Big Data Analytics Quality Factors in Enhancing Healthcare Organizational Performance: A Pilot Study with Rasch Model Analysis

Wan Mohd Haffiz Mohd Nasir (1), Yusmadi Yah Jusoh (2), Rusli Abdullah (3), Salfarina Abdullah (4)
(1) Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
(2) Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
(3) Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
(4) Faculty of Computer Science and Information Technology, Universiti Putra Malaysia, Serdang, Selangor, Malaysia
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Mohd Nasir , Wan Mohd Haffiz, et al. “Big Data Analytics Quality Factors in Enhancing Healthcare Organizational Performance: A Pilot Study With Rasch Model Analysis”. International Journal on Advanced Science, Engineering and Information Technology, vol. 14, no. 6, Dec. 2024, pp. 2076-83, doi:10.18517/ijaseit.14.6.11983.
Big Data Analytics (BDA) plays a pivotal role in the digital transformation of healthcare, significantly boosting organizational performance within the sector. As healthcare organizations increasingly adopt BDA to leverage data-driven decision-making, understanding the factors contributing to BDA quality becomes imperative. Thus, this study has proposed and developed the BDA quality conceptual model, and a pilot study is part of the process of completing the conceptual model development. The instrument, which is the questionnaire that has been designed, needs to be tested for reliability. Therefore, the pilot study aims to evaluate and refine the instrument used to assess BDA practitioners’ comprehension of the constructs and the reliability of the items. This study utilized the probabilistic approach of Item Response Theory (IRT), explicitly employing the Rasch Measurement Model analysis to enhance the accuracy of measurement instruments, assess respondents' performance, and ensure instrument reliability. The survey instrument comprised 11 constructs and 64 items, which were designed to measure all the constructs: reliability, accuracy, completeness, timeliness, format, accessibility, usability, maintainability, portability, user satisfaction, and healthcare organizational performance. Data were collected from 20 respondents and synthesized according to their responses to each questionnaire item. The analyses were performed using Rasch analysis software, specifically Winsteps. The results of the Rasch analysis included findings on the reliability of persons and items, the distribution map of person-item relationships, identification of misfitting items, and assessment of unidimensionality. Ten items were removed from the initial set of 64 due to misfit, leaving 54 items that effectively measured respondents' understanding of BDA quality in healthcare organizational performance. Thus, Rasch measurement model analysis has confirmed the instrument was well constructed, valid, and reliable for actual study.

D. Tosi, R. Kokaj, and M. Roccetti, “15 years of Big Data: a systematic literature review,” Journal of Big Data, vol. 11, no. 1, May 2024, doi:10.1186/s40537-024-00914-9.

I. Taleb, M. A. Serhani, and R. Dssouli, “Big Data Quality: A Survey,” 2018 IEEE International Congress on Big Data (BigData Congress), pp. 166–173, Jul. 2018, doi: 10.1109/bigdatacongress.2018.00029.

S. Sarker, M. S. Arefin, M. Kowsher, T. Bhuiyan, P. K. Dhar, and O.-J. Kwon, “A Comprehensive Review on Big Data for Industries: Challenges and Opportunities,” IEEE Access, vol. 11, pp. 744–769, 2023, doi: 10.1109/access.2022.3232526.

A. Rehman, S. Naz, and I. Razzak, “Leveraging big data analytics in healthcare enhancement: trends, challenges and opportunities,” Multimedia Systems, vol. 28, no. 4, pp. 1339–1371, Jan. 2021, doi:10.1007/s00530-020-00736-8.

A. Ahmed, R. Xi, M. Hou, S. A. Shah, and S. Hameed, “Harnessing Big Data Analytics for Healthcare: A Comprehensive Review of Frameworks, Implications, Applications, and Impacts,” IEEE Access, vol. 11, pp. 112891–112928, 2023, doi: 10.1109/access.2023.3323574.

P. Goyal and R. Malviya, “Challenges and opportunities of big data analytics in healthcare,” Health Care Science, vol. 2, no. 5, pp. 328–338, Oct. 2023, doi: 10.1002/hcs2.66.

W. Raghupathi and V. Raghupathi, “Big data analytics in healthcare: promise and potential,” Health Information Science and Systems, vol. 2, no. 1, Feb. 2014, doi: 10.1186/2047-2501-2-3.

P. B. Dharmawan, I. G. A. N. S. Maharani, and C. Tho, “Big Data Capabilities for Hospital: A Systematic Literature Review,” Procedia Computer Science, vol. 227, pp. 272–281, 2023, doi:10.1016/j.procs.2023.10.525.

K. E. Alhajaj and I. A. Moonesar, “The power of big data mining to improve the health care system in the United Arab Emirates,” Journal of Big Data, vol. 10, no. 1, Feb. 2023, doi: 10.1186/s40537-022-00681-5.

Y. Wang, L. Kung, S. Gupta, and S. Ozdemir, “Leveraging Big Data Analytics to Improve Quality of Care in Healthcare Organizations: A Configurational Perspective,” British Journal of Management, vol. 30, no. 2, pp. 362–388, Apr. 2019, doi: 10.1111/1467-8551.12332.

K. Batko and A. Ślęzak, “The use of Big Data Analytics in healthcare,” Journal of Big Data, vol. 9, no. 1, Jan. 2022, doi: 10.1186/s40537-021-00553-4.

J. W. Cortada, D. Gordon, and B. Lenihan, The Value of Analytics in Healthcare. IBM Institute for Business Value, Armonk, NY, USA, 2012, Report No.: GBE03476-USEN-00.

N. Côrte-Real, P. Ruivo, and T. Oliveira, “Leveraging internet of things and big data analytics initiatives in European and American firms: Is data quality a way to extract business value?,” Information & Management, vol. 57, no. 1, p. 103141, Jan. 2020, doi:10.1016/j.im.2019.01.003.

N. Mehta and A. Pandit, “Concurrence of big data analytics and healthcare: A systematic review,” International Journal of Medical Informatics, vol. 114, pp. 57–65, Jun. 2018, doi:10.1016/j.ijmedinf.2018.03.013.

H. Kang and S. Sibbald, “Challenges to Using Big Data in Health Services Research,” University of Western Ontario Medical Journal, vol. 87, no. 2, pp. 18–20, Mar. 2019, doi: 10.5206/uwomj.v87i2.1140.

G. Manikandan, S. Abirami, K. Gokul, and G. Deepalakshmi, “Big data analytics in healthcare,” Big Data Analytics for Healthcare, pp. 3–11, 2022, doi: 10.1016/b978-0-323-91907-4.00008-x.

Cloudera, "Welcome to the Revolution in Healthcare," [Online]. Available: https://fr.cloudera.com/content/dam/www/marketing/resources/solution-briefs/cloudera-ibm-welcome-to-the-revolution-in-healthcare.pdf?daqp=true. [Accessed: Aug. 24, 2024].

J. A. McCall, P. K. Richards, and G. F. Walters, Factors in Software Quality, Concept and Definitions of Software Quality, 1977. [Online]. Available: http://www.dtic.mil/dtic/tr/fulltext/u2/a049014.pdf. Accessed: Aug. 24, 2024.

R. Raja, I. Mukherjee, and B. K. Sarkar, “A Systematic Review of Healthcare Big Data,” Scientific Programming, vol. 2020, pp. 1–15, Jul. 2020, doi: 10.1155/2020/5471849.

N. Mehta, A. Pandit, and M. Kulkarni, “Elements of Healthcare Big Data Analytics,” Big Data Analytics in Healthcare, pp. 23–43, Oct. 2019, doi: 10.1007/978-3-030-31672-3_2.

Y. Wang, “Leveraging Big Data Analytics to Improve Quality of Care In Health Care: A fsQCA Approach,” Proceedings of the 51st Hawaii International Conference on System Sciences, 2018, doi:10.24251/hicss.2018.097.

ISO, "ISO/IEC 25010:2011 (SQuaRE) Quality Model," Geneva, Switzerland, 2011. [Online]. Available: https://www.iso.org/obp/ui/#iso:std:iso-iec:25010:ed-1:v1:en.

B. W. Boehm, J. R. Brown, H. Kaspar, M. Lipow, G. McLeod and M. Merritt, “Characteristics of Software Quality,” North Holland, 1978.

S. Fosso Wamba, S. Akter, and M. de Bourmont, “Quality dominant logic in big data analytics and firm performance,” Business Process Management Journal, vol. 25, no. 3, pp. 512–532, Jun. 2018, doi:10.1108/bpmj-08-2017-0218.

S. F. Wamba, A. Gunasekaran, S. Akter, S. J. Ren, R. Dubey, and S. J. Childe, “Big data analytics and firm performance: Effects of dynamic capabilities,” Journal of Business Research, vol. 70, pp. 356–365, Jan. 2017, doi: 10.1016/j.jbusres.2016.08.009.

S. Ji-fan Ren, S. Fosso Wamba, S. Akter, R. Dubey, and S. J. Childe, “Modelling quality dynamics, business value and firm performance in a big data analytics environment,” International Journal of Production Research, vol. 55, no. 17, pp. 5011–5026, Mar. 2016, doi:10.1080/00207543.2016.1154209.

P. H. Shariat Panahy, F. Sidi, L. S. Affendey, M. A. Jabar, H. Ibrahim, and A. Mustapha, “A Framework to Construct Data Quality Dimensions Relationships,” Indian Journal of Science and Technology, vol. 6, no. 5, pp. 1–10, May 2013, doi:10.17485/ijst/2013/v6i5.10.

M. Wook, “Big Data Analytics Application Model Based on Data Quality Dimensions and Big Data Traits in Public Sector,” International Journal of Advanced Trends in Computer Science and Engineering, vol. 9, no. 2, pp. 1247–1256, Apr. 2020, doi:10.30534/ijatcse/2020/53922020.

A. Ameen, D. Al-Ali, O. Isaac, and F. Mohammed, “Examining relationship between service quality, user satisfaction, and performance impact in the context of smart government in UAE,” International Journal of Electrical and Computer Engineering (IJECE), vol. 10, no. 6, p. 6026, Dec. 2020, doi:10.11591/ijece.v10i6.pp6026-6033.

S. Fosso Wamba, S. Akter, L. Trinchera, and M. De Bourmont, “Turning information quality into firm performance in the big data economy,” Management Decision, vol. 57, no. 8, pp. 1756–1783, Sep. 2019, doi: 10.1108/md-04-2018-0394.

S. Akter, S. Fosso Wamba, and S. Dewan, “Why PLS-SEM is suitable for complex modelling? An empirical illustration in big data analytics quality,” Production Planning & Control, vol. 28, no. 11–12, pp. 1011–1021, Jul. 2017, doi: 10.1080/09537287.2016.1267411.

M. Ghasemaghaei and G. Calic, “Can big data improve firm decision quality? The role of data quality and data diagnosticity,” Decision Support Systems, vol. 120, pp. 38–49, May 2019, doi: 10.1016/j.dss.2019.03.008.

W. M. H. M. Nasir, Y. Y. Jusoh, R. Abdullah, and S. Abdullah, “Towards Healthcare Organizational Performance Deriving by Big Data Analytics Quality Factors: A Systematic Literature Review,” 2022 Applied Informatics International Conference (AiIC), pp. 1–6, May 2022, doi: 10.1109/aiic54368.2022.9914026.

D. Pratt, Curriculum Design and Development. Harcourt Brace, 1980.

M.-C. Boudreau, D. Gefen, and D. W. Straub, “Validation in Information Systems Research: A State-of-the-Art Assessment,” MIS Quarterly, vol. 25, no. 1, p. 1, Mar. 2001, doi: 10.2307/3250956.

D. Straub and D. Gefen, “Validation Guidelines for IS Positivist Research,” Communications of the Association for Information Systems, vol. 13, 2004, doi: 10.17705/1cais.01324.

E. R. Van Teijlingen, A. Rennie, V. Hundley, and W. Graham, “The importance of conducting and reporting pilot studies: the example of the Scottish Births Survey,” Journal of Advanced Nursing, vol. 34, no. 3, pp. 289–295, May 2001, doi: 10.1046/j.1365-2648.2001.01757.x.

J. A. Gliem and R. R. Gliem, "Calculating, interpreting, and reporting Cronbach’s alpha reliability coefficient for Likert-type scales," in Midwest Research-to-Practice Conference in Adult, Continuing, and Community Education, Ohio State University, Columbus, OH, USA, 2003.

W. Wiersma and S. G. Jurs, Research Methods in Education: An Introduction, 9th edition. Pearson, 2009.

D. Andrich, “A rating formulation for ordered response categories,” Psychometrika, vol. 43, no. 4, pp. 561–573, Dec. 1978, doi: 10.1007/bf02293814.

M. A. Hertzog, “Considerations in determining sample size for pilot studies,” Research in Nursing & Health, vol. 31, no. 2, pp. 180–191, Jan. 2008, doi: 10.1002/nur.20247.

J. M. Linacre, A User's Guide to Winsteps/Ministep: Rasch-Model Computer Programs. 2015. [Online]. Available: http://www.winsteps.com/manuals.htm.

G. Rasch, Probabilistic Models for Some Intelligence and Attainment Tests. Copenhagen: Danish Institute for Educational Research, 1960.

C. Bond, T. & Fox, Applying the Rasch Model: Fundamental Measurement in the Human Sciences. Mahwah, NJ: Lawrence Erlbaum Associates, 2007.

I. H. Nunnally, J. C., & Bernstein, Psychometric Theory, 3rd edn. New York: New York, NY: McGraw-Hill, 1994.

W. P. Fisher, “Rating Scale Instrument Quality Criteria,” Rasch Meas. Trans., vol. 21, no. 1, p. 1095, 2007.

G. N. Wright, B.D. & Masters, Rating Scale Analysis: Rasch Measurement. Chicago: Mesa Press, 1982.

E. Brentari, S. Golia, and M. Manisera, "Models for categorical data: A comparison between the Rasch model and nonlinear principal component analysis," Statistica & Applicazioni, vol. 5, no. 1, pp. 53–77, 2007.

A. A. Aziz, M. S. Masodi and A. Zaharim, Asas Model Pengukuran Rasch: Pembentukan Skala Dan Struktur Pengukuran, Bangi, Malaysia, Penerbit Universiti Kebangsaan Malaysia, 2017.

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