Improved blood utilization using real-time clinical decision support

Transfusion ◽  
2013 ◽  
Vol 54 (5) ◽  
pp. 1358-1365 ◽  
Author(s):  
Lawrence T. Goodnough ◽  
Lisa Shieh ◽  
Eric Hadhazy ◽  
Nathalie Cheng ◽  
Paul Khari ◽  
...  
Keyword(s):  
Decision Support ◽  
Real Time ◽  
Clinical Decision Support ◽  
Clinical Decision ◽  
Blood Utilization

scholarly journals Improving Blood Transfusion Practices in a Community Hospital Setting: Our Experience with Real-Time Clinical Decision Support

2018 ◽  
Vol 6 (3) ◽  
pp. 67
Author(s):  
Muhammad Sardar ◽  
Muhammad Azharuddin ◽  
Ananta Subedi ◽  
Prateek Ghatage ◽  
Doantarang Du ◽  
...  
Keyword(s):  
Decision Support ◽  
Real Time ◽  
Clinical Decision Support ◽  
Community Hospital ◽  
Statistical Significance ◽  
Hospital Setting ◽  
Clinical Decision ◽  
Good Evidence ◽  
Blood Utilization ◽  
The Impact

There is good evidence that 50% or more of red blood cell (RBC) transfusions are unnecessary. To curtail inappropriate RBC transfusions at our hospital, real-time clinical decision support was implemented in our electronic medical record (EMR) that alerts clinicians to the patient’s most recent pretransfusion hemoglobin value upon order entry and provides Best Practice Advisory. This is a soft pop-up alert which is activated when the hemoglobin exceeds 7 g/dL. The ordering clinician can either honor (by cancelling the order) or override the alert. We studied the impact of the alert on blood utilization during a 3-month period (November 2016 to January 2017). For patients who were transfused despite the alert, a retrospective review of the medical chart was performed to determine whether or not the transfusion was clinically indicated. During the study period, 178 of the 895 RBC transfusion orders (20%) triggered the alert. After excluding duplicates, 144 orders were included in our analysis. Most of these orders (124/144, 86%) were carried out despite the alert. According to our chart review, 48% of the alert transfusions could be considered inappropriate, with hemodynamically stable, asymptomatic anemia being the leading indication. Of clinical services, orthopedic surgery had the highest rate of overriding the alert with no clinical justification (70%). The number of RBC transfusions dropped from 313.5 units per month (preintervention period) to 293.2 units per month (postintervention period)—a 6.5% decrease. Real-time clinical decision support may reduce the number of inappropriate RBC transfusions in a community hospital setting, though in our study, the decrease in blood utilization (6.5%) did not reach statistical significance.

scholarly journals Transfusion Medicine Informatics: Analysis of User Burden from Clinical Decision Support Alerts in Promoting Patient Blood Management

2020 ◽  
Vol 154 (Supplement_1) ◽  
pp. S123-S124
Author(s):  
H C Tsang ◽  
P Mathias ◽  
N Hoffman ◽  
M B Pagano
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Blood Product ◽  
Clinical Decision ◽  
Blood Products ◽  
Blood Management ◽  
Before And After ◽  
Blood Utilization ◽  
Transfusion Ratio ◽  
Major Project

Abstract Introduction/Objective To increase efficiency of blood product ordering and delivery processes and improve appropriateness of orders, a major project to implement clinical decision support (CDS) alerts in the electronic medical record (EMR) was undertaken. A design team was assembled including hospital and laboratory medicine information technology and clinical informatics, transfusion services, nursing and clinical services from medical and surgical specialties. Methods Consensus-derived thresholds in hemoglobin/hematocrit, platelet count, INR, and fibrinogen for red blood cell (RBC), platelet, plasma, and cryoprecipitate blood products CDS alerts were determined. Data from the EMR and laboratory information system were queried from the 12-month period before and after implementation and the data was analyzed. Results During the analysis period, 5813 RBC (avg. monthly = 484), 1040 platelet (avg. monthly = 87), 423 plasma (avg. monthly = 35), and 88 cryoprecipitate (avg. monthly = 7) alerts fired. The average time it took for a user to respond was 5.175 seconds. The total amount of time alerts displayed over 12 months was 5813 seconds (~97 minutes of user time) compared to 56503 blood products transfused. Of active CDS alerts, hemoglobin/RBC alerts fired most often with ~1:5 (31141 RBC units) alert to transfusion ratio and 4% of orders canceled (n=231) when viewing the alert, platelet alerts fired with ~1:15 (15385 platelet units) alert to transfusion ratio and 6% orders canceled (n=66), INR/plasma alerts fired with ~1:21 (8793 plasma units) alert to transfusion ratio and 10% orders canceled (n=41), cryoprecipitate alerts fired with ~1:13 (1184 cryoprecipitate units) alert to transfusion ratio and 10% orders canceled (n=9). Overall monthly blood utilization normalized to 1000 patient discharges did not appear to have statistically significant differences comparing pre- versus post-go-live, except a potentially significant increase in monthly plasma usage at one facility with p = 0.34, although possibly due to an outlier single month of heavy usage. Conclusion Clinical decision support alerts can guide provider ordering with minimal user burden. This resulted in increased safety and quality use of the ordering process, although overall blood utilization did not appear to change significantly.

Real-Time Clinical Decision Support at the Point of Care

2019 ◽  
pp. 125-133
Author(s):  
Ana Margarida Pereira ◽  
Cristina Jácome ◽  
Rita Amaral ◽  
Tiago Jacinto ◽  
João A Fonseca
Keyword(s):  
Decision Support ◽  
Real Time ◽  
Clinical Decision Support ◽  
Point Of Care ◽  
Clinical Decision

scholarly journals A scoping review of clinical decision support tools that generate new knowledge to support decision making in real time

2020 ◽  
Vol 27 (12) ◽  
pp. 1968-1976
Author(s):  
Anna Ostropolets ◽  
Linying Zhang ◽  
George Hripcsak
Keyword(s):  
Decision Support ◽  
Real Time ◽  
Clinical Decision Support ◽  
Scoping Review ◽  
Clinical Care ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
The United States ◽  
Quality Outcomes ◽  
New Knowledge

Abstract Objective A growing body of observational data enabled its secondary use to facilitate clinical care for complex cases not covered by the existing evidence. We conducted a scoping review to characterize clinical decision support systems (CDSSs) that generate new knowledge to provide guidance for such cases in real time. Materials and Methods PubMed, Embase, ProQuest, and IEEE Xplore were searched up to May 2020. The abstracts were screened by 2 reviewers. Full texts of the relevant articles were reviewed by the first author and approved by the second reviewer, accompanied by the screening of articles’ references. The details of design, implementation and evaluation of included CDSSs were extracted. Results Our search returned 3427 articles, 53 of which describing 25 CDSSs were selected. We identified 8 expert-based and 17 data-driven tools. Sixteen (64%) tools were developed in the United States, with the others mostly in Europe. Most of the tools (n = 16, 64%) were implemented in 1 site, with only 5 being actively used in clinical practice. Patient or quality outcomes were assessed for 3 (18%) CDSSs, 4 (16%) underwent user acceptance or usage testing and 7 (28%) functional testing. Conclusions We found a number of CDSSs that generate new knowledge, although only 1 addressed confounding and bias. Overall, the tools lacked demonstration of their utility. Improvement in clinical and quality outcomes were shown only for a few CDSSs, while the benefits of the others remain unclear. This review suggests a need for a further testing of such CDSSs and, if appropriate, their dissemination.

scholarly journals A 12-hospital prospective evaluation of a clinical decision support prognostic algorithm based on logistic regression as a form of machine learning to facilitate decision making for patients with suspected COVID-19

PLoS ONE ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. e0262193
Author(s):  
Monica I. Lupei ◽  
Danni Li ◽  
Nicholas E. Ingraham ◽  
Karyn D. Baum ◽  
Bradley Benson ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Decision Support ◽  
Real Time ◽  
Acute Care ◽  
Clinical Decision Support ◽  
Invasive Mechanical Ventilation ◽  
Clinical Decision ◽  
Hospital System ◽  
Temporal Validation

Objective To prospectively evaluate a logistic regression-based machine learning (ML) prognostic algorithm implemented in real-time as a clinical decision support (CDS) system for symptomatic persons under investigation (PUI) for Coronavirus disease 2019 (COVID-19) in the emergency department (ED). Methods We developed in a 12-hospital system a model using training and validation followed by a real-time assessment. The LASSO guided feature selection included demographics, comorbidities, home medications, vital signs. We constructed a logistic regression-based ML algorithm to predict “severe” COVID-19, defined as patients requiring intensive care unit (ICU) admission, invasive mechanical ventilation, or died in or out-of-hospital. Training data included 1,469 adult patients who tested positive for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) within 14 days of acute care. We performed: 1) temporal validation in 414 SARS-CoV-2 positive patients, 2) validation in a PUI set of 13,271 patients with symptomatic SARS-CoV-2 test during an acute care visit, and 3) real-time validation in 2,174 ED patients with PUI test or positive SARS-CoV-2 result. Subgroup analysis was conducted across race and gender to ensure equity in performance. Results The algorithm performed well on pre-implementation validations for predicting COVID-19 severity: 1) the temporal validation had an area under the receiver operating characteristic (AUROC) of 0.87 (95%-CI: 0.83, 0.91); 2) validation in the PUI population had an AUROC of 0.82 (95%-CI: 0.81, 0.83). The ED CDS system performed well in real-time with an AUROC of 0.85 (95%-CI, 0.83, 0.87). Zero patients in the lowest quintile developed “severe” COVID-19. Patients in the highest quintile developed “severe” COVID-19 in 33.2% of cases. The models performed without significant differences between genders and among race/ethnicities (all p-values > 0.05). Conclusion A logistic regression model-based ML-enabled CDS can be developed, validated, and implemented with high performance across multiple hospitals while being equitable and maintaining performance in real-time validation.

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