Clinical Decision-MakingADAM's APPLENCT05657002
A Study to the Impact of Accuracy Problem Lists in Electronic Health Records on Correctness and Speed of Clinical Decision-making Performed by Dutch Healthcare Providers
A study testing patient A with accurate problem list, patient B with inaccurate problem list and patient A with inaccurate problem list in people with Clinical Decision-Making and Decision Making, Computer-assisted, run by Eva Klappe at 1 site in Netherlands.
Trial summary
The primary objective of this study is to determine whether patient records with complete, structured and up-to-date problem lists ('accurate problem lists'), result in better clinical decision-making, compared to patient records that convey the same information in a less structured way where the problem list has… Read more about this study
- Who is it for?
- Any ageHealthy volunteers can take part
- Conditions the study is for
- Clinical Decision-Making, Decision Making, Computer-assisted, Medical Records, Problem-Oriented
- What is being tested?
- patient A with accurate problem list, patient B with inaccurate problem list and patient A with inaccurate problem list
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 160 participants
- Where
- 1 siteNetherlands
- Study dates
- 1 Dec 2022to 21 Dec 2022
- Phase
- Interventional
- Are there results?
- None posted in the registry
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
- Healthcare professionals who are allowed to prescribe medication, thus hold a position as: medical specialist, medical resident, nurse specialist or physician assistant, research-specialists
- Healthcare professionals must have followed at least the 'basic EHR Epic course'. This electronic health record course lasts for three days and includes how to send letters, register diagnoses in a record, request testing, all in the software system EPIC, which concludes with an exam on the theory.
Who cannot join
- Non-Dutch speaking employees as the patient cases and the exercises are described in Dutch
Trial locations and contacts
1 site in 1 country.
Netherlands1 site
Amsterdam UMC, Location AMC
Amsterdam, North Holland, 1105AZ
About this study
Official title: a Randomized Controlled Trial Study to Determine the Impact of Accuracy of Problem Lists in Electronic Health Records on Clinical Decision-making
The primary objective of this study is to determine whether patient records with complete, structured and up-to-date problem lists ('accurate problem lists'), result in better clinical decision-making, compared to patient records that convey the same information in a less structured way where the problem list has missing and/or duplicate diagnoses ('inaccurate problem lists'). The secondary objective is to determine whether the time required to make a correct decision is less for patient records with accurate problem lists compared to patient records with inaccurate problem lists.
Read the detailed description
A problem list in Electronic Health Records (EHRs) is considered an essential feature in the collection of structured data. The problem list provides a centralized summary of each patient's medical problems and these problems or diagnoses are selected from the terminology underlying the problem list, such as SNOMED CT. If well maintained and structured, the problem list is a valuable tool for reviewing records of (unfamiliar) patients as it quickly shows the required information when needed. While studies have shown that the use of structured formats can serve as prompt for extra details, greater consistency of information and clinical decision-making there is little evidence whether a patient record with complete and structured problem lists results in more accurate and faster clinical decision making.
In the study (ADAM's APPLE: Adequate Data registration And Monitoring, subproject: Accurate Presentation of Problem List Elements), the investigators will perform a crossover randomized controlled trial in which a laboratory experiment will be performed among individual healthcare professionals to assess the impact of patient records with accurate and inaccurate problem lists on clinical decision-making. The participants will be presented with two records of two different patients in a training environment of the software system EPIC, one of them with an accurate problem list and the other that conveys the complete information in the patient record (in free text notes) but with an inaccurate problem list with missing diagnoses and duplicate information. The participants do not know which of the two records includes the accurate problem list and which record includes the inaccurate problem list. Participants are asked to decide whether or not to prescribe two medications for those two patients. One medication is not allowed per patient because the patient is allergic to that medication, which is documented on the allergy list. For the first patient record, the other medication is not allowed, because of a contraindicated diagnosis and for the second patient record the other medication is not allowed, because of a side effect that has occurred using that medication in the medical history. Based on the correctness of the motivation for correct answers and the time to the right answers, the research question if accurate problem lists in patient records lead to better and faster decision-making is answered.
Prior to this study, two healthcare professionals in the research team determined suitable use cases and questions for this study. These use cases were based on real-world unstructured versions of patient records. Two optimized accurate problem lists were also created for both patient records, which was defined according to the problem list policy at our institution (i.e. all current active problems and relevant medical history should be documented on the problem list).
- Conditions
- Clinical Decision-Making, Decision Making, Computer-assisted, Medical Records, Problem-Oriented, Quality of Health Care, Evidence-Based Practice, Data Accuracy
- Being tested
- patient A with accurate problem list, patient B with inaccurate problem list, patient A with inaccurate problem list, patient B with accurate problem list
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
patient A with accurate problem listOther
A problem list that contains a diagnosis code that is contraindicated with a type of medication (Y). Also, all other relevant diagnoses and medical history for the patient are up-to-date on the problem list, which was defined according to the problem list policy at our institution (i.e. all current active problems and relevant medical history should be documented on the problem list). Additionally, the problem list is included in eight out of thirteen notes using so-called smart phrases that can automatically import a (part of a) problem list. One note includes the problem list with the diagnosis relevant for the question asked.
patient B with inaccurate problem listOther
A problem list that does not contain the diagnosis code and corresponding details explaining medical history of this diagnosis caused by a type of medication (Y). Additionally, the problem list is included in three out of thirteen notes using so-called smart phrases that can automatically import a (part of a) problem list. The relevant diagnosis is not documented on the problem list and hence is not included in the imported problem list in the notes.
The expert panel provided and anonymized two real-world representative examples of hematology patient records that included inaccurate problem lists and that had many free-text notes. An 'inaccurate problem list' is defined as a problem list where diagnoses are missing resulting in missed trigger medication or order-alerts, where diagnoses are 'active' although they should be closed or removed and/or where the problem list contained duplicated diagnoses.
patient A with inaccurate problem listOther
A problem list that does not contain the diagnosis code that is contraindicated with the type of medication (Y). Additionally, the problem list is included in eight out of thirteen notes using so-called smart phrases that can automatically import a (part of a) problem list. The relevant diagnosis is not documented on the problem list and hence is not included in the imported problem list in the notes.
The expert panel provided and anonymized two real-world representative examples of hematology patient records that included inaccurate problem lists and that had many free-text notes. An 'inaccurate problem list' is defined as a problem list where diagnoses are missing resulting in missed trigger medication or order-alerts, where diagnoses are 'active' although they should be closed or removed and/or where the problem list contained duplicated diagnoses.
patient B with accurate problem listOther
A problem list that contains the diagnosis code and corresponding details explaining medical history of this diagnosis caused by a type of medication (Y). Also, all other relevant diagnoses and medical history for the patient are up-to-date on the problem list, which was defined according to the problem list policy at our institution (i.e. all current active problems and relevant medical history should be documented on the problem list). Additionally, the problem list is included in three out of thirteen notes using so-called smart phrases that can automatically import a (part of a) problem list. One note includes the problem list with the diagnosis and details relevant for the question asked.
| Study group | Role | What they receive |
|---|---|---|
| accurate problem list, then inaccurate problem list | Active comparator | in round 1, the participants will use the patient record of patient A, with an accurate problem list and answer the question: can the patient be prescribed Medication X and Y where medication X is a control question and medication Y is related to a contraindicated diagnosis (on problem list)
In round 2, the participants will use the patient record of patient B, with an inaccurate problem list and answer the question: can the patient be prescribed Medication X and Y where medication X is a control question and medication Y is related to medical history (not on problem list) Receives: Other: patient A with accurate problem list; Other: patient B with inaccurate problem list |
| inaccurate problem list, then accurate problem list | Active comparator | in round 1, the participants will use the patient record of patient A, with an inaccurate problem list and answer the question: can the patient be prescribed Medication X and Y where medication X is a control question and medication Y is related to a contraindicated diagnosis (not on problem list)
In round 2, the participants will use the patient record of patient B, with an accurate problem list and answer the question: can the patient be prescribed Medication X and Y where medication X is a control question and medication Y is related to medical history (on problem list) Receives: Other: patient A with inaccurate problem list; Other: patient B with accurate problem list |
What is being measured
The primary outcome is the main question the study is designed to answer.
the correctness of the answer of medication B including the right motivation
Measured using a questionnaire showing the two question(s) per patient record on a separate tablet during the experiment. Two Yes/No questions per patient record (so a total of four questions per participant) are answered. Answers are stored using an automatically assigned unique anonymized identifier. The question for both patient records is: "can the patient be prescribed medication A and/or B?". Medications A and B can both not be prescribed, but medication B is related to the problem list diagnoses and medication A is related to the allergy list which is the same for both versions of the patient records. A motivation is required per Yes/No answer to determine the correctness of the answer and prevent from a chance of gambling. An independent researcher from the research team who did not perform the experiments will categorize the motivation of the answers for medication B.
Measured: during the experiment/questionnaire
Secondary outcomes (4)
the total time to answer the two questions correctly, where the answer of medication B also includes the right motivation
The time to enter the motivation was not measured. However, the participants might give the right answer but the wrong motivation. For this secondary outcome measure, only the answers that have the right motivation are considered as 'right answer'.
Measured: during the experiment/questionnaire
the total time to answer the two questions correctly including the right motivations for medication A and B
A time stamp is registered when the participant opens the question, and a time stamp is registered when the participant confirms the answers. A time stamp is therefore registered for the total of two Yes/No answers per patient record.
Measured: during the experiment/questionnaire
the correctness of both the answer for medication A and B including the right motivations for A and B
Measured using a questionnaire showing the two question(s) per patient record on a separate tablet during the experiment. Two Yes/No questions per patient record (so a total of four questions per participant) are answered. Answers are stored using an automatically assigned unique anonymized identifier. The question for both patient records is: "can the patient be prescribed medication A and/or B?". Medications A and B can both not be prescribed, but medication B is related to the problem list diagnoses and medication A is related to the allergy list which is the same for both versions of the patient records. A motivation is required per Yes/No answer to determine the correctness of the answer and prevent from a chance of gambling. An independent researcher from the research team who did not perform the experiments will categorize the motivation of the answers for medication A and B.
Measured: during the experiment/questionnaire
the correctness of the answer for medication A including the right explanation
Measured using a questionnaire showing the two question(s) per patient record on a separate tablet during the experiment. Two Yes/No questions per patient record (so a total of four questions per participant) are answered. Answers are stored using an automatically assigned unique anonymized identifier. The question for both patient records is: "can the patient be prescribed medication A and/or B?". Medications A and B can both not be prescribed, but medication B is related to the problem list diagnoses and medication A is related to the allergy list which is the same for both versions of the patient records. A motivation is required per Yes/No answer to determine the correctness of the answer and prevent from a chance of gambling. An independent researcher from the research team who did not perform the experiments will categorize the motivation of the answers for medication A.
Measured: during the experiment/questionnaire
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted20 Dec 2022
- Study start1 Dec 2022
- Primary completion21 Dec 2022
- Study completion21 Dec 2022
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Clinical Decision-MakingStudies of the same condition, open and under way.
- 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
- Health services research
- Allocation
- Randomised
- Intervention model
- Crossover
- Model description
- Participants will be presented with 2 patient records in EPIC, one with an accurate problem list and the other that conveys the complete information in the patient record (in free text notes) but with an inaccurate problem list with missing diagnoses and duplicates. Each participant answers a total of 4 questions for 2 separate patient cases (2 questions per case), one of those cases having the inaccurate and the other having the accurate problem list. Using 2 separate patient cases rather than one was decided to prevent a memory effect, i.e., participants would have had time to understand the patient's conditions described in the record which could impact the time required to answer questions in round 2. Additionally, the order in which patients cases are provided is not randomized, since in practice it also happens that a new patient is followed up by another new patient which requires professionals to systematically go through two separate records with little time in between.
- Masking
- Single blind
- Who is masked
- Participant
- Enrolment
- 160 participants (actual)
Sponsor and investigators
- Lead sponsor
- Eva Klappe
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Eva Klappe, Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)
- Investigators
- Eva Klappe, MSc, Principal investigator, Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05657002
- Sponsor's protocol ID
- Amsterdam UMC 2019-AMC-JK-7
- Other IDs
- 2019-AMC-JK-7
- Acronym
- ADAM's APPLE
- Status last verified
- Dec 2022
- First submitted
- 22 Nov 2022
- Last updated
- 23 Dec 2022
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 23 Dec 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- a Randomized Controlled Trial Study to Determine the Impact of Accuracy of Problem Lists in Electronic Health Records on Clinical Decision-making
- ClinicalTrials.gov ID
- NCT05657002
- Acronym / sponsor ID
- ADAM's APPLE / Amsterdam UMC 2019-AMC-JK-7
- Conditions
- Clinical Decision-Making, Decision Making, Computer-assisted, Medical Records, Problem-Oriented, Quality of Health Care, Evidence-Based Practice, Data Accuracy
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 160 participants (actual)
- Lead sponsor
- Eva KlappeUniversity, hospital or other
- Study start
- 1 Dec 2022 (actual)
- Primary completion
- 21 Dec 2022 (actual)
- Study completion
- 21 Dec 2022 (actual)
- First posted
- 20 Dec 2022 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
The primary objective of this study is to determine whether patient records with complete, structured and up-to-date problem lists ('accurate problem lists'), result in better clinical decision-making, compared to patient records that convey the same information in a less structured way where the problem list has missing and/or duplicate diagnoses ('inaccurate problem lists'). The secondary objective is to determine whether the time required to make a correct decision is less for patient records with accurate problem lists compared to patient records with inaccurate problem lists.
Detailed description
A problem list in Electronic Health Records (EHRs) is considered an essential feature in the collection of structured data. The problem list provides a centralized summary of each patient's medical problems and these problems or diagnoses are selected from the terminology underlying the problem list, such as SNOMED CT. If well maintained and structured, the problem list is a valuable tool for reviewing records of (unfamiliar) patients as it quickly shows the required information when needed. While studies have shown that the use of structured formats can serve as prompt for extra details, greater consistency of information and clinical decision-making there is little evidence whether a patient record with complete and structured problem lists results in more accurate and faster clinical decision making.
In the study (ADAM's APPLE: Adequate Data registration And Monitoring, subproject: Accurate Presentation of Problem List Elements), the investigators will perform a crossover randomized controlled trial in which a laboratory experiment will be performed among individual healthcare professionals to assess the impact of patient records with accurate and inaccurate problem lists on clinical decision-making. The participants will be presented with two records of two different patients in a training environment of the software system EPIC, one of them with an accurate problem list and the other that conveys the complete information in the patient record (in free text notes) but with an inaccurate problem list with missing diagnoses and duplicate information. The participants do not know which of the two records includes the accurate problem list and which record includes the inaccurate problem list. Participants are asked to decide whether or not to prescribe two medications for those two patients. One medication is not allowed per patient because the patient is allergic to that medication, which is documented on the allergy list. For the first patient record, the other medication is not allowed, because of a contraindicated diagnosis and for the second patient record the other medication is not allowed, because of a side effect that has occurred using that medication in the medical history. Based on the correctness of the motivation for correct answers and the time to the right answers, the research question if accurate problem lists in patient records lead to better and faster decision-making is answered.
Prior to this study, two healthcare professionals in the research team determined suitable use cases and questions for this study. These use cases were based on real-world unstructured versions of patient records. Two optimized accurate problem lists were also created for both patient records, which was defined according to the problem list policy at our institution (i.e. all current active problems and relevant medical history should be documented on the problem list).
Study design
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Crossover
- Masking
- Single blind (participant)
- Number of arms
- 2
- Model description
- Participants will be presented with 2 patient records in EPIC, one with an accurate problem list and the other that conveys the complete information in the patient record (in free text notes) but with an inaccurate problem list with missing diagnoses and duplicates. Each participant answers a total of 4 questions for 2 separate patient cases (2 questions per case), one of those cases having the inaccurate and the other having the accurate problem list. Using 2 separate patient cases rather than one was decided to prevent a memory effect, i.e., participants would have had time to understand the patient's conditions described in the record which could impact the time required to answer questions in round 2. Additionally, the order in which patients cases are provided is not randomized, since in practice it also happens that a new patient is followed up by another new patient which requires professionals to systematically go through two separate records with little time in between.
| Arm | Type | Receives |
|---|---|---|
| accurate problem list, then inaccurate problem list | Active comparator | Other: patient A with accurate problem list, Other: patient B with inaccurate problem listin round 1, the participants will use the patient record of patient A, with an accurate problem list and answer the question: can the patient be prescribed Medication X and Y where medication X is a control question and medication Y is related to a contraindicated diagnosis (on problem list) In round 2, the participants will use the patient record of patient B, with an inaccurate problem list and answer the question: can the patient be prescribed Medication X and Y where medication X is a control question and medication Y is related to medical history (not on problem list) |
| inaccurate problem list, then accurate problem list | Active comparator | Other: patient A with inaccurate problem list, Other: patient B with accurate problem listin round 1, the participants will use the patient record of patient A, with an inaccurate problem list and answer the question: can the patient be prescribed Medication X and Y where medication X is a control question and medication Y is related to a contraindicated diagnosis (not on problem list) In round 2, the participants will use the patient record of patient B, with an accurate problem list and answer the question: can the patient be prescribed Medication X and Y where medication X is a control question and medication Y is related to medical history (on problem list) |
Who may qualify
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
Key inclusion criteria (2 listed)
- Healthcare professionals who are allowed to prescribe medication, thus hold a position as: medical specialist, medical resident, nurse specialist or physician assistant, research-specialists
- Healthcare professionals must have followed at least the 'basic EHR Epic course'. This electronic health record course lasts for three days and includes how to send letters, register diagnoses in a record, request testing, all in the software system EPIC, which concludes with an exam on the theory.
Key exclusion criteria (1 listed)
- Non-Dutch speaking employees as the patient cases and the exercises are described in Dutch
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 |
|---|---|---|
| patient A with accurate problem listA problem list that contains a diagnosis code that is contraindicated with a type of medication (Y). Also, all other relevant diagnoses and medical history for the patient are up-to-date on the problem list, which was defined according to the problem list policy at our institution (i.e. all current active problems and relevant medical history should be documented on the problem list). Additionally, the problem list is included in eight out of thirteen notes using so-called smart phrases that can automatically import a (part of a) problem list. One note includes the problem list with the diagnosis relevant for the question asked. | Other | accurate problem list, then inaccurate problem list |
| patient B with inaccurate problem listA problem list that does not contain the diagnosis code and corresponding details explaining medical history of this diagnosis caused by a type of medication (Y). Additionally, the problem list is included in three out of thirteen notes using so-called smart phrases that can automatically import a (part of a) problem list. The relevant diagnosis is not documented on the problem list and hence is not included in the imported problem list in the notes. The expert panel provided and anonymized two real-world representative examples of hematology patient records that included inaccurate problem lists and that had many free-text notes. An 'inaccurate problem list' is defined as a problem list where diagnoses are missing resulting in missed trigger medication or order-alerts, where diagnoses are 'active' although they should be closed or removed and/or where the problem list contained duplicated diagnoses. | Other | accurate problem list, then inaccurate problem list |
| patient A with inaccurate problem listA problem list that does not contain the diagnosis code that is contraindicated with the type of medication (Y). Additionally, the problem list is included in eight out of thirteen notes using so-called smart phrases that can automatically import a (part of a) problem list. The relevant diagnosis is not documented on the problem list and hence is not included in the imported problem list in the notes. The expert panel provided and anonymized two real-world representative examples of hematology patient records that included inaccurate problem lists and that had many free-text notes. An 'inaccurate problem list' is defined as a problem list where diagnoses are missing resulting in missed trigger medication or order-alerts, where diagnoses are 'active' although they should be closed or removed and/or where the problem list contained duplicated diagnoses. | Other | inaccurate problem list, then accurate problem list |
| patient B with accurate problem listA problem list that contains the diagnosis code and corresponding details explaining medical history of this diagnosis caused by a type of medication (Y). Also, all other relevant diagnoses and medical history for the patient are up-to-date on the problem list, which was defined according to the problem list policy at our institution (i.e. all current active problems and relevant medical history should be documented on the problem list). Additionally, the problem list is included in three out of thirteen notes using so-called smart phrases that can automatically import a (part of a) problem list. One note includes the problem list with the diagnosis and details relevant for the question asked. | Other | inaccurate problem list, then accurate problem list |
Primary outcome
the correctness of the answer of medication B including the right motivation
Time frame: during the experiment/questionnaire
Measured using a questionnaire showing the two question(s) per patient record on a separate tablet during the experiment. Two Yes/No questions per patient record (so a total of four questions per participant) are answered. Answers are stored using an automatically assigned unique anonymized identifier. The question for both patient records is: "can the patient be prescribed medication A and/or B?". Medications A and B can both not be prescribed, but medication B is related to the problem list diagnoses and medication A is related to the allergy list which is the same for both versions of the patient records. A motivation is required per Yes/No answer to determine the correctness of the answer and prevent from a chance of gambling. An independent researcher from the research team who did not perform the experiments will categorize the motivation of the answers for medication B.
4 secondary outcomes
the total time to answer the two questions correctly, where the answer of medication B also includes the right motivation
Time frame: during the experiment/questionnaire
the total time to answer the two questions correctly including the right motivations for medication A and B
Time frame: during the experiment/questionnaire
the correctness of both the answer for medication A and B including the right motivations for A and B
Time frame: during the experiment/questionnaire
the correctness of the answer for medication A including the right explanation
Time frame: during the experiment/questionnaire
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 23 Dec 2022.
- 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.
Eva Klappe, MSc principal investigator
Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing patient A with accurate problem list, patient B with inaccurate problem list and patient A with inaccurate problem list in people with Clinical Decision-Making and Decision Making, Computer-assisted, run by Eva Klappe at 1 site in Netherlands.
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 2 inclusion and 1 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 23 Dec 2022. 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 Netherlands. 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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
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?
the correctness of the answer of medication B including the right motivation (time frame: during the experiment/questionnaire). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
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 23 Dec 2022.