Short Communication - Journal of Clinical Research and Pharmacy (2023) Volume 6, Issue 2

Development and validation of a clinical decision support system for optimizing antibiotic prescribing in hospitalized patients.

Chunyan Li ^*

Department of Pharmacy Practice, Marshall University, Huntington, USA

*Corresponding Author:

Chunyan Li
Department of Pharmacy Practice, Marshall University, Huntington, USA
E-mail: chunyanli.mu12@edu

Received: 07-April-2023, Manuscript No. AAJCRP-23-100270; Editor assigned: 07-April-2023, PreQC No. AAJCRP-23-100270 (PQ); Reviewed:21-April-2023, QC No. AAJCRP-23-100270; Revised:22-April-2023, Manuscript No. AAJCRP-23-100270 (R); Published:28-April-2023, DOI:10.35841/ aajcrp-6.2.143

Citation: Li C. Development and validation of a clinical decision support system for optimizing antibiotic prescribing in hospitalized patients. J Clin Res Pharm. 2023;6(2):143

Abstract

Antibiotic prescribing practices play a crucial role in patient outcomes and the emergence of antimicrobial resistance. This study aims to develop and validate a clinical decision support system (CDSS) to optimize antibiotic prescribing in hospitalized patients. The CDSS will provide evidence-based recommendations, taking into account patient characteristics, microbiology data, and local resistance patterns. This paper describes the development and validation process of the CDSS, highlighting its potential impact on improving patient care and reducing the misuse of antibiotics. The study involved a comprehensive literature review, development of decision algorithms, and rigorous validation using real-world patient data. The CDSS demonstrated high accuracy and reliability in providing antibiotic recommendations, and its integration into clinical workflows has the potential to enhance antibiotic stewardship and patient safety.

Introduction

Antimicrobial resistance poses a significant global health challenge, with antibiotic overuse and misuse being key drivers of this phenomenon. Hospitalized patients are particularly vulnerable to infections, and appropriate antibiotic prescribing is crucial for improving patient outcomes and reducing the emergence of resistance. However, prescribing decisions in complex clinical settings are often challenging due to the diverse factors that must be considered, including patient characteristics, local microbiology data, and resistance patterns [1].

Clinical decision support systems (CDSS) have emerged as promising tools to assist healthcare providers in making evidence-based prescribing decisions. This paper presents the development and validation of a CDSS specifically designed to optimize antibiotic prescribing in hospitalized patients. The development process of the CDSS involved several steps. Firstly, a comprehensive literature review was conducted to identify evidence-based guidelines and recommendations for antibiotic prescribing in various clinical scenarios [2].

This review formed the foundation for developing decision algorithms within the CDSS. The algorithms were designed to take into account patient-specific factors such as age, comorbidities, and laboratory results, as well as local resistance patterns and microbiology data. The integration of these multiple variables ensured a holistic approach to antibiotic decision-making. To validate the CDSS, a large dataset of real-world patient information was collected from diverse healthcare settings. This dataset included patient demographics, clinical characteristics, laboratory results, microbiology data, and antibiotic prescriptions [3].

The CDSS was then applied to this dataset, and its recommendations were compared to the actual antibiotic prescriptions made by healthcare providers. The accuracy and reliability of the CDSS in providing appropriate recommendations were assessed through various statistical analyses, including sensitivity, specificity, positive predictive value, and negative predictive value. The validation process demonstrated the CDSS's ability to consistently deliver evidence-based recommendations, aligning with best practices in antibiotic prescribing. [4].

Pharmacists' involvement in patient care significantly improves medication adherence rates, reduces medication-related problems, and enhances overall health outcomes. These interventions often involve personalized medication reviews, patient education, and ongoing monitoring of medication use. Pharmacist-led MTM interventions also lead to a reduction in healthcare costs, as improved adherence reduces hospitalizations and emergency department visits. [5].

conclusion

In conclusion, the development and validation of a CDSS for optimizing antibiotic prescribing in hospitalized patients offer significant potential benefits. By integrating evidence based recommendations, patient specific data, and local resistance patterns, the CDSS can guide healthcare providers towards more appropriate antibiotic prescribing decisions. The CDSS demonstrated high accuracy and reliability during the validation process, indicating its potential as a valuable tool for improving patient care and combating antimicrobial resistance. Further research and implementation studies are warranted to assess the impact of the CDSS on patient outcomes, antibiotic use, and resistance rates in real-world clinical settings.

References

1. Castro-Sánchez E, Moore LS, Husson F, et al. Development and validation of a clinical decision tool to predict the presence of extended-spectrum β-lactamase-producing Enterobacteriaceae in hospitalized patients. Antimicrob. Agents Chemother. 2016;60(2):888-897

   Google Scholar

2. Leibovici L, Gitelman V, Yehezkelli Y,et al. Improving empirical antibiotic treatment: prospective, nonintervention testing of a decision support system. J Intern Med. 1997;242(5):395–400.

Indexed at, Google Scholar, Cross Ref

3. Mullett CJ, Thomas JG, Smith CL, et al. Computerized antimicrobial decision support: an offline evaluation of a database-driven empiric antimicrobial guidance program in hospitalized patients with a bloodstream infection. Int J Med Inform. 2004;73(5):455–460.

Indexed at, Google Scholar, Cross Ref

4. Paul M, Andreassen S, Tacconelli E, et al. Improving empirical antibiotic treatment using TREAT, a computerized decision support system: cluster randomized trial. J Antimicrob Chemother. 2006;58(6):1238–1245.

Indexed at, Google Scholar, Cross Ref

5. 5. Sintchenko V, Coiera E, Iredell JR, et al. Comparative impact of guidelines, clinical data, and decision support on prescribing decisions: an interactive web experiment with simulated cases. J Am Med Inform Assoc. 2004;11(1):71–77.

Indexed at, Google Scholar, Cross Ref