Digitalized Differential Diagnosis Broadening in Emergency Rooms

NCT ID: NCT05346523

Last Updated: 2023-07-21

Basic Information

• Recruitment Status: COMPLETED
• Clinical Phase: NA
• Total Enrollment: 1218 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2022-06-09
• Study Completion Date: 2023-07-13

Brief 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. 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.

Conditions

Emergencies; Diagnoses Disease

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: CROSSOVER
• Primary Study Purpose: DIAGNOSTIC
• Blinding Strategy: DOUBLE

Study Groups

Usual Care + CDDS usage

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.

Group Type: EXPERIMENTAL

Isabel Pro - The DDx generator (CDDS)

Intervention Type: 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.

Usual Care

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.

Group Type: NO_INTERVENTION

No interventions assigned to this group

Interventions

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.

Intervention Type: DEVICE

Eligibility Criteria

Inclusion Criteria

• 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.

Exclusion Criteria

• 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

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Swiss National Science Foundation

OTHER

Sponsor Role: collaborator

Insel Gruppe AG, University Hospital Bern

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Wolf Hautz, Prof. MD

Role: PRINCIPAL_INVESTIGATOR

Prof. MD

Locations

Dept. of internal and emergency medicine, Spital Münsigen

Münsingen, Canton of Bern, Switzerland

Dept. of Internal and Emergency Medicine, Spital Tiefenau

Bern, , Switzerland

Dept. of Emergency Medicine, Inselspital, University Hospital Bern

Bern, , Switzerland

Dept. of Internal and Emergency Medicine, Buergerspital Solothurn

Solothurn, , Switzerland

Countries

Switzerland

References

Hautz 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.

Reference Type: DERIVED

PMID: 39890244 (View on PubMed)

Marcin 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.

Reference Type: DERIVED

PMID: 36990482 (View on PubMed)

Provided Documents

Document Type: Statistical Analysis Plan

View Document

Other Identifiers

407740_187284/1

Identifier Type: -

Identifier Source: org_study_id