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OpenTrials
Completed

NCT Number: NCT05493072

RxConnect User Testing Study

Background

Medication errors are the leading cause of preventable harm in healthcare settings worldwide. An estimated 237 million medication errors occur in England alone every year, with 66 million considered clinically significant. There is an estimated cost to the NHS from definitely avoidable adverse drug reactions as a result of these errors of £98.5 million per year, consuming 181,626 bed-days and causing to 712 deaths.

Medication related clinical decision support systems, often integrated with electronic prescribing systems, are rapidly increasing in number over the last few decades, ranging from drug-drug interaction alerts to allergy checks and formulary support. A recent systematic review summarised that these systems are still relatively immature, with limited use of patient-specific input or human factors research used to develop them. There is an opportunity to improve these systems significantly for the benefit of the user and for patient safety. The World Health Organization propose that interventions to reduce medication error should include the development of technologies that are well understood and designed for the systems and practice they are applied to.

Human factors and usability engineering is an integral part of developing medical devices, such as clinical decision support (CDS) systems, to ensure that such devices are easy to use and can be used safely as intended. User testing / usability testing, which may incorporate several methods, should be conductive throughout the development process (at formative, summative assessment, and during post-market surveillance). These methods are now becoming more common place in healthcare technology research and should continue to support the development of new technologies.

RxConnect

RxConnect, a newly registered UKCA marked medical device, is an on-demand clinical decision support tool that receives medication and patient inputs and uses them to filter an underlying formulary, such as the BNF, and perform dosing calculations, as needed, to return patient-specific dosing recommendations. RxConnect does not have a user interface and relies on an integration with third-party systems, such as electronic prescribing systems, to deliver CDS services to clinical end users. For this study a prototype user interface for RxConnect that emulates a typical electronic prescribing system will be used.

The study team hypothesise that use of RxConnect as a digital prescribing aid is quicker, easier, and as safe to use as currently available prescribing aids. This study aims to utilise user testing to prove or disprove the above hypothesis and to generate quantitative and qualitative outputs to support the continued development of RxConnect prior to clinical deployment.

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Key information

Conditions

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

Imperial College NHS Healthcare Trust

London, United Kingdom

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Willingness to consent and participate
  • Medical doctor - Foundation year 1 and above OR registered non-medical prescriber (e.g. nurses or pharmacists)
  • Regular (at least weekly) experience in prescribing medications as part of working role

Exclusion criteria

  • Infrequent prescribing practice (less than once a week)
  • Not willing to participate

Treatment and study plan

RxConnect

Other

Participants use RxConnect, an on-demand clinical decision support tool that receives medication and patient inputs and uses them to filter an underlying formulary, such as the BNF, and perform dosing calculations, as needed, to return patient-specific dosing recommendations.

Primary outcomes

  1. Number of Prescribing Errors by Study Arm

    Time frame: 60 minutes

    Sub analysis of errors by type available in full report

Secondary outcomes

  1. Number of Medication Orders With a Large Magnitude Error (Greater Than 25% of the Recommended Dosing Range)

    Time frame: 60 minutes

    Dosing errors with a deviation of more than 25% from the recommended range were categorised as large magnitude errors.

  2. Time Taken to Prescribe Each Medication

    Time frame: 60 minutes

    For the first scenario, TTP was calculated from the moment the participant began reading the scenario to task completion, while for subsequent scenarios, timing started from the completion of the previous scenario. The endpoint for each scenario was marked by the participant's submission of the medication order on the electronic prescribing (eP) system.

  3. Measurement of the Prescribers Perceived Mental Load Per Prescribing Scenario

    Time frame: 60 minutes

    Measurement of the Prescribers perceived mental load per prescribing scenario, Using NASA task load index (TLX).

    An overall workload score combining all 6 NASA TLX domains was calculated (minimum 0 lower workload - maximum 126 highest workload).

Other outcomes

  1. Erroneous Medication Orders by Hierarchial Task Analysis (Identifying Vulnerable Steps in the Prescribing Workflow).

    Time frame: 60 minutes

    Erroneous orders identified as a primary outcome of the study will then be analysed using hierarchical task analysis (HTA). The HTA is a qualitative outcome, different from the primary outcome (error yes/no) by instead identifying 'where' within the prescribing process an error occurred.

    Workflow steps, representing tasks or actions in both the control and intervention arms, were developed based on established and anticipated prescribing workflows and refined as new, unanticipated steps emerged during study observations. These workflows were then employed for hierarchical task analysis, as detailed in the data analysis section.

    Hierarchical task analysis was conducted by reviewing recordings of all erroneous medication orders, breaking down the prescribing process into discrete steps. This structured approach allowed for identification of potential risks or inefficiencies in the workflow, helping trace each error's likely origin within the process.

  2. Number of Participants That Gave Qualitative Feedback

    Time frame: 60 minutes

    Audio of interviews will be transcribed verbatim and thematically analyses to provide insights from participants that can be utilised for recommendations for practice and research.

Sponsors and collaborators

Lead sponsor

Imperial College London

Other

Collaborators

  • National Institute for Health Research, United Kingdom

Registry information

Official study title

Safety, Performance, and User Perceptions of RxConnect When Used to Provide Patient-specific, Indication Based Prescribing Support

Important dates

Study start
2022
Primary completion
2023
Study completion
2023
First posted
Aug 9, 2022
Registry last updated
Mar 24, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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