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Journal of Imaging Informatics in Medicine
Published in: Journal of Digital Imaging 6/2011

01-12-2011

Assessing Task–Technology Fit in a PACS Upgrade: Do Users’ and Developers’ Appraisals Converge?

Authors: Luigi Lepanto, Claude Sicotte, Pascale Lehoux

Published in: Journal of Imaging Informatics in Medicine | Issue 6/2011

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Abstract

The purpose of this study was to measure users’ perceived benefits of a picture archiving and communication system (PACS) upgrade, and compare their responses to those predicted by developers. The Task–Technology Fit (TTF) model served as the theoretical framework to study the relation between TTF, utilization, and perceived benefits. A self-administered survey was distributed to radiologists working in a university hospital undergoing a PACS upgrade. Four variables were measured: impact, utilization, TTF, and perceived net benefits. The radiologists were divided into subgroups according to their utilization profiles. Analysis of variance was performed and the hypotheses were tested with regression analysis. Interviews were conducted with developers involved in the PACS upgrade who were asked to predict impact and TTF. Users identified only a moderate fit between the PACS enhancements and their tasks, while developers predicted a high level of TTF. The combination of a moderate fit and an underestimation of the potential impact of changes in the PACS led to a low score for perceived net benefits. Results varied significantly among user subgroups. Globally, the data support the hypotheses that TTF predicts utilization and perceived net benefits, but not that utilization predicts perceived net benefits. TTF is a valid tool to assess perceived benefits, but it is important to take into account the characteristics of users. In the context of a technology that is rapidly evolving, there needs to be an alignment of what users perceive as a good fit and the functionality developers incorporate into their products.
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Literature
1.
(2010) IMV: Peripherals, upgrades dominate mature PACS market. HealthImaging.com, 2010
2.
Mattern CW, et al: Electronic imaging impact on image and report turnaround times. J Digit Imaging 12(2 Suppl 1):155–9, 1999PubMedCrossRef
3.
Reiner BI, et al: Radiologists’ productivity in the interpretation of CT scans: a comparison of PACS with conventional film. AJR Am J Roentgenol 176(4):861–4, 2001PubMed
4.
Andriole KP: Productivity and cost assessment of computed radiography, digital radiography, and screen-film for outpatient chest examinations. J Digit Imaging 15(3):161–9, 2002PubMedCrossRef
5.
Lepanto L, et al: Impact of PACS on dictation turnaround time and productivity. J Digit Imaging 19(1):92–7, 2006PubMedCrossRef
6.
Bauman RA, Gell G: The reality of picture archiving and communication systems (PACS): a survey. J Digit Imaging 13(4):157–69, 2000PubMedCrossRef
7.
Crivianu-Gaita D, et al: User acceptability—a critical success factor for picture archiving and communication system implementation. J Digit Imaging 13(2 Suppl 1):13–6, 2000PubMedCrossRef
8.
Pilling JR: Picture archiving and communication systems: the users’ view. Br J Radiol 76(908):519–24, 2003PubMedCrossRef
9.
Pare G, et al: Toward a multidimensional assessment of picture archiving and communication system success. Int J Technol Assess Health Care 21(4):471–9, 2005PubMedCrossRef
10.
Duyck P, et al: User acceptance of a picture archiving and communication system. Applying the unified theory of acceptance and use of technology in a radiological setting. Methods Inf Med 47(2):149–56, 2008PubMed
11.
Holden RJ, Karsh BT: The technology acceptance model: its past and its future in health care. J Biomed Inform 43(1):159–72, 2010PubMedCrossRef
12.
Goodhue DL: Understanding user evaluations of information systems. Management Science 41(12):1827–1844, 1995CrossRef
13.
Goodhue DL, Thompson RL: Task–Technology Fit and Individual Performance. MIS Quarterly, 1995, pp 213–236
14.
Ammenwerth E, Iller C, Mahler C: IT-adoption and the interaction of task, technology and individuals: a fit framework and a case study. BMC Med Inform Decis Mak 6:3, 2006PubMedCrossRef
15.
Ammenwerth E, et al: Factors affecting and affected by user acceptance of computer-based nursing documentation: results of a two-year study. J Am Med Inform Assoc 10(1):69–84, 2003PubMedCrossRef
16.
Tsiknakis M, Kouroubali A: Organizational factors affecting successful adoption of innovative eHealth services: a case study employing the FITT framework. Int J Med Inform 78(1):39–52, 2009PubMedCrossRef
17.
Fuller RM, Dennis AR: Does fit matter? The impact of task–technology fit and appropriation on team performance in repeated tasks. Information Systems Research 20(1):2–17, 2009CrossRef
Metadata
Title
Assessing Task–Technology Fit in a PACS Upgrade: Do Users’ and Developers’ Appraisals Converge?
Authors
Luigi Lepanto
Claude Sicotte
Pascale Lehoux
Publication date
01-12-2011
Publisher
Springer-Verlag
Published in
Journal of Imaging Informatics in Medicine / Issue 6/2011
Print ISSN: 2948-2925
Electronic ISSN: 2948-2933
DOI
https://doi.org/10.1007/s10278-011-9378-x

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