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| Name | Class |
|---|---|
| University of Kansas | OTHER |
| Beckman Coulter, Inc. | INDUSTRY |
| Truman Medical Center | OTHER |
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The primary objective of this study is to validate the use of an electronic clinical decision support (CDS) tool, TriageGO with Monocyte Distribution Width (TriageGO-MDW), in the emergency department (ED). TriageGO-MDW is non-device CDS designed to support emergency clinicians (nurses, physicians and advanced practice providers) in performing risk-based assessment and prioritization of patients during their ED visit. This study will follow an effectiveness-implementation hybrid design via the following three aims (phases), to be executed sequentially:
(Aim 1) Validate the TriageGO-MDW algorithm locally using retrospective data at ED study sites.
(Aim 2) Deploy TriageGO-MDW integrated with the electronic medical record (EMR) and perform user assessment.
(Aim 3) Evaluate TriageGO-MDW in steady state with respect to clinical, process, and perceived utility outcomes.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Pre-Implementation | Usual care will be provided during all ED patient encounters. |
| |
| Post-Implementation | TriageGo-MDW CDS will be made available during all ED patient encounters at two points in the ED care continuum: (1) shortly after arrival during initial ED triage (First Triage) and (2) after initial laboratory results have been populated within the EHR. General illness severity estimates will be provided to nurses at ED triage in the form of recommended triage acuity scores (CDS for First Triage). General illness severity estimates along with estimated risk for specific outcomes including sepsis and septic shock will be presented to clinicians after laboratory results have populated (CDS for Early Assessment). TriageGO-MDW risk estimates will be generated by machine learning algorithms using routinely available clinical data as predictor inputs. Nurses and clinicians will receive risk estimates within existing EHR workflows, along with brief and rapidly interpretable explanations of the logic driving each risk estimate. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| TriageGO-MDW Clinical Decision Support | Other | TriageGO-MDW is non-device clinical decision support that provides patient-level clinical risk estimates based on clinical data derived from the electronic health record |
| Measure | Description | Time Frame |
|---|---|---|
| Critical Care | Admission to an intensive care unit within 24 hours of ED disposition; Prediction performance of machine learning algorithms that underly TriageGO-MDW for this outcome will be measured | baseline (pre-intervention) |
| Critical Care | Admission to an intensive care unit within 24 hours of ED disposition; Prediction performance of machine learning algorithms that underly TriageGO-MDW for this outcome will be measured | during post-implementation steady state (approximately 3 months after intervention) |
| In-Hospital Mortality | Death during index hospital encounter; Prediction performance of machine learning algorithms that underly TriageGO-MDW for this outcome will be measured | baseline (pre-intervention) |
| In-Hospital Mortality | Death during index hospital encounter; Prediction performance of machine learning algorithms that underly TriageGO-MDW for this outcome will be measured | during post-implementation steady state (approximately 3 months after intervention) |
| Emergent Surgery | procedure in the operating room within 12 hours of ED arrival; Prediction performance of machine learning algorithms that underly TriageGO-MDW for this outcome will be measured | baseline (pre-intervention) |
| Emergent Surgery | procedure in the operating room within 12 hours of ED arrival; Prediction performance of machine learning algorithms that underly TriageGO-MDW for this outcome will be measured | during post-implementation steady state (approximately 3 months after intervention) |
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| Measure | Description | Time Frame |
|---|---|---|
| Critical care triage capture rate | Proportion of patients with critical care admission, emergency surgery or in-hospital mortality identified as high acuity at ED triage | baseline (pre-intervention) |
| Critical care triage capture rate |
Inclusion Criteria: Adult patients receiving care at a study site ED
Exclusion Criteria: None
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All emergency department visits by adult patients during the study period will be included in our analysis.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Eric Hamrock | Contact | 4013420373 | eric.hamrock@stocastic.com |
| Name | Affiliation | Role |
|---|---|---|
| Scott Levin, PhD | Stocastic, LLC | Principal Investigator |
| Jeremiah Hinson, PhD/MD | Stocastic, LLC | Principal Investigator |
| Nima Sarani, MD |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Kansas University Medical Center | Recruiting | Kansas City | Kansas | 66160 | United States |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 28888332 | Background | Levin S, Toerper M, Hamrock E, Hinson JS, Barnes S, Gardner H, Dugas A, Linton B, Kirsch T, Kelen G. Machine-Learning-Based Electronic Triage More Accurately Differentiates Patients With Respect to Clinical Outcomes Compared With the Emergency Severity Index. Ann Emerg Med. 2018 May;71(5):565-574.e2. doi: 10.1016/j.annemergmed.2017.08.005. Epub 2017 Sep 6. | |
| 27133736 |
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| Usual Care | Other | Clinical care without decision support provided by TriageGo-MDW |
|
| Sepsis | Prediction performance of machine learning algorithms that underlie TriageGO-MDW for this outcome will be measured | baseline (pre-intervention) |
| Sepsis | Prediction performance of machine learning algorithms that underlie TriageGO-MDW for this outcome will be measured | during post-implementation steady state (approximately 3 months after intervention) |
| Septic Shock | Meeting septic shock criteria within 24 hours of ED arrival; Prediction performance of machine learning algorithms that underly TriageGO-MDW for this outcome will be measured | baseline (pre-intervention) |
| Septic Shock | Meeting septic shock criteria within 24 hours of ED arrival; Prediction performance of machine learning algorithms that underly TriageGO-MDW for this outcome will be measured | during post-implementation steady state (approximately 3 months after intervention) |
| Viral Infection | Testing positive for influenza or Covid-19 (SARS-CoV-2) infection within 24 hours of ED arrival; Prediction performance of machine learning algorithms that underly TriageGO-MDW for this outcome will be measured | baseline (pre-intervention) |
| Viral Infection | Testing positive for influenza or Covid-19 (SARS-CoV-2) infection within 24 hours of ED arrival; Prediction performance of machine learning algorithms that underly TriageGO-MDW for this outcome will be measured | during post-implementation steady state (approximately 3 months after intervention) |
Proportion of patients with critical care admission, emergency surgery or in-hospital mortality identified as high acuity at ED triage
| during post-implementation steady state (approximately 3 months after intervention) |
| Hospital admission triage capture rate | Proportion of patients requiring hospital admission identified as moderate or high acuity at ED triage | baseline (pre-intervention) |
| Hospital admission triage capture rate | Proportion of patients requiring hospital admission identified as moderate or high acuity at ED triage | during post-implementation steady state (approximately 3 months after intervention) |
| ED patient flow metrics | Intervals between major ED care events, including arrival to disposition, arrival to treatment space, arrival to treatment provider, arrival to intensive care unit transfer, arrival to ED departure will be measured | baseline (pre-intervention) |
| ED patient flow metrics | Intervals between major ED care events, including arrival to disposition, arrival to treatment space, arrival to treatment provider, arrival to intensive care unit transfer, arrival to ED departure will be measured | during post-implementation steady state (approximately 3 months after intervention) |
| Sepsis care quality metrics | Standard sepsis care quality metrics including time to diagnosis and treatment and rates of compliance with the Centers for Medicare and Medicaid Services (CMS) Sepsis-1 (SEP-1) Core Measure and its components will be measured | baseline (pre-intervention) |
| Sepsis care quality metrics | Standard sepsis care quality metrics including time to diagnosis and treatment and rates of compliance with the Centers for Medicare and Medicaid Services (CMS) Sepsis-1 (SEP-1) Core Measure and its components will be measured | during post-implementation steady state (approximately 3 months after intervention) |
| University of Kansas |
| Principal Investigator |
| Kevin O'Rourke, MD | Truman Medical Center | Principal Investigator |
| University Health Truman Medical Center | Recruiting | Kansas City | Missouri | 64108 | United States |
|
| Dugas AF, Kirsch TD, Toerper M, Korley F, Yenokyan G, France D, Hager D, Levin S; New Collective Author. An Electronic Emergency Triage System to Improve Patient Distribution by Critical Outcomes. J Emerg Med. 2016 Jun;50(6):910-8. doi: 10.1016/j.jemermed.2016.02.026. Epub 2016 Apr 25. |
| 28625579 | Background | Crouser ED, Parrillo JE, Seymour C, Angus DC, Bicking K, Tejidor L, Magari R, Careaga D, Williams J, Closser DR, Samoszuk M, Herren L, Robart E, Chaves F. Improved Early Detection of Sepsis in the ED With a Novel Monocyte Distribution Width Biomarker. Chest. 2017 Sep;152(3):518-526. doi: 10.1016/j.chest.2017.05.039. Epub 2017 Jun 15. |
| ID | Term |
|---|---|
| D018805 | Sepsis |
| D012772 | Shock, Septic |
| ID | Term |
|---|---|
| D007239 | Infections |
| D018746 | Systemic Inflammatory Response Syndrome |
| D007249 | Inflammation |
| D010335 | Pathologic Processes |
| D013568 | Pathological Conditions, Signs and Symptoms |
| D012769 | Shock |
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