Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

At a glance: The ONWARDS insulin icodec trials

A round-up of the ONWARDS phase 3 clinical trials evaluating the novel weekly insulin icodec in people with diabetes.
ADVANCED SEARCH
Log in
Top
BMC Medical Informatics and Decision Making
Published in: BMC Medical Informatics and Decision Making 1/2020

01-12-2020 | Care | Research article

MHealth and perceived quality of care delivery: a conceptual model and validation

Authors: Yvonne O’Connor, Pavel Andreev, Philip O’Reilly

Published in: BMC Medical Informatics and Decision Making | Issue 1/2020

Login to get access

Abstract

Background

The objective of this research is to examine, conceptualize, and empirically validate a model of mobile health (mHealth) impacts on physicians’ perceived quality of care delivery (PQoC).

Methods

Observational quasi-experimental one group posttest-only design was implemented through the empirical testing of the conceptual model with nine hypotheses related to the association of task and technology characteristics, self-efficacy, m-health utilization, task-technology fit (TTF), and their relationships with PQoC. Primary data was collected over a four-month period from acute care physicians in The Ottawa Hospital, Ontario, Canada. The self-reported data was collected by employing a survey and distributed through the internal hospital channels to physicians who adopted iPads for their daily activities.

Results

Physicians’ PQoC was found to be positively affected by the level of mHealth utilization and TTF, while the magnitude of the TTF direct effect was two times stronger than utilization. Additionally, self-efficacy has the highest direct and total effect on mHealth utilization; in the formation of TTF, technological characteristics dominate followed by task characteristics.

Conclusion

To date, the impact of utilized mHealth on PQoC has neither been richly theorized nor explored in depth. We address this gap in existing literature. Realizing how an organization can improve TTF will lead to better PQoC.
Appendix
Available only for authorised users
Literature
1.
Lee J-H. Future of the smartphone for patients and healthcare providers. Healthcare Inform Res. 2016;22(1):1–2.CrossRef
2.
Sherer S, Meyerhoefer C, Levick D. Challenges to aligning coordination technology with organizations, people, and processes in healthcare. In: Proceedings of the 50th Hawaii international conference on system sciences; 2017.
3.
Seyedi H, Mohebbifar R, Rafiei S. Quality management system and its role in the quality maturity of training hospitals. J Client-Centered Nursing Care. 2019;5(2):113–22.CrossRef
4.
Kohli R, Tan SS-L. Electronic health records: how can IS researchers contribute to transforming healthcare? MIS Q. 2016;40(3):553–73.CrossRef
5.
Sarre S, Maben J, Griffiths P, Chable R, Robert G. The 10-year impact of a ward-level quality improvement intervention in acute hospitals: a multiple methods study. Southampton: NIHR Journals Library, Health Services and Delivery Research. 2019;7(28).
6.
Abu-Dalbouh H. A proposed mhealth model for improving the quality care in hospitals. Res J Appl Sci Eng Technol. 2014;7(7):1401–5.CrossRef
7.
Rahurkar S, Vest JR, Menachemi N. Despite the spread of health information exchange, there is little evidence of its impact on cost, use, and quality of care. Health Aff. 2015;34(3):477–83.CrossRef
8.
Kumar S, Nilsen WJ, Abernethy A, Atienza A, Patrick K, Pavel M, et al. Mobile health technology evaluation: the mHealth evidence workshop. Am J Prev Med. 2013;45(2):228–36.PubMedPubMedCentralCrossRef
9.
Hall CS, Fottrell E, Wilkinson S, Byass P. Assessing the impact of mHealth interventions in low-and middle-income countries–what has been shown to work? Glob Health Action. 2014;7:25606.PubMedCrossRef
10.
Dick S, O’Connor Y, Heavin C. Approaches to Mobile health evaluation: a comparative study a mHealth evaluation comparison study. Inf Syst Manag. 2019;37:1–18.
11.
Boudreaux ED, Waring ME, Hayes RB, Sadasivam RS, Mullen S, Pagoto S. Evaluating and selecting mobile health apps: strategies for healthcare providers and healthcare organizations. Transl Behav Med. 2014;4(4):363–71.PubMedPubMedCentralCrossRef
12.
Keikhosrokiani P, Zakaria N, Mustaffa N, Venkat I. Study of the effective factors in Mobile health-care success: sociotechnical perspective. In: mHealth Multidisciplinary Verticals; 2014. p. 237.
13.
Anglada-Martinez H, Riu-Viladoms G, Martin-Conde M, Rovira-Illamola M, Sotoca-Momblona J, Codina-Jane C. Does mHealth increase adherence to medication? Results of a systematic review. Int J Clin Pract. 2015;69(1):9–32.PubMedCrossRef
14.
Franz-Vasdeki J, Pratt BA, Newsome M, Germann S. Taking mHealth solutions to scale: enabling environments and successful implementation. J Mobile Technol Med. 2015;4(1):35–8.CrossRef
15.
Hamine S, Gerth-Guyette E, Faulx D, Green BB, Ginsburg AS. Impact of mHealth chronic disease management on treatment adherence and patient outcomes: a systematic review. J Med Internet Res. 2015;17(2):e52.PubMedPubMedCentralCrossRef
16.
Turner T, Spruijt-Metz D, Wen C, Hingle M. Prevention and treatment of pediatric obesity using mobile and wireless technologies: a systematic review. Pediatr Obes. 2015;10:403–9.PubMedPubMedCentralCrossRef
17.
Hsieh JP-A, Zmud R. Understanding post-Adtopive usage behaviors: a two-dimensional view. DIGIT 2006 proceedings; 2006.
18.
O’Connor Y, O’Reilly P. Examining the infusion of mobile technology by healthcare practitioners in a hospital setting. Inf Syst Front. 2018;20(6):1297–317.CrossRef
19.
Goodhue DL, Thompson RL. Task-technology fit and individual performance. MIS Q. 1995;19(2):213–36.CrossRef
20.
Serrano CI, Karahanna E. The compensatory interaction between user Capabiities and technology capabilities in influencing task performance: an empirical assessment in telemedicine consultations. MIS Q. 2016;40(3):597–621.CrossRef
21.
Sundin P, Callan J, Mehta K. Why do entrepreneurial mHealth ventures in the developing world fail to scale? J Med Eng Technol. 2016;40(7-8):1–14.CrossRef
22.
Varshney U. Mobile health: four emerging themes of research. Decis Support Syst. 2014;66(0):20–35.CrossRef
23.
Sun H, Fang Y, Zou HM. Choosing a fit technology: understanding mindfulness in technology adoption and continuance. J Assoc Inf Syst. 2016;17(6):377.
24.
Bhuyan S, Kim H, Isehunwa OO, Kumar N, Bhatt J, Wyant DK, et al. Privacy and security issues in mobile health: current research and future directions. In: Health policy and technology; 2017.
25.
Restuccia JD, Cohen AB, Horwitt JN, Shwartz M. Hospital implementation of health information technology and quality of care: are they related? BMC Med Inform Decis Mak. 2012;12(1):109.PubMedPubMedCentralCrossRef
26.
Miah SJ, Gammack J, Hasan N. Extending the framework for Mobile health information systems research: a content analysis. Inf Syst. 2017;69:1–24.CrossRef
27.
Cook VE, Ellis AK, Hildebrand KJ. Mobile health applications in clinical practice: pearls, pitfalls, and key considerations. Ann Allergy Asthma Immunol. 2016;117(2):143–9.PubMedCrossRef
28.
Donabedian A. Commentary on some studies of the quality of care. Health Care Financ Rev. 1987;SUPPL:75–85.
29.
Chang JT, Hays RD, Shekelle PG, MacLean CH, Solomon DH, Reuben DB, et al. Patients' global ratings of their health care are not associated with the technical quality of their care. Ann Intern Med. 2006;144(9):665–72.PubMedCrossRef
30.
Hanefeld J, Powell-Jackson T, Balabanova D. Understanding and measuring quality of care: dealing with complexity. Bull World Health Organ. 2017;95(5):368.PubMedPubMedCentralCrossRef
31.
Scott Morton MS. The corporation of the 1990s. Information technology and organizational transformation; 1991.
32.
Chatfield AT, Yetton P. Strategic payoff from EDI as a function of EDI embeddedness. J Manag Inf Syst. 2000;16(4):195–224.CrossRef
33.
Bensaou M, Venkatraman N. Inter-organizational relationships and information technology: a conceptual synthesis and a research framework. Eur J Inf Syst. 1996;5(2):84–91.CrossRef
34.
Doran D, Haynes BR, Estabrooks CA, Kushniruk A, Dubrowski A, Bajnok I, et al. The role of organizational context and individual nurse characteristics in explaining variation in use of information technologies in evidence based practice. Implement Sci. 2012;7(1):122.PubMedPubMedCentralCrossRef
35.
Chiasson M, Kelley H, Downey A. Understanding task-performance chain feed-forward and feedback relationships in E-health. AIS Transact Human-Comput Interact. 2015;7(3):167–90.CrossRef
36.
Furneaux B. Task-technology fit theory: a survey and synopsis of the literature. In: Dwivedi Y., Wade M., Schneberger S. (eds) Information Systems Theory. Integrated Series in Information Systems, vol 28. New York: Springer; 2012.
37.
Kutney-Lee A, Sloane DM, Bowles KH, Burns LR, Aiken LH. Electronic health record adoption and nurse reports of usability and quality of care: the role of work environment. Appl Clin Inform. 2019;10(01):129–39.PubMedPubMedCentralCrossRef
38.
Abraham D. A grounded theory for the impacts of ubiquitous information systems (IS) access on task performance. Austin: Austin Mobility Roundtable; 2004.
39.
Basole RC. The value and impact of mobile information and communication technologies. Atlanta: Proceedings of the 2004 International Federation of Automatic Control Symposium; 2004; 2004.
40.
Hsiao J-L, Chen R-F. An investigation on task-technology fit of Mobile nursing information Systems for Nursing Performance. Comput Inform Nurs. 2012;30(5):265–73.PubMedCrossRef
41.
Rossi M, Tuunainen VK, Pesonen M. Mobile technology in field customer service: big improvements with small changes. Bus Process Manag J. 2007;13(6):853–65.CrossRef
42.
Kim H-W, Chan HC, Lee S-H. A user commitment approach to information systems infusion: PACIS 2012 Proceedings; 2012. Paper 101. Ho Chi Minh City, Vietnam.
43.
Thatcher JB, Wright RT, Sun H, Zagenczyk TJ, Klein R. Mindfulness in information technology use: definitions, distinctions, and a new measure. MIS Q. 2018;42(3):831–47.CrossRef
44.
McLean E, Sedera D, Tan F. Reconceptualizing system use for contemporary information systems. In: PACIS 2011 proceedings; 2011. p. 130.
45.
Oakley R, Palvia P. A study of the impact of Mobile self-efficacy and emotional attachment on Mobile device infusion. Seattle: AMCIS 2012; 2012. Paper 15.
46.
Limayem M, Hirt SG. Force of habit and information systems usage: theory and initial validation. J Assoc Inf Syst. 2003;4(1):Article 3.
47.
Prgomet M, Georgiou A, Westbrook JI. The impact of mobile handheld technology on hospital physicians' work practices and patient care: a systematic review. J Am Med Inform Assoc. 2009;16(6):792–801.PubMedPubMedCentralCrossRef
48.
Bell H, Garfield S, Khosla S, Patel C, Franklin BD. Mixed methods study of medication-related decision support alerts experienced during electronic prescribing for inpatients at an English hospital. Eur J Hosp Pharm. 2019;26(6):318–22.PubMedCrossRef
49.
Mans R, van der Aalst W, Russell N, Bakker P. Implementation of a healthcare process in four different workflow systems. Technical report. Eindhoven: Technische Universiteit Eindhoven; 2009.
50.
Han S, Harkke V, Mustonen P, Seppanen M, Kallio M. Mobilizing medical information and knowledge: some insights from a survey. In: ECIS 2004 proceedings; 2004. p. Paper 69.
51.
Venkatesh V, Brown SA, Maruping LM, Bala H. Predicting different conceptualizations of system use: the competing roles of behavioral intention, facilitating conditions, and behavioral expectation. MIS Q. 2008;32(3):483–502.CrossRef
52.
Agarwal R, Venkatesh V. Assessing a firm’s web presence: a heuristic evaluation procedure for the measurement of usability. Inf Syst Res. 2002;13(2):168–86.CrossRef
53.
Cooper RB, Zmud RW. Information technology implementation research: a technological diffusion approach. Manag Sci. 1990;36(2):123–39.CrossRef
54.
Lewis W, Agarwal R, Sambamurthy V. Sources of influence on beliefs about information technology use: an empirical study of knowledge workers. MIS Q. 2003;27(4):657–78.CrossRef
55.
Tsai M-F, Hung S-Y, Yu W-J, Chen C, Yen DC. Understanding physicians’ adoption of electronic medical records: healthcare technology self-efficacy, service level and risk perspectives. Comput Stand Interfaces. 2019;66(103342).CrossRef
56.
Compeau DR, Higgins CA. Computer self-efficacy: development of a measure and initial test. MIS Q. 1995;19(2):189–211.CrossRef
57.
Shaw N, Manwani S. Extending feature usage: a study of the post-adoption of electronic medical records CIS 2011 proceedings; 2011. p. Paper 125.
58.
Bandura A. Self-efficacy: toward a unifying theory of behavioral change. Psychol Rev. 1977;84(2):191–215.PubMedCrossRef
59.
Stajkovic AD, Luthans F. Self-efficacy and work-related performance: a meta-analysis. Psychol Bull. 1988;124(2):240–61.CrossRef
60.
Vannatta R, Beyerbach B, Walsh C. From teaching technology to using technology to enhance student learning: preservice teachers’ changing perceptions of technology infusion. J Technol Teach Educ. 2001;9(1):105–27.
61.
Pongpattrachai D, Cragg P, Fisher R. Spreadsheet infusion in small audit firms in Thailand. In: Americas conference on information systems Paper; 2009. p. 432.
62.
Pierce JL, Kostova T, Dirks KT. The state of psychological ownership: integrating and extending a century of research. Rev Gen Psychol. 2003;7(1):84–107.CrossRef
63.
Podsakoff PM, MacKenzie SB, Lee J-Y, Podsakoff NP. Common method biases in behavioral research: a critical review of the literature and recommended remedies. J Appl Psychol. 2003;88(5):879.PubMedCrossRef
64.
Cunningham CT, Quan H, Hemmelgarn B, Noseworthy T, Beck CA, Dixon E, et al. Exploring physician specialist response rates to web-based surveys. BMC Med Res Methodol. 2015;15(1):32.PubMedPubMedCentralCrossRef
65.
Al-Janabi S, Alkaim AF. A nifty collaborative analysis to predicting a novel tool (DRFLLS) for missing values estimation. Soft Comput. 2019;24:555–69.CrossRef
66.
Cohen J. Statistical power analysis for the behavioral sciences ed. Hillsdale: Lawrence Erlbaum Associates, NJ; 1988.
67.
Tenenhaus M, Vinzi VE, Chatelin YM, Lauro C. PLS path modeling. Comput Stat Data Anal. 2005;48(1):159–205.CrossRef
68.
Diamantopoulos A, Siguaw JA. Formative versus reflective indicators in organizational measure development: a comparison and empirical illustration. Br J Manag. 2006;17(4):263–82.CrossRef
69.
Fornell C, Bookstein FL. Two structural equation models: LISREL and PLS applied to consumer exit-voice theory. J Market Res. 1982;19(4):440–52.CrossRef
70.
Henseler J, Ringle CM, Sinkovics RR. The use of partial least squares path modeling in international marketing. Adv Int Mark. 2009;20(2009):277–319.CrossRef
71.
Andreev P, Heart T, Maoz H, Pliskin N. Validating formative partial least squares (PLS) models: methodological review and empirical illustration. Phoenix: ICIS 2009 Proceedings; 2009. p. 193.
72.
Chatelin YM, Vinzi VE, Tenenhaus M. State-of-art on PLS path modeling through the available software. In: Les cahiers de Recherche. Paris: Groupe HEC; 2002. p. 764.
73.
Gefen D, Straub DW, Boudreau M-C. Structural equation modeling and regression: guidelines for research and practice. CAIS. 2000;4(7):1–70.
74.
Chin WW. The partial least squares approach to structural equation modeling. In: G.E M, editor. Modern methods for business research Mahwah. New Jersey: Lawrence Erlbaum Associates; 1998. p. 295–336.
75.
Hair JF Jr, Hult GTM, Ringle C, Sarstedt M. A primer on partial least squares structural equation modeling (PLS-SEM): sage publications; 2016.
76.
Becker J-M, Klein K, Wetzels M. Hierarchical latent variable models in PLS-SEM: guidelines for using reflective-formative type models. Long Range Plann. 2012;45(5):359–94.CrossRef
77.
Ali SB, Romero J, Morrison K, Hafeez B, Ancker JS. Focus section health IT usability: applying a task-technology fit model to adapt an electronic patient portal for patient work. Appl Clin Inform. 2018;9(01):174–84.PubMedPubMedCentralCrossRef
78.
Dishaw M, Strong D, Bandy DB. Extending the task-technology fit model with self-efficacy constructs. In: AMCIS 2002 proceedings; 2002. p. 143.
79.
Lin T-C, Huang C-C. Understanding knowledge management system usage antecedents: an integration of social cognitive theory and task technology fit. Inf Manage. 2008;45(6):410–7.CrossRef
80.
Gagnon M-P, Ngangue P, Payne-Gagnon J, Desmartis M. M-health adoption by healthcare professionals: a systematic review. J Am Med Inform Assoc. 2015;23(1):212–20.PubMedPubMedCentralCrossRef
81.
Murray E, Treweek S, Pope C, MacFarlane A, Ballini L, Dowrick C, et al. Normalisation process theory: a framework for developing, evaluating and implementing complex interventions. BMC Med. 2010;8(1):63.PubMedPubMedCentralCrossRef
82.
Beaudry A, Pinsonneault A. Advancing the theory of infusion: an appropriation model of the infusion process. Paris: Cahier du GreSI M, Canada; 1999.
83.
Igbaria M, Iivari J. The effects of self-efficacy on computer usage. Omega. 1995;23(6):587–605.CrossRef
84.
Agarwal R, Karahanna E. Time flies when you're having fun: cognitive absorption and beliefs about information technology usage. MIS Q. 2000;24:665–94.CrossRef
85.
Jain V, Kanungo S. IS-enabled performance improvement at the individual level: evidence of complementarity. In: Proceedings of the 2006 ACM SIGMIS CPR conference on computer personnel research: forty four years of computer personnel research: achievements, challenges and the future. Claremont. 1125181: ACM; 2006. p. 25–33.
86.
Sun H. Understanding user revisions when using information system features: adaptive system use and triggers. MIS Q. 2012;36(2):453–78.CrossRef
87.
Honeybourne C, Sutton S, Ward L. Knowledge in the palm of your hands: PDAs in the clinical setting. Health Info Libr J. 2006;23(1):51–9.PubMedCrossRef
88.
Strong DM, Dishaw MT, Bandy DB. Extending task technology fit with computer self-efficacy. ACM SIGMIS Database. 2006;37(2–3):96–107.CrossRef
89.
Varshney U. A model for improving quality of decisions in mobile health. Decis Support Syst. 2014;62:66–77.CrossRef
90.
Wakefield RL, Whitten D. Mobile computing: a user study on hedonic/utilitarian mobile device usage. Eur J Inf Syst. 2006;15(3):292–300.CrossRef
Metadata
Title
MHealth and perceived quality of care delivery: a conceptual model and validation
Authors
Yvonne O’Connor
Pavel Andreev
Philip O’Reilly
Publication date
01-12-2020
Publisher
BioMed Central
Keyword
Care
Published in
BMC Medical Informatics and Decision Making / Issue 1/2020
Electronic ISSN: 1472-6947
DOI
https://doi.org/10.1186/s12911-020-1049-8

Other articles of this Issue 1/2020

BMC Medical Informatics and Decision Making 1/2020 Go to the issue

Research article

The efficiency and effectiveness of surgery information systems in Iran

Research article

Use of online knowledge base in primary health care and correlation to health care quality: an observational study

Research article

Constructing fine-grained entity recognition corpora based on clinical records of traditional Chinese medicine

Software

Monitoring stance towards vaccination in twitter messages

Research article

Predicting diabetes clinical outcomes using longitudinal risk factor trajectories

Research article

Implementation of a national electronic health information system in Gabon: a survey of healthcare providers’ perceptions