BehaviorNCT05493072
RxConnect User Testing Study
A study testing RxConnect in people with Behavior, run by Imperial College London at 1 site in United Kingdom.
Trial summary
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. Read more about this study
- Who is it for?
- Any ageHealthy volunteers can take part
- Conditions the study is for
- Behavior
- What is being tested?
- RxConnect
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 24 participants
- Where
- 1 siteUnited Kingdom
- Study dates
- 12 Dec 2022to 30 Mar 2023
- Phase
- Interventional
- Are there results?
- Yes, posted in the registrySummary in the doctors' view
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
Who may be able to join
- 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
Who cannot join
- Infrequent prescribing practice (less than once a week)
- Not willing to participate
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Safety, Performance, and User Perceptions of RxConnect When Used to Provide Patient-specific, Indication Based Prescribing Support
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.
Read more
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.
- Conditions
- Behavior
- Being tested
- RxConnect
- Keywords
- Medication safety, Prescribing practice, Prescribing behaviour
- Medical subject headings
- Behavior
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
RxConnectOther
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.
| Study group | Role | What they receive |
|---|---|---|
| Control | No intervention | Observation of control arm practice for 5 medication scenarios |
| Intervention | Experimental | Observation of intervention practice for 5 medication scenarios Receives: Other: RxConnect |
What is being measured
The primary outcome is the main question the study is designed to answer.
Number of Prescribing Errors by Study Arm
Sub analysis of errors by type available in full report
Measured: 60 minutes
Secondary outcomes (3)
Number of Medication Orders With a Large Magnitude Error (Greater Than 25% of the Recommended Dosing Range)
Dosing errors with a deviation of more than 25% from the recommended range were categorised as large magnitude errors.
Measured: 60 minutes
Time Taken to Prescribe Each Medication
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.
Measured: 60 minutes
Measurement of the Prescribers Perceived Mental Load Per Prescribing Scenario
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).
Measured: 60 minutes
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted9 Aug 2022
- Study start12 Dec 2022
- Primary completion30 Mar 2023
- Study completion30 Mar 2023
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Posted resultsParticipant flow, outcome measures and adverse events reported by the sponsor.
- Publications (8)Papers the sponsor has linked to this study.
- Other trials for BehaviorStudies of the same condition, open and under way.
- More trials run by Imperial College LondonThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Basic science
- Allocation
- Randomised
- Intervention model
- Crossover
- Masking
- Single blind
- Who is masked
- Participant
- Enrolment
- 24 participants (actual)
Sponsor and investigators
- Lead sponsor
- Imperial College London
- Sponsor type
- University, hospital or other
- Collaborators
- National Institute for Health Research, United Kingdom
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05493072
- Sponsor's protocol ID
- 22CX7758
- Status last verified
- Mar 2025
- First submitted
- 25 Jul 2022
- Last updated
- 24 Mar 2025
- Results posted
- Yes, on ClinicalTrials.gov
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Donaldson LJ, Kelley ET, Dhingra-Kumar N, Kieny MP, Sheikh A. Medication Without Harm: WHO's Third Global Patient Safety Challenge. Lancet. 2017 Apr 29;389(10080):1680-1681. doi: 10.1016/S0140-6736(17)31047-4. No abstract available.
PubMed 28463129MHRA. Guidance on applying human factors and usability engineering of medical devices including drug-device combination products in Great Britain. 2021;(January):35. Available from: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/970563/Human-Factors_Medical-Devices_v2.0.pdf
Holden RJ, Abebe E, Russ-Jara AL, Chui MA. Human factors and ergonomics methods for pharmacy research and clinical practice. Res Social Adm Pharm. 2021 Dec;17(12):2019-2027. doi: 10.1016/j.sapharm.2021.04.024. Epub 2021 May 2.
PubMed 33985892Novak LL, Holden RJ, Anders SH, Hong JY, Karsh BT. Using a sociotechnical framework to understand adaptations in health IT implementation. Int J Med Inform. 2013 Dec;82(12):e331-44. doi: 10.1016/j.ijmedinf.2013.01.009. Epub 2013 Apr 3.
PubMed 23562140Elliott RA, Camacho E, Jankovic D, Sculpher MJ, Faria R. Economic analysis of the prevalence and clinical and economic burden of medication error in England. BMJ Qual Saf. 2021 Feb;30(2):96-105. doi: 10.1136/bmjqs-2019-010206. Epub 2020 Jun 11.
PubMed 32527980Tolley CL, Slight SP, Husband AK, Watson N, Bates DW. Improving medication-related clinical decision support. Am J Health Syst Pharm. 2018 Feb 15;75(4):239-246. doi: 10.2146/ajhp160830.
PubMed 29436470Aufegger L, Serou N, Chen S, Franklin BD. Evaluating users' experiences of electronic prescribing systems in relation to patient safety: a mixed methods study. BMC Med Inform Decis Mak. 2020 Apr 3;20(1):62. doi: 10.1186/s12911-020-1080-9.
PubMed 32245467Feather C, Clarke J, Appelbaum N, Darzi A, Franklin BD. Comparing safety, performance and user perceptions of a patient-specific indication-based prescribing tool with current practice: a mixed methods randomised user testing study. BMJ Qual Saf. 2025 Oct 17;34(11):737-746. doi: 10.1136/bmjqs-2024-017733.
PubMed 39577867
Study at a glance
From the registry record, last updated by the sponsor on 24 Mar 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Safety, Performance, and User Perceptions of RxConnect When Used to Provide Patient-specific, Indication Based Prescribing Support
- ClinicalTrials.gov ID
- NCT05493072
- Acronym / sponsor ID
- 22CX7758
- Conditions
- Behavior
- Keywords
- Medication safety, Prescribing practice, Prescribing behaviour
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 24 participants (actual)
- Lead sponsor
- Imperial College LondonUniversity, hospital or other
- Collaborators
- National Institute for Health Research, United Kingdom
- Study start
- 12 Dec 2022 (actual)
- Primary completion
- 30 Mar 2023 (actual)
- Study completion
- 30 Mar 2023 (actual)
- First posted
- 9 Aug 2022 (actual)
- Results
- Posted 24 Mar 2025
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Primary purpose
- Basic science
- Allocation
- Randomised
- Intervention model
- Crossover
- Masking
- Single blind (participant)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Control | No intervention | –Observation of control arm practice for 5 medication scenarios |
| Intervention | Experimental | Other: RxConnectObservation of intervention practice for 5 medication scenarios |
Who may qualify
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
Key inclusion criteria (3 listed)
- 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
Key exclusion criteria (2 listed)
- Infrequent prescribing practice (less than once a week)
- Not willing to participate
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| RxConnectParticipants 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. | Other | Intervention |
Primary outcome
Number of Prescribing Errors by Study Arm
Time frame: 60 minutes
Sub analysis of errors by type available in full report
3 secondary outcomes
Number of Medication Orders With a Large Magnitude Error (Greater Than 25% of the Recommended Dosing Range)
Time frame: 60 minutes
Time Taken to Prescribe Each Medication
Time frame: 60 minutes
Measurement of the Prescribers Perceived Mental Load Per Prescribing Scenario
Time frame: 60 minutes
Results and safety
Loading the posted results from ClinicalTrials.gov…
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 24 Mar 2025.
- Posted resultsParticipant flow, baseline, all outcome measures and adverse events.
- Background: PubMed 28463129Donaldson LJ, Kelley ET, Dhingra-Kumar N, Kieny MP, Sheikh A. Medication Without Harm: WHO's Third Global Patient Safety Challenge. Lancet. 2017 Apr 29;389(10080):1680-1681. doi: 10.1016/S0140-6736(17)31047-4. No abstract available.
- BackgroundMHRA. Guidance on applying human factors and usability engineering of medical devices including drug-device combination products in Great Britain. 2021;(January):35. Available from: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/970563/Human-Factors_Medical-Devices_v2.0.pdf
- Background: PubMed 33985892Holden RJ, Abebe E, Russ-Jara AL, Chui MA. Human factors and ergonomics methods for pharmacy research and clinical practice. Res Social Adm Pharm. 2021 Dec;17(12):2019-2027. doi: 10.1016/j.sapharm.2021.04.024. Epub 2021 May 2.
- Background: PubMed 23562140Novak LL, Holden RJ, Anders SH, Hong JY, Karsh BT. Using a sociotechnical framework to understand adaptations in health IT implementation. Int J Med Inform. 2013 Dec;82(12):e331-44. doi: 10.1016/j.ijmedinf.2013.01.009. Epub 2013 Apr 3.
- Results publication: PubMed 32527980Elliott RA, Camacho E, Jankovic D, Sculpher MJ, Faria R. Economic analysis of the prevalence and clinical and economic burden of medication error in England. BMJ Qual Saf. 2021 Feb;30(2):96-105. doi: 10.1136/bmjqs-2019-010206. Epub 2020 Jun 11.
- Results publication: PubMed 29436470Tolley CL, Slight SP, Husband AK, Watson N, Bates DW. Improving medication-related clinical decision support. Am J Health Syst Pharm. 2018 Feb 15;75(4):239-246. doi: 10.2146/ajhp160830.
- Results publication: PubMed 32245467Aufegger L, Serou N, Chen S, Franklin BD. Evaluating users' experiences of electronic prescribing systems in relation to patient safety: a mixed methods study. BMC Med Inform Decis Mak. 2020 Apr 3;20(1):62. doi: 10.1186/s12911-020-1080-9.
- Derived from this study: PubMed 39577867Feather C, Clarke J, Appelbaum N, Darzi A, Franklin BD. Comparing safety, performance and user perceptions of a patient-specific indication-based prescribing tool with current practice: a mixed methods randomised user testing study. BMJ Qual Saf. 2025 Oct 17;34(11):737-746. doi: 10.1136/bmjqs-2024-017733.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing RxConnect in people with Behavior, run by Imperial College London at 1 site in United Kingdom.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 24 Mar 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United Kingdom. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Yes. Results are posted in the registry (first posted 24 Mar 2025). A summary is in the For doctors view, and the full results are on ClinicalTrials.gov.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Number of Prescribing Errors by Study Arm (time frame: 60 minutes). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05493072, which also lists 8 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 24 Mar 2025.