Digitalized Differential Diagnosis Broadening in Emergency Rooms
- Conditions
- EmergenciesDiagnoses Disease
- Interventions
- Device: Isabel Pro - The DDx generator (CDDS)
- Registration Number
- NCT05346523
- Lead Sponsor
- Insel Gruppe AG, University Hospital Bern
- 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.
Recruitment & Eligibility
- Status
- COMPLETED
- Sex
- All
- Target Recruitment
- 1218
- 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.
- 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
Study & Design
- Study Type
- INTERVENTIONAL
- Study Design
- CROSSOVER
- Arm && Interventions
Group Intervention Description Usual Care + CDDS usage 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.
- Primary Outcome Measures
Name Time Method Diagnostic quality risk score 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)
- Secondary Outcome Measures
Name Time Method Death within 14 days after Emergency Room discharge (yes/no) From emergency room discharge to 14 days after emergency room discharge Patient died within the timeframe of emergency discharge
Unexpected intensive care unit admission Within 24 hours from emergency room transfer to hospital ward Number of patients with unexpected intensive care unit admission from ward within 24 hours when hospitalized (yes/no)
Length of emergency room stay Time from emergency room admission to emergency room discharge, up to 24 hours Number of hours the patient spent in emergency room routine care
Length of hospital stay Time from hospital admission to hospital discharge, up to 18 days Number of days the patient was hospitalized (if hospitalized)
Resource consumption in the Emergency Department Time from emergency room admission to emergency room discharge, up to 24 hours Resource consumption (total costs for personnel and diagnostics) during emergency room
Diagnostic discrepancy From emergency room discharge to 14 days after emergency room discharge Number of patients with diagnostic discrepancy between the Emergency Room discharge diagnosis and the current diagnosis 14 days after ER discharge (yes/no)
Unscheduled medical care 72 hours, 7 days and 14 days From emergency room discharge to 72 hours, 7 days and 14 days after emergency room discharge Number of patients with unscheduled medical care 72 hours, 7 days and 14 days after emergency room discharge
CDDS potential Time from emergency room admission to 14 days after emergency room discharge Number of cases where the generated differential diagnosis list entails the diagnoses after 14 days
Diagnostic error From emergency room discharge to 14 days after emergency room discharge Diagnostic error based on full chart review for a random subset
Diagnostic tests Time from emergency room admission to emergency room discharge, up to 24 hours Number of diagnostic tests performed during emergency room routine care
Resource consumption Time from emergency room admission to emergency room discharge, up to 18 days Resource consumption (total costs for personnel and diagnostics) during hospitalization
Discharge destination Timepoint of emergency room discharge (according to clinical routine, up to 24 hours) Home / Hospital (intern) / Hospital (extern) / Nursing home / Rehabilitation / Other
Number of differential diagnoses Timepoint of emergency room discharge (according to clinical routine, up to 24 hours) Number of differential diagnoses provided by the physicians at emergency room discharge
CDDS usage Time from emergency room admission to emergency room discharge From 0 up to 24 hours. Number of CDDS queries
Physician confidence calibration, advice seeking and collaboration Exact timepoints to be defined, up to a maximum of 9 months. From June 2022 to March 2023 Assessed by qualitative methods such as observations of physicians or interviews and focus groups with physicians (no patients directly involved).
Trial Locations
- Locations (4)
Dept. of Internal and Emergency Medicine, Spital Tiefenau
🇨ðŸ‡Bern, Switzerland
Dept. of Internal and Emergency Medicine, Buergerspital Solothurn
🇨ðŸ‡Solothurn, Switzerland
Dept. of internal and emergency medicine, Spital Münsigen
🇨ðŸ‡Münsingen, Bern, Switzerland
Dept. of Emergency Medicine, Inselspital, University Hospital Bern
🇨ðŸ‡Bern, Switzerland