scholarly journals Use of quick sequential organ failure assessment score-based sepsis clinical decision support system may be helpful to predict sepsis development

2021 ◽  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Compliance Rate ◽  
Clinical Decision ◽  
Suspected Infection ◽  
Before And After ◽  
Bundle Compliance ◽  
Resuscitation Bundle

Objectives: A sepsis clinical decision support system (CDSS) can facilitate quicker sepsis detection and treatment and consequently improve outcomes. We developed a qSOFA-based sepsis CDSS and evaluated its impact on compliance with a 3-hour resuscitation bundle for patients with sepsis. Methods: This before-and-after study included consecutive adult patients with suspected infection and qSOFA scores of ≥ 2 at their emergency department (ED) presentation of a tertiary care hospital. Sepsis was defined according to the Sepsis-3 criteria. We evaluated the 3-hour resuscitation bundle compliance rate for control patients from July through August 2016, for patients using the qSOFA-based sepsis CDSS from September through December 2016, and the impact of the system using multivariable logistic regression analysis. Results: Of 306 patients with suspected infection and positive qSOFA scores at presentation, 265 patients (86.6%) developed sepsis (including 71 patients with septic shock). The 3-hour resuscitation bundle compliance rate did not differ significantly between the patients before and after the routine implementation of the qSOFA-based sepsis CDSS (63.7% vs. 52.6%; P = 0.071). Multivariate analysis showed that age (AOR [adjusted odds ratio], 1.033; P = 0.002) and body temperature (AOR, 1.677; P < 0.001) were associated with bundle compliance. Conclusions: Among patients with a positive qSOFA score at presentation, sepsis developed in 86.6%, which means the qSOFA-based sepsis CDSS may be helpful; however, it was not associated with improved bundle compliance. Future quality improvement studies with multifactorial, hospital-wide approaches using sepsis CDSS tools are warranted.

scholarly journals 1004. Clinical Decision Support System Alerts for HIV Retention in Care – A Pilot Implementation Research Study

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S530-S531
Author(s):  
Michael Leonard ◽  
Rachel P Weber ◽  
Laurence Brunet ◽  
Bernard Davis ◽  
Christopher Polk ◽  
...  
Keyword(s):  
Decision Support ◽  
Pilot Study ◽  
Clinical Decision Support ◽  
Support System ◽  
Implementation Research ◽  
Clinical Decision Support System ◽  
Retention In Care ◽  
Clinical Decision ◽  
Before And After ◽  
Over Time

Abstract Background Clinical decision support system (CDSS) alerts may help retain people living with HIV (PLWH) in care. A system of CDSS alerts utilizing the CHORUS™ portal was developed to identify PLWH at risk of being lost to care. To evaluate feasibility for a larger scale study, a before and after implementation research pilot study was implemented in the OPERA Cohort at three clinic sites in a southeastern US city. Methods Periods without intervention (before) or with CDSS alerts (after) were followed by 3 months of follow up. The study population consisted of PLWH with ≥ 1 electronic health record entry in the 2 years prior to, or during, the before or after period (Fig 1). To support clinicians through a discrete implementation strategy, alerts warning of suboptimal patient attendance were generated daily for the eligible PLWH at each site; providers or other clinic staff could respond to the alerts (Fig 2). Alerts, responses, and visits (i.e., meeting with provider or HIV lab measurement) were characterized. The proportion of PLWH with ≥ 1 visit in the before and after periods were compared at each site by Pearson’s Chi-square. Figure 1. Pilot study timeline Figure 2. CDSS alert criteria and response options Results A total of 12,230 PLWH were eligible (sites A: 11,271; B: 733; C: 1,344 PLWH), with &gt; 75% in both the before and after periods. The ratio of alerts to responses was 11.9 at site A (2,245 alerts to 189 responses in 309 days; Fig 3A), and comparatively lower at sites B (756 alerts to 334 responses in 352 days, ratio=2.2; Fig 3B) and C (1,305 alerts to 896 responses in 246 days, ratio=1.5; Fig 3C). Responses to alerts were sporadic at sites A and B and consistent at site C. After the intervention, the proportion of PLWH with ≥ 1 visit stayed the same at site A (46% in both periods; p=0.47), decreased at site B (91% to 80%; p&lt; 0.01), and increased at site C (72% to 81%; p&lt; 0.01). Figure 3. Alerts and responses over time in (A) Site A, (B) Site B, and (C) Site C Conclusion This pilot study was ecological by design: measures of retention in care were compared over two calendar periods, without accounting for changes in study populations, clinic characteristics, and policies in place over time (which could have impacted clinic attendance). Though engagement with the CDSS was suboptimal at some sites, this implementation pilot study has demonstrated the ability to implement a CDSS aimed at identifying at-risk PLWH, while highlighting areas for improvement in future larger scale studies. Disclosures Joel Wesley Thompson, MHS, PA-C, AAHIVS, DFAAPA, MHS, PA-C, AAHIVS, DFAAPA, Gilead (Shareholder, Speaker’s Bureau)Janssen (Speaker’s Bureau)Theratechnologies (Speaker’s Bureau)ViiV (Speaker’s Bureau) Tammeka Evans, MoP, ViiV Healthcare (Employee)

1309-P: Utilizing a Clinical Decision Support System for Hypoglycemia Management

Diabetes ◽  
2019 ◽  
Vol 68 (Supplement 1) ◽  
pp. 1309-P
Author(s):  
JACQUELYN R. GIBBS ◽  
KIMBERLY BERGER ◽  
MERCEDES FALCIGLIA
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision

A Clinical Decision Support System for Assessing the Risk of Cardiovascular Diseases in Diabetic Hemodialysis Patients

2020 ◽  
Vol 16 (3) ◽  
pp. 262-269
Author(s):  
Tahere Talebi Azad Boni ◽  
Haleh Ayatollahi ◽  
Mostafa Langarizadeh
Keyword(s):  
Decision Support ◽  
Cardiovascular Diseases ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Diabetic Patients ◽  
K Value ◽  
Care Plans

Background: One of the greatest challenges in the field of medicine is the increasing burden of chronic diseases, such as diabetes. Diabetes may cause several complications, such as kidney failure which is followed by hemodialysis and an increasing risk of cardiovascular diseases. Objective: The purpose of this research was to develop a clinical decision support system for assessing the risk of cardiovascular diseases in diabetic patients undergoing hemodialysis by using a fuzzy logic approach. Methods: This study was conducted in 2018. Initially, the views of physicians on the importance of assessment parameters were determined by using a questionnaire. The face and content validity of the questionnaire was approved by the experts in the field of medicine. The reliability of the questionnaire was calculated by using the test-retest method (r = 0.89). This system was designed and implemented by using MATLAB software. Then, it was evaluated by using the medical records of diabetic patients undergoing hemodialysis (n=208). Results: According to the physicians' point of view, the most important parameters for assessing the risk of cardiovascular diseases were glomerular filtration, duration of diabetes, age, blood pressure, type of diabetes, body mass index, smoking, and C reactive protein. The system was designed and the evaluation results showed that the values of sensitivity, accuracy, and validity were 85%, 92% and 90%, respectively. The K-value was 0.62. Conclusion: The results of the system were largely similar to the patients’ records and showed that the designed system can be used to help physicians to assess the risk of cardiovascular diseases and to improve the quality of care services for diabetic patients undergoing hemodialysis. By predicting the risk of the disease and classifying patients in different risk groups, it is possible to provide them with better care plans.

Massive transfusion experience, current practice and decision support: A survey of Australian and New Zealand anaesthetists

2021 ◽  
pp. 0310057X2097403
Author(s):  
Brenton J Sanderson ◽  
Jeremy D Field ◽  
Lise J Estcourt ◽  
Erica M Wood ◽  
Enrico W Coiera
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Response Rate ◽  
Massive Transfusion ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Transfusion Practice

Massive transfusions guided by massive transfusion protocols are commonly used to manage critical bleeding, when the patient is at significant risk of morbidity and mortality, and multiple timely decisions must be made by clinicians. Clinical decision support systems are increasingly used to provide patient-specific recommendations by comparing patient information to a knowledge base, and have been shown to improve patient outcomes. To investigate current massive transfusion practice and the experiences and attitudes of anaesthetists towards massive transfusion and clinical decision support systems, we anonymously surveyed 1000 anaesthetists and anaesthesia trainees across Australia and New Zealand. A total of 228 surveys (23.6%) were successfully completed and 227 were analysed for a 23.3% response rate. Most respondents were involved in massive transfusions infrequently (88.1% managed five or fewer massive transfusion protocols per year) and worked at hospitals which have massive transfusion protocols (89.4%). Massive transfusion management was predominantly limited by timely access to point-of-care coagulation assessment and by competition with other tasks, with trainees reporting more significant limitations compared to specialists. The majority of respondents reported that they were likely, or very likely, both to use (73.1%) and to trust (85%) a clinical decision support system for massive transfusions, with no significant difference between anaesthesia trainees and specialists ( P = 0.375 and P = 0.73, respectively). While the response rate to our survey was poor, there was still a wide range of massive transfusion experience among respondents, with multiple subjective factors identified limiting massive transfusion practice. We identified several potential design features and barriers to implementation to assist with the future development of a clinical decision support system for massive transfusion, and overall wide support for a clinical decision support system for massive transfusion among respondents.

scholarly journals An Evolutionary Belief Rule-Based Clinical Decision Support System to Predict COVID-19 Severity under Uncertainty

2021 ◽  
Vol 11 (13) ◽  
pp. 5810
Author(s):  
Faisal Ahmed ◽  
Mohammad Shahadat Hossain ◽  
Raihan Ul Islam ◽  
Karl Andersson
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Clinical Decision ◽  
Experimental Result ◽  
Rule Base ◽  
Rule Based ◽  
Belief Rule Base

Accurate and rapid identification of the severe and non-severe COVID-19 patients is necessary for reducing the risk of overloading the hospitals, effective hospital resource utilization, and minimizing the mortality rate in the pandemic. A conjunctive belief rule-based clinical decision support system is proposed in this paper to identify critical and non-critical COVID-19 patients in hospitals using only three blood test markers. The experts’ knowledge of COVID-19 is encoded in the form of belief rules in the proposed method. To fine-tune the initial belief rules provided by COVID-19 experts using the real patient’s data, a modified differential evolution algorithm that can solve the constraint optimization problem of the belief rule base is also proposed in this paper. Several experiments are performed using 485 COVID-19 patients’ data to evaluate the effectiveness of the proposed system. Experimental result shows that, after optimization, the conjunctive belief rule-based system achieved the accuracy, sensitivity, and specificity of 0.954, 0.923, and 0.959, respectively, while for disjunctive belief rule base, they are 0.927, 0.769, and 0.948. Moreover, with a 98.85% AUC value, our proposed method shows superior performance than the four traditional machine learning algorithms: LR, SVM, DT, and ANN. All these results validate the effectiveness of our proposed method. The proposed system will help the hospital authorities to identify severe and non-severe COVID-19 patients and adopt optimal treatment plans in pandemic situations.

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