Integration of Explicit Criteria in a Clinical Decision Support System Through Evaluation of Acute Kidney Injury Events

In Clinical Decision Support System (CDSS), relevance of alerts is essential to limit alert fatigue and risk of overriding relevant alerts by health professionals. Detection of acute kidney injury (AKI) situations is of great importance in clinical practice and could improve quality of care. Nevertheless, to our knowledge, no explicit rule has been created to detect AKI situations in CDSS. The objective of the study was to implement an AKI detection rule based on KDIGO criteria in a CDSS and to optimize this rule to increase its relevance in clinical pharmacy use. Two explicit rules were implemented in a CDSS (basic AKI rule and improved AKI rule), based on KDIGO criteria. Only the improved rule was optimized by a group of experts during the two-month study period. The CDSS provided 1,125 alerts on AKI situations (i.e. 643 were triggered for the basic AKI rule and 482 for the improved AKI rule). As the study proceeds, the pharmaceutically and medically relevance of alerts from the improved AKI rule increased. A ten-fold increase was shown for the improved AKI rule compared to the basic AKI rule. The study highlights the usefulness of a multidisciplinary review to enhance explicit rules integrated in CDSS. The improved AKI is able to detect AKI situations and can improve workflow of health professionals.

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Sustained effects of a clinical decision support system for acute kidney injury

Nephrology Dialysis Transplantation ◽

10.1093/ndt/gfaa099 ◽

2020 ◽

Vol 35 (10) ◽

pp. 1819-1821

Author(s):

Ayham Bataineh ◽

Dilhari Dealmeida ◽

Andrew Bilderback ◽

Richard Ambrosino ◽

Mohammed J Al-Jaghbeer ◽

...

Keyword(s):

Acute Kidney Injury ◽

Decision Support ◽

Decision Support System ◽

Clinical Decision Support ◽

Support System ◽

Clinical Decision Support System ◽

Kidney Injury ◽

Clinical Decision ◽

Sustained Effects

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Effect of clinical decision support systems on clinical outcome for acute kidney injury: a systematic review and meta-analysis

BMC Nephrology ◽

10.1186/s12882-021-02459-y ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Youlu Zhao ◽

Xizi Zheng ◽

Jinwei Wang ◽

Damin Xu ◽

Shuangling Li ◽

...

Keyword(s):

Acute Kidney Injury ◽

Decision Support ◽

Decision Support System ◽

Decision Support Systems ◽

Clinical Decision Support ◽

Support System ◽

Clinical Decision Support System ◽

Kidney Injury ◽

Clinical Decision Support Systems ◽

Clinical Decision

Abstract Background Clinical decision support systems including both electronic alerts and care bundles have been developed for hospitalized patients with acute kidney injury. Methods Electronic databases were searched for randomized, before-after and cohort studies that implemented a clinical decision support system for hospitalized patients with acute kidney injury between 1990 and 2019. The studies must describe their impact on care processes, patient-related outcomes, or hospital length of stay. The clinical decision support system included both electronic alerts and care bundles. Results We identified seven studies involving 32,846 participants. Clinical decision support system implementation significantly reduced mortality (OR 0.86; 95 % CI, 0.75–0.99; p = 0.040, I2 = 65.3 %; n = 5 studies; N = 30,791 participants) and increased the proportion of acute kidney injury recognition (OR 3.12; 95 % CI, 2.37–4.10; p < 0.001, I2 = 77.1 %; n = 2 studies; N = 25,121 participants), and investigations (OR 3.07; 95 % CI, 2.91–3.24; p < 0.001, I2 = 0.0 %; n = 2 studies; N = 25,121 participants). Conclusions Nonrandomized controlled trials of clinical decision support systems for acute kidney injury have yielded evidence of improved patient-centered outcomes and care processes. This review is limited by the low number of randomized trials and the relatively short follow-up period.

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

Applied Sciences ◽

10.3390/app11135810 ◽

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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