1
|
Duffy J, Berger FH, Cheng I, Shelton D, Galanaud JP, Selby R, Laing K, Fedorovsky T, Matelski J, Hall J. Implementation of the YEARS algorithm to optimise pulmonary embolism diagnostic workup in the emergency department. BMJ Open Qual 2023; 12:bmjoq-2022-002119. [PMID: 37217241 DOI: 10.1136/bmjoq-2022-002119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2022] [Accepted: 04/29/2023] [Indexed: 05/24/2023] Open
Abstract
BACKGROUND Excessive use of CT pulmonary angiography (CTPA) to investigate pulmonary embolism (PE) in the emergency department (ED) contributes to adverse patient outcomes. Non-invasive D-dimer testing, in the context of a clinical algorithm, may help decrease unnecessary imaging but this has not been widely implemented in Canadian EDs. AIM To improve the diagnostic yield of CTPA for PE by 5% (absolute) within 12 months of implementing the YEARS algorithm. MEASURES AND DESIGN Single centre study of all ED patients >18 years investigated for PE with D-dimer and/or CTPA between February 2021 and January 2022. Primary and secondary outcomes were the diagnostic yield of CTPA and frequency of CTPA ordered compared with baseline. Process measures included the percentage of D-dimer tests ordered with CTPA and CTPAs ordered with D-dimers <500 µg/L Fibrinogen Equivalent Units (FEU). The balancing measure was the number of PEs identified on CTPA within 30 days of index visit. Multidisciplinary stakeholders developed plan- do-study-act cycles based on the YEARS algorithm. RESULTS Over 12 months, 2695 patients were investigated for PE, of which 942 had a CTPA. Compared with baseline, the CTPA yield increased by 2.9% (12.6% vs 15.5%, 95% CI -0.06% to 5.9%) and the proportion of patients that underwent CTPA decreased by 11.4% (46.4% vs 35%, 95% CI -14.1% to -8.8%). The percentage of CTPAs ordered with a D-dimer increased by 26.3% (30.7% vs 57%, 95% CI 22.2% 30.3%) and there were two missed PE (2/2695, 0.07%). IMPACT Implementing the YEARS criteria may safely improve the diagnostic yield of CTPAs and reduce the number of CTPAs completed without an associated increase in missed clinically significant PEs. This project provides a model for optimising the use of CTPA in the ED.
Collapse
Affiliation(s)
- Juliana Duffy
- Division of Emergency Medicine, Department of Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Ferco Henricus Berger
- Department of Medical Imaging, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Department of Medical Imaging, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
| | - Ivy Cheng
- Division of Emergency Medicine, Department of Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Department of Emergency Services, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
| | - Dominick Shelton
- Department of Emergency Services, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
- Department of Family & Community Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Jean-Philippe Galanaud
- Department of Medicine, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
- Department of Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Rita Selby
- Department of Laboratory Medicine & Pathobiology, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
- Department of Laboratory Medicine & Pathobiology, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Kristine Laing
- Department of Emergency Services, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
| | - Tali Fedorovsky
- Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - John Matelski
- Biostatistics Research Unit, University Health Network, Toronto, Ontario, Canada
| | - Justin Hall
- Division of Emergency Medicine, Department of Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- Department of Emergency Services, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
| |
Collapse
|
2
|
Gurazada SG, Gao SC, Burstein F, Buntine P. Predicting Patient Length of Stay in Australian Emergency Departments Using Data Mining. SENSORS 2022; 22:s22134968. [PMID: 35808458 PMCID: PMC9269793 DOI: 10.3390/s22134968] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 06/14/2022] [Accepted: 06/28/2022] [Indexed: 02/01/2023]
Abstract
Length of Stay (LOS) is an important performance metric in Australian Emergency Departments (EDs). Recent evidence suggests that an LOS in excess of 4 h may be associated with increased mortality, but despite this, the average LOS continues to remain greater than 4 h in many EDs. Previous studies have found that Data Mining (DM) can be used to help hospitals to manage this metric and there is continued research into identifying factors that cause delays in ED LOS. Despite this, there is still a lack of specific research into how DM could use these factors to manage ED LOS. This study adds to the emerging literature and offers evidence that it is possible to predict delays in ED LOS to offer Clinical Decision Support (CDS) by using DM. Sixteen potentially relevant factors that impact ED LOS were identified through a literature survey and subsequently used as predictors to create six Data Mining Models (DMMs). An extract based on the Victorian Emergency Minimum Dataset (VEMD) was used to obtain relevant patient details and the DMMs were implemented using the Weka Software. The DMMs implemented in this study were successful in identifying the factors that were most likely to cause ED LOS > 4 h and also identify their correlation. These DMMs can be used by hospitals, not only to identify risk factors in their EDs that could lead to ED LOS > 4 h, but also to monitor these factors over time.
Collapse
Affiliation(s)
- Sai Gayatri Gurazada
- Faculty of Information Technology, Monash University, Clayton, Melbourne, VIC 3800, Australia
| | - Shijia Caddie Gao
- Faculty of Information Technology, Monash University, Clayton, Melbourne, VIC 3800, Australia
| | - Frada Burstein
- Faculty of Information Technology, Monash University, Clayton, Melbourne, VIC 3800, Australia
| | - Paul Buntine
- Eastern Health Clinical School Monash University, Box Hill, Melbourne, VIC 3128, Australia
| |
Collapse
|
3
|
Lunn Y, Patel R, Sokphat TS, Bourn L, Fields K, Fitzgerald A, Sundaresan V, Thomas G, Korvink M, Gunn LH. Assessing Hospital Resource Utilization with Application to Imaging for Patients Diagnosed with Prostate Cancer. Healthcare (Basel) 2022; 10:healthcare10020248. [PMID: 35206863 PMCID: PMC8872431 DOI: 10.3390/healthcare10020248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 01/25/2022] [Accepted: 01/25/2022] [Indexed: 02/04/2023] Open
Abstract
Resource utilization measures are typically modeled by relying on clinical characteristics. However, in some settings, those clinical markers are not available, and hospitals are unable to explore potential inefficiencies or resource misutilization. We propose a novel approach to exploring misutilization that solely relies on administrative data in the form of patient characteristics and competing resource utilization, with the latter being a novel addition. We demonstrate this approach in a 2019 patient cohort diagnosed with prostate cancer (n = 51,111) across 1056 U.S. healthcare facilities using Premier, Inc.’s (Charlotte, NC, USA) all payor databases. A multivariate logistic regression model was fitted using administrative information and competing resources utilization. A decision curve analysis informed by industry average standards of utilization allows for a definition of misutilization with regards to these industry standards. Odds ratios were extracted at the patient level to demonstrate differences in misutilization by patient characteristics, such as race; Black individuals experienced higher under-utilization compared to White individuals (p < 0.0001). Volume-adjusted Poisson rate regression models allow for the identification and ranking of facilities with large departures in utilization. The proposed approach is scalable and easily generalizable to other diseases and resources and can be complemented with clinical information from electronic health record information, when available.
Collapse
Affiliation(s)
- Yazmine Lunn
- School of Data Science, University of North Carolina at Charlotte, Charlotte, NC 28223, USA; (Y.L.); (R.P.); (T.S.S.); (L.B.); (K.F.); (A.F.); (V.S.)
| | - Rudra Patel
- School of Data Science, University of North Carolina at Charlotte, Charlotte, NC 28223, USA; (Y.L.); (R.P.); (T.S.S.); (L.B.); (K.F.); (A.F.); (V.S.)
| | - Timothy S. Sokphat
- School of Data Science, University of North Carolina at Charlotte, Charlotte, NC 28223, USA; (Y.L.); (R.P.); (T.S.S.); (L.B.); (K.F.); (A.F.); (V.S.)
- Department of Public Health Sciences, University of North Carolina at Charlotte, Charlotte, NC 28223, USA;
| | - Laura Bourn
- School of Data Science, University of North Carolina at Charlotte, Charlotte, NC 28223, USA; (Y.L.); (R.P.); (T.S.S.); (L.B.); (K.F.); (A.F.); (V.S.)
- Department of Public Health Sciences, University of North Carolina at Charlotte, Charlotte, NC 28223, USA;
| | - Khalil Fields
- School of Data Science, University of North Carolina at Charlotte, Charlotte, NC 28223, USA; (Y.L.); (R.P.); (T.S.S.); (L.B.); (K.F.); (A.F.); (V.S.)
- Department of Public Health Sciences, University of North Carolina at Charlotte, Charlotte, NC 28223, USA;
| | - Anna Fitzgerald
- School of Data Science, University of North Carolina at Charlotte, Charlotte, NC 28223, USA; (Y.L.); (R.P.); (T.S.S.); (L.B.); (K.F.); (A.F.); (V.S.)
- Department of Public Health Sciences, University of North Carolina at Charlotte, Charlotte, NC 28223, USA;
| | - Vandana Sundaresan
- School of Data Science, University of North Carolina at Charlotte, Charlotte, NC 28223, USA; (Y.L.); (R.P.); (T.S.S.); (L.B.); (K.F.); (A.F.); (V.S.)
- Department of Public Health Sciences, University of North Carolina at Charlotte, Charlotte, NC 28223, USA;
| | - Greeshma Thomas
- Department of Public Health Sciences, University of North Carolina at Charlotte, Charlotte, NC 28223, USA;
| | | | - Laura H. Gunn
- School of Data Science, University of North Carolina at Charlotte, Charlotte, NC 28223, USA; (Y.L.); (R.P.); (T.S.S.); (L.B.); (K.F.); (A.F.); (V.S.)
- Department of Public Health Sciences, University of North Carolina at Charlotte, Charlotte, NC 28223, USA;
- Faculty of Medicine, School of Public Health, Imperial College London, London W6 8RP, UK
- Correspondence:
| |
Collapse
|