Blood glucose monitoring devices: current considerations.

Measuring blood glucose concentrations via capillary (fingerprick) blood glucose monitoring or continuous (interstitial) glucose monitoring is an important aspect of management for many people with diabetes. Blood glucose monitoring informs patient self-management strategies, which can improve the patient's engagement in their own care and reduce barriers to achieving recommended blood glucose targets. Blood glucose monitoring also informs clinician-guided management plans. Compared to capillary blood glucose monitoring, continuous glucose monitoring in people using insulin significantly improves glycaemic metrics and is associated with improved patient-reported outcomes. Even with good glycaemic metrics, patients using continuous glucose monitoring should still have access to capillary blood glucose monitoring for correlation of hypoglycaemic readings when accuracy may be compromised or if there is a malfunction with the continuous blood glucose monitor.

Digital interventions to improve safety and quality of inpatient diabetes management: A systematic review.

IMPORTANCE: Diabetes is common amongst hospitalised patients and contributes to increased length of stay and poorer outcomes. Digital transformation, particularly the implementation of electronic medical records (EMRs), is rapidly occurring across the healthcare sector and provides an opportunity to improve the safety and quality of inpatient diabetes care. Alongside this revolution has been a considerable and ongoing evolution of digital interventions to optimise care of inpatients with diabetes including optimisation of EMRs, digital clinical decision support systems (CDSS) and solutions utilising data visibility to allow targeted patient review. OBJECTIVE: To systematically appraise the recent literature to determine which digitally-enabled interventions including EMR, CDSS and data visibility solutions improve the safety and quality of non-critical care inpatient diabetes management. METHODS: Pubmed, Embase and Cochrane databases were searched for suitable articles. Selected articles underwent quality assessment and analysis with results grouped by intervention type. RESULTS: 1202 articles were identified with 42 meeting inclusion criteria. Four key interventions were identified; computerised physician order entry (n = 4), clinician decision support systems (n = 21), EMR driven active case finding (data visibility solutions) and targeted patient review (n = 10) and multicomponent system interventions (n = 7). Studies reported on glucometric outcomes, evidence-based medication ordering including medication errors, and patient and user outcomes. An improvement in glucometric measures particularly mean blood glucose and proportion of target range blood glucose levels and rates of evidence-based insulin prescribing were consistently demonstrated. CONCLUSION: Digitally-enabled interventions utilised to improve quality and safety of inpatient diabetes care were heterogenous in design. The majority of studies across all intervention types reported positive effects for evidence-based prescribing and glucometric outcomes. There was less evidence for digital interventions reducing diabetes medication administration errors or impacting patient outcomes (length of stay).

Machine learning models for diabetes management in acute care using electronic medical records: A systematic review.

BACKGROUND: Machine learning (ML) is a subset of Artificial Intelligence (AI) that is used to predict and potentially prevent adverse patient outcomes. There is increasing interest in the application of these models in digital hospitals to improve clinical decision-making and chronic disease management, particularly for patients with diabetes. The potential of ML models using electronic medical records (EMR) to improve the clinical care of hospitalised patients with diabetes is currently unknown. OBJECTIVE: The aim was to systematically identify and critically review the published literature examining the development and validation of ML models using EMR data for improving the care of hospitalised adult patients with diabetes. METHODS: The Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA) guidelines were followed. Four databases were searched (Embase, PubMed, IEEE and Web of Science) for studies published between January 2010 to January 2022. The reference lists of the eligible articles were manually searched. Articles that examined adults and both developed and validated ML models using EMR data were included. Studies conducted in primary care and community care settings were excluded. Studies were independently screened and data was extracted using Covidence® systematic review software. For data extraction and critical appraisal, the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) was followed. Risk of bias was assessed using the Prediction model Risk Of Bias Assessment Tool (PROBAST). Quality of reporting was assessed by adherence to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guideline. The IJMEDI checklist was followed to assess quality of ML models and the reproducibility of their outcomes. The external validation methodology of the studies was appraised. RESULTS: Of the 1317 studies screened, twelve met inclusion criteria. Eight studies developed ML models to predict disglycaemic episodes for hospitalized patients with diabetes, one study developed a ML model to predict total insulin dosage, two studies predicted risk of readmission, and one study improved the prediction of hospital readmission for inpatients with diabetes. All included studies were heterogeneous with regard to ML types, cohort, input predictors, sample size, performance and validation metrics and clinical outcomes. Two studies adhered to the TRIPOD guideline. The methodological reporting of all the studies was evaluated to be at high risk of bias. The quality of ML models in all studies was assessed as poor. Robust external validation was not performed on any of the studies. No models were implemented or evaluated in routine clinical care. CONCLUSIONS: This review identified a limited number of ML models which were developed to improve inpatient management of diabetes. No ML models were implemented in real hospital settings. Future research needs to enhance the development, reporting and validation steps to enable ML models for integration into routine clinical care.

Toilet Paper, Minced Meat and Diabetes Medicines: Australian Panic Buying Induced by COVID-19.

The COVID-19 pandemic has impacted the management of non-communicable diseases in health systems around the world. This study aimed to understand the impact of COVID-19 on diabetes medicines dispensed in Australia. Publicly available data from Australia's government subsidised medicines program (Pharmaceutical Benefits Scheme), detailing prescriptions by month dispensed to patients, drug item code and patient category, was obtained from January 2016 to November 2020. This study focused on medicines used in diabetes care (Anatomical Therapeutical Chemical code level 2 = A10). Number of prescriptions dispensed were plotted by month at a total level, by insulins and non-insulins, and by patient category (general, concessional). Total number of prescriptions dispensed between January and November of each year were compared. A peak in prescriptions dispensed in March 2020 was identified, an increase of 35% on March 2019, compared to average growth of 7.2% in previous years. Prescriptions dispensed subsequently fell in April and May 2020 to levels below the corresponding months in 2019. These trends were observed across insulins, non-insulins, general and concessional patient categories. The peak and subsequent dip in demand have resulted in a small unexpected overall increase for the period January to November 2020, compared to declining growth for the same months in prior years. The observed change in consumer behaviour prompted by COVID-19 and the resulting public health measures is important to understand in order to improve management of medicines supply during potential future waves of COVID-19 and other pandemics.