Keywords
safety - quality - electronic prescribing - technology acceptance model
Introduction
Electronic prescribing (e-prescribing) systems used in conjunction with decision support
are increasingly being adopted in primary care and hospital settings around the world
to reduce prescribing errors.[1]
[2]
[3] These systems eliminate the problem of illegible handwriting, increase the accuracy
and completeness of information, and improve response times and the continuity of
patient care. Many safety problems arise from poor communication between and across
the professions (e.g., physicians, nurses, and pharmacist groups) and departments
(e.g., pharmacy and medicine).[4]
[5] Information technology (IT), particularly electronic health record (EHR) and e-prescribing
systems, have great potential to enhance communication and improve the quality and
safety of care.[6]
[7] However, some studies suggest the existence of serious problems and deficiencies
associated with e-prescribing, which may significantly hinder the efficiency of EHR
use and physicians' routine work,[8]
[9] such as creating usability problems and introducing new error types.[9] This article's focus is on physicians' perception of patient safety and quality
of care (PSQ).
In this article, our aim is to investigate the influence of electronic prescription
(e-prescription) on PSQ on physician's work as e-prescribing is used in primary health
care settings and in a university hospital district. The first survey took place in
2012 when the e-prescribing system was initially introduced in Finland, followed by
the second survey conducted in 2014, when the system was implemented nationally. Our
work contributes to the research literature by exploring the relationship between
key concepts (i.e., PSQ) as well as the technology acceptance model (TAM) and the
interoperability of the EHR, e-prescribing, and the national Pharmaceutical Database.
Conceptual Framework
The conceptual framework that forms the basis of this study is the TAM. The TAM model
was developed based on existing behavioral intention models in the social sciences:
the theory of reasoned action[10] and theory of planned behavior.[11]
[12] The TAM model is based on two fundamental beliefs, namely systems' perceived usefulness
(PU) and perceived ease of use (PEoU).[13] Davis defined PU as “the degree to which a person believes that using a particular
system would enhance his or her job performance.” PEoU can be defined as “the degree
to which a person believes that using a particular system would be free of effort.”[13] These beliefs determine an individual's attitude toward the use, behavioral intention,
and actual usage of information systems. The TAM model posits a relationship (1) between
users' perceptions of technology and acceptance and (2) between acceptance and actual
use.
Today, the TAM model is widely used as a conceptual framework in studies of the acceptance
of various health information technologies (HITs). Prior research involving the TAM
model explored the relationships between TAM concepts and the impact of national cultural
differences,[14] perceived importance of data security,[15] a person's privacy concerns,[16] and perceived threat to professional autonomy.[17]
[18] Additionally, PSQ were studied and noted as having a relationship with concepts
described in the TAM model.[19] Most studies have focused on health professionals' behavioral intention to use health
care technology.[20]
[21] Holden and Karsh highlighted the necessity of contextualizing the TAM model for
health care settings and suggested that this could be done by differentiating between
performance benefits accruing at the personal and group levels, focusing on health
care outcomes, and allowing for different sources of social influences.[12] For example, Sun et al extended IT usage models to include the role of IT's perceived
work compatibility in shaping users' IT usage intention, usage, and performance in
a work setting.[22] The authors suggested that it was important to contextualize IT usage within a work
setting to maximize its power in organizations.
HIT has been shown to potentially improve PSQ.[3]
[23]
[24] Quality of health care can be defined as doing the right thing at the right time
and in the right way to achieve the best possible results for a patient.[23] In this study, patient safety means the continuous identification, analysis, and
management of patient-related risks and incidents to minimize harm to patients.[6]
[23] One of the main HIT issues is the heterogeneity of available systems;[5] interoperability and adaptation have to be introduced into the model through compliance
with technological and health-oriented standards.[5]
[25]
[26] For instance, an e-prescribing system that helps physicians enter orders on a computer
could help prevent medication errors.[3]
[27]
[28]
In Finland, e-prescriptions are stored in a centralized database, the Prescription
Centre. The Prescription Centre is one part of the National Archive of Health Information
(also called Kanta services). In addition to e-prescriptions, Kanta services comprise
the Pharmaceutical Database and the Patient Data Repository. The Pharmaceutical Database
includes information necessary for prescribing and dispensing, such as the process
of administering medication and reimbursement status.[29]
[30]
Interoperability issues may arise if patient care requirements, clinical processes,
and administrative requirements are not adequately addressed.[19]
[31] Interoperability refers to the ability of various information systems and software
applications to communicate, exchange data, and use information that has been exchanged.[32] Definitions for e-prescriptions contain the use cases, requirements and data contents
of the pharmacy system, Patient Records Archive, and HL7 definitions.[29] Integration is a combination of diverse application entities into a relationship,
which functions as a whole. This study attempts to use a conceptual framework for
e-prescribing, patient safety, and quality of care in physicians' work and to illustrate
the usefulness of e-prescription in improving the quality and safety of care.[6]
[7] This study extends the TAM model to analyze the acceptance of e-prescribing and
adds an understanding of what kind of impact the external variables, patient identification,
and interoperability of applications (i.e., interoperability between the EHR, e-prescribing,
and the pharmaceutical database) have on physicians' individual work performance.
The difference between previous studies and this study is it focuses on e-prescribing's
impact on physicians' perception of PSQ rather than on the intention to use or usage
of a system. PSQ could be viewed as measures of the usefulness of a system, but this
study uses PSQ as estimates for e-prescribing's usefulness.
Research Model Hypotheses
Research Model Hypotheses
The present study hypothesizes a model for predicting physicians' attitudes toward
using an e-prescribing system as one for providing safer care. The study seeks to
assess the impact of an e-prescribing system on PSQ, as well as assess effects between
PU and PEoU and external variables, including the interoperability of an EHR system
and patient identification.
A gap between the postulated and objectively demonstrated benefits of electronic health
(e-health) systems, including e-prescribing, was found in Black et al's systematic
review that assessed the impact of an e-health system on perceived patient safety.[33] Davies et al suggested a potential for deterioration rather than improvement in
perceived patient safety and safety attitudes among staff with the introduction of
e-prescribing.[28] The authors of the study found problems with systems' usability, nonstandardized
implementation, and competence assessment strategies. They conducted a cross-sectional
study, which targeted all hospital staff involved in the care of surgical patients
in southwestern England.[28] Similar results have been found with e-prescribing systems' usability in Finland
and Sweden.[8]
[24]
[34]
Our research model ([Fig. 1]) seeks to assess and explain the impact of an e-prescribing system on PSQ on physicians'
individual work performance. Two factors, patient safety and quality of care, will
reveal the consequences of e-prescription as assessed by physicians. Two external
variables, EHRs' interoperability (iEHR) and patient identification (ID), highlight
the consequence of using the technology. The architecture of national social and health
services in Finland describes the sector's common procedures, data contents, and data
system services.[29] Physician uses the information by accessing patients' records, where patients' medication
list, prescribing history, and known medication allergies are documented, and patient
records to prescribing are accessible. PU and PEoU, which are two factors in the TAM,
emphasize the importance of physicians' opinions on the use of e-prescribing software
in their work.
Fig. 1 Research model for predicting e-prescribing's influence with patient safety and quality
of care.
PSQ are the focus of this study. The original TAM was developed to predict users'
organizational information system use. In this study, we are seeking to understand
the kind of impact PU and PEoU have on physicians' work performance, including PSQ.
Based on previous studies, seven hypotheses were identified.
Perceived Usefulness of Electronic Prescribing Systems
Davis linked PU with outcome expectations, instrumentality, and extrinsic motivation.[13] PU refers to one's belief that using a technology will lead to desirable outcomes,
usually involving an increase in personal effectiveness or productivity.[13] An IT system's usefulness was found to be significant for users' acceptance in health
care–related TAM model studies.[16]
[19]
[35] For example, participants believed that the new system could enhance job performance
or improve work quality.[14]
[35] However, Sicotte et al found that one weakness of the TAM model's factors (PU, PEoU)
was they did not predict new technology use among physicians.[36] Regardless of the weakness of the TAM model's factors, using an e-prescribing system
is expected to improve PSQ on medication prescribing.
Thus, we hypothesized:
H1. e-Prescribing's PU is positively associated with physicians' perceptions of PSQ.
Perceived Ease of Use of Electronic Prescribing Systems
Studies on TAM model use in health care found a significant positive influence of
PEoU on PU.[12]
[36] In health care, ease of use, like time efficiency, would also be relevant.[37] If systems are easy to use and suitable for integration into daily work, they deliver
adequate performance in most cases.[37] This finding is relevant, and it indicates that e-health applications would benefit
physicians.[12]
[19]
[36] Contradictory reporting has been found in which there was no significant association
between PEoU and actual use of information systems.[19]
[38] PEoU could indirectly influence technology adoption through PU and should be considered
a determining factor in technology acceptance.[19] Therefore, improving the usability of e-health systems is important. We hypothesized:
H2. e-Prescribing's PEoU is positively associated with PSQ.
H3. e-Prescribing's PEoU is positively associated with PU.
Interoperability of the Electronic Health Record System and Patient Identification
EHR systems are computer systems that allow one to create, store, edit, and retrieve
patient charts on a computer.[32] These systems facilitate the organization and rapid retrieval of information by
serving as digital repositories for physicians' notes and laboratory results as well
as patients' medications, allergies, and essential sociodemographic and contact data.[12] Interoperability has been defined as the ability of different IT systems and software
applications to communicate, exchange data, and use information that has been exchanged.[32] Interoperability is no longer alone a technological option; it is a fundamental
requirement for delivering effective care and ensuring the health and well-being of
patients.[39] Therefore, our hypotheses are as follows:
H4. An EHR system's interoperability is positively associated with the PU of e-prescribing.
H5. An EHR system's interoperability is positively associated with the PEoU of e-prescribing.
Recording a patient's demographic, treatment, and outcome data makes it possible to
assess the results of changes to treatments or to the organization of care.[12] Consequently, the failure to correctly identify patients continues to result in
medication errors, testing errors, and wrong-person procedures. The ultimate goal
is to accurately identify the patient and link all related information to the individual
within and across systems.[31] Therefore, we hypothesize:
H6. Patient identification is positively associated with the PU of e-prescribing.
H7. Patient identification is positively associated with the PEoU of e-prescribing.
Data Collection and Analysis
Data Collection and Analysis
The empirical analysis uses data from surveys conducted in 2012 and 2014. The participants
were all physicians who worked in primary health care and in a university hospital
district. e-Prescribing was the only method that could be used for prescribing medication
in Finland at the time of these studies. e-Prescriptions are sent electronically from
a physician's office to a central database (i.e., the National Prescription Center).
Pharmacies also have electronic access to prescriptions. The Finnish e-prescribing
system uses a pull model;[40] it allows any authorized pharmacy to retrieve prescription information into its
system for processing.
At the time of the data collection (2012 and 2014), the e-prescribing system had been
used for more than 1 year and all physicians had experience with e-prescribing. The
first study (2012) took place in two primary health care organizations (n = 69/269, 26%). These primary health care organizations were the first in Finland
to implement the e-prescribing system in 2010 and 2011. The second study (2014) took
place in the university hospital district area, including a university hospital and
primary health care enterprises (n = 131/1,135, 12%). The university hospital district area was among the last in Finland
to implement the e-prescribing system in 2013. The two studies had different participants
due to their affiliations in primary care and hospital settings. However, participants
in both studies had a little more than 1 year of experience with the e-prescribing
system. A questionnaire was used as a collection method to obtain an overview of the
participants' status.
A questionnaire was developed following a review of previously published questionnaires,[24]
[41] prior research, and studies involving the TAM model. The study questionnaire was
pretested twice in 2012 and 2014 by clinicians, senior health care officials, and
two faculty members. One faculty member had a background in health and human services
informatics and the other in pharmacology. The pretest allowed the researchers to
identify confusing or redundant items through discussion with the individuals who
were in the pretest. In this study, e-prescribing was investigated by utilizing a
combination of questions inspired by the TAM model (i.e., n = 18 items), questions from a survey by Hellström and her colleagues (i.e., n = 10 items),[24] and structured questions collected by a 5-point Likert-type scale where 1 “strongly
disagree” and 5 “strongly agree.” Seven structured questions were used to collect
demographic data.
Physicians were invited to complete the surveys by email. All physicians in the organizations
received an email with a hyperlink to the survey (n = 269 in 2012, n = 1,135 in 2014). The e-mail also included information about the purpose of the study
and the researchers. No incentives were offered for participation. The web-based survey
was available for completion for a period of 2 weeks and subsequently for an additional
week after two reminder emails were sent inviting physicians to participate in the
survey study. The first survey took place between September and October 2012, and
the second study took place between April and May 2014.
This article provides a comparative analysis of how e-prescribing influenced physicians'
work and how TAM factors and external variables correlate with PSQ. The TAM suggests
that external variables indirectly determine one's attitude toward technology acceptance
by influencing PU and PEoU. In our study, PSQ ([Table 1]), the iEHR, and ID ([Table 2]) were formulated by exploratory factor analysis. The model's validity was tested
with the Kaiser–Meyer–Olkin test and Bartlett's test of sphericity, and all factors
presented here fulfilled the tests' assumptions. Additionally, Cronbach's α values
were calculated for the factors to test the scale's reliability. The α values were
in the range of 0.65 to 0.96, which indicates adequate consistency.[42] The survey data were combined for statistical analysis, and when the survey was
completed, it was a background variable.
Table 1
Factor loading of patient safety and quality of care
|
2012 (KMO 0.647, p < 0.000)
|
2014 (KMO 0.764, p < 0.000)
|
Combined (KMO 0.752, p < 0.000)
|
|
Patient safety and quality of care (PSQ)
|
Factor loading
|
Factor loading
|
Factor loading
|
|
PSQ1
|
Compared with handwritten prescription, e-prescriptions written in the EHR system
are safer
|
0.586
|
0.587
|
0.581
|
|
PSQ2
|
e-Prescribing has decreased errors in patients' medication
|
0.914
|
0.961
|
0.933
|
|
PSQ3
|
e-Prescribing has decreased “near misses” situations
|
0.934
|
0.883
|
0.913
|
|
PSQ4
|
e-Prescribing will provide better service to the patients
|
0.613
|
0.624
|
0.624
|
Abbreviations: EHR, electronic health record; KMO, Kaiser–Meyer–Olkin.
Table 2
Factor loading of external variables (iEHR and ID)
|
2012 (KMO 0.647, p < 0.000)
|
2014 (KMO 0.764, p < 0.000)
|
Combined (KMO 0.752, p < 0.000)
|
|
Factor loading
|
Factor loading
|
Factor loading
|
|
Interoperability of EHR system (iEHR)
|
|
iEHR1
|
The EHR system clearly displays the basic information for each medication
|
0.998
|
0.706
|
0.765
|
|
iEHR2
|
The EHR system clearly displays the price for each medication
|
0.588
|
0.748
|
0.711
|
|
iEHR3
|
The EHR system clearly displays the medicine package for each medication
|
0.438
|
0.695
|
0.629
|
|
iEHR4
|
The EHR supports the e-prescribing
|
0.421
|
0.405
|
0.357
|
|
Patient Identification (ID)
|
|
ID1
|
When e-prescribing medication, the EHR system clearly displays the personal identity
code of the current patient
|
0.950
|
0.950
|
0.963
|
|
ID2
|
When e-prescribing medication, the EHR system clearly displays the name of the current
patient
|
0.972
|
0.970
|
0.959
|
Abbreviation: KMO, Kaiser–Meyer–Olkin.
An exploratory factor analysis was also conducted with PU and PEoU variables, and
it revealed some variation between 2012 and 2014 data. Despite some slight variation
in the PU and PEoU factor loadings between the two years ([Table 3]), the TAM's two main factors were kept in the initial structure.
Table 3
Measures of constructs, Cronbach's α, and factory of items used by studies
|
TAM model: theoretical constructs items
|
2012 (Cronbach's α 0.97, KMO 0.925, sig. 0.000)
|
2014 (Cronbach's α 0.96, KMO 0.925, sig. 0.000)
|
Combined (KMO 0.929, sig. 0.000)
|
|
Perceived usefulness (PU)
|
Factor loading
|
Factor loading
|
Factor loading
|
|
PU1
|
Using e-prescribing improves the quality of the work
|
0.836
|
0.759
|
0.777
|
|
PU2
|
Using e-prescribing gives me greater control over my work
|
0.877
|
0.827
|
0.842
|
|
PU3
|
e-Prescribing enables me to accomplish tasks more quickly
|
0.895
|
0.825
|
0.881
|
|
PU4
|
e-Prescribing supports critical aspects of my job
|
0.835
|
0.788
|
0.798
|
|
PU5
|
Using e-prescribing increases my productivity
|
0.905
|
0.880
|
0.886
|
|
PU6
|
Using e-prescribing allows me to accomplish more work than would otherwise be possible
|
0.855
|
0.825
|
0.836
|
|
PU7
|
Using e-prescribing enhances my effectiveness on the job
|
0.970
|
0.883
|
0.912
|
|
PU8
|
Using e-prescribing makes it easier to do my job
|
0.927
|
0.795
|
0.849
|
|
PU9
|
Overall, I find the e-prescribing useful in my job
|
0.914
|
0.817
|
0.850
|
|
Perceived ease of use (PEoU)
|
(Cronbach's α 0.82, KMO 0.756, sig. 0.000)
|
(Cronbach's α 0.882, KMO 0.837, sig. 0.000)
|
(KMO 0.848, sig. 0.000)
|
|
PeoU1
|
Learning to operate e-prescribing is easy for me
|
0.354
|
0.787
|
0.322
|
|
PeoU2
|
I find e-prescribing cumbersome to use
|
0.766
|
0.412
|
0.692
|
|
PeoU3
|
Interacting with the e-prescribing is often frustrating
|
0.859
|
0.316
|
0.783
|
|
PeoU4
|
I find it easy to get the e-prescribing to do what I want it to do
|
0.506
|
0.505
|
0.556
|
|
PeoU5
|
The e-prescribing is rigid and inflexible to interact with
|
0.668
|
0.719
|
0.671
|
|
PeoU6
|
It is easy for me to remember how to perform tasks using the e-prescribing
|
0.723
|
0.804
|
0.805
|
|
PeoU7
|
My interaction with the e-prescribing is clear and understandable
|
0.511
|
0.564
|
0.506
|
|
PeoU8
|
I find it takes a lot of effort to become skillful at using the e-prescribing
|
0.520
|
0.505
|
0.434
|
|
PeoU9
|
Overall, I find the e-prescribing easy to use
|
0.481
|
0.627
|
0.508
|
Abbreviations: KMO, Kaiser–Meyer–Olkin; sig., significance.
Additionally, the researcher tested the effect of background variables (i.e., gender,
age, setting, date of graduation, experience with e-prescribing and EHR, personal
computing (PC) skills, and years the survey was taken) on the revealed significant
association with the independent variables. To ensure content validity, the measurement
items used to capture the theoretical construct were adopted from scales validated
in previous health informatics research.[13]
[24]
[36]
[41] Measures of the iEHR and ID were adapted from the national strategy of health informatics[43] and the Healthcare Information and Management Systems Society.[31]
First, we conducted descriptive analyses to explore the demographic and theoretical
data distribution. Next, we performed a path analysis using the maximum likelihood
method of parameter estimation to test direct and indirect impacts on the extended
TAM model. In the first step, we entered dimensions pertaining to the TAM (PU and
PEoU). Then, two other dimensions, namely iEHR and ID, were added to the research
model to improve its predictive power. All associations with predictors and PSQ were
hypothesized as direct in iEHR and ID. We tested the influence of experience and demographic
characteristics on physicians' attitudes toward the PSQ to detect potential modifying
effects.
To evaluate the effectiveness of our research model, we employed an analysis of covariance
(ANCOVA) to test the hypothesis that the research model will demonstrate physicians'
perception of the e-prescribing system's impact on PSQ and test how the extended research
model works with these data. The final model presented in the article includes only
the significant predictors of PSQ. All statistical analyses were performed with SAS
21 (IBM, Amok, NY) and AMOS. A p-value of 0.05 was set as the level of statistical significance.
Results
Most participants (n = 139, 70%) were female. The participants' mean age was 44.57 years (median = 46
years). About two-thirds of the participants (n = 123, 66%) worked in primary care at the time of the study. The participants' mean
experience with an EHR system was 7.82 years (median = 7 years), and half of the participants
(n = 115, 59%) had more than 1 year of experience with e-prescribing technology. For
a full overview of the participants' demographic information, see [Table 4].
Table 4
Participants' profile (n = 200)
|
Characteristics
|
|
2012, n (%)
|
2014, n (%)
|
2012–2014, n (%)
|
|
Gender (n = 198)
|
Male
|
17 (25)
|
42 (33)
|
59 (30)
|
|
Female
|
52 (75)
|
87 (67)
|
139 (70)
|
|
Age group (y, n = 200)
|
Under 34
|
18 (26)
|
54 (41)
|
72 (36)
|
|
35–54
|
36 (52)
|
77 (59)
|
113 (56)
|
|
55 or over
|
15 (22)
|
|
15 (8)
|
|
Setting (n = 188)
|
Primary health care
|
69 (100)
|
59 (48)
|
123 (66)
|
|
Hospital
|
|
65 (52)
|
65 (34)
|
|
Qualified as a physician (y, n = 199)
|
Under 4
|
11 (16)
|
14 (11)
|
27 (14)
|
|
5–14
|
17 (25)
|
30 (23)
|
46 (23)
|
|
15–24
|
8 (12)
|
33 (25)
|
45 (23)
|
|
25–34
|
18 (26)
|
44 (33)
|
67 (33)
|
|
35 or over
|
14 (21)
|
10 (8)
|
14 (7)
|
|
Experience on EHR system (y, n = 160)
|
<3
|
14 (22)
|
26 (27)
|
40 (25)
|
|
4–9
|
31 (49)
|
30 (31)
|
61 (38)
|
|
>10
|
18 (29)
|
41 (42)
|
59 (37)
|
|
Experience on e-prescribing (y, n = 195)
|
≤1
|
23 (33)
|
57 (45)
|
80 (41)
|
|
>1
|
46 (67)
|
69 (55)
|
115 (59)
|
|
e-Prescription per day (n = 194)
|
≤5
|
19 (28)
|
54 (43)
|
73 (38)
|
|
6–9
|
23 (35)
|
33 (26)
|
56 (29)
|
|
>10
|
25 (37)
|
40 (31)
|
65 (33)
|
|
PC skills (n = 199)
|
Moderate or poor
|
28 (41)
|
50 (38)
|
78 (39)
|
|
Excellent
|
40 (59)
|
81 (62)
|
121 (61)
|
|
Moment of surveys (n = 200)
|
2012
|
69 (26)
|
|
69 (35)
|
|
2014
|
|
131 (12)
|
131 (65)
|
Abbreviations: EHR, electronic health record; PC, personal computing.
In general, the participants used the e-prescribing system daily, and over half (n = 121, 62%) used e-prescribing more frequently than five times per day. Over half
of the participants (n = 121, 61%) self-evaluated their PC skills as excellent.
[Table 5] shows the results from the ANCOVA model. PU had a significant association with PSQ,
and iEHR had a significant association with both PEoU and PU. The findings show that
experience with an e-prescribing system has positive effects on the participants'
PEoU and PU. The research also found that participants' PEoU and PU were higher when
the participants worked in a hospital. The findings suggest that PEoU was higher for
female respondents, and PSQ was higher for males. In the second measurement completed
in 2014, the iEHR system had a positive effect on participants' PEoU. A weak but statistically
significant association was found between the year of graduation and experience with
an EHR system. This occurred when the participant was a newly qualified physician
(i.e., under 4 years of experience as a physician) and their experience with using
an EHR system was low; the iEHR system's importance was emphasized.
Table 5
ANCOVA results (coefficients): PU, PEoU, PSQ, iEHR, and ID
|
Perceived of usefulness (n = 165)
|
Perceived ease of use (n = 162)
|
Patient safety and quality
of care (n = 160)
|
Interoperability of EHR (n = 168)
|
Patient identification (n = 168)
|
|
B
|
Sig.
|
B
|
Sig.
|
B
|
Sig.
|
B
|
Sig.
|
B
|
Sig.
|
|
Experience of e-prescribing
|
−0.365
|
0.012
|
−0.265
|
0.046
|
|
|
|
|
−0.260
|
0.090
|
|
Setting
|
−0.566
|
0.000
|
−0.504
|
0.000
|
|
|
|
|
|
|
|
Interoperability of EHR
|
0.186
|
0.022
|
0.353
|
0.000
|
|
|
|
|
|
|
|
Gender
|
|
|
−0.282
|
0.047
|
0.482
|
0.000
|
|
|
|
|
|
2012–2014
|
|
|
−0.469
|
0.001
|
|
|
0.252
|
0.079
|
|
|
|
Perceived of usefulness
|
|
|
|
|
0.571
|
0.000
|
|
|
|
|
|
Graduation
|
|
|
|
|
|
|
0.663
|
0.042
|
|
|
|
Experience with EHR
|
|
|
|
|
|
|
−0.445
|
0.033
|
|
|
Abbreviations: ANCOVA, analysis of covariance; ID, identification; iEHR, interoperability
of electronic health record; PEoU, perceived of ease of use; PSQ, patient safety and
quality of care; PU, perceived of usefulness; sig., significance.
In statistics, a path analysis is used to describe direct dependencies among a set
of variables.[42] The paths in this model, denoted by one-headed arrows, contain assumptions of the
directions of the associations between the variables. The results of the path analysis
model testing the research model for predicting physicians' perception of e-prescribing's
impact on PSQ are reported in [Fig. 2]. A test of the structural model includes estimates for the path coefficients, which
indicate the strengths of association between the independent variables. The path
diagram supported a positive association for four of the hypotheses ([Fig. 2]). PU has a direct and positive effect on PSQ, while PEoU has a direct positive effect
on PU and an indirect effect on PSQ. iEHR and ID have a direct positive effect on
PEoU, and they have an indirect effect on PU and PSQ. PEoU's direct effect on PSQ
did not appear to be significant.
Fig. 2 Assessment of the research model (standardized solution).
[Table 6] shows the path coefficient's direct, indirect, and total effects. This reflects
an association in hypotheses H1, H3, H5, and H7 with a direct effect, while hypotheses
H2, H4, and H6 yield an indirect effect.
Table 6
Effects of research model (hypothesis test results for the research model)
|
Hypotheses
|
Path
|
Total
|
Direct
|
Indirect
|
Results
|
|
H1
|
PU → PSQ
|
0.542
|
0.542
|
0.000
|
Supported
|
|
H2
|
PEoU → PSQ
|
0.392
|
0.000
|
0.392
|
No supported
|
|
H3
|
PEoU → PU
|
0.724
|
0.724
|
0.000
|
Supported
|
|
H4
|
iEHR → PU
|
0.216
|
0.000
|
0.216
|
No supported
|
|
H5
|
iEHR → PEoU
|
0.298
|
0.298
|
0.000
|
Supported
|
|
H6
|
ID → PU
|
0.085
|
0.000
|
0.085
|
No supported
|
|
H7
|
ID → PEoU
|
0.118
|
0.118
|
0.000
|
Supported
|
Abbreviations: ID, identification; iEHR, interoperability of electronic health record;
PEoU, perceived of ease of use; PSQ, patient safety and quality of care; PU, perceived
of usefulness.
Discussion
The aim of this study was to investigate the factors that can predict an e-prescribing
system's influence on PSQ. The study is founded on physicians' perceptions. The results
illustrate interoperability is an essential requirement for HIT because of the need
to integrate patient care across a variety of settings and providers.[27]
[32] e-Prescribing has improved PSQ as expected.[27]
[37] Physicians assessed e-prescribing as safer than handwritten prescribing,[1]
[24] and it enabled them to provide better service.[24] Some studies showed that e-prescribing systems cause fewer prescription errors compared
with handwritten prescriptions.[1]
[27] Experience with using e-prescribing, as hypothesized, is positively associated with
PU and PEoU. Human factors, such as experience, influence the probability of e-prescribing
errors in unintentionally entering incorrect information. The selection of an incorrect
option or dosage by prescribers who are unfamiliar with the functionalities of a given
e-prescribing system can endanger patient safety.[1]
[44] The positive impact of e-prescribing and EHR systems is associated with e-prescribing,
especially in hospital settings, and interoperability among systems is assessed as
workable.[12]
[32] National Kanta services comprise the Pharmaceutical Database and the Patient Data
Repository, which improves physician's access to patients' medical information.[29] EHR coverage reached 100% in public health care in 2010, and the vast majority of
private health care providers use EHR systems.[30]
Physicians' e-prescribing experiences are an important aspect of technology acceptance
when it comes to the expected usefulness of e-prescribing and attitudes toward PSQ.[1]
[2]
[25] Our findings are similar to those reported in recent studies; PU is the most significant
factor affecting physicians' intention to use IT.[16]
[17] Our study differs from previous studies that have shown PEoU is a significant determinant
of physicians' intention to use IT.[12]
[16] The PEoU of e-prescribing had an indirect association on PSQ.[21] A possible explanation might be that participants do not consider usability an important
reason to use a technology. The serious implications of their actions for patients
and the considerable responsibility they assume could mean that intention to use a
technology might depend on factors related to improving PSQ and not on usability issues.[20] Furthermore, Gururajan showed that the ease-of-use factor was not strongly significant
in the health care domain when determining wireless technology adoption. The health
care environment is complex, sensitive, and time critical. He assumed that these issues
could be some of the reasons why the TAM model did not perform as expected in health
care settings.[45] It has been shown that 24% of prior studies did not find a significant relationship
between PEoU and behavioral intention.[38]
Interestingly, the results revealed that newly qualified physicians, who had little
experience with EHRs, found the support of the interoperability of the EHR helpful.
Physicians' resistance to IT such as EHRs has been reported in other studies.[18]
[35] This research highlights the importance of attitudinal factors and cognitive instrumental
processes where the medical professionals' adoption and utilization of health information
systems with technology acceptance is concerned. Newly qualified physicians may use
the information systems without prejudice. Prior research has shown that computer
skills and young age were positively associated with technology adoption.[16] IT acceptance studies in the health care field have reported that higher levels
of HIT experience led to a better understanding of where the components and functions
of HIT were useful in daily routines.[12]
[32]
[44] These facts did not emerge with participants who have little experience with using
EHRs in this study. This phenomenon could have arisen as the first sign of digital
natives who think highly of the interoperability of technology.
The interoperability of applications (i.e., EHR, e-prescribing, and the pharmaceutical
database) and technological solutions as patient identification factors had an association
with PEoU and an indirect association with PU. Technological solutions could be seen
as relevant in physicians' work processes, whereas PSQ may be considered the benefits
of electronic development.[12]
[19]
[32] In this study, e-prescribing was a positive example of HIT development, and the
PU of e-prescribing had a clear association with PSQ.
A path analysis was used to analyze the complex association between different factors.
Methods such as ANCOVA and path analysis are suitable for research on e-prescribing
systems, where the objects of the research are multidimensional. Path analysis made
it possible to investigate effects among complex factors and their structures describing
PSQ. By selecting PSQ as dependent variables in our extended model, the importance
of developing an e-prescribing system has been explained.[42]
[46] PSQ could also be considered usefulness, which is an independent variable in the
TAM model.
This study highlights potential safety and efficiency benefits associated with integrated
e-prescribing systems in health care[6]
[9] used by physicians' during national implementation. The study examined physicians'
experiences involving an e-prescribing system and their attitudes toward PSQ. The
PU of e-prescribing had an association with PSQ. e-Prescribing's PEoU had an indirect
association with PSQ, whereas there was a direct effect on the PU of e-prescribing.
The iEHR and ID had a direct effect on the PEoU of e-prescribing. Physicians' EHR
and e-prescribing experiences proved to be significant.[1]
[2] The TAM model[13] established the presence of associations between PEoU and PU and modified their
association with PSQ. Previous research focusing on health IT acceptance has shown
that the TAM model is suitable for predicting and explaining physicians' acceptance
of e-prescribing[36]
[41] and EHR systems.[12]
[16]
Limitations and Future Directions
Limitations and Future Directions
Although the response rate was relatively small in the single surveys (26% in 2012
and 12% in 2014), the combined data made it possible to conduct statistical analyses
and allowed a preliminary interpretation of the study results. This was possible because
the settings in both facilities (primary care and a hospital) were the same regarding
e-prescribing system implementation. The web surveys were based on physicians' willingness
to participate. Users' responses may not be actual perceptions but rather the subjects'
reports of their perceptions. Therefore, the physicians answering the web survey might
have had a more positive attitude toward innovative technologies when compared with
those who did not answer the survey. Analyzing why so many physicians did not choose
to participate in the survey was beyond the limits of this study. The link to the
web-based survey was sent to a contact person who forwarded the email to the physicians.
He or she received an automatic reply if the email did not reach a physician. The
electronic survey was opened by physicians 29 times in 2012 (11%, 29/269) and 63 times
in 2014 (6%, 63/1,135) without submitting the responses. Differences in gender groups
may also be a source of bias in the study results. There was some association between
female physicians and PEoU. In addition, male gender and PSQ were also associated
with each other.
From a theoretical viewpoint, this study underlines the importance of instrumental
and attitudinal factors on physicians' technology acceptance decisions. The TAM model's
factors of attitude toward PSQ and PU appear to be predictive and mediating mechanisms
in medical professionals' acceptance of health information systems such as e-prescribing.
Many studies have looked at how the TAM model and extended TAM model could explain
the acceptance of new technology.[14]
[15]
[16]
[17]
[18] Future research may focus on determining whether the TAM model can also be used
to explain the usefulness of a particular technology or “job performance” such as
e-prescribing.
Conclusion
The study shows that the PU of an e-prescribing system was significantly associated
with PSQ. The participants' opinions regarding e-prescribing and EHR systems were
positive. The extended TAM model demonstrated that when experience with e-prescribing
increased, the e-prescribing system's PEoU had an indirect effect on its PU and strengthened
PSQ. Additionally, the research has focused on interoperability issues and improving
the continuity of care to better understand technical effects and interoperability
of work processes during HIT implementation.
