Implementation hurdles of an interactive, integrated, point-of-care computerised decision support system for hospital antibiotic prescription

Chow, Angela; Ang, Aloysius Jian Feng; Chow, Chengzi; Ng, Tat Ming; Teng, Christine B; L, Li; Ang, Brenda; Lye, David C.

doi:10.1016/j.ijantimicag.2015.12.006

Cited by 25 publications

(39 citation statements)

References 27 publications

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“…[38,46,50,51,59,70,71,72] Six studies showed improvement of user uptake and satisfaction. In a study by Chow and co-workers, the proportion of times when CDS was used when antibiotics were prescribed increased from 23% in phase (1) to 38% in phase (2) and to 87% in phase (3).…”

Section: Resultsmentioning

confidence: 99%

“…In a study by Chow and co-workers, the proportion of times when CDS was used when antibiotics were prescribed increased from 23% in phase (1) to 38% in phase (2) and to 87% in phase (3). [70] CDS recommendations were accepted in 40% to 89% of cases. Buising and co-workers showed that the use of an approval system increased between 2005 and 2006 and reached a plateau of 250–300 new approvals per month.…”

Section: Resultsmentioning

confidence: 99%

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The effectiveness of computerised decision support on antibiotic use in hospitals: A systematic review

2017

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BackgroundInappropriate antimicrobial use has been shown to be an important determinant of the emergence of antimicrobial resistance (AMR). Health information technology (HIT) in the form of Computerised Decision Support (CDS) represents an option for improving antimicrobial prescribing and containing AMR.ObjectivesTo evaluate the evidence for CDS in improving quantitative and qualitative measures of antibiotic prescribing in inpatient hospital settings.MethodsA systematic literature search was conducted of articles published from inception to 20th December 2014 using eight electronic databases: MEDLINE, EMBASE, PUBMED, Web of Science, CINAHL, Cochrane Library, HMIC and PsychINFo. An updated systematic literature search was conducted from January 1st 2015 to October 1st 2016 using PUBMED. The search strategy used combinations of the following terms: (electronic prescribing) OR (clinical decision support) AND (antibiotic or antibacterial or antimicrobial) AND (hospital or secondary care or inpatient). Studies were evaluated for quality using a 10-point rating scale.ResultsEighty-one studies were identified matching the inclusion criteria. Seven outcome measures were evaluated: adequacy of antibiotic coverage, mortality, volume of antibiotic usage, length of stay, antibiotic cost, compliance with guidelines, antimicrobial resistance, and CDS implementation and uptake. Meta-analysis of pooled outcomes showed CDS significantly improved the adequacy of antibiotic coverage (n = 13; odds ratio [OR], 2.11 [95% CI, 1.67 to 2.66, p ≤ 0.00001]). Also, CDS was associated with marginally lowered mortality (n = 20; OR, 0.85 [CI, 0.75 to 0.96, p = 0.01]). CDS was associated with lower antibiotic utilisation, increased compliance with antibiotic guidelines and reductions in antimicrobial resistance. Conflicting effects of CDS on length of stay, antibiotic costs and system uptake were also noted.ConclusionsCDS has the potential to improve the adequacy of antibiotic coverage and marginally decrease mortality in hospital-related settings.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

The effectiveness of computerised decision support on antibiotic use in hospitals: A systematic review

2017

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“…(73)(74)(75) The first local CDSS, ARUS-C (Antibiotic Resistance Utilisation and Surveillance-Control), was developed by Tan Tock Seng Hospital in 2009, prior to MOH's ASP fundingwhich spurred attempts to adopt ARUS-C or develop new CDSSs after 2011. (76) By using appropriate algorithms, CDSSs could potentially provide patient-specific, evidence-based antibiotic recommendations at the point of prescribing, an advantage that is potentially magnified by the availability of other patient data within electronic medical records such as drug allergies, culture results, and renal and/or liver function.…”

Section: Hospital Antibiotic Prescriptionmentioning

confidence: 99%

Ten-year narrative review on antimicrobial resistance in Singapore

Chua¹,

Kwa²,

Tan³

et al. 2019

smedj

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Antimicrobial resistance (AMR) results in drug-resistant infections that are harder to treat, subsequently leading to increased morbidity and mortality. In 2008, we reviewed the problem of AMR in Singapore, limiting our discussion to the human healthcare sector. Ten years later, we revisit this issue again, reviewing current efforts to contain it in order to understand the progress made as well as current and emerging challenges. Although a significant amount of work has been done to control AMR and improve antibiotic prescribing in Singapore, most of it has focused on the hospital setting, with mixed impact. The role of antibiotic use and AMR in food animals and the environment-and the link to human health-is better understood today. This issue of AMR encompasses both human health as well as animal/food safety, and efforts to control it will need to continually evolve to maintain or improve on current gains.

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“…In Singapore, considerable efforts have been made to address antibiotic resistance [ 20 ]. Although computerized decision support systems have been developed to guide antibiotic prescribing, they are largely based on guidelines drawn by expert consensus and not on actual data derived from local patients [ 21 ]. Furthermore, most studies on antibiotic prescribing focus on understanding behaviors and perceptions or finding associative factors for antibiotic prescribing decisions [ 22 – 25 ].…”

Section: Introductionmentioning

confidence: 99%

Risk prediction models to guide antibiotic prescribing: a study on adult patients with uncomplicated upper respiratory tract infections in an emergency department

Wong

Aung

Lian

et al. 2020

Antimicrob Resist Infect Control

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Background Appropriate antibiotic prescribing is key to combating antimicrobial resistance. Upper respiratory tract infections (URTIs) are common reasons for emergency department (ED) visits and antibiotic use. Differentiating between bacterial and viral infections is not straightforward. We aim to provide an evidence-based clinical decision support tool for antibiotic prescribing using prediction models developed from local data. Methods Seven hundred-fifteen patients with uncomplicated URTI were recruited and analysed from Singapore’s busiest ED, Tan Tock Seng Hospital, from June 2016 to November 2018. Confirmatory tests were performed using the multiplex polymerase chain reaction (PCR) test for respiratory viruses and point-of-care test for C-reactive protein. Demographic, clinical and laboratory data were extracted from the hospital electronic medical records. Seventy percent of the data was used for training and the remaining 30% was used for validation. Decision trees, LASSO and logistic regression models were built to predict when antibiotics were not needed. Results The median age of the cohort was 36 years old, with 61.2% being male. Temperature and pulse rate were significant factors in all 3 models. The area under the receiver operating curve (AUC) on the validation set for the models were similar. (LASSO: 0.70 [95% CI: 0.62–0.77], logistic regression: 0.72 [95% CI: 0.65–0.79], decision tree: 0.67 [95% CI: 0.59–0.74]). Combining the results from all models, 58.3% of study participants would not need antibiotics. Conclusion The models can be easily deployed as a decision support tool to guide antibiotic prescribing in busy EDs.

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Implementation hurdles of an interactive, integrated, point-of-care computerised decision support system for hospital antibiotic prescription

Cited by 25 publications

References 27 publications

The effectiveness of computerised decision support on antibiotic use in hospitals: A systematic review

The effectiveness of computerised decision support on antibiotic use in hospitals: A systematic review

Ten-year narrative review on antimicrobial resistance in Singapore

Risk prediction models to guide antibiotic prescribing: a study on adult patients with uncomplicated upper respiratory tract infections in an emergency department

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