RANKING THE MICRO LEVEL CRITICAL FACTORS OF ELECTRONIC MEDICAL RECORDS ADOPTION USING TOPSIS METHOD

Health Informatics- An International Journal (HIIJ) Vol.2,No.4,November 2013
DOI: 10.5121/hiij.2013.2402 19
RANKING THE MICRO LEVEL CRITICAL FACTORS
OF ELECTRONIC MEDICAL RECORDS ADOPTION
USING TOPSIS METHOD
Hossein Ahmadi1
, Maryam Salahshour Rad2
, Mehrbakhsh Nilashi3,*
, Othman
Ibrahim4
, Alireza Almaee5
1,2,3,4
Faculty of Computing, Universiti Technologi Malaysia, Johor, Malaysia
5
Department of Management, Rasht Payame Noor University, Rasht, Iran
ABSTRACT
In many countries, the health care sector is entering into a time of unprecedented change. Electronic
Medical Record (EMR) has been introduced into healthcare organizations in order to incorporate better
use of technology, to aid decision making, and to facilitate the search for medical solution. This needs
those professionals in healthcare organizations to be in the process of changing from the use of paper to
maintain medical records into computerized medical recordkeeping opportunities. However, the adoption
of these electronic medical records systems has been slow throughout the healthcare field. The critical
users are physicians which play an important role to success of health information technology including
Electronic Medical Record systems. As a result user adoption is necessary in order to understand the
benefits of an EMR. Therefore, in the current paper, a model of ranking factors of micro-level in EMRs
adoption was developed. Surveys distributed to physicians as this study’s respondent in two private
hospitals in Malaysia. The findings indicate that physicians have a high perception means for the
technology and showed that EMR would increase physician’s performance regarding to decision making.
They have been and continue to be positively motivated to adopt and use the system. The relevant factors
according to micro-level perspective prioritized and ranked by using the Technique for Order of
Preference by Similarity to Ideal Solution (TOPSIS). The aim of ranking and using this approach is to
investigate which factors are more important in EMRs adoption from the micro-level perspectives. The
results of performing TOPSIS is as a novelty which assist health information systems (HIS) success and
also healthcare organizations to motivate their users in accepting of new technology.
KEYWORDS
EMRs, Adoption, TOPSIS, Micro-Level Adoption Factors
1. INTRODUCTION
The health care industry’s growing adoption of Electronic Medical Records (EMR) is becoming a
new perspective on the role of healthcare professionals. Information technology has been proved
to be as an imperative element in the administration of healthcare [34]. In particular, some private
hospitals in Malaysia are adopting information systems that offer more accurate and timely
information concerning patient care [5]. By utilizing information technology hospitals are capable
to retain documentation of their daily transactions such as in data storage, retrieving and
communication. Currently, the midst of a landmark shift in record keeping, with driving for
electronic medical records well in progress [6]. An EMR system was introduced as a way to make
possible a centralized patient information repository. For many purposes EMR is utilized

20
including administration, patient care, quality improvement, research, and reimbursement [35].
These applications need knowledge of the underlying quality of the data within the EMR so as to
avoid misinterpretation [35]. EMRs would remedy the intrinsic flaws of the conventional paper
system through improvements in accessibility, efficiency, quality of data capture and cost saving.
As a result, an EMR system should be able to appropriately capturing, processing and storing
information and also should be compatible with other related systems [6]. It affects the quality
outputs in health care provider which users by using the patient information can be able to make
decisions. By increasing the accuracy of patient information, it is possible to less likely that they
face large differences in errors and consequently decreases the marginal revenue from quality
growing [6].
In relation with EMR, the concept of clinical system places reduction of medical error into the
wider context of quality of care and safety by giving a framework to evaluate and assess the
structure, process and outcomes of care. The purpose of this paper is to describe the factors that
have more priority in affecting EMR to adopt in private hospitals in Malaysia. The critical
elements of this paper include HIS quality, use and net benefits with their sub-factors.
The remainder of this paper is structured as follows. Section 1 describes the EMR and gives an
overview of this research. The section 2 introduces the proposed research model. In Section 3, we
explain the research methodology step by step. Section 4 and 5 are allocated to the background
mathematical of The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)
and data collection, respectively. Finally, we present the results of TOPSIS and conclusions in
sections 6 and 7, respectively.
2. PROPOSED RESEARCH MODEL
The physician adoption model provides a conceptual model to identify the factors that have more
influence on health information systems (HIS) success. It extends Info way Benefits Evaluation
(BE) Framework [18] (adapted from the DeLone and McLean information system success model
[9] which Thereafter, [12] in his study review developed Clinical Adoption (CA) framework
based on three dimensions. The framework comprised of micro, meso and macro-level
dimensions. Each dimension has its own factors and sub-factors which could affect physicians in
EMR adoption. In this study it has been focused on micro-level factors. Physician adoption model
at the micro-level explains HIS success related to HIS quality, use and net benefits. HIS quality
divided in information, system and service quality respectively; use covers HIS usage and
satisfaction; net benefits covers care quality, access, and productivity. The physician adoption
model was developed with a range of HIS in mind, including EMRs. In this review, we examined
EMR and its success in health centre thru the lens of the physician adoption model. EMR
adoption has been described and influence on physician practice, according to evaluation
measures utilized in the studies. In regarding of Factors that have been caused to this impact, it
has been described as the reasons cited that could explain the adoption and effect. Hence, in this
study we have concentrated on Micro level factors that affects on EMR adoption. At the end the
proposed model has been developed and shown in Figure 1.
It has been required for system quality to sustain high quality health service delivery that meets
the request of the people. System quality affects the quality of care by capturing, transferring,
storing, managing and displaying medical information. In growing the quality of these processes,
the system should give higher quality (12).
System quality factors included the availability of templates [2], interface design [6, 12], Newby
[36] and technical performance (e.g. speed and reliability) [24, 35].

21
Information quality plays a critical role in hospital. The organization, accuracy, completeness and
accessibility of the patient record are the sub factors of information quality [1, 6, 11, 12, 13, 24,
27].
Service quality is a comparison of expectations with performance. Health care provider with high
service quality will meet patient needs whilst remaining economically competitive. Improved
quality may increase economic competitiveness. If patients who have not been satisfied with
preferred hospitals can deliver quality services, they would look for the services elsewhere. Thus
it is imperative to inquire patients in a straight line about the perceived quality of services
provided by the country’s hospitals [31]. Service quality factors included training and technical
support [32, 38] system backup and unexpected downtime [31].
Electronic medical records usage can differ depending on how they utilize it and who the user is.
Electronic medical records would assist to advance the quality of medical care given to patients.
Removing the traditional paper records are denied by Many doctors and office-based physicians
[22]. Factors in EMR usage covers its intent (e.g. quality improvement versus record keeping)
[21], actual strategies for optimal use, ease of use [11, 29] and usage patterns that appeared
gradually [17]. The relevant factors of interaction included patient-physician encounters like
patients’ ability to schedule appointments [10], the kind of consult (e.g. psychological) [3, 22,
23], and consult room layout [22].
Figure 1. TOPSIS Framework of Physician Adoption Model in Micro-Level
Net benefits, care quality factors covered patient safety [14], care effectiveness [17], quality
improvement [27] and guideline compliance [8, 36]. Productivity factors covered care efficiency
[14, 33], coordination [35], and net cost including billing, staffing and maintenance costs [2, 27,
24, 31].

22
Micro-level factors that found in previous research which has an effect on EMR adoption and
effect were shown (See Table 1).
Table 1.Micro -level factors that influenced EMR success
HIS quality HIS quality sub-factors References
System quality
Template [2, 22]
Design/performance [6, 12, 21, 24, 28, , 30, 35, 37]
Information
quality
Access [6]
Content [1, 6, 11, 12, 13, 24, 27, 28, 33, 35, 37]
Service quality
Support [32, 38, 39]
Downtime [31, 38]
HIS Use HIS quality Sub-factors References
Use
Use strategies [3]
Use pattern [17]
Use intention [21]
Satisfaction Ease of use [11, 29]
Interaction [3, 6, 22, 23]
Net benefits Net benefits Sub-factors References
Care quality
Safety [14]
Effectiveness [17]
Quality improvement [27]
Guidelines [8, 27, 36]
Productivity
Care coordination [2, 35]
Efficiency [16, 4, 14, 33]
Net cost [2, 27]
Cost Savings/Profits [27]
Maintenance cost [31]
Access
Communication [6]
Patient acceptance [4]
Patient choice [10]
3. RESEARCH METHODOLOGY
Researcher covered the topic of Electronic Medical Record adoptions shown that EMR are being
accepted by private hospital of Malaysia. A quantitative, survey-based research study was
performed and was analysed to explaining the factors that have an effect on EMR adoption. The
two hospitals have been chosen to conduct this research. Survey distributed to 150 physicians
who had experience using EMRs. 90 physicians fulfilled the questionnaire in this study and the
rest did not complete the survey study because of their time constrain. The survey contains
number of questions that were design to capture information about the constructs in the research
model. The questions that measured were HIS quality, HIS use and net benefits besides their sub-
factors. TOPSIS was use to obtain the ranking of these factors. Figure 2 contains a description of
each step in this study.

23
Figure 2. Research methodology
4. MATHEMATICAL BACKGROUND OF TOPSIS
TOPSIS is one of the famous classical Multi-Criteria Decision Making (MCDM) method, which
was initiated for the first time by Hwang and Yoon [40] that shall be used with both normal
numbers and fuzzy numbers [41, 42]. Furthermore, TOPSIS is more applicable in that limited
subjective input is required from decision makers. The only subjective input required is weights.
The TOPSIS procedure is shown in Figure 3 in five main steps.
Figure 3. Procedure of TOPSIS method

24
Using entropy method, objective weights were calculated. The following equation calculates
entropy measure of every index.
[ ]










=
=
∀
⇒
−
= ∑ =
)
m
(
Ln
1
K
n
,...
2
,
1
)
n
(
Ln
n
K
E
j
m
1
i
ij
ij
j (1)
The degree of divergence dj of the intrinsic information of each criterion C (j= 1, 2, …, n) may
be calculated as
j
j E
1
d −
= (2)
The value dj represents the inherent contrast intensity of cj. The higher the dj is, the more
important the criterion cj is for the problem. The objective weight for each criterion can be
obtained. Accordingly, the normalized weights of indexes may be calculated as
∑ =
=
n
1
k
k
j
j
d
d
W
(3)
5. DATA COLLECTION
The primary data in this study were collected through questionnaire that distributed to the
physicians through web based questionnaire who have some experiences in using EMR. For this
study, a number of respondents, were approximately 150 (n=150) physicians. Sixty percent (60%)
of the respondents provided answers to all the questions in the instrument.
The first section comprise of information on respondent demographic profile, eightsections on the
independent variable namely, system quality, information quality, service quality, use,
satisfaction, care quality, productivity and access. Five options (index) ranked by 1-5 (1= very
low important 2=low important 3=moderately important 4= high important 5= very high
important) were used for the raised questions.

25
Table 2. The respondents’ demographic profile
Aspects Category Respondents (n) Respondents (%)
Gender Male 75 75%
Female 25 25%
Age 26-33
34-50
51-65
20
45
85
13.4%
30%
56.6%
Years of electronic medical
records experience
1-5 54 56.8%
6-10 15 15.8%
Over 10 3 3.2%
Medical specialization Generalist 68 67%
Specialist 34 33%
Table 2 provides the respondents’ demographic profile. About seventy five percent of physicians
were male and twenty five percent were female, generalist and specialist physicians in with one to
five years of experience with Electronic Medical Records technology.
6. RESULTS OF TOPSIS
In this section, we provide the results of TOPSIS for ranking the factors presented in the TOPSIS
Framework of physician adoption model in micro-level. According to the Figure 1, the aim of
applying TOPSIS is to rank the 23 factors to show the importance of these factors in EMRs
adoption in micro-level.
In addition, based on five steps of TOPSIS shown in Figure 3 and formulas presented in
equations 1, 2 and 3, we calculated the weights of five indices as following:
m
ij ij
i
E k (n ln(n )) .
=
= − = −
∑
1
1
3 26
m
ij ij
i
E k (n ln(n )) .
=
= − = −
∑
2
1
4 13
m
ij ij
i
E k (n ln(n )) .
=
= − = −
∑
3
1
2 68
m
ij ij
i
E k (n ln(n )) .
=
= − = −
∑
4
1
2 29
m
ij ij
i
E k (n ln(n )) .
=
= − = −
∑
1
5 3 25
Thus, using Entropy method, the weights are obtained as:
w 0.236
=
1
w 0.220
=
2
w 0.178
=
1
w 0.168
=
4
w 0.196
=
5
where
1
=
+
+
+
+
⇒
=
∑ 5
4
3
2
1
1 w
w
w
w
w
wi

26
W
0.238
0.222
0.178
0.169
0.196
 
 
 
 
= ⇒
 
 
 
 
0 0 0 0
0 0 0 0
0 0 0 0
0 0 0 0
0 0 0 0
d n n
V N W
0.071 0.200 0.057 0.114 0.014
0.000 0.089 0.057 0.204 0.055
0.071 0.089 0.057 0.013 0.221
0.071 0.089 0.057 0.050 0.123
0.071 0.089 0.057 0.114 0.055
0.000 0.089 0.229 0.050 0.055
0.000 0.355 0.057 0.000 0.221
0.071 0.089 0.057 0
×
= ×
.114 0.055
0.000 0.022 0.229 0.204 0.014
0.000 0.022 0.057 0.320 0.055
0.071 0.022 0.229 0.013 0.123
0.000 0.022 0.014 0.204 0.221
0.000 0.089 0.057 0.050 0.221
0.000 0.089 0.057 0.050 0.221
0.071 0.022 0.014 0.114 0.221
0.071 0.089 0.057 0.114 0.055
0.071 0.089 0.057 0.050 0.123
0.071 0.089 0.057 0.114 0.055
0.000 0.089 0.229 0.050 0.055
0.071 0.022 0.229 0.013 0.123
0.000 0.022 0.014 0.204 0.221
0.000 0.089 0.229 0.050 0.055
0.000 0.355 0.057 0.000 0.221































 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

and where d
N denotes the normalized ratings of responses’ participants and V denotes the non-
scaled weight matrix.
According to the third step of TOPSIS shown in Figure 1, we calculated the positive and negative
ideals as following:
Positive Ideal =A+ = {(maxVij), (max Vij),i=1,2,..,m}={V1+,V2+,…Vn+} (4)
Table 3. Positive ideal
Max Vi1 Max Vi2 Max Vi3 Max Vi4 Max Vi5
0.071 0.355 0.229 0.204 0.221
Negative Ideal =A- = {(minxVij), (min Vij),i=1,2,..,m}={V1-,V2-,…Vn-} (5)

27
Table 4.Negative ideal
Min Vi1 Min Vi2 Min Vi3 Min Vi4 Min Vi5
0 0.022 0.014 0 0.014
As shown in the Table 3 and Table 4, we selected the maximum and the minimum of each
column of matrix V as positive and negative ideals. Thus, A+ and A- denote all the maximum and
minimum numbers of each column of matrix V.
For step 4 of TOPSIS procedure, we calculate the distance i from positive ideal as following:
2
1
2
1
}
)
(
{
Ideal
positive
from
i
Distance +
− −
= ∑ = j
j ij
v
v
Table 5 presents the distance i from positive ideal for 23 factors. In this table, the square of
difference between distance between max point and each point ideal are provided.
Table 5. Distance i from positive ideal
2
)
( +
− j
ij
v
v 2
)
( +
− j
ij
v
v 2
)
( +
− j
ij
v
v 2
)
( +
− j
ij
v
v 2
)
( +
− j
ij
v
v
0.00000 0.02413 0.02965 0.00802 0.04299
0.00504 0.07089 0.02965 0.00000 0.02768
0.00000 0.07089 0.02965 0.03630 0.00000
0.00000 0.07089 0.02965 0.02357 0.00962
0.00000 0.07089 0.02965 0.00802 0.02768
0.00504 0.07089 0.00000 0.02357 0.02768
0.00504 0.00000 0.02965 0.04162 0.00000
0.00000 0.07089 0.02965 0.00802 0.02768
0.00504 0.11077 0.00000 0.00000 0.04299
0.00504 0.11077 0.02965 0.01339 0.02768
0.00000 0.11077 0.00000 0.03630 0.00962
0.00504 0.11077 0.04614 0.00000 0.00000
0.00504 0.07089 0.02965 0.02357 0.00000
0.00504 0.07089 0.02965 0.02357 0.00000
0.00000 0.11077 0.04614 0.00802 0.00000
0.00000 0.07089 0.02965 0.00802 0.02768
0.00000 0.07089 0.02965 0.02357 0.00962
0.00000 0.07089 0.02965 0.00802 0.02768
0.00504 0.07089 0.00000 0.02357 0.02768
0.00000 0.11077 0.00000 0.03630 0.00962
0.00504 0.11077 0.04614 0.00000 0.00000
0.00504 0.07089 0.00000 0.02357 0.02768
0.00000 0.00000 0.02965 0.04162 0.00000
Similar to distance i from positive ideal, we calculate the distance i from negative ideal as
following:

28
2
1
2
1
Ideal
negative
from
i
Distance }
)
(
{ −
=
−
= ∑ − j
j ij
v
v
Table 6 presents the distance i from negative ideal for 23 factors. In this table, the square of
difference between distance between min point and each point are provided.
Table 6. Distance i from negative ideal
2
)
( +
− j
ij
v
v 2
)
( +
− j
ij
v
v 2
)
( +
− j
ij
v
v 2
)
( +
− j
ij
v
v 2
)
( +
− j
ij
v
v
0.0051 0.0316 0.0018 0.0131 0.0000
0.0000 0.0045 0.0018 0.0415 0.0017
0.0051 0.0045 0.0018 0.0002 0.0426
0.0051 0.0045 0.0018 0.0025 0.0119
0.0051 0.0045 0.0018 0.0131 0.0017
0.0000 0.0045 0.0462 0.0025 0.0017
0.0000 0.1109 0.0018 0.0000 0.0426
0.0051 0.0045 0.0018 0.0131 0.0017
0.0000 0.0000 0.0462 0.0415 0.0000
0.0000 0.0000 0.0018 0.1022 0.0017
0.0051 0.0000 0.0462 0.0002 0.0119
0.0000 0.0000 0.0000 0.0415 0.0426
0.0000 0.0045 0.0018 0.0025 0.0426
0.0000 0.0045 0.0018 0.0025 0.0426
0.0051 0.0000 0.0000 0.0131 0.0426
0.0051 0.0045 0.0018 0.0131 0.0017
0.0051 0.0045 0.0018 0.0025 0.0119
0.0051 0.0045 0.0018 0.0131 0.0017
0.0000 0.0045 0.0462 0.0025 0.0017
0.0051 0.0000 0.0462 0.0002 0.0119
0.0000 0.0000 0.0000 0.0415 0.0426
0.0000 0.0045 0.0462 0.0025 0.0017
0.0000 0.1109 0.0018 0.0000 0.0426
In the next step of TOPSIS, we calculate the sum of id+ and id- as presented in Table 7.

29
Table 7. Sum of positive ideal and negative ideal
From the Table 7, di+ and di- stand for distance i from positive ideal and di- that stands for
distance i from negative ideal, respectively. In the last step we rank 23 factors by calculating the
distance between Ai and ideal solution as following:
m
i
cli
d
d
d
cli ,...
2
,
1
1
0
1
1
1
=
≤
≤
+
= +
−
−
(6)
Finally, in Table 8, we present the ranking of factors in the micro-level of ERMs adoption. The
ranking in this table demonstrates that based on physicians’ perception, ten important factors in
micro level of electronic medical records adoption are patient choice, use strategies, ease of use,
use intention, safety, communication, template, downtime and cost savings/profits. In addition,
according to the ranking presented in Table 8, the patient choice is ranked with a high priority and
this confirms the result of work developed by Dennison et al., 2006. They showed that enhanced
patient choice of appointment date and time significantly enhances the electronic surgical referral
system can improve efficiency. Thus, it is important for adopter of EMRs that patient choice can
play important role in their goals, mission and vision.

30
Table 8. Final ranking of factors in the micro-level of ERMs adoption
7. CONCLUSION
The current study was done to develop the body of research related to technology adoption inside
a professional environment in context of hospitals in private sector which could be applied in
regard to public sector. This study has focused on micro-level factors which influence on EMR
adoption and effect which is based on [12]. The limitation of the study confined the physicians
who have not yet adopted the EMR or stop using this technology. The findings of the present
study were used to address the adoption and effect of electronic medical records technology
within the physician community in private hospitals in Malaysian. Physicians had very high
perception means for the technology and showed that EMR would increase physician’s
performance. They have been and continue to be positively motivated to adopt and use the
system. The TOPSIS Framework of Physician Adoption Model in Micro-Level, factors, finding
discussed in this research give the essential components to make sense of EMR in the private
hospitals.
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