Printer Friendly

MEDICAL STUDENTS' BEHAVIORAL INTENTION TO ADOPT ELECTRONIC HEALTH RECORD SYSTEM.

Byline: M. N. Sakib and M. J. M. Razi

ABSTRACT: Electronic Health Record (EHR) is a computerized system that collect, store and display the data of patients. The use of EHR systems is increasing globally and numbers of countries, both developed and developing, have invested in EHR with a hope of developing their health sector. Accordingly, Bangladesh, a developing country, has shown its keen interest on digitization of health sector. However, very limited information is available on medical professionals' perception towards EHR. Therefore, the current study aims to understand the level of behavioral intention towards EHR among medical students of two selected medical colleges in Bangladesh. The study also investigates the predictors of behavioral intention. The study adapts Unified Theory of Acceptance and Use of Technology (UTAUT) to match the context. Self-administered questionnaire survey method was used to collect data among 238 medical students from the two selected medical colleges using convenient sampling.

Upon completion of data collection, the data were analyzed using SPSS 16 (Descriptive analysis) and AMOS 16 (Measurement model and Structural model). The results showed that the respondents are moderately intended to adopt EHR while Performance Expectancy (PE) and Effort Expectancy (EE) are strong determinants of intention to adopt EHR. EE's positive effects on EE and Social Influence (SI) also established. The findings of this study might help the policy makers in Bangladesh in their strategy formulation, especially in the health sector, in the future.

1. INTRODUCTION

Digitization has transformed the world order almost in every sector, including health sector. There are numbers of Information and Communication Technology (ICT) based tools and applications in the health sector. Electronic Health Record (EHR), one of such popular ICT based systems, is an electronic record system that acts as a depository of patients' past and present medical records. Clinical documentation, clinical test and imaging results, computerized order entry system and decision support system are all comprised in HER [1]. The EHR can construct an errorless data management system for quality, safety and efficient health sector.

EHR has both merits and demerits. From merit prospective, trying to avoid the fragile ambiguity of the human mind to process a larger amount of data EHR will help the physicians. Patient can achieve the leverage of esthetic knowledge in medical science and also can augment the learning capacity of medical students [2]. Meantime, it helps information portability, accessibility, epidemic decease control, and averting unnecessary test [2]. From the negative perspective, to establish such a system with hardware and software, and upgrade and maintain the whole system on a regular basis is very challenging and costly. It may cause unwanted pause or break in the middle of their work [2] and create security concern of patient's medical records [3].

Nevertheless, numbers of countries already have started to adopt such systems in their heath sector. For example, the family physician's adoption rate of EHR reached 68% in USA [4, 5]. To embrace the EHR US government financed $30 billion to persuade physicians, Australian government financed AU$1 billion and United Kingdom (in 2005) invested $18 billion in the health care industry to reduce medical errors and increase efficiency [6]. In Asia pacific region except India and Philippine's, almost every country use EHR at least partially [7].

Similarly, the government of Bangladesh has also initiated a new era of automation in the health sector by introducing ICT in three hospitals; National Institute of Kidney Diseases and Urology (NIKDU), Government Employees' Hospital, and Azimpur Maternity Hospital from 2012 (Directorate General of Health Service,. n.d.b). As one of the third world countries, Bangladesh face a daunting challenge to provides a healthier health service. Over 159 million people live in Bangladesh; population density is 881 people per square KM, more than 60% people live below the poverty line, and 77% people lives in rural areas [8]. The doctor patient ratio is 1: 2000 [9], thus, the Bangladesh is one of 55 countries which have a shortage of health workforce [10]. As a result, on aggregate level almost 40.21% people obtain their treatment from dispensary or pharmacy, 24.46% visit private doctors, 14.34% go to government doctors and 15.57% people are not getting any treatment at all [11].

Hence, to overcome the barrier in the health sector the government has initiated few projects; mHealth or Health Service via Mobile Phone for 24/7, Geographical Information System (GIS) for disease surveillance, SMS pregnancy care, telemedicine care, electronic birth registration in one district, Health Enterprise Architecture system which collected the 120 million patient data in rural areas [10]. Similarly the private sector also has taken some initiatives to digitize the patient records. For example, Bangladesh Secretariat Clinic, Apollo Hospital, Square Hospital, United Hospital, Medinova hospital and popular diagnostic center have their own database system of patient record system [10]. However, most of the Governmental and private hospitals yet to introduce ICT [12]. Most of the manual record at hospitals been destroyed time to time; Bangabandhu Sheikh Mujib Medical University Hospital and Holy Family Hospital are stored the patient record in paper and destroy the document after 5 years [12].

In this context, it is believed that the EHR adoption can improve the health sector. However, according to some studies in Bangladesh, the adoption of EHR among the physicians is a myth despite the fact of cost, workload, and stagnation of innovation [12, 13]. Nevertheless, it is hard to find information on the perception of medical students, the future medical professionals, of Bangladesh towards EHR.

To bridge the above mentioned gap, the objectives of this study are two folds; 1st to understand the level of perceived behavioral intention to adopt EHR by the medical students, and the 2nd is to understand the predictors of the behavioral intention.

2. RESEARCH FRAMEWORK

The research framework for this study mainly developed based on the unified theory of Acceptance and Use of Technology (UTAUT) [14] to study the behavioral intention to adopt EHR and the factors that influence such intention among students in two selected medical colleges in Bangladesh. Since, the respondents (students) yet to be in practice and not yet get into a real working environment, this study limits to investigate their intention to adopt EHR though the original UTAUT measures up to use of technology (behavior). Similarly, only Performance Expectancy (PE), Effort Expectancy (EE) and Social Influence (SI) were considered as independent variables as facilitating condition, the other variable in the UTAUT, is linked to use behavior. The moderating effects in the original UTAUT also not tested in this study. Fig.1 depicts the research model used in this study.

PE has been defined as EHR use will increase the medical student job performance while EE has been defined as the degree of feeling easy to adapt to EHR environment. The SI means other people like organization (hospitals), friends, and family will influence to use EHR. In the UTAUT model PE is one of the stronger predictors of Behavior intention. By using EHR will accomplish their task quickly, increase productivity, increase chances of getting raises. EE and SI also proved to have a positive effect on behavior intention. There are number of studies in different context, such as [15, 16, 17] and, including in the medical field, [18] that prove these significant relationships. In addition, the Technology Acceptance Model (TAM) posits that EE has a positive effect on PE. Accordingly the studies of [15, 16] has tested these relationships while some other studies, such as [19, 20] have tested the relationship between EE and SI.

Based on these theoretical and empirical supports, the following hypotheses were proposed:

H1: Performance Expectancy will have a positive effect on the behavior intention to use EHR.

H2: Effort Expectancy will have a positive effect on the behavior intention to use EHR.

H3: Social Influence will have a positive effect on the behavior intention to use EHR.

H4: Effort Expectancy will a have positive effect on the Performance Expectancy

H5: Effort Expectancy will a have positive effect on the Social Influence.

3. METHODOLOGY

Self-administered questionnaire survey method was used to collect data from 238 medical students from the Ibn Sina Medical College and Aici Medical College in Bangladesh, using the convenient sampling. The questionnaire items for all variables; Performance expectancy, Effort Expectancy, Social Influence and Behavior intention, were adapted from UTAUT [13] and the respondents were asked to indicate (on a 5-point Likert scale ranging from "strongly disagree" to "strongly agree") their level of agreements on statements.

93.7% of respondents (238) are in the age range of 20 to 25 years and the rest are in their 26 to 30 years. Most of them (62.2%) are female (37.8% are male). 31.5% of them are 1st year students while 14.3% (2nd year), 9.7% (3rd year), 29.8 (4th year) and 14.7% (fifth year) their studies.

Initially, exploratory factor analysis was performed to see the data pattern. All the questionnaire items, except EHR15 and EHR 16, loaded cleanly to the respective variables. The questionnaire items EHR15 and EHR16 are supposed to measure SI (Social Influence), however, these two items loaded with BI (Behavioral Intention). With the intention of getting good model fit indices, both these items were treated as BI items when performing the confirmatory factor analysis using AMOS 16. To assess the convergent validity, factor loadings, composite reliability (CR) and the average variance extracted (AVE) were examined. The factor loadings exceeded the recommended value of 0.5 as shown in Table 1.

Table 1: Results of Convergent Validity Assessment

###Model constructs###Questionnaire Item###Code###Loading###Composite###Average Variance

###Reliability###Extracted (AVE)

###(CR)

###I would find the EHR useful in my###EHR_5###.775

###future medical profession.

###Using the EHR would enable me###EHR_6###.758

###Performance###to accomplish tasks more quickly.

###0.857###0.600

###Expectancy (PE)###Using the EHR would increase my###EHR_7###.769

###productivity.

###If I use the EHR, I would increase###EHR_8###.795

###my chances of getting a raise.

###My interaction with the EHR###EHR_9###.680

###Effort Expectancy###would be clear and

###0.760###0.442

###(EE)###understandable.

###It would be easy for me to become###EHR_10###.669

###skillful at using EHR.

###I would find the EHR easy to use.###EHR_11###.609

###Learning to operate the EHR will###EHR_12###.698

###be easy for me.

###People who influence my behavior###EHR_13###.834

###would think that I should use

###Social Influence###EHR.

###0.815###0.688

###(SI)###People who are important to me###EHR_14###.825

###would think that I should use

###EHR.

###The senior management would be###EHR_15###.590

###helpful in the use of EHR

###In general the organization will###EHR_16###.600

###(Behavioral)

###support the EHR.

###Intention to adopt###0.856###0.549

###I intend to use EHR in future###EHR_17###.762

###EHR (BI)

###I predict I would use EHR in###EHR_18###.858

###future.

###I plan to use HER in Future.###EHR_19###.849

Table 2: Results of Discriminant Validity Assessment

###PE###EE###BI###SI

PE###0.774

EE###0.739###0.665

BI###0.634###0.693###0.741

SI###0.527###0.754###0.492###0.830

Table 3: Fit Indices of Measurement and Structural Model

Fit Index###Fit###Measurement

###Criteria###Model

Chi Square (2)###197.602

P-value (probability)###[greater than or equal to] 0.5###.000

###[greater than or equal to] 0.9

GFI (Goodness of Fit###.906

Index)

RMSEA (Root Mean###a$? 0.05###.076

Square Error of

Approximation)

RMR (Root Mean Square###a$? 0.05###.047

Residual)

###[greater than or equal to] 0.9

NFI (Normed Fit Index)###.891

###[greater than or equal to] 0.9

CFI (Comparative Fit###.934

Index)

AGFI (Adjusted Goodness###[greater than or equal to] 0.8###.866

of Fit Index)

PNFI (Parsimonious###[greater than or equal to] 0.5###.713

Normed Fit Index)

Finally the measurement model (Fig. 2) was assessed in terms of its fitness. As shown in Table 3, the model fulfilled most of the fit indices requirements.

With the intention of achieving the 1st research objective of understanding the level of behavioral intention to adopt EHR, a descriptive analysis was performed using SPSS 16 and the results are shown in Table 4.

Table 4: Findings of Descriptive Analysis (Behavioral Intention)

###Mean###Std. Deviation

EHR_15###3.70###.91

EHR_16###3.58###.91

EHR_17###3.77###.95

EHR_18###3.71###.93

EHR_19###3.79###.92

Average (BI)###3.71###.73

It shows that the respondents' perceived intention to adopt EHR is moderate with the mean value of 3.71 (SD 0.73). To test the hypotheses, a path analysis was performed through the structural model (Fig. 3). As shown in Table 5, the overall results indicate that all hypotheses, except H3 (Social Influence will have a positive effect on the behavior intention to use EHR) were fully supported as the p-values for all paths are well below 0.05 and the coefficient values ([beta]) range between 0.291 and 0.993.

Table 5: Results of Hypotheses Testing

Hypothesi###Estimat###S.E.###C.R.###p-###Results

s###es###value

H1: PE a###.291###.111###2.624###.009###Support

BI###ed

H2: EE a###.663###.208###3.188###.001###Support

BI###ed

H3: SI a###-.040###.110###-.364###.716###Not

BI###Supported

H4: EE a###.893###.110###8.084###.000###Support

PE###ed

H5: EE a###.993###.123###8.098###.000###Support

SI###ed

4. CONCLUSION

The current study was performed to investigate the level of behavioral intention towards EHR by the selected two Bangladesh medical college students and to examine the influencing factors or the predictors of their behavioral intention. Accordingly, the results of the study have revealed that the respondents are moderately willing to adopt I.

Similarly, the findings suggest that PE (Performance Expectancy) and EE (Effort Expectancy) are strong determinants of intention to adopt I and EE positively influences PE and SI. Therefore, if the Bangladesh policy makers or the government or the hospital management really want to implement I in their health sector in future, they have to educate the current and future medical professional about the benefit of I to perform their daily duties and how it can benefit for their career as well. Furthermore, the policy makers have to show them (practitioners) that the adoption of I doesn't require addition effort and it will be easy to customize to it. However, similar kinds of studies have to be performed at different context and with different sample to generalize the findings throughout the country. The current researchers believe the findings of this study should be useful to both researchers and practitioners.

5. REFERENCES

[1] Hayrinen, K., Saranto, K., and Nykanen, P., Definition, structure, content, use and impacts of electronic health records: a review of the research literature. International journal of medical informatics, 77(5), 291-304, (2008).

[2] Menachemi, N., and Collum, T. H., Benefits and drawbacks of electronic health record systems. Risk Manag Healthc Policy, 4, 47-55, (2011).

[3] Ozair, F. F., Jamshed, N., Sharma, A., and Aggarwal, P., Ethical issues in electronic health records: A general overview. Perspectives in clinical research, 6(2), 73, (2015).

[4] Xierali, I. M., Hsiao, C. J., Puffer, J. C., Green, L. A., Rinaldo, J. C., Bazemore, A. W., Burke, M. T., and Phillips, R. L., The rise of electronic health record adoption among family physicians. The Annals of Family Medicine, 11(1), 14-19, (2013).

[5] Xierali, I. M., Phillips, R. L., Green, L. A., Bazemore, A. W., and Puffer, J. C., Factors influencing family physician adoption of electronic health records (EHRs). The Journal of the American Board of Family Medicine, 26(4), 388-393, (2013).

[6] Soumerai, S., and Avery, T., Don't Repeat the UK's Electronic Health Records Failure. The Huffington Post, (2013).

[7] Kimura, M., Croll, P., Li, B., Wong, C. P., Gogia, S., Faud, A., Kwak, Y.-S, Chu, Marcelo, S., Chow, Y.-H., and Paoin, W., Survey on medical records and I in Asia-Pacific region. Methods of information in medicine, 50(4), 386-391, (2011).

[8] Siddiqua, P., and Awal, M. A., A portable telemedicine system in the context of rural Bangladesh. In Informatics, Electronics and Vision (ICIEV), 2012 International Conference on (pp. 608-611). IEEE, (2012).

[9] Directorate General of Health Services. (n.d.a). Government of the People's Republic of Bangladesh Ministry of Health and Family Welfare Health Bulletin, Retrieved December 26, 2015, from http://www.dghs.gov.bd/images/docs/Publicaations/ HB_2014_2nd_Edition_060115.pdf (2014).

[10] Hoque, M. R., Mazmum A., M. F., and Bao, Y., e-Health in Bangladesh: current status, challenges, and future direction. International Tech Management Review, 4(2), 87-96., (2014).

[11] Sikder, M. K. A., Chy, A. N., and Seddiqui, M. H., Electronic health record system for human disease prediction and healthcare improvement in Bangladesh. In Informatics, Electronics and Vision (ICIEV), 2013 International Conference on (pp. 1-5). IEEE, (2013).

[12] Khan, S. Z., Shahid, Z., Hedstrom, K., and Andersson, A. Hopes and fears in implementation of electronic health records in Bangladesh. The Electronic Journal of Information Systems in Developing Countries, 54, 1-8, (2012).

[13] Mandl, K. D., and Kohane, I. S., Escaping the I trap-the future of health IT. New England Journal of Medicine, 366(24), 2240-2242, (2012).

[14] Venkatesh, V., Morris, M. G., Davis, G. B., and Davis, F. D., User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425-478, (2003).

[15] Cheung, R., and Vogel, D. Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning. Computers and Education, 63, 160-175, (2013).

[16] Padilla-MeleNdez, A., Del Aguila-Obra, A. R., and Garrido-Moreno, A. Perceived playfulness, gender differences and technology acceptance model in a blended learning scenario. Computers and Education, 63, 306-317, (2013).

[17] Razi, M., Applicability of technology acceptance in knowledge management implementation. Paper presented at the 6th International Conference on Information Technology (ICIT 2013), Amman, Jordan, (2013).

[18] Jeyakodi, T., and Herath, D., Acceptance and Use of Electronic Medical Records in Sri Lanka. Scientific Research Journal (SCIRJ), Volume IV, Issue I, January 2016, (2016).

[19] Jeng, D. J. F., and Tzeng, G. H., Social influence on the use of clinical decision support systems: revisiting the unified theory of acceptance and use of technology by the fuzzy DEMATEL technique. Computers and Industrial Engineering, 62(3), 819-828, (2012).

[20] Bilgihan, A., Barreda, A., Okumus, F., and Nusair, K. Consumer perception of knowledge-sharing in travel-related Online Social Networks. Tourism Management, 52, 287-296, (2016).
COPYRIGHT 2017 Asianet-Pakistan
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2017 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Author:Sakib, M.N.; Razi, M.J.M.
Publication:Science International
Article Type:Report
Geographic Code:9BANG
Date:Oct 31, 2017
Words:3407
Previous Article:SIGNIFICANCE OF CUSTOMER RELATIONSHIP MANAGEMENT AND ADVERTISEMENT ON CONSUMER BUYING BEHAVIOUR IN ENTREPRENEURIAL APPAREL FIRMS.
Next Article:OCCUPATIONAL STRESS AND ITS EFFECTS ON JOB PERFORMANCE: A CASE STUDY ON FACULTY OF JAMSHORO EDUCATION CITY (JEC).
Topics:

Terms of use | Privacy policy | Copyright © 2019 Farlex, Inc. | Feedback | For webmasters