EmergenciesDDX-BRONCT05346523
Digitalized Differential Diagnosis Broadening in Emergency Rooms
A study testing Isabel Pro - The DDx generator (CDDS) in people with Emergencies and Diagnoses Disease, run by Insel Gruppe AG, University Hospital Bern at 4 sites in Switzerland.
Trial summary
10 to 35% of patients admitted to an emergency department receive an incorrect diagnosis. Not surprisingly, given the wide variety of health conditions encountered in emergency medicine, physicians often do not consider, remember, or know all possible diagnoses that fit the patient's symptoms. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Emergencies, Diagnoses Disease
- What is being tested?
- Isabel Pro - The DDx generator (CDDS)
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 1,218 participants
- Where
- 4 sitesSwitzerland
- Study dates
- 9 Jun 2022to 13 Jul 2023
- 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 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Informed Consent signed by the subject
- Presentation to the ER with fever, abdominal pain, syncope or Non-specific complaint (NSC) as chief complaint
- Triaged as "not vitally threatened"
- The study subject is 18 years old or older.
Who cannot join
- Trauma as chief complaint
- Pregnancy
- Worsening of a known pre-existing condition or medical referral with a definite diagnosis
- Inability to follow the informed consent and investigation procedures
- Previous enrolment into the current investigation
Trial locations and contacts
4 sites in 1 country.
Switzerland4 sites
Dept. of Internal and Emergency Medicine, Spital Tiefenau
Bern, 3004
Dept. of Emergency Medicine, Inselspital, University Hospital Bern
Bern, 3010
Dept. of internal and emergency medicine, Spital Münsigen
Münsingen, Canton of Bern, 3110
Dept. of Internal and Emergency Medicine, Buergerspital Solothurn
Solothurn, 3004
About this study
Official title: Effects of Digitalized Differential Diagnosis Broadening Using a Computerized Diagnostic Decision Support Tool on Diagnostic Quality in Emergency Room Patients - a Multi-centre Cluster Randomized Cross-over Trial.
10 to 35% of patients admitted to an emergency department receive an incorrect diagnosis. Not surprisingly, given the wide variety of health conditions encountered in emergency medicine, physicians often do not consider, remember, or know all possible diagnoses that fit the patient's symptoms. Nowadays, computer software (CDDS) is able to support physicians with a list of possible diagnoses by matching entered patient data to a large database with diagnoses. However, it is still unclear how the use of such a CDDS actually affects the diagnostic quality and workflow in 'real world' ER routine care. Therefore, the aim of this cluster-randomized cross-over trial is to evaluate the consequences of CDDS usage on diagnostic quality, patient outcomes and diagnostic workflow within the ER. Four ER's will provide a CDDS to the diagnosing physicians for specific periods (randomly and alternatingly allocated) in which physicians will be asked to use it for all included study patients. Outcomes between periods with and without the CDDS will be compared. Primary outcome is a diagnostic quality risk score composed of unscheduled ER revisits, unexpected hospitalization (both within 14 days), unexpected intensive medical care unit admission if hospitalized and diagnostic discrepancy between the ER discharge diagnosis and the current diagnosis after 14 days. In total, 1'184 patients will be included.
Read the detailed description
Background:
Misdiagnosis occurs in about 5% of outpatients, and in 10% to 35% of emergency room (ER) patients, sometimes with devastating medical and economic consequences. Nowadays, computerized diagnostic decision support programs (CDDS) exist, which suggest differential diagnoses (DDx) to physicians and thus have potential to improve diagnoses and hence, outcomes of patient care. The effects of such CDDS in 'real-world' ER settings are unknown. Controlled clinical trials investigating their effectiveness and safety are absent. In addition, most available CDDS are overcautious and suggest a wide variety of diagnostic options, likely increasing diagnostic resource consumption.
Objectives:
With this project, the investigators aim to understand the intended and unintended consequences of CDDS use by physicians on diagnostic quality and workflow in emergency medicine
- on the micro-level, how CDDS affect diagnostic quality by physicians in individual emergency patients.
- on the meso-level, how CDDS affect the diagnostic workflow in emergency departments.
- on the macro-level, the economic and educational impact of CDDS utilization in ERs
Outcomes: Details given below
Design:
Cross sectional, multi-center, four-period cross-over controlled cluster-randomized trial. Four ER sites will randomly be allocated to one of two sequences with alternating intervention and control periods (ABAB vs. BABA) with each period lasting for two months. Recruitment will target 74 patients per period and cluster and 1'184 patients total.
Inclusion / Exclusion Criteria: Details given below
Intervention period: Details given below
Control period: Details given below.
Measurements and procedures:
For the primary outcome, data will be extracted from the electronic health records (i.e. ER diagnosis, intensive care unit admission or diagnosis after 14d if patients are still hospitalized). Additionally, patients and their general practitioner will be contacted via telephone by study nurses after 14d of study inclusion in order to collect information about patients' current diagnoses, and re-visits or hospitalization related to the initial ER visit. Data for secondary endpoints will be retrieved from the routinely collected data in the electronic health record system (e.g mortality, time to ER diagnosis, resource consumption). Additionally, interviews and focus groups with physicians will be performed to investigate diagnostic workflow changes, physician confidence and other process outcomes.
Statistical Analysis:
Statistical analysis will be based on multi-level general linear mixed modelling (GLMM) methods using appropriate post hoc techniques (e.g for subgroup analyses).
For the primary outcome (presence or no presence of a positive diagnostic quality risk score), a generalized linear mixed model (GLMM) with a binomial distribution family and exchangeable correlation structure will be performed. The GLMM takes into account a random effect for each site, resident and attending physician. Diagnosing resident and attending physicians are nested within sites. The condition (intervention and control) and the period (period 1 to 4) will be included as fixed factors under the assumption of equality of carry-over effects. Additionally, presenting chief complaint, patient's age, sex and comorbidity index will be added as covariates.
For all secondary endpoints, summary statistics appropriate to the distribution will be tabulated by treatment group. Analysis of secondary endpoints will parallel the primary analysis.
- Conditions
- Emergencies, Diagnoses Disease
- Being tested
- Isabel Pro - The DDx generator (CDDS)
- Keywords
- Emergency, Diagnostic Error, Diagnostic Safety, Misdiagnosis, CDDS, Computerized Decision Support System, Diagnostic Quality
- Medical subject headings
- Emergencies
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Isabel Pro - The DDx generator (CDDS)Device
Isabel Pro - the DDx generator is a software developped for health professionals with the intention to support them in broadening their differential diagnoses. After the first patient examination, the resident is asked to enter patient symptoms into Isabel Pro, which returns a list of possible diagnoses from its underlying database that matches the entered data. The diagnosing resident physicians will be asked to consult Isabel Pro at least once within the first hour after the first patient assessement. After entering patient symptoms into the software, Isabel Pro will itself return a list with possible diagnoses derived from their underlying database. It is then free to the physician to decide whether one or more of the suggested DDx should be considered for further diagnostic or treatment procedure based on clinical judgement.
| Study group | Role | What they receive |
|---|---|---|
| Usual Care + CDDS usage | Experimental | Patients presenting to the ER and included in the study during the ER's intervention period will be treated and diagnosed by the ER physicians as usual but with support of the CDDS. Receives: Device: Isabel Pro - The DDx generator (CDDS) |
| Usual Care | No intervention | Patients presenting to the ER and included in the study during the ER's intervention period will be treated and diagnosed by the ER physicians as usual without support of the CDDS. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic quality risk score
Primary endpoint is a binary score indicating a diagnostic quality risk, composed of:
- Death within 14 days after emergency room discharge (yes/no)
- Unscheduled medical care (emergency room revisits, General Practitioner visits or hospitalization) within 14 days after emergency room discharge (yes/no)
- Unexpected intensive care unit admission from ward within 24 hours when hospitalized (yes/no)
- Diagnostic discrepancy between the emergency room discharge diagnosis and the current diagnosis 14 days after emergency room discharge (yes/no)
Measured: From emergency room discharge to 14 days after emergency room discharge
Secondary outcomes (15)
Death within 14 days after Emergency Room discharge (yes/no)
Patient died within the timeframe of emergency discharge
Measured: From emergency room discharge to 14 days after emergency room discharge
Unexpected intensive care unit admission
Number of patients with unexpected intensive care unit admission from ward within 24 hours when hospitalized (yes/no)
Measured: Within 24 hours from emergency room transfer to hospital ward
Diagnostic discrepancy
Number of patients with diagnostic discrepancy between the Emergency Room discharge diagnosis and the current diagnosis 14 days after ER discharge (yes/no)
Measured: From emergency room discharge to 14 days after emergency room discharge
Unscheduled medical care 72 hours, 7 days and 14 days
Number of patients with unscheduled medical care 72 hours, 7 days and 14 days after emergency room discharge
Measured: From emergency room discharge to 72 hours, 7 days and 14 days after emergency room discharge
Length of emergency room stay
Number of hours the patient spent in emergency room routine care
Measured: Time from emergency room admission to emergency room discharge, up to 24 hours
Length of hospital stay
Number of days the patient was hospitalized (if hospitalized)
Measured: Time from hospital admission to hospital discharge, up to 18 days
Diagnostic tests
Number of diagnostic tests performed during emergency room routine care
Measured: Time from emergency room admission to emergency room discharge, up to 24 hours
Resource consumption in the Emergency Department
Resource consumption (total costs for personnel and diagnostics) during emergency room
Measured: Time from emergency room admission to emergency room discharge, up to 24 hours
Resource consumption
Resource consumption (total costs for personnel and diagnostics) during hospitalization
Measured: Time from emergency room admission to emergency room discharge, up to 18 days
Discharge destination
Home / Hospital (intern) / Hospital (extern) / Nursing home / Rehabilitation / Other
Measured: Timepoint of emergency room discharge (according to clinical routine, up to 24 hours)
Number of differential diagnoses
Number of differential diagnoses provided by the physicians at emergency room discharge
Measured: Timepoint of emergency room discharge (according to clinical routine, up to 24 hours)
CDDS potential
Number of cases where the generated differential diagnosis list entails the diagnoses after 14 days
Measured: Time from emergency room admission to 14 days after emergency room discharge
Diagnostic error
Diagnostic error based on full chart review for a random subset
Measured: From emergency room discharge to 14 days after emergency room discharge
CDDS usage
Number of CDDS queries
Measured: Time from emergency room admission to emergency room discharge From 0 up to 24 hours.
Physician confidence calibration, advice seeking and collaboration
Assessed by qualitative methods such as observations of physicians or interviews and focus groups with physicians (no patients directly involved).
Measured: Exact timepoints to be defined, up to a maximum of 9 months. From June 2022 to March 2023
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted26 Apr 2022
- Study start9 Jun 2022
- Primary completion13 Jul 2023
- Study completion13 Jul 2023
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (3)Papers the sponsor has linked to this study.
- Other trials for EmergenciesStudies of the same condition, open and under way.
- More trials run by Insel Gruppe AG, University Hospital BernThe 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
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Crossover
- Model description
- Cluster-Randomized Cross-Over Trial
- Masking
- Double blind
- Who is masked
- Participant, Outcomes assessor
- Enrolment
- 1,218 participants (actual)
Sponsor and investigators
- Lead sponsor
- Insel Gruppe AG, University Hospital Bern
- Sponsor type
- University, hospital or other
- Collaborators
- Swiss National Science Foundation
- Responsible party
- Sponsor
- Investigators
- Wolf Hautz, Prof. MD, Principal investigator, Prof. MD
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05346523
- Sponsor's protocol ID
- 407740_187284/1
- Acronym
- DDX-BRO
- Status last verified
- Jul 2023
- First submitted
- 21 Mar 2022
- Last updated
- 21 Jul 2023
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: Researchers who wish to use IPD for a nested study need to submit a proposal to the Sponsor-Investigator and request permission. A concept sheet describing the planned analyses must be approved by the sponsor-investigator and PIs of the participating sites. Nested studies might need separate ethics permission.
Publications
Marcin T, Werthmuller N, Kolbener F, Muller M, Zwaan L, Hautz SC, Schuster A, Exadaktylos AK, Hautz WE. Identification of diagnostic discrepancies as a quality assurance measure in emergency medicine - a validation study. Scand J Trauma Resusc Emerg Med. 2026 Feb 11;34(1):56. doi: 10.1186/s13049-026-01572-x.
PubMed 41673906Hautz WE, Marcin T, Hautz SC, Schauber SK, Krummrey G, Muller M, Sauter TC, Lambrigger C, Schwappach D, Nendaz M, Lindner G, Bosbach S, Griesshammer I, Schonberg P, Pluss E, Romann V, Ravioli S, Werthmuller N, Kolbener F, Exadaktylos AK, Singh H, Zwaan L. Diagnoses supported by a computerised diagnostic decision support system versus conventional diagnoses in emergency patients (DDX-BRO): a multicentre, multiple-period, double-blind, cluster-randomised, crossover superiority trial. Lancet Digit Health. 2025 Feb;7(2):e136-e144. doi: 10.1016/S2589-7500(24)00250-4.
PubMed 39890244Marcin T, Hautz SC, Singh H, Zwaan L, Schwappach D, Krummrey G, Schauber SK, Nendaz M, Exadaktylos AK, Muller M, Lambrigger C, Sauter TC, Lindner G, Bosbach S, Griesshammer I, Hautz WE. Effects of a computerised diagnostic decision support tool on diagnostic quality in emergency departments: study protocol of the DDx-BRO multicentre cluster randomised cross-over trial. BMJ Open. 2023 Mar 29;13(3):e072649. doi: 10.1136/bmjopen-2023-072649.
PubMed 36990482
Study at a glance
From the registry record, last updated by the sponsor on 21 Jul 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Effects of Digitalized Differential Diagnosis Broadening Using a Computerized Diagnostic Decision Support Tool on Diagnostic Quality in Emergency Room Patients - a Multi-centre Cluster Randomized Cross-over Trial.
- ClinicalTrials.gov ID
- NCT05346523
- Acronym / sponsor ID
- DDX-BRO / 407740_187284/1
- Conditions
- Emergencies, Diagnoses Disease
- Keywords
- Emergency, Diagnostic Error, Diagnostic Safety, Misdiagnosis, CDDS, Computerized Decision Support System, Diagnostic Quality
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 1,218 participants (actual)
- Lead sponsor
- Insel Gruppe AG, University Hospital BernUniversity, hospital or other
- Collaborators
- Swiss National Science Foundation
- Study start
- 9 Jun 2022 (actual)
- Primary completion
- 13 Jul 2023 (actual)
- Study completion
- 13 Jul 2023 (actual)
- First posted
- 26 Apr 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.
10 to 35% of patients admitted to an emergency department receive an incorrect diagnosis. Not surprisingly, given the wide variety of health conditions encountered in emergency medicine, physicians often do not consider, remember, or know all possible diagnoses that fit the patient's symptoms. Nowadays, computer software (CDDS) is able to support physicians with a list of possible diagnoses by matching entered patient data to a large database with diagnoses. However, it is still unclear how the use of such a CDDS actually affects the diagnostic quality and workflow in 'real world' ER routine care. Therefore, the aim of this cluster-randomized cross-over trial is to evaluate the consequences of CDDS usage on diagnostic quality, patient outcomes and diagnostic workflow within the ER. Four ER's will provide a CDDS to the diagnosing physicians for specific periods (randomly and alternatingly allocated) in which physicians will be asked to use it for all included study patients. Outcomes between periods with and without the CDDS will be compared. Primary outcome is a diagnostic quality risk score composed of unscheduled ER revisits, unexpected hospitalization (both within 14 days), unexpected intensive medical care unit admission if hospitalized and diagnostic discrepancy between the ER discharge diagnosis and the current diagnosis after 14 days. In total, 1'184 patients will be included.
Detailed description
Background:
Misdiagnosis occurs in about 5% of outpatients, and in 10% to 35% of emergency room (ER) patients, sometimes with devastating medical and economic consequences. Nowadays, computerized diagnostic decision support programs (CDDS) exist, which suggest differential diagnoses (DDx) to physicians and thus have potential to improve diagnoses and hence, outcomes of patient care. The effects of such CDDS in 'real-world' ER settings are unknown. Controlled clinical trials investigating their effectiveness and safety are absent. In addition, most available CDDS are overcautious and suggest a wide variety of diagnostic options, likely increasing diagnostic resource consumption.
Objectives:
With this project, the investigators aim to understand the intended and unintended consequences of CDDS use by physicians on diagnostic quality and workflow in emergency medicine
- on the micro-level, how CDDS affect diagnostic quality by physicians in individual emergency patients.
- on the meso-level, how CDDS affect the diagnostic workflow in emergency departments.
- on the macro-level, the economic and educational impact of CDDS utilization in ERs
Outcomes: Details given below
Design:
Cross sectional, multi-center, four-period cross-over controlled cluster-randomized trial. Four ER sites will randomly be allocated to one of two sequences with alternating intervention and control periods (ABAB vs. BABA) with each period lasting for two months. Recruitment will target 74 patients per period and cluster and 1'184 patients total.
Inclusion / Exclusion Criteria: Details given below
Intervention period: Details given below
Control period: Details given below.
Measurements and procedures:
For the primary outcome, data will be extracted from the electronic health records (i.e. ER diagnosis, intensive care unit admission or diagnosis after 14d if patients are still hospitalized). Additionally, patients and their general practitioner will be contacted via telephone by study nurses after 14d of study inclusion in order to collect information about patients' current diagnoses, and re-visits or hospitalization related to the initial ER visit. Data for secondary endpoints will be retrieved from the routinely collected data in the electronic health record system (e.g mortality, time to ER diagnosis, resource consumption). Additionally, interviews and focus groups with physicians will be performed to investigate diagnostic workflow changes, physician confidence and other process outcomes.
Statistical Analysis:
Statistical analysis will be based on multi-level general linear mixed modelling (GLMM) methods using appropriate post hoc techniques (e.g for subgroup analyses).
For the primary outcome (presence or no presence of a positive diagnostic quality risk score), a generalized linear mixed model (GLMM) with a binomial distribution family and exchangeable correlation structure will be performed. The GLMM takes into account a random effect for each site, resident and attending physician. Diagnosing resident and attending physicians are nested within sites. The condition (intervention and control) and the period (period 1 to 4) will be included as fixed factors under the assumption of equality of carry-over effects. Additionally, presenting chief complaint, patient's age, sex and comorbidity index will be added as covariates.
For all secondary endpoints, summary statistics appropriate to the distribution will be tabulated by treatment group. Analysis of secondary endpoints will parallel the primary analysis.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Crossover
- Masking
- Double blind (participant, outcomes assessor)
- Number of arms
- 2
- Model description
- Cluster-Randomized Cross-Over Trial
| Arm | Type | Receives |
|---|---|---|
| Usual Care + CDDS usage | Experimental | Device: Isabel Pro - The DDx generator (CDDS)Patients presenting to the ER and included in the study during the ER's intervention period will be treated and diagnosed by the ER physicians as usual but with support of the CDDS. |
| Usual Care | No intervention | –Patients presenting to the ER and included in the study during the ER's intervention period will be treated and diagnosed by the ER physicians as usual without support of the CDDS. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (4 listed)
- Informed Consent signed by the subject
- Presentation to the ER with fever, abdominal pain, syncope or Non-specific complaint (NSC) as chief complaint
- Triaged as "not vitally threatened"
- The study subject is 18 years old or older.
Key exclusion criteria (5 listed)
- Trauma as chief complaint
- Pregnancy
- Worsening of a known pre-existing condition or medical referral with a definite diagnosis
- Inability to follow the informed consent and investigation procedures
- Previous enrolment into the current investigation
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 |
|---|---|---|
| Isabel Pro - The DDx generator (CDDS)Isabel Pro - the DDx generator is a software developped for health professionals with the intention to support them in broadening their differential diagnoses. After the first patient examination, the resident is asked to enter patient symptoms into Isabel Pro, which returns a list of possible diagnoses from its underlying database that matches the entered data. The diagnosing resident physicians will be asked to consult Isabel Pro at least once within the first hour after the first patient assessement. After entering patient symptoms into the software, Isabel Pro will itself return a list with possible diagnoses derived from their underlying database. It is then free to the physician to decide whether one or more of the suggested DDx should be considered for further diagnostic or treatment procedure based on clinical judgement. | Device | Usual Care + CDDS usage |
Primary outcome
Diagnostic quality risk score
Time frame: From emergency room discharge to 14 days after emergency room discharge
Primary endpoint is a binary score indicating a diagnostic quality risk, composed of: * Death within 14 days after emergency room discharge (yes/no) * Unscheduled medical care (emergency room revisits, General Practitioner visits or hospitalization) within 14 days after emergency room discharge (yes/no) * Unexpected intensive care unit admission from ward within 24 hours when hospitalized (yes/no) * Diagnostic discrepancy between the emergency room discharge diagnosis and the current diagnosis 14 days after emergency room discharge (yes/no)
15 secondary outcomes
Death within 14 days after Emergency Room discharge (yes/no)
Time frame: From emergency room discharge to 14 days after emergency room discharge
Unexpected intensive care unit admission
Time frame: Within 24 hours from emergency room transfer to hospital ward
Diagnostic discrepancy
Time frame: From emergency room discharge to 14 days after emergency room discharge
Unscheduled medical care 72 hours, 7 days and 14 days
Time frame: From emergency room discharge to 72 hours, 7 days and 14 days after emergency room discharge
Length of emergency room stay
Time frame: Time from emergency room admission to emergency room discharge, up to 24 hours
Length of hospital stay
Time frame: Time from hospital admission to hospital discharge, up to 18 days
Diagnostic tests
Time frame: Time from emergency room admission to emergency room discharge, up to 24 hours
Resource consumption in the Emergency Department
Time frame: Time from emergency room admission to emergency room discharge, up to 24 hours
Resource consumption
Time frame: Time from emergency room admission to emergency room discharge, up to 18 days
Discharge destination
Time frame: Timepoint of emergency room discharge (according to clinical routine, up to 24 hours)
Number of differential diagnoses
Time frame: Timepoint of emergency room discharge (according to clinical routine, up to 24 hours)
CDDS potential
Time frame: Time from emergency room admission to 14 days after emergency room discharge
Diagnostic error
Time frame: From emergency room discharge to 14 days after emergency room discharge
CDDS usage
Time frame: Time from emergency room admission to emergency room discharge From 0 up to 24 hours.
Physician confidence calibration, advice seeking and collaboration
Time frame: Exact timepoints to be defined, up to a maximum of 9 months. From June 2022 to March 2023
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 21 Jul 2023.
- Derived from this study: PubMed 41673906Marcin T, Werthmuller N, Kolbener F, Muller M, Zwaan L, Hautz SC, Schuster A, Exadaktylos AK, Hautz WE. Identification of diagnostic discrepancies as a quality assurance measure in emergency medicine - a validation study. Scand J Trauma Resusc Emerg Med. 2026 Feb 11;34(1):56. doi: 10.1186/s13049-026-01572-x.
- Derived from this study: PubMed 39890244Hautz WE, Marcin T, Hautz SC, Schauber SK, Krummrey G, Muller M, Sauter TC, Lambrigger C, Schwappach D, Nendaz M, Lindner G, Bosbach S, Griesshammer I, Schonberg P, Pluss E, Romann V, Ravioli S, Werthmuller N, Kolbener F, Exadaktylos AK, Singh H, Zwaan L. Diagnoses supported by a computerised diagnostic decision support system versus conventional diagnoses in emergency patients (DDX-BRO): a multicentre, multiple-period, double-blind, cluster-randomised, crossover superiority trial. Lancet Digit Health. 2025 Feb;7(2):e136-e144. doi: 10.1016/S2589-7500(24)00250-4.
- Derived from this study: PubMed 36990482Marcin T, Hautz SC, Singh H, Zwaan L, Schwappach D, Krummrey G, Schauber SK, Nendaz M, Exadaktylos AK, Muller M, Lambrigger C, Sauter TC, Lindner G, Bosbach S, Griesshammer I, Hautz WE. Effects of a computerised diagnostic decision support tool on diagnostic quality in emergency departments: study protocol of the DDx-BRO multicentre cluster randomised cross-over trial. BMJ Open. 2023 Mar 29;13(3):e072649. doi: 10.1136/bmjopen-2023-072649.
- Individual participant dataSharing planned: Yes. Researchers who wish to use IPD for a nested study need to submit a proposal to the Sponsor-Investigator and request permission. A concept sheet describing the planned analyses must be approved by the sponsor-investigator and PIs of the participating sites. Nested studies might need separate ethics permission.
Related trials for Emergencies
Not yet recruiting
A Pre-post Study of a Lethal Means Safety Planning Intervention for Youth at Elevated Suicide Risk in the Emergency Department
Suicidal Ideas, Suicidal Ideation
Not yet recruiting
Miniature In-line Respirator
Brain Death, Respiration, Artificial
Recruiting
Development and Preliminary Validation of Intelligent Video Laryngoscope for Emergency Use
Critically Ill Patients Requiring Mechanical Ventilation
RecruitingObservational study
AIDE: Decision Support for Anastomosis or Colostomy in Emergency Surgery for Complicated Acute Diverticulitis
Complicated Diverticulitis, Acute Diverticulitis
Recruiting
Developing a Support Application for Food Pantries (SAFPAS) to Improve Client Access to Healthy Foods & Enhance Emergency Preparedness
Improving Healthy Food Access in Food Insecurity Populations in Normal and Emergency Situations
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.
Wolf Hautz, Prof. MD principal investigator
Prof. MD
4 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Isabel Pro - The DDx generator (CDDS) in people with Emergencies and Diagnoses Disease, run by Insel Gruppe AG, University Hospital Bern at 4 sites in Switzerland.
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 4 inclusion and 5 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 21 Jul 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 4 sites in Switzerland. 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?
Diagnostic quality risk score (time frame: From emergency room discharge to 14 days after emergency room discharge). 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 NCT05346523, which also lists 3 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 21 Jul 2023.