionality including storage,

retrieval of test results, dictated notes, electronic

prescribing, shared medication and allergy lists

increases motivation to use and trust the system.

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Usability is explained by S45: Van Velsen et al.

(2016) as a set of attributes that bear on the effort

needed for use, and on the individual assessment of

such use, by a stated or implied set of users.

Usability has previously been identified as a pivotal

part of trust in e-services for the healthcare

professional. It is also identified as an important

antecedent for creating trust by a physician in HCT.

One of the most discussed trust barriers is the

privacy concern that has been discussed 14 times in

the literature. S13: Hsieh (2015) describes this as the

potential loss of confidential patient data in EMR

exchange systems leading towards low trust by the

healthcare professional in the system.

Another highly cited trust barrier is cost and

issues related with cost. S14: Ross et al. (2010)

describe some practices which identified capital

costs, such as installing and supporting new

computers and upgraded networking in the practice,

as a significant barrier to healthcare exchange use.

Lack of efficiency and poor quality has been

discussed regularly in the literature. Poor technology

quality is one of the factors defined by S9: Egea and

González (2011) for clinician’s resistance to use and

trust technology. They explain, “a physician who

uses telemedicine is concerned by the quality of

patient’s care which causes distrust about the

telemedicine”.

‘Multilevel’ influencing factors include security

S13: Hsieh (2015) explain that physicians’

intentions to use the system are based on the

importance of implementing security measures.

Security requirements, such as authentication, data

integrity, and encryption increases trust beliefs

among physicians. Where the integrity of healthcare

data – especially patient identifiable information is

not assured, it creates distrust by physicians.

5.2 Individual Factors

In the context of trust factors for healthcare

professionals, perception of the benefits of the

technology is the most frequent trust facilitator

factor encountered in the study, discussed 43 times.

Perceived usefulness, perceived ease of use/

complexity, intention to use and perceived

behavioural control/facilitating conditions all fall

under the umbrella of usefulness of the system.

Behavioural intention to use is defined as the

individual's interest in using the system for future

work. Perceived usefulness is defined as the degree

to which a person believes that using a particular

system would enhance his or her job performance,

while perceived ease of use is defined as the degree

to which a person believes that using a particular

system would be free of effort S5: (Wu et al., 2008).

Successful cases of HCT trust are usually

characterised by a clear understanding of the

benefits of the innovation by healthcare

professionals. Perceived ease of use is a strong and

significant determinant of [nurses’] intention to use

and trust electronic health records and also

influences the perceived usefulness of the system

S37: (Hung et al., 2014).

Perceived risks of usage and task complexity are

also trust barriers. Perceived risk is explained as the

uncertainty of a user [physician] or risks associated

with the usage of information system S14: (Hsieh,

2015). They have explained that the [physicians’]

perceived risk has a negative effect on their trust and

intention to use an electronic medical record

exchange system as perceived risk increases the

anticipation of negative outcomes, leading to an

unfavourable attitude that typically results in a

negative effect on a user's trust. S15: Saleem et al.

(2009) explain nine instances where complexity of a

task was not supported by the routine workflow or

computerized patient record system functionality,

resulting in the distrust of the system.

‘Multilevel’ influencing factors include

knowledge (experience, awareness) and attitude.

S45: Van Velsen et al. (2016) explain that they

found an indication of prior experience with

telemedicine playing a role in the formation of trust

beliefs among healthcare professionals, where lack

of knowledge and bad experiences led to low

trust. S14: Hsieh (2015) describe how positive or

negative correlation exists between [physicians’]

attitudes toward using the EMR exchange.

5.3 Organizational Factors

The main ‘multilevel’ factors, that may act as a

facilitator or barrier to HCT trust on organizational

level, is training and technical support. It is reported

a little more often as contributing positively as the

facilitator of trust and when it is a negative factor,

training could be non-existent, but also inadequate.

S17: Kayyali et al. (2017) describe that, when

healthcare professionals used telehealth, it also

raised the need for telehealth training packages for

clinicians. It is therefore not surprising that in a

context where healthcare professionals have very

limited time to learn to use a new HCT, training and

technical support plays an important role in forming

trust in the technology. Other influencing trust

barriers include governance/regulatory compliance

and policies.

Trust Factors in Healthcare Technology: A Healthcare Professional Perspective

459

6 CONCLUSION

HCT trust is complex, multi-dimensional, and

influenced by a variety of factors at individual and

organizational levels. Based on the trust factors

identified in this study, the main ingredients for a

successful HCT strategy for any healthcare

professional should include: perceived usefulness,

usability and training and technical support. The

strategy should recognise main trust barriers

including lack of privacy, cost issues, perceived risks

and security issues.

The mapping presented in this paper can guide

decision makers through HCT implementation,

providing them with issues to avoid to ensure

implementation success. HCT trust is complex,

multi-dimensional, and influenced by a variety of

factors at individual and organisational levels

(Kukafka et al., 2003), underscoring the importance

of developing interventions aimed at different levels

simultaneously.

One limitation of this study is that we did not

assess the extent to which proposed interventions

addressed trust barriers or the extent to which they

built on trust facilitators. This would constitute an

interesting avenue for further research in trust in

HCT. Other limitations are the unanswered

questions that are related to the impact of

interventions taking the barriers and the facilitators

identified into account. The relative importance of

each trust factor in specific HCT contexts remains to

be explored by studies using prospective designs. It

is also important to consider how these factors

change over time with the use of a specific

technology and with overall computer literacy.

In this study, we focused on trust in HCT by

healthcare professionals, but we have to

acknowledge that trust in HCT in healthcare

organizations is a multifaceted process since various

stakeholders are involved (Menachemi et al., 2004).

Also, trust is just the first step to consider for the

adoption of the healthcare technology. As noted by

Menachemi et al. (2009), it is important to consider

the viewpoints of all key adopter groups, because

resistance in any of these groups could slow the

overall trust and would not provide essential

information for decision-makers.

For future work, we plan to undertake a

systematic literature review to synthesize evidence,

considering the strength of evidence in assessing the

extent to which interventions addressed the trust

facilitators and barriers in HCT.

ACKNOWLEDGEMENT

This work was supported with the financial support

of the Science Foundation Ireland grant 13/RC/2094

and co-funded under the European Regional

Development Fund through the Southern & Eastern

Regional Operational Programme to Lero - the Irish

Software Research Centre (www.lero.ie).

REFERENCES

Abbas, R. M., Carroll, N., Richardson, I. & Beecham, S.

2017. Protocol for Mapping Study of Trust Factors in

Healthcare Technology: A Healthcare Preofessio-nal

Perspective [Online]. LERO Technical Report.

Available: http://www.lero.ie/sites/default/files/-

TR_2017_04.pdf [Accessed 29/10/2017].

Alford, J. Building Trust in Partnerships Between

Community Orgnization and Government. Changing

the Way Government Works Seminar, Melbourne,

2004.

Almenárez, F., Marín, A., Campo, C. & Garcia, C. PTM:

A pervasive trust management model for dynamic

open environments. First Workshop on Pervasive

Security, Privacy and Trust PSPT, 2004. 1-8.

Anderson, J. G. 2007. Social, ethical and legal barriers to

E-health. International Journal of Medical

Informatics, 76, 480-483.

Bélanger, F. & Carter, L. 2008. Trust and risk in e-

government adoption. The Journal of Strategic

Information Systems, 17, 165-176.

Berner, E. S., Detmer, D. E. & Simborg, D. 2005. Will the

Wave Finally Break? A Brief View of the Adoption of

Electronic Medical Records in the United States.

Journal of the American Medical Informatics

Association, 12, 3-7.

Brooks, R. G. & Menachemi, N. 2006. Physicians’ Use of

Email with Patients: Factors Influencing Electro-nic

Communication and Adherence to Best Practi-ces.

Journal of Medical Internet Research, 8, e2.

Car, J. & Sheikh, A. 2004. Email Consultations In Health

Care: 2-Acceptability And Safe Application. BMJ:

British Medical Journal, 329, 439-442.

Carroll, N. 2016. Key success factors for smart and

connected health software solutions. Computer, 49,

22-28.

Carroll, N., Kennedy, C. & Richardson, I. 2016.

Challenges towards a connected community healthcare

ecosystem (CCHE) for managing long-term

conditions.

Cocosila, M. & Archer, N. 2014. Perceptions of

chronically ill and healthy consumers about electronic

personal health records: a comparative empirical

investigation. BMJ Open, 4.

Egea, J. M. O. & González, M. V. R. 2011. Explaining

physicians’ acceptance of EHCR systems: an

extension of TAM with trust and risk factors.

HEALTHINF 2018 - 11th International Conference on Health Informatics

460

Computers in Human Behavior, 27, 319-332.

Gagnon, M.-P., Desmartis, M., Labrecque, M., Car, J.,

Pagliari, C., Pluye, P., Frémont, P., Gagnon, J.,

Tremblay, N. & Légaré, F. 2012. Systematic review of

factors influencing the adoption of information and

communication technologies by healthcare profession-

als. Journal of medical systems, 36, 241-277.

Gollmann, D. 2006. Why Trust is Bad for Security.

Electronic Notes in Theoretical Computer Science,

157, 3-9.

Grandison, T. & Sloman, M. 2000. A survey of trust in

internet applications. IEEE Communications Surveys

& Tutorials, 3, 2-16.

Hall, M. A., Zheng, B., Dugan, E., Camacho, F., Kidd, K.

E., Mishra, A. & Balkrishnan, R. 2002. Measuring

patients’ trust in their primary care providers. Medical

care research and review, 59, 293-318.

Hsieh, P.-J. 2015. Physicians’ acceptance of electronic

medical records exchange: An extension of the

decomposed TPB model with institutional trust and

perceived risk. International Journal of Medical

Informatics, 84, 1-14.

Hung, S.-Y., Tsai, J. C.-A. & Chuang, C.-C. 2014.

Investigating primary health care nurses' intention to

use information technology: An empirical study in

Taiwan. Decision Support Systems, 57, 331-342.

Jøsang, A., Ismail, R. & Boyd, C. 2007. A survey of trust

and reputation systems for online service provision.

Decision Support Systems, 43, 618-644.

Kayyali, R., Hesso, I., Mahdi, A., Hamzat, O., Adu, A. &

Nabhani Gebara, S. 2017. Telehealth: misconceptions

and experiences of healthcare professionals in

England. International Journal of Pharmacy Practice,

25, 203-209.

Krishna, K. & Maarof, M. A. 2002. An Hybrid Trust

Management Model for MAS Based Trading Society,

Universiti Teknologi Malaysia.

Kukafka, R., Johnson, S. B., Linfante, A. & Allegrante, J.

P. 2003. Grounding a new information technology

implementation framework in behavioral science: a

systematic analysis of the literature on IT use. Journal

of Biomedical Informatics, 36, 218-227.

Leroy, G., Chen, H. & Rindflesch, T. C. 2014. Smart and

connected health. IEEE Intelligent Systems, 29, 2-5.

Massa, P. 2007. Trust in E-services: Technologies

Practices and Challenges. A Survey of Trust Use and

Modeling in Real Online Systems, 51-83.

Mays, N., Pope, C. & Popay, J. 2005. Systematically

reviewing qualitative and quantitative evidence to

inform management and policy-making in the health

field. Journal of health services research & policy, 10,

6-20.

Mcknight, D. H., Choudhury, V. & Kacmar, C. 2002.

Developing and validating trust measures for e-

commerce: An integrative typology. Information

systems research, 13, 334-359.

Menachemi, N., Burke, D. E. & Ayers, D. J. 2004. Factors

affecting the adoption of telemedicine—a multiple

adopter perspective. Journal of medical systems,

28, 617-632.

Menachemi, N., Matthews, M., Ford, E. W., Hikmet, N. &

Brooks, R. G. 2009. The relationship between local

hospital IT capabilities and physician EMR adoption.

Journal of medical systems, 33, 329.

Michael, J. B., Hestad, D. R., Pedersen, C. M. & Gaines,

L. T. 2002. Incorporating the Human Element of Trust

into Information Systems. IAnewsletter, 5, 4-8.

Miles, A. & Asbridge, J. E. 2014. The European Society

for Person Centered Healthcare (ESPCH)–raising the

bar of health care quality in the Century of the Patient.

Journal of Evaluation in Clinical Practice, 20, 729-

733.

Misztal, B. 2013. Trust in modern societies: The search for

the bases of social order, John Wiley & Sons.

Pagliari, C. 2005. Implementing the National Programme

for IT: what can we learn from the Scottish

experience? Informatics in Primary Care, 13, 105-111.

Park, H., Chon, Y., Lee, J., Choi, I.-J. & Yoon, K.-H.

2011. Service design attributes affecting diabetic

patient preferences of telemedicine in South Korea.

Telemedicine and e-Health, 17, 442-451.

Petersen, K., Vakkalanka, S. & Kuzniarz, L. 2015.

Guidelines for conducting systematic mapping studies

in software engineering: An update. Information and

Software Technology, 64, 1-18.

Pluye, P., Gagnon, M.-P., Griffiths, F. & Johnson-Lafleur,

J. 2009. A scoring system for appraising mixed

methods research, and concomitantly appraising

qualitative, quantitative and mixed methods primary

studies in Mixed Studies Reviews. International

Journal of Nursing Studies, 46, 529-546.

Raya, M., Papadimitratos, P., Gligor, V. D. & Hubaux, J.-

P. On data-centric trust establishment in ephemeral ad

hoc networks. INFOCOM 2008. The 27th Conference

on Computer Communications. IEEE, 2008. IEEE,

1238-1246.

Robinson, S. L. 1996. Trust and breach of the

psychological contract. Administrative science

quarterly, 574-599.

Ross, S. E., Schilling, L. M., Fernald, D. H., Davidson, A.

J. & West, D. R. 2010. Health information exchange

in small-to-medium sized family medicine practices:

Motivators, barriers, and potential facilitators of

adoption. International Journal of Medical

Informatics, 79, 123-129.

Saleem, J. J., Russ, A. L., Justice, C. F., Hagg, H., Ebright,

P. R., Woodbridge, P. A. & Doebbeling, B. N. 2009.

Exploring the persistence of paper with the electronic

health record. International Journal of Medical

Informatics, 78, 618-628.

Van Velsen, L., Van Der Geest, T., Van De Wijngaert, L.,

Van Den Berg, S. & Steehouder, M. 2015.

Personalization has a Price, Controllability is the

Currency: Predictors for the Intention to use

Personalized eGovernment Websites. Journal of

Organizational Computing and Electronic Commerce,

25, 76-97.

Van Velsen, L., Wildevuur, S., Flierman, I., Van

Schooten, B., Tabak, M. & Hermens, H. 2016. Trust in

telemedicine portals for rehabilitation care: an

Trust Factors in Healthcare Technology: A Healthcare Professional Perspective

461

exploratory focus group study with patients and

healthcare professionals. BMC Medical Informatics

and Decision Making, 16, 11.

Who. 2017. Available: http://www.who.int/topics/-

technology_medical/en/ [Accessed 5th July 2017].

Wu, I.-L. & Chen, J.-L. 2005. An extension of Trust and

TAM model with TPB in the initial adoption of on-

line tax: An empirical study. International Journal of

Human-Computer Studies, 62, 784-808.

Wu, J.-H., Shen, W.-S., Lin, L.-M., Greenes, R. A. &

Bates, D. W. 2008. Testing the technology acceptance

model for evaluating healthcare professionals'

intention to use an adverse event reporting system.

International Journal for Quality in Health Care, 20,

123-129.

Yarbrough, A. K. & Smith, T. B. 2007. Technology

Acceptance among Physicians. Medical Care

Research and Review, 64, 650-672.

Yusof, M. M., Stergioulas, L. & Zugic, J. 2007. Health

information systems adoption: findings from a

systematic review. Studies in health technology and

informatics, 129, 262.

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