Skip to main content
  • Research article
  • Open access
  • Published:

Reliability, ease of use and usefulness of I-MeDeSA for evaluating drug-drug interaction alerts in an Australian context



Recently, attention has shifted to improving the design of computerized alerts via the incorporation of human factors design principles. The Instrument for Evaluating Human Factors Principles in Medication-Related Decision Support Alerts (I-MeDeSA) is a tool developed in the United States to guide improvements to alert design and facilitate selection of electronic systems with superior design. In this study, we aimed to determine the reliability, ease of use and usefulness of I-MeDeSA for assessing drug-drug interaction (DDI) alerts in an Australian context.


Using the I-MeDeSA, three reviewers independently evaluated DDI alert interfaces of seven electronic systems used in Australia. Inter-rater reliability was assessed and reviewers met to discuss difficulties in using I-MeDeSA and the tool’s usefulness.


Inter-rater reliability was high (Krippendorff’s alpha = 0.76), however, ambiguous wording and the inclusion of conditional items impacted ease of use. A number of items were not relevant to Australian implementations and as a result, most systems achieved an I-MeDeSA score of less than 50%.


The I-MeDeSA proved to be reliable, but item wording and structure made application difficult. Future studies should investigate potential modifications to the I-MeDeSA to improve ease of use and increase applicability to a variety of system configurations.

Peer Review reports


Drug-drug interactions (DDIs) occur when two or more drugs are taken concurrently and the result is a change in the effect of one or more of the drugs. DDIs can result in adverse effects (e.g. bleeding) or to one or both of the drugs not achieving their therapeutic effect [1]. DDIs are a significant cause of patient morbidity and mortality worldwide [2,3,4,5].

Despite being predictable in nature, potential DDI errors are often missed by prescribers and pharmacists [6]. The sheer volume of known drug interactions is likely to contribute to poor DDI detection. Electronic systems are increasingly being adopted by hospitals all over the world as a means of reducing medication errors, including DDIs. One of the core benefits of these systems is the ability to provide clinicians with information and guidance at the point of care. Computerised decision support can take many forms, the most common being computerised alerts. DDI alerts are often included as a form of decision support in electronic prescribing and dispensing systems to warn prescribers and pharmacists of potential DDIs [7]. Although frequently implemented, previous studies have shown that users override most DDI alerts presented [8,9,10,11]. That is, most alerts are clicked past without alert recommendations being followed.

A variety of factors are likely to be contributing to poor DDI alert acceptance, however alert design has been identified as one of the most important factors [10]. Alert design relates to multiple aspects of alert implementation, including the mechanisms underlying alert generation, alert visual appearance, and the options available to users to accept or reject alert recommendations. Poor alert design is a frequent complaint by users and is viewed as a priority area to enhance alert potential to improve medication safety [12,13,14,15].

Recently, attention has shifted to improving DDI alert design via the incorporation of human factors (HF) design principles [16]. In a recent series of studies, [16, 17] researchers in the United States developed a standardised tool for evaluating DDI alerts in terms of their compliance to HF principles [18]. This tool, the Instrument for Evaluating Human Factors Principles in Medication-Related Decision Support Alerts (I-MeDeSA; see Additional file 1), was developed to guide improvements to DDI alert design and facilitate selection of electronic systems with superior HF design [18].

The I-MeDeSA assesses compliance with nine HF design principles (see Table 1) and is composed of 26 items with binary scoring (i.e. 1 or 0 to indicate a yes or no answer). Initial validation of the tool involved content validation by three HF experts, pilot testing with three electronic medical record (EMR) systems, inter-rater reliability testing, and an evaluation of construct validity via the assessment of alerts in two EMRs of various ages [18]. A subsequent US study utilized the I-MeDeSA to evaluate 14 systems and showed that HF compliance was generally poor [19]. In one of the few applications of the I-MeDeSA outside the US, a Korean-language version of the tool was used by two medical informatics reviewers to assess DDI alerts in a Korean EMR [20]. The tool was found to be useful and generalizable but reviewers identified a number of problems with the tool, including the need for more concrete definitions, clearer rationale for each item and more explicit examples [20].

Table 1 Human factors principles assessed by the Instrument for Evaluating Human Factors Principles in Medication-Related Decision Support Alerts (I-MeDeSA) [18]

In this study, we aimed to assess the reliability, ease of use and usefulness of the I-MeDeSA for evaluating DDI alerts in an Australian context.


Electronic systems evaluated

Three reviewers, two HF researchers (MB, WYZ) and a medical science honours student (DL), utilised I-MeDeSA to assess DDI alert interfaces in seven electronic systems currently in use in Australian hospitals, primary care settings and pharmacies. Hospital computerised provider order entry (CPOE) systems were Cerner’s PowerChart (, DXR Technology’s MedChart (, and InterSystem’s Trakcare ( Primary care EMR systems were Best Practice ( and Medical Director (, and pharmacy dispensing systems were FRED ( and iPharmacy (


Prior to commencing formal data collection, the three reviewers undertook a pilot test of the I-MeDeSA. The three reviewers independently rated DDI alerts in the oncology information system MOSAIQ (Elekta) (, and then came together to discuss any issues or difficulties in utilizing the tool. This led to the identification of ambiguous terms in a number of items and a consensus was reached among reviewers on how these criteria would be applied during assessments.

For formal data collection, multiple site visits were undertaken to hospitals, clinics and offices to evaluate the DDI alerts in each system. Reviewers received a ‘walk through’ of each system by an experienced user or administrator who also answered any queries about alerts that could not be ascertained from a demonstration of the system (e.g. whether a catalogue of unsafe events was available to users). To generate DDI alerts in each system, reviewers provided demonstrators with a list of drug pairs known to potentially interact. Both major and minor DDIs were inputted into systems during demonstrations. Reviewers took hand-written notes during walk-throughs and were provided with screenshots of DDI alerts to assist with subsequent evaluations.

Following each demonstration, reviewers independently assessed DDI alerts in each system using I-MeDeSA. Reviewers then came together to discuss scores, to reach a consensus on a final score for each system if disagreements arose, and to identify any additional difficulties or problems encountered while using the I-MeDeSA.

Inter-rater reliability

To determine inter-rater reliability between the three reviewers, Krippendorff’s alpha was used to assess consistency between reviewers on overall I-MeDeSA scores awarded to the seven alert interfaces.


I-MeDeSA reliability

Reviewers were highly consistent in their application of I-MeDeSA. Krippendorff’s alpha found to be 0.7584, 95% CI [0.7035, 0.8133].

I-MeDeSA ease of use

Reviewers identified two primary issues with the I-MeDeSA tool. Firstly, phrasing of a number of items was perceived to be ambiguous, leading to differences in how the items were interpreted by reviewers. Items identified to be problematic appear in Table 2.

Table 2 I-MeDeSA [18] items perceived to be ambiguous leading to differences in interpretation among reviewers

Secondly, several of the I-MeDeSA items included related items so that a negative score on one item automatically impacted the score on another. That is, some items were automatically scored zero if a preceding item was not scored a one. Some examples appear in Table 3. As a result of the inclusion of conditional items, some systems were penalized multiple times for missing a single design feature. In total, 5 (20%) items were ‘related’ to preceding items.

Table 3 Examples of conditional items in the I-MeDeSA [18]

I-MeDeSA usefulness

Evaluation of the seven DDI alert interfaces revealed that scores were low, with the majority (five of seven interfaces) scoring 50% or less. The average I-MeDeSA score was 49%. The I-MeDeSA proved to be useful in identifying several areas where alerts were non-compliant with HF principles, including for example, placement and corrective actions (see Additional file 2). However, the items in I-MeDeSA relating to prioritization were not relevant to all systems evaluated as some had only one level of DDI alert in place. In the same way, items relating to other alert types (e.g. allergy alerts) were not relevant for systems that only had DDI alerts operational. In total, 8 (31%) items were applicable to only some systems, contingent on the configuration in place.


Evaluation of seven systems using the I-MeDeSA allowed reviewers to identify a number of design issues that may be contributing to poor alert acceptance in Australian settings. Most systems achieved I-MeDeSA scores of less than 50%. However, due to the tool’s structure and content, systems were penalised multiple times for missing a single design feature and approximately a third of the items were not relevant to the system configurations in use in Australia.

Reviewers perceived a key difficulty with I-MeDeSA to be the use of ambiguous wording in some items, which led to differences in interpretation and inconsistent scores. For example, what one reviewer perceived to be an ‘appropriate’ font, another reviewer considered to be inappropriate. The inclusion of ambiguous items was also identified to be a problem in a previous study where I-MeDeSA was utilized to assess Korean DDI alerts [20]. As was the case in previous studies utilising I-MeDeSA, [18,19,20] inter-rater reliability was high in our application, however this was likely to be due to the in-depth discussions held during piloting. For example, during piloting, item 2i) ‘Are different types of alerts meaningfully grouped?’ was found to be problematic as reviewers interpreted ‘meaningful’ differently. Disagreements arose when one reviewer judged alphabetical grouping of interactions to be meaningful, while another disagreed, focusing instead on grouping interactions by severity level. Discussion between reviewers was required to come to an agreement on what the term ‘meaningful’ encompassed for subsequent assessments. If I-MeDeSA is intended to be used as an ‘off-the-shelf’ instrument by a single reviewer in the absence of rigorous pilot testing, more explicit terms and examples are needed to minimize confusion and facilitate more consistent application of the tool. For example, the instrument could specify the conditions under which a font is considered ‘appropriate’ for an alert (e.g. size, colour, style, etc).

Another factor that impacted ease of use of the I-MeDeSA was the use of conditional scoring (i.e. scoring a one (‘yes’) for a number of I-MeDeSA items was dependent on scoring a one for a preceding item). This scoring system penalized systems multiple times for missing a single design feature (e.g. the absence of colour) and in turn contributed to poor HF compliance scores. Similar concerns were raised by Cho et al. [20]. To ensure I-MeDeSA scores reflect true HF compliance, denominators should be revised based on the applicability of dependent items. For example, if a conditional item is not applicable to a system because the parent item has been marked ‘no’, the system should be scored out of 25 for HF compliance, not 26.

The main factor perceived to have impacted on the usefulness of the I-MeDeSA was the inclusion of irrelevant items. A key problem was identified to be that some items assumed that systems included alert types of various levels of severity. Five items in the tool relate to prioritization of alerts, and assess whether more severe alerts are easily distinguishable from less severe alerts (i.e. with colours, shapes and words). A number of systems evaluated in this study had only one level of DDI alert in place. Although technically possible to include multiple levels, the systems were tested in situ and sites had chosen to implement only ‘severe’ DDI alerts, so as to minimise the risk of user frustration and alert fatigue [21]. Adopting larger numbers of alerts with clear prioritization is not more complaint with HF principles than adopting fewer, more meaningful alerts. Thus, it seems counterintuitive to penalise these systems for not prioritizing alerts. In the Korean application of the I-MeDeSA, it also proved difficult to assess systems that did not employ multiple severity levels, and the authors suggested that a branch question be included at the start of the tool [20]. Assessing systems on only one level of alert severity would allow comparisons to be made across all systems, regardless of configuration. However, results from these comparisons would provide limited information on whether systems with multiple levels of alert severity use techniques to assist users to distinguish between these levels.

Similarly, several items in the I-MeDeSA relate to other alerts operational in systems (e.g. allergy alerts) and assess whether DDI alerts are easily distinguishable from other alert types. These items are not relevant in implementations that include only DDI alerts and so an additional branching question is also needed for this. Filter questions are important elements of good survey/tool design as they guide respondents away from questions that are not applicable [22]. Ideally, multiple branches, via the inclusion of appropriate filter questions, should be made available in the I-MeDeSA, with alert configuration dictating which branch is followed. This would make alert assessment more logical and streamlined, but would make comparisons of I-MeDeSA scores across systems with variable alert configurations more difficult.


Overall, our results indicate that computerised alerts in use in Australian healthcare settings require significant redesign to incorporate human factors principles of good warning design. As a tool for assessing computerised medication alerts, the I-MeDeSA is reliable but suffers from several problems that negatively impact on ease of use and usefulness. Although a need clearly exists for a tool that allows easy assessment of HF compliance of computerised alerts, additional work is needed to ensure this US instrument is useful for evaluating alerting systems currently being used in other healthcare contexts, like Australia. In moving forward, we plan to adopt an evidence-based approach to guide the development of a more user-friendly and useful tool for alert evaluation.



Computerised Provider Order Entry


Drug-Drug interaction


Electronic Medical Record


Human Factors


Instrument for Evaluating Human Factors Principles in Medication-Related Decision Support Alerts


  1. Zwart-van Rijkom J, Uijtendaal E, ten Berg M, van Solinge W, Egberts A. Frequency and nature of drug-drug interactions in a Dutch univeristy hospital. Br J Clin Pharmacol. 2009;68(2):187–93.

    Article  CAS  Google Scholar 

  2. Guédon-Moreau L, Ducrocq D, Duc M-F, Quieureux Y, L’Hôte C, Deligne J, Caron J. Absolute contraindications in relation to potential drug interactions in outpatient prescriptions: analysis of the first five million prescriptions in 1999. Eur J Clin Pharmacol. 2003;59(8):689–95.

    Article  Google Scholar 

  3. Pirmohamed M, James S, Meakin S, Green C, Scott AK, Walley TJ, Farrar K, Park BK, Breckenridge AM. Adverse drug reactions as cause of admission to hospital: prospective analysis of 18 820 patients. BMJ (Clinical research ed). 2004;329(7456):15–9.

    Article  Google Scholar 

  4. Leone R, Magro L, Moretti U, Cutroneo P, Moschini M, Motola D, Tuccori M, Conforti A. Identifying adverse drug reactions associated with drug-drug interactions. Drug Saf. 2010;33(8):667–75.

    Article  CAS  Google Scholar 

  5. Dechanont S, Maphanta S, Butthum B, Kongkaew C. Hospital admissions/visits associated with drug-drug interactions: a systematic review and meta-analysis. Pharmacoepidemiol Drug Saf. 2014;23(5):489–97.

    Article  Google Scholar 

  6. Ko Y, Malone D, Skrepnek G. Prescribers’ knowledge of and sources of information for potential drug-drug interactions; a postal survey of US prescribers. Drug Saf. 2008;31:525–36.

    Article  Google Scholar 

  7. Bates DW, Cullen DJ, Laird N, et al. Incidence of adverse drug events and potential adverse drug events: implications for prevention. JAMA. 1995;274(1):29–34.

    Article  CAS  Google Scholar 

  8. Payne TH, Nichol WP, Hoey P, Savarino J. Characteristics and override rates of order checks in a practitioner order entry system. Proceedings of the AMIA Symposium. 2002:602–6.

  9. Weingart SN, Simchowitz B, Padolsky H, et al. AN empirical model to estimate the potential impact of medication safety alerts on patient safety, health care utilization, and cost in ambulatory care. Arch Intern Med. 2009;169(16):1465–73.

    Article  Google Scholar 

  10. Kuperman GJ, Bobb A, Payne TH, Avery AJ, Gandhi TK, Burns G, Classen DC, Bates DW. Medication-related clinical decision support in computerized provider order entry systems: a review. J Am Med Inform Assoc. 2007;14(1):29–40.

    Article  Google Scholar 

  11. van der Sijs H, Aarts J, Vulto A, Berg M. Overriding of drug safety alerts in computerized physician order entry. J Am Med Inform Assoc. 2006;13(2):138–47.

    Article  Google Scholar 

  12. Glassman PA, Simon B, Belperio P, Lanto A. Improving recognition of drug interactions: benefits and barriers to using automated drug alerts. Med Care. 2002;40(12):1161–71.

    Article  Google Scholar 

  13. Yu KH, Sweidan M, Williamson M, Fraser A. Drug interaction alerts in software—what do general practitioners and pharmacists want? Med J Aust. 2011;195(11–12):676–80.

    Article  Google Scholar 

  14. Coleman JJ, van der Sijs H, Haefeli WE, Slight SP, McDowell SE, Seidling HM, Eiermann B, Aarts J, Ammenwerth E, Ferner RE, et al. On the alert: future priorities for alerts in clinical decision support for computerized physician order entry identified from a European workshop. BMC Med Inform Decis Mak. 2013;13(1):1–8.

    Article  Google Scholar 

  15. Payne TH, Hines LE, Chan RC, Hartman S, Kapusnik-Uner J, Russ AL, Chaffee BW, Hartman C, Tamis V, Galbreth B, et al. Recommendations to improve the usability of drug-drug interaction clinical decision support alerts. J Am Med Inform Assoc. 2015;22(6):1243–50.

    Article  Google Scholar 

  16. Phansalkar S, Edworthy J, Hellier E, Seger DL, Schedlbauer A, Avery AJ, Bates DW. A review of human factors principles for the design and implementation of medication safety alerts in clinical information systems. J Am Med Inform Assoc. 2010;17(5):493–501.

    Article  Google Scholar 

  17. Seidling HM, Phansalkar S, Seger DL, Paterno MD, Shaykevich S, Haefeli WE, Bates DW. Factors influencing alert acceptance: a novel approach for predicting the success of clinical decision support. J Am Med Inform Assoc. 2011;18(4):479–84.

    Article  Google Scholar 

  18. Zachariah M, Phansalkar S, Seidling HM, Neri PM, Cresswell KM, Duke J, Bloomrosen M, Volk LA, Bates DW. Development and preliminary evidence for the validity of an instrument assessing implementation of human-factors principles in medication-related decision-support systems—I-MeDeSA. J Am Med Inform Assoc. 2011;18(Supplement 1):i62–72.

    Article  Google Scholar 

  19. Phansalkar S, Zachariah M, Seidling HM, Mendes C, Volk L, Bates DW. Evaluation of medication alerts in electronic health records for compliance with human factors principles. J Am Med Inform Assoc. 2014;21(e2):e332–40.

    Article  Google Scholar 

  20. Cho I, Lee J, Han H, Phansalkar S, Bates DW. Evaluation of a Korean version of a tool for assessing the incorporation of human factors into a medication-related decision support system: the I-MeDeSA. Appl Clin Inform. 2014;5(2):571–88.

    Article  CAS  Google Scholar 

  21. Baysari MT, Westbrook JI, Richardson KL, Day RO. The influence of computerized decision support on prescribing during ward-rounds: are the decision-makers targeted? JAMIA. 2011;18:754–9.

    PubMed  Google Scholar 

  22. Iarossi G. The power of survey design: A User’s Guide for Managing Surveys, Interpeting Results and Influencing Respondents: World Bank Publications; 2006.

Download references


This research was supported by the National Health and Medical Research Council (Program Grant 1054146). The funding source played no role in study design, in the collection, analysis and interpretation of data, in the writing of this manuscript, or in the decision to submit this article for publication.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Author information

Authors and Affiliations



MTB, and ROD designed the study, MTB, DL and WYZ undertook data collection, DL and WYZ analysed the data, all authors contributed to interpretation of findings, writing of the manuscript, and read and approved the final manuscript.

Corresponding author

Correspondence to Melissa T Baysari.

Ethics declarations

Ethics approval and consent to participate

Ethics approval was obtained from Macquarie University’s Human Research Ethics Committee (#5201600140), St Vincent’s Hospital Human Research Ethics Committee (LNR/16/SVH/75) and Concord Repatriation General Hospital’s Research Governance Committee (2016–132).

Consent for publication

As this study involved no participants, consent for publication is not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Additional files

Additional file 1:

I-MeDeSA. This is a table including all items of the I-MeDeSA [18] and their descriptions. (DOCX 18 kb)

Additional file 2:

I-MeDeSA scores for all systems in terms of human factors principles assessed. This is a table which includes a breakdown of the scores obtained by the seven electronic systems we assessed. (DOCX 94 kb)

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver ( applies to the data made available in this article, unless otherwise stated.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Baysari, M.T., Lowenstein, D., Zheng, W.Y. et al. Reliability, ease of use and usefulness of I-MeDeSA for evaluating drug-drug interaction alerts in an Australian context. BMC Med Inform Decis Mak 18, 83 (2018).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: