Renal Drug Dosing. Effectiveness of Outpatient Pharmacist-Based vs. Prescriber-Based Clinical Decision Support Systems

Pharmacists as important prescribers of coronavirus disease 2019 (COVID-19) antivirals

Although pharmacists are key members of the healthcare team, they are currently ineligible to independently prescribe the oral coronavirus disease 2019 (COVID-19) antivirals. We report the roles pharmacists have undertaken during the COVID-19 pandemic and provide evidence for the support of independent oral COVID-19 antiviral prescribing.

Clinical Decision Support System Based on Hybrid Knowledge Modeling: A Case Study of Chronic Kidney Disease-Mineral and Bone Disorder Treatment

Clinical decision support systems (CDSSs) represent the latest technological transformation in healthcare for assisting clinicians in complex decision-making. Several CDSSs are proposed to deal with a range of clinical tasks such as disease diagnosis, prescription management, and medication ordering. Although a small number of CDSSs have focused on treatment selection, areas such as medication selection and dosing selection remained under-researched. In this regard, this study represents one of the first studies in which a CDSS is proposed for clinicians who manage patients with end-stage renal disease undergoing maintenance hemodialysis, almost all of whom have some manifestation of chronic kidney disease-mineral and bone disorder (CKD-MBD). The primary objective of the system is to aid clinicians in dosage prescription by levering medical domain knowledge as well existing practices. The proposed CDSS is evaluated with a real-world hemodialysis patient dataset acquired from Kyung Hee University Hospital, South Korea. Our evaluation demonstrates overall high compliance based on the concordance metric between the proposed CKD-MBD CDSS recommendations and the routine clinical practice. The concordance rate of overall medication dosing selection is 78.27%. Furthermore, the usability aspects of the system are also evaluated through the User Experience Questionnaire method to highlight the appealing aspects of the system for clinicians. The overall user experience dimension scores for pragmatic, hedonic, and attractiveness are 1.53, 1.48, and 1.41, respectively. A service reliability for the Cronbach's alpha coefficient greater than 0.7 is achieved using the proposed system, whereas a dependability coefficient of the value 0.84 reveals a significant effect.

Scoping review exploring the impact of digital systems on processes and outcomes in the care management of acute kidney injury and progress towards establishing learning healthcare systems

OBJECTIVES

Digital systems have long been used to improve the quality and safety of care when managing acute kidney injury (AKI). The availability of digitised clinical data can also turn organisations and their networks into learning healthcare systems (LHSs) if used across all levels of health and care. This review explores the impact of digital systems i.e. on patients with AKI care, to gauge progress towards establishing LHSs and to identify existing gaps in the research.

METHODS

Embase, PubMed, MEDLINE, Cochrane, Scopus and Web of Science databases were searched. Studies of real-time or near real-time digital AKI management systems which reported process and outcome measures were included.

RESULTS

Thematic analysis of 43 studies showed that most interventions used real-time serum creatinine levels to trigger responses to enable risk prediction, early recognition of AKI or harm prevention by individual clinicians (micro level) or specialist teams (meso level). Interventions at system (macro level) were rare. There was limited evidence of change in outcomes.

DISCUSSION

While the benefits of real-time digital clinical data at micro level for AKI management have been evident for some time, their application at meso and macro levels is emergent therefore limiting progress towards establishing LHSs. Lack of progress is due to digital maturity, system design, human factors and policy levers.

CONCLUSION

Future approaches need to harness the potential of interoperability, data analytical advances and include multiple stakeholder perspectives to develop effective digital LHSs in order to gain benefits across the system.

Use of machine learning to predict clinical decision support compliance, reduce alert burden, and evaluate duplicate laboratory test ordering alerts

Objectives

While well-designed clinical decision support (CDS) alerts can improve patient care, utilization management, and population health, excessive alerting may be counterproductive, leading to clinician burden and alert fatigue. We sought to develop machine learning models to predict whether a clinician will accept the advice provided by a CDS alert. Such models could reduce alert burden by targeting CDS alerts to specific cases where they are most likely to be effective.

Materials and Methods

We focused on a set of laboratory test ordering alerts, deployed at 8 hospitals within the Partners Healthcare System. The alerts notified clinicians of duplicate laboratory test orders and advised discontinuation. We captured key attributes surrounding 60 399 alert firings, including clinician and patient variables, and whether the clinician complied with the alert. Using these data, we developed logistic regression models to predict alert compliance.

Results

We identified key factors that predicted alert compliance; for example, clinicians were less likely to comply with duplicate test alerts triggered in patients with a prior abnormal result for the test or in the context of a nonvisit-based encounter (eg, phone call). Likewise, differences in practice patterns between clinicians appeared to impact alert compliance. Our best-performing predictive model achieved an area under the receiver operating characteristic curve (AUC) of 0.82. Incorporating this model into the alerting logic could have averted more than 1900 alerts at a cost of fewer than 200 additional duplicate tests.

Conclusions

Deploying predictive models to target CDS alerts may substantially reduce clinician alert burden while maintaining most or all the CDS benefit.

Clinical Decision Support System with Renal Dose Adjustment Did Not Improve Subsequent Renal and Hepatic Function among Inpatients: The Japan Adverse Drug Event Study

Background  Medication dose adjustment is crucial for patients with renal dysfunction (RD). The assessment of renal function is generally mandatory; however, the renal function may change during the hospital stay and the manual assessment is sometimes challenging.

Objective  We developed the clinical decision support system (CDSS) that provided a recommended dose based on automated calculated renal function.

Methods  We conducted a prospective cohort study in a single teaching hospital in Japan. All hospitalized patients were included except for obstetrics/gynecology and pediatric wards between September 2013 and February 2015. The CDSS was implemented on December 2013. Renal and hepatic dysfunction (HD) were defined as changes in the estimated glomerular filtration rate (eGFR) and alanine aminotransferase or alkaline phosphatase levels based on these measurements during hospital stay. These measurements were obtained before (phase I), after (phase II), and 1 year after (phase III) the CDSS implementation.

Results  We included 6,767 patients (phase I: 2,205; phase II: 2,279; phase III: 2,283). The patients' characteristics were similar among phases. Changes in eGFR were similar among phases, but the incidence of RD increased in phase III (phase I: 228 [10.3%]; phase II: 260 [11.4%]; phase III: 296 [13.0%], p  = 0.02). However, the differences in incidences of RD were not statistically significant after adjusting for eGFR at baseline and age. The incidences of HD were also similar among phases (phase I: 175 [13.2%]; phase II: 171 [12.9%]; phase III: 167 [12.2%], p  = 0.72).

Conclusion  The CDSS implementation did not affect the incidence of renal and HD and changes in renal and hepatic function among hospitalized patients. The effectiveness of the CDSS with renal-guided doses should be investigated with respect to other endpoints.