RxConnect User Testing Study

Study Overview
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Brief Summary
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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.

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

Conditions
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Behavior

Intervention / Treatment
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* Other: RxConnect


Participation Criteria
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Eligibility Criteria
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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
Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Basic Science
Allocation: Randomized
Intervention Model: Crossover Assignment
Masking: Single


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Other: 1 - Control (scenarios 1-5) then intervention (scenarios 6-10)<br>Observation of control arm practice for scenarios 1-5, followed by intervention arm for scenarios 6-10. | Other: RxConnect<br>* 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.<br>|
| Other: 2- Intervention (scenarios 6-10) then control (scenarios 1-5)<br>Observation of intervention arm for scenarios 6-10, followed by control arm practice for scenarios 1-5 | Other: RxConnect<br>* 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.<br>|
| Other: 3- Intervention (scenarios 1-5) then control (scenarios 6-10)<br>Observation of intervention arm practice for scenarios 1-5, followed by control arm for scenarios 6-10. | Other: RxConnect<br>* 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.<br>|
| Other: 4- Control (scenarios 6-10) then intervention (scenarios 1-5)<br>Observation of control arm practice for scenarios 6-10, followed by intervention arm for scenarios 1-5. | Other: RxConnect<br>* 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.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Number of prescribing errors per scenario | Subcategorised as - Incorrect drug, dose, route, frequency or patient. | 90 minutes | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| The magnitude of any dose errors (deviation from recommended dosing range) |  | 90 minutes | 
| Time taken to prescribe each medication |  | 90 minutes | 
| Prescribers perceived mental load per prescribing scenario | Using NASA task load index | 90 minutes | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Medication safety, Prescribing practice, Prescribing behaviour