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| Name | Class |
|---|---|
| National Institute for Health Research, United Kingdom | OTHER_GOV |
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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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Control | No Intervention | Observation of control arm practice for 5 medication scenarios | |
| Intervention | Experimental | Observation of intervention practice for 5 medication scenarios |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| 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. |
| Measure | Description | Time Frame |
|---|---|---|
| Number of Prescribing Errors by Study Arm | Sub analysis of errors by type available in full report | 60 minutes |
| Measure | Description | Time Frame |
|---|---|---|
| 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. | 60 minutes |
| Time Taken to Prescribe Each Medication |
| Measure | Description | Time Frame |
|---|---|---|
| Erroneous Medication Orders by Hierarchial Task Analysis (Identifying Vulnerable Steps in the Prescribing Workflow). | 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. |
Inclusion Criteria:
Exclusion Criteria:
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Imperial College NHS Healthcare Trust | London | United Kingdom |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 28463129 | Background | 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. | |
| Background | MHRA. 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 | ||
| 33985892 |
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| ID | Title | Description |
|---|---|---|
| FG000 | 1 - Control First, Then Intervention | Observation of control arm practice for 5 medication scenarios (using standard prescribing practice), then intervention arm for 5 medication scenarios (using the intevention to support prescribing practice). |
| FG001 | 2- Intervention First, Then Control | Observation of intervention arm for 5 medication scenarios (using the intevention to support prescribing practice). Then Observation of control arm practice for 5 medication scenarios (using standard prescribing practice) |
| Title | Milestones | Reasons Not Completed | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Overall Study |
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| ID | Title | Description |
|---|---|---|
| BG000 | All Study Participants | All study participants exposed to both control and intervention, therefore reported together |
| Units | Counts |
|---|---|
| Participants |
|
| Title | Description | Population Description | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Denominator Units Selected | Denominators | Classes |
|---|---|---|---|---|---|---|---|---|---|
| Age, Categorical | Count of Participants |
| Type | Title | Description | Population Description | Reporting Status | Anticipated Posting Date | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Time Frame | Units Analyzed | Denominator Units Selected | Arm/Group Information | Denominators | Classes | Analyses | |||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Primary | Number of Prescribing Errors by Study Arm | Sub analysis of errors by type available in full report | Posted | Count of Units | Medication orders | 60 minutes | Medication orders | Medication orders |
|
60 minutes
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| ID | Title | Description | Deaths (Affected) | Deaths (At Risk) | Serious Events (Affected) | Serious Events (At Risk) | Other Events (Affected) | Other Events (At Risk) |
|---|---|---|---|---|---|---|---|---|
| EG000 | Current Practice (Control Arm) | Participants exposed to both arms but in alternate orders. Participants were asked to complete 5 test scenarios using the usual resources available to them in their prescribing practice. Access was provided to the prescribers usual electronic prescribing platform, including links to the British National Formulary, MedicinesComplete, local antimicrobial stewardship application, local intranet, and a generic online search engine. A hard copy of the BNF and BNFc was also readily available. Participants entered the dose recommendation for each medication scenario for the required test patient on the Cerner Millennium Power Chart currently used at the study site for electronic prescribing. |
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| Title | Organization | Phone | Extension | |
|---|---|---|---|---|
| Calandra Feather | Imperial College NHS Healthcare Trust | 07977185577 | calandra.feather@nhs.net |
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| Type | Includes Protocol | Includes SAP | Includes ICF | Document Label | Document Date | Document Uploaded Date | Document File Name |
|---|---|---|---|---|---|---|---|
| Prot_SAP | Yes | Yes | No | Study Protocol and Statistical Analysis Plan | Jun 15, 2022 | Apr 15, 2024 | Prot_SAP_000.pdf |
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| ID | Term |
|---|---|
| D001519 | Behavior |
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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. |
| 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). | 60 minutes |
| 60 minutes |
| Number of Participants That Gave Qualitative Feedback | 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. | 60 minutes |
| Background |
| 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. |
| 23562140 | Background | Novak 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. |
| 32527980 | Result | Elliott 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. |
| 29436470 | Result | Tolley 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. |
| 32245467 | Result | Aufegger 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. |
| 39577867 | Derived | Feather 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. |
| Participants |
|
| Sex/Gender, Customized | Count of Participants | Participants |
|
| Race and Ethnicity Not Collected | Race and Ethnicity were not collected from any participant. | Count of Participants | Participants |
|
| Profession | Count of Participants | Participants |
|
| Speciality | Count of Participants | Participants |
|
| Participant grade (self reported titles used) | Count of Participants | Participants |
|
| Years using Cerner | Count of Participants | Participants |
|
Participants were asked to complete 5 test scenarios using the usual resources available to them in their prescribing practice. Access was provided to the prescribers usual electronic prescribing platform, including links to the British National Formulary, MedicinesComplete, local antimicrobial stewardship application, local intranet, and a generic online search engine. A hard copy of the BNF and BNFc was also readily available. Participants entered the dose recommendation for each medication scenario for the required test patient on the Cerner Millennium Power Chart currently used at the study site for electronic prescribing. |
|
|
| Secondary | 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. | Posted | Count of Units | Medication orders | 60 minutes | Medication orders | Medication orders |
|
|
|
| Secondary | 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. | Posted | Mean | 95% Confidence Interval | seconds | 60 minutes | Medication orders | Medication orders |
|
|
|
| Secondary | 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). | Posted | Mean | 95% Confidence Interval | score on a scale | 60 minutes |
|
|
|
| Other Pre-specified | Erroneous Medication Orders by Hierarchial Task Analysis (Identifying Vulnerable Steps in the Prescribing Workflow). | 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. | Posted | Number | Medication orders | 60 minutes | Medication orders | Medication orders |
|
|
|
| Other Pre-specified | Number of Participants That Gave Qualitative Feedback | 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. | There is only one arm reported for the qualitative feedback as all participants provided feedback. | Posted | Number | participants | 60 minutes |
|
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| 0 |
| 24 |
| 0 |
| 24 |
| 0 |
| 24 |
| EG001 | RxConnect (Experiment Arm) | Participants exposed to both arms but in alternate orders. Participants were asked to complete 5 test scenarios using Touchdose to determine the required dose recommendation. Participants were asked to 'trust' Touchdose and were reminded that they were not required to check or confirm the dose recommendations with any other resources or calculators. Participants entered the dose recommendation for each medication scenario for the required test patient on the Cerner Millennium Power Chart currently used at the study site for electronic prescribing. | 0 | 24 | 0 | 24 | 0 | 24 |
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| Access prefered/necesary dosing resource |
|
| Identify medication, appropriate indication and dose recommendation |
|
| Identify any relevant dose considerations |
|
| Calculate dose as per resource directions |
|
| Condsider if any min/max dose constraints need taking into account |
|
| Select/search for or confirm route from unfiltered list |
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| Select/search for form from unfiltered list |
|
| Launch required patient in Cerner |
|
| Select one or more dose recommendation(s) |
|
| Enter/confirm dose |
|