Design of Decision Support Interventions for Medication Prescribing

From Clinfowiki
Revision as of 06:37, 19 March 2015 by Liyu Geresu (Talk | contribs)

(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)
Jump to: navigation, search

This is a review for Horsky, J., Phansalkar, S., Desai, A., Bell, D., & Middleton, B. (2013). Design of decision support interventions for medication prescribing. International Journal of Medical Informatics, 82(6), 492–503. doi:10.1016/j.ijmedinf.2013.02.003


Horsky et al.’s stated objective in this study is to describe optimal design attributes of clinical decision support(CDS) interventions for medication prescribing, emphasizing perceptual, cognitive and functional characteristics that improve human–computer interaction(HCI) and patient safety.[1]

Clinical decision support (CDS) systems can safely and effectively support CPOE. Many contemporary installations, however, have poor user interface design. Horsky et al states this makes the receiving and responding to decision support interventions difficult. Few systems have substantially delivered on the promise to improve healthcare processes and outcomes. The challenges of designing effective but potentially work-disruptive alerts and notifications are manifold and often require the reconciliation of contradictory goals, such as the need for succinctness with the need to adequately support complex medical decisions. [1]

Designers and developers of health information technology (HIT) need a cohesive, widely accepted and reliable set of industry standards, recommendations and best practices to substantially increase the usability, effectiveness and safety of electronic health records (EHRs) and CDS systems. This report describes design recommendations for CDS interventions that are activated during medication prescribing, such as alerts to drug and allergy interactions, according to Horsky et al. [1]


Horsky et al. performed a background investigation with these findings: There is somewhat scant but increasingly more reported evidence of medical errors, adverse drug events, near misses and other patient safety problems that can be at least in part attributed to failures in human interaction with poorly designed EHR and CDS interfaces. Published reports include descriptions of decreased cognitive performance, medication prescribing errors, unsafe workarounds and poor handling of safety alerts. Existing standards do provide an authoritative source of reference but are difficult to apply by designers without usability training. [1]


Horsky et al. found from published reports on success, failures and lessons learned during implementation of CDS systems were reviewed and interpreted with regard to HCI and software usability principles. The authors then formulated design recommendations for CDS alerts that would reduce unnecessary workflow interruptions and allow clinicians to make informed decisions quickly, accurately and without extraneous cognitive and interactive effort. [1]

Horsky et al. searched PubMed, Web of Science, PsychInfo, Books @ Ovid and ACM Digital library databases for peer-reviewed articles and trade literature and articles published online by private and public healthcare institutions and usability organizations. The search returned 1544 articles of which Horsky et al. reviewed 421 either in brief (abstract only) or in detail for statements about design, software development or lessons learned from implementation that described positive and negative findings related to specific design characteristics of EHR and decision support systems. [1]


Excessive alerting that tends to distract clinicians rather than provide effective CDS can be reduced by designing only high severity alerts as interruptive dialog boxes and less severe warnings without explicit response requirement, by curating system knowledge bases to suppress warnings with low clinical utility and by integrating contextual patient data into the decision logic. Recommended design principles include parsimonious and consistent use of color and language, minimalist approach to the layout of information and controls, the use of font attributes to convey hierarchy and visual prominence of important data over supporting information, the inclusion of relevant patient data in the context of the alert and allowing clinicians to respond with one or two clicks. [1]

Horsky et al. categorized the research findings with the following headings: reducing excessive alerting, alerts tiered by levels of interaction severity, Interruptive high-severity alerts, non-interruptive alerts for low-severity interactions, filtering of alerts and rule maintenance, alert content, language and typography, visual and perceptual characteristics. [1]


Healthcare has been incorporating best practices and proven design principles into IT development at a much slower pace than is necessary to maintain a high level of function and safety for increasingly more complex systems and HIT is therefore often considered as having low reliability, states Horsky et al. Basic HCI standards and guidelines that Horsky et al. review in this report need to be complemented by socio-technical, observational and ethnographic methods to give designers realistic insight into the conditions in which care is provided and the complexities of treating patients with a multitude of comorbid conditions. Safety analyses should not look for a single cause of problems but should consider the system as a whole when looking for ways to make a safer system and avoid unintended consequences of poorly designed HIT. The high rate of drug interaction alerting to even minor possibility of personal discomfort or adverse reaction may in practice counteract the primary objective of CDS to safeguard patients from severe drug injuries.[1]


Horsky et al. highlighted the findings as follows: Alerts should be tiered by severity, have concise text, justification, clear response options, prioritize concurrent alerts, use controlled color sets, consistent terminology, format text to visually associate drug categories, show clinical context data, maintain manageable pick lists, allow multiple entry options and custom order sets.[1]

Reviewer’s Comments

Jonzy’s Comments

Horsky et al. succeeds in its objectives. This article was thorough and informative. The findings of this study should impact all CDS systems in a positive way. I highly recommend reading the entire article. The website link can be found in the references.

Other Comments

Other studies suggest that use of human factors design principles may also improve receptiveness to alerts. The two studies appear to compliment one another. It would be interesting to see a study combining optimized "mechanics" with optimized "fit and feel".

Other comment

The article I reviewed states that, the value of the CDS depends on the quality of its design. And, it recommends three points to be considered during the design phase of CDS • Problem identification • Select a specific approach to the identified problem • And, a corresponding solution should be designed in a way that fits into the workflow at hand. I believe having those three points during designing phase will result in a CDS that lives up to its expectation. Eberhardt, J, Bilchick, A, Stojadinovic, A. (March, 2012). Clinical decision support systems: Potential with Pitfalls. Journal of Surgical Oncology, Vol#105, 502-510 DOI: 10.1002/jso.23053


  1. 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 Horsky, J., Phansalkar, S., Desai, A., Bell, D., & Middleton, B. (2013). Design of decision support interventions for medication prescribing. International Journal of Medical Informatics, 82(6), 492–503. doi:10.1016/j.ijmedinf.2013.02.003