Feasibility and acceptability of a computerised system with automated reminders for prescribing behaviour in primary care

Martens, Jody; Weijden, Trudy van der; Winkens, Ron; Kester, A.D.M.; Geerts, P.J.H.; Evers, Silvia M. A. A.; Severens, Johan L.

doi:10.1016/j.ijmedinf.2007.05.013

Cited by 25 publications

(41 citation statements)

References 15 publications

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“…Mechanisms to identify patients who need a closer follow-up and evaluation have been identified as an important need for primary healthcare 3 34 35. Future initiatives can include linking administrative databases to decision support systems that can help identify individuals who need closer monitoring and follow-up and allow for targeted services such as visit reminders sent to patients or to their care provider.…”

Section: Discussionmentioning

confidence: 99%

Using decision support for population tracking of adherence to recommended asthma guidelines

2014

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ObjectiveDecision support systems linked to administrative databases provide a unique opportunity to monitor adherence to guidelines and target disease management strategies towards patients not receiving guideline-based therapy. The objective of this study was to evaluate the discrepancy between actual asthma treatments prescribed by primary care physicians compared to those recommended by evidence-based guidelines using a decision support tool linked to a provincial health administrative database.DesignThe drug and medical services information of individuals with asthma was identified from the provincial health database and was pushed through an asthma decision support system (ADSS). Recommendations aimed at optimising asthma treatment were generated on two index dates, 15 September 2007 (index date 1) and 15 March 2008 (index date 2).SettingPrimary care settings in a large Canadian metropolitan area.ParticipantsIndividuals with asthma and provincial health insurance primary and secondary outcome measures: well controlled asthma.Results16 803 eligible individuals were identified on index date 1, and 18 103 on index date 2. The distribution of recommendation categories was similar on both index dates. 94% were classified as well controlled and 7% as not well controlled. Among well-controlled individuals, the largest proportion was in the maintain treatment category (63.8%), followed by the maintain/decrease treatment category (28.2%) and the decrease treatment category (2.7%). Almost all individuals who were not well controlled had the recommendation to increase treatment (88%) with a small proportion in the refer category (1%).ConclusionsThe ADSS was able to identify subgroups of patients from an administrative database that could benefit from a medication review and possible change. Decision support systems linked to an administrative database can be used to identify individuals with uncontrolled asthma or prescriptions that deviate from recommended treatment. When connected to the point of care, this can provide an opportunity for physicians to intervene early.

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Section: Discussionmentioning

confidence: 99%

Using decision support for population tracking of adherence to recommended asthma guidelines

2014

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“…'Fit' between user, technology and organization could serve as a determinant of those factors that influence user acceptance. By understanding 'fit' between these three players, we may understand why (Lorenzi and Riley, 2003;Bowns et al, 1999;Aggelidis and Chatzoglou, 2009;Meade et al, 2009) System provides sufficient speed to accomplish jobs (Folz-Murphy et al, 1998;Martens et al, 2008;Ash et al, 2000) Systems is ease to use and useful (Dishaw and Strong, 1999;Carayon et al, 2010;Yen et al, 2010;Chang, 2010) User gets support from top management/managerial commitment (Travers and M.Downs, 2000;Yusof et al, 2008;Tsiknakis and Kouroubali, 2008 (Randell and Dowding, 2010) Management provides right technology which meets the requirements of the job (Folz-Murphy et al, 1998) Technology is designed for all level of users (Carayon et al, 2010) Good help desk support by vendor/technical support/administrative support (Martens et al, 2008;Aggelidis and Chatzoglou, 2009) Factor(s) Reference(s) System negatively impacted staffs' work flow (Travers and M.Downs, 2000;Bleich and Slack, 2010;Randell and Dowding, 2010) System's problem such as content, computer generated forms, hardware and interface (Lee et al, 2008) System did not meet user's practice requirement (Folz-Murphy et al, 1998) Poorly designed system which increases workload/paperwork (Bossen, 2007;Meade et al, 2009;Lammintakanen et al, 2010) Information systems which is not ready to be used and does not support management. (Ellis and May, 1999;Lammintakanen et al, 2010) Lack of standardized terminologies which clinicians used to work with (Tsiknakis and Kouroubali, 2008) User who has less/insufficient experienc...…”

Section: Discussionmentioning

confidence: 99%

“…(Ellis and May, 1999;Lammintakanen et al, 2010) Lack of standardized terminologies which clinicians used to work with (Tsiknakis and Kouroubali, 2008) User who has less/insufficient experience with technology, limited skills to use the systems (Bossen, 2007;Short et al, 2004) Technology that does not meet clinical needs or match with work flow (George R. (Vishwanath and Scamurra, 2007) Insufficient number of computer, printer problems, system downtime, system breakdown (Lee et al, 2008) Mismatch or misalignment between facilities and social organization it meant to support (Southon et al, 1999) Interaction problem between new system and existing system -complex, time consuming, susceptible for error (Heeks, 2006;Jr. et al, 2010) Prototype lacking in functionality or usability (Bossen, 2007) Technical problems/multiple updates to the information systems/operating system (Martens et al, 2008;Lorenzi and Riley, 2003;Carayon et al, 2010) the same system implemented in two different setting results in two different outcome. Most of the previous studies only identify those acceptance factors, but these factors could not stand on their own.…”

Section: Discussionmentioning

confidence: 99%

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Understanding and Addressing the ‘Fit’ Between User, Technology and Organization in Evaluating User Acceptance of Healthcare Technology

Mohamadali

Garibaldi

2012

Proceedings of the International Conference on Health Informatics

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Abstract:In this paper, we discuss the importance of addressing the 'fit' between user, technology and organization in evaluating user acceptance of healthcare technology. We give an overview of evaluation dimensions and explore two models that are related to 'fit'. We demonstrate how users' acceptance factors in previous studies could be better explained through the perspective of 'fit' between user, technology and organization. We believe that the importance of 'fit' needs to be understood in greater detail among the evaluation research community. The 'fit' between user, technology, and organization needs to be addressed together with the factors that influence user acceptance. This paper attempts to gain empirical support for the inclusion of 'fit' between user, technology and organization when evaluating user acceptance of the healthcare technology.

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“…The issue of user acceptance of healthcare technology is often the main focus of research in evaluation studies (Yi et al, 2006;Martens et al, 2008). Evaluation outcomes may allow decision makers to take appropriate courses of action.…”

Section: Introductionmentioning

confidence: 99%

Including Explicitly the Question of ‘Which’ in Evaluation Studies

2012

Proceedings of the International Conference on Health Informatics

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Abstract:Existing studies of user acceptance factors try to provide answers to the questions of 'why', 'what', 'who', 'when' and 'how'. In this paper, we propose to include the explicit question of 'which' into future evaluation studies. Two distinct approaches are discussed to address the question of 'which'. The aim is not to show which is the best, but rather to demonstrate the potential of some alternative approaches to addressing 'which'.

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Feasibility and acceptability of a computerised system with automated reminders for prescribing behaviour in primary care

Cited by 25 publications

References 15 publications

Using decision support for population tracking of adherence to recommended asthma guidelines

Using decision support for population tracking of adherence to recommended asthma guidelines

Understanding and Addressing the ‘Fit’ Between User, Technology and Organization in Evaluating User Acceptance of Healthcare Technology

Including Explicitly the Question of ‘Which’ in Evaluation Studies

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