Outpatient assessment of Karlsburg Diabetes Management System-based decision support

Applications of Clinical Decision Support Systems in Diabetes Care: Scoping Review

BACKGROUND

Providing comprehensive and individualized diabetes care remains a significant challenge in the face of the increasing complexity of diabetes management and a lack of specialized endocrinologists to support diabetes care. Clinical decision support systems (CDSSs) are progressively being used to improve diabetes care, while many health care providers lack awareness and knowledge about CDSSs in diabetes care. A comprehensive analysis of the applications of CDSSs in diabetes care is still lacking.

OBJECTIVE

This review aimed to summarize the research landscape, clinical applications, and impact on both patients and physicians of CDSSs in diabetes care.

METHODS

We conducted a scoping review following the Arksey and O'Malley framework. A search was conducted in 7 electronic databases to identify the clinical applications of CDSSs in diabetes care up to June 30, 2022. Additional searches were conducted for conference abstracts from the period of 2021-2022. Two researchers independently performed the screening and data charting processes.

RESULTS

Of 11,569 retrieved studies, 85 (0.7%) were included for analysis. Research interest is growing in this field, with 45 (53%) of the 85 studies published in the past 5 years. Among the 58 (68%) out of 85 studies disclosing the underlying decision-making mechanism, most CDSSs (44/58, 76%) were knowledge based, while the number of non-knowledge-based systems has been increasing in recent years. Among the 81 (95%) out of 85 studies disclosing application scenarios, the majority of CDSSs were used for treatment recommendation (63/81, 78%). Among the 39 (46%) out of 85 studies disclosing physician user types, primary care physicians (20/39, 51%) were the most common, followed by endocrinologists (15/39, 39%) and nonendocrinology specialists (8/39, 21%). CDSSs significantly improved patients' blood glucose, blood pressure, and lipid profiles in 71% (45/63), 67% (12/18), and 38% (8/21) of the studies, respectively, with no increase in the risk of hypoglycemia.

CONCLUSIONS

CDSSs are both effective and safe in improving diabetes care, implying that they could be a potentially reliable assistant in diabetes care, especially for physicians with limited experience and patients with limited access to medical resources.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.37766/inplasy2022.9.0061.

Quality improvement strategies for diabetes care: Effects on outcomes for adults living with diabetes

BACKGROUND

There is a large body of evidence evaluating quality improvement (QI) programmes to improve care for adults living with diabetes. These programmes are often comprised of multiple QI strategies, which may be implemented in various combinations. Decision-makers planning to implement or evaluate a new QI programme, or both, need reliable evidence on the relative effectiveness of different QI strategies (individually and in combination) for different patient populations.

OBJECTIVES

To update existing systematic reviews of diabetes QI programmes and apply novel meta-analytical techniques to estimate the effectiveness of QI strategies (individually and in combination) on diabetes quality of care.

SEARCH METHODS

We searched databases (CENTRAL, MEDLINE, Embase and CINAHL) and trials registers (ClinicalTrials.gov and WHO ICTRP) to 4 June 2019. We conducted a top-up search to 23 September 2021; we screened these search results and 42 studies meeting our eligibility criteria are available in the awaiting classification section.

SELECTION CRITERIA

We included randomised trials that assessed a QI programme to improve care in outpatient settings for people living with diabetes. QI programmes needed to evaluate at least one system- or provider-targeted QI strategy alone or in combination with a patient-targeted strategy. - System-targeted: case management (CM); team changes (TC); electronic patient registry (EPR); facilitated relay of clinical information (FR); continuous quality improvement (CQI). - Provider-targeted: audit and feedback (AF); clinician education (CE); clinician reminders (CR); financial incentives (FI). - Patient-targeted: patient education (PE); promotion of self-management (PSM); patient reminders (PR). Patient-targeted QI strategies needed to occur with a minimum of one provider or system-targeted strategy.

DATA COLLECTION AND ANALYSIS

We dual-screened search results and abstracted data on study design, study population and QI strategies. We assessed the impact of the programmes on 13 measures of diabetes care, including: glycaemic control (e.g. mean glycated haemoglobin (HbA1c)); cardiovascular risk factor management (e.g. mean systolic blood pressure (SBP), low-density lipoprotein cholesterol (LDL-C), proportion of people living with diabetes that quit smoking or receiving cardiovascular medications); and screening/prevention of microvascular complications (e.g. proportion of patients receiving retinopathy or foot screening); and harms (e.g. proportion of patients experiencing adverse hypoglycaemia or hyperglycaemia). We modelled the association of each QI strategy with outcomes using a series of hierarchical multivariable meta-regression models in a Bayesian framework. The previous version of this review identified that different strategies were more or less effective depending on baseline levels of outcomes. To explore this further, we extended the main additive model for continuous outcomes (HbA1c, SBP and LDL-C) to include an interaction term between each strategy and average baseline risk for each study (baseline thresholds were based on a data-driven approach; we used the median of all baseline values reported in the trials). Based on model diagnostics, the baseline interaction models for HbA1c, SBP and LDL-C performed better than the main model and are therefore presented as the primary analyses for these outcomes. Based on the model results, we qualitatively ordered each QI strategy within three tiers (Top, Middle, Bottom) based on its magnitude of effect relative to the other QI strategies, where 'Top' indicates that the QI strategy was likely one of the most effective strategies for that specific outcome. Secondary analyses explored the sensitivity of results to choices in model specification and priors.  Additional information about the methods and results of the review are available as Appendices in an online repository. This review will be maintained as a living systematic review; we will update our syntheses as more data become available.

MAIN RESULTS

We identified 553 trials (428 patient-randomised and 125 cluster-randomised trials), including a total of 412,161 participants. Of the included studies, 66% involved people living with type 2 diabetes only. Participants were 50% female and the median age of participants was 58.4 years. The mean duration of follow-up was 12.5 months. HbA1c was the commonest reported outcome; screening outcomes and outcomes related to cardiovascular medications, smoking and harms were reported infrequently. The most frequently evaluated QI strategies across all study arms were PE, PSM and CM, while the least frequently evaluated QI strategies included AF, FI and CQI. Our confidence in the evidence is limited due to a lack of information on how studies were conducted.  Four QI strategies (CM, TC, PE, PSM) were consistently identified as 'Top' across the majority of outcomes. All QI strategies were ranked as 'Top' for at least one key outcome. The majority of effects of individual QI strategies were modest, but when used in combination could result in meaningful population-level improvements across the majority of outcomes. The median number of QI strategies in multicomponent QI programmes was three.  Combinations of the three most effective QI strategies were estimated to lead to the below effects:  - PR + PSM + CE: decrease in HbA1c by 0.41% (credibility interval (CrI) -0.61 to -0.22) when baseline HbA1c < 8.3%; - CM + PE + EPR: decrease in HbA1c by 0.62% (CrI -0.84 to -0.39) when baseline HbA1c > 8.3%;  - PE + TC + PSM: reduction in SBP by 2.14 mmHg (CrI -3.80 to -0.52) when baseline SBP < 136 mmHg; - CM + TC + PSM: reduction in SBP by 4.39 mmHg (CrI -6.20 to -2.56) when baseline SBP > 136 mmHg;  - TC + PE + CM: LDL-C lowering of 5.73 mg/dL (CrI -7.93 to -3.61) when baseline LDL < 107 mg/dL; - TC + CM + CR: LDL-C lowering by 5.52 mg/dL (CrI -9.24 to -1.89) when baseline LDL > 107 mg/dL. Assuming a baseline screening rate of 50%, the three most effective QI strategies were estimated to lead to an absolute improvement of 33% in retinopathy screening (PE + PR + TC) and 38% absolute increase in foot screening (PE + TC + Other).

AUTHORS' CONCLUSIONS

There is a significant body of evidence about QI programmes to improve the management of diabetes. Multicomponent QI programmes for diabetes care (comprised of effective QI strategies) may achieve meaningful population-level improvements across the majority of outcomes. For health system decision-makers, the evidence summarised in this review can be used to identify strategies to include in QI programmes. For researchers, this synthesis identifies higher-priority QI strategies to examine in further research regarding how to optimise their evaluation and effects. We will maintain this as a living systematic review.

Patient-Tailored Decision Support System Improves Short- and Long-Term Glycemic Control in Type 2 Diabetes

BACKGROUND

The increasing prevalence of type 2 diabetes mellitus (T2D) and specialist shortage has caused a healthcare gap that can be bridged by a decision support system (DSS). We investigated whether a diabetes DSS can improve long- and/or short-term glycemic control.

METHODS

This is a retrospective observational cohort study of the Diabetiva program, which offered a patient-tailored DSS using Karlsburger Diabetes-Management System (KADIS) once a year. Glycemic control was analyzed at baseline and after 12 months in 452 individuals with T2D. Time in range (TIR; glucose 3.9-10 mmol/L) and Q-Score, a composite metric developed for analysis of continuous glucose profiles, were short-term and HbA1c long-term measures of glycemic control. Glucose variability (GV) was also measured.

RESULTS

At baseline, one-third of patients had good short- and long-term glycemic control. Q-Score identified insufficient short-term glycemic control in 17.9% of patients with HbA1c <6.5%, mainly due to hypoglycemia. GV and hyperglycemia were responsible in patients with HbA1c >7.5% and >8%, respectively. Application of DSS at baseline improved short- and long-term glycemic control, as shown by the reduced Q-Score, GV, and HbA1c after 12 months. Multiple regression demonstrated that the total effect on GV resulted from the single effects of all influential parameters.

CONCLUSIONS

DSS can improve short- and long-term glycemic control in individuals with T2D without increasing hypoglycemia. The Q-Score allows identification of individuals with insufficient glycemic control. An effective strategy for therapy optimization could be the selection of individuals with T2D most at need using the Q-Score, followed by offering patient-tailored DSS.

Decision coaching for people making healthcare decisions

BACKGROUND

Decision coaching is non-directive support delivered by a healthcare provider to help patients prepare to actively participate in making a health decision. 'Healthcare providers' are considered to be all people who are engaged in actions whose primary intent is to protect and improve health (e.g. nurses, doctors, pharmacists, social workers, health support workers such as peer health workers). Little is known about the effectiveness of decision coaching.

OBJECTIVES

To determine the effects of decision coaching (I) for people facing healthcare decisions for themselves or a family member (P) compared to (C) usual care or evidence-based intervention only, on outcomes (O) related to preparation for decision making, decisional needs and potential adverse effects.

SEARCH METHODS

We searched the Cochrane Library (Wiley), Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE (Ovid), Embase (Ovid), PsycINFO (Ovid), CINAHL (Ebsco), Nursing and Allied Health Source (ProQuest), and Web of Science from database inception to June 2021.

SELECTION CRITERIA

We included randomised controlled trials (RCTs) where the intervention was provided to adults or children preparing to make a treatment or screening healthcare decision for themselves or a family member. Decision coaching was defined as: a) delivered individually by a healthcare provider who is trained or using a protocol; and b) providing non-directive support and preparing an adult or child to participate in a healthcare decision. Comparisons included usual care or an alternate intervention. There were no language restrictions.

DATA COLLECTION AND ANALYSIS

Two authors independently screened citations, assessed risk of bias, and extracted data on characteristics of the intervention(s) and outcomes. Any disagreements were resolved by discussion to reach consensus. We used the standardised mean difference (SMD) with 95% confidence intervals (CI) as the measures of treatment effect and, where possible, synthesised results using a random-effects model. If more than one study measured the same outcome using different tools, we used a random-effects model to calculate the standardised mean difference (SMD) and 95% CI. We presented outcomes in summary of findings tables and applied GRADE methods to rate the certainty of the evidence.

MAIN RESULTS

Out of 12,984 citations screened, we included 28 studies of decision coaching interventions alone or in combination with evidence-based information, involving 5509 adult participants (aged 18 to 85 years; 64% female, 52% white, 33% African-American/Black; 68% post-secondary education). The studies evaluated decision coaching used for a range of healthcare decisions (e.g. treatment decisions for cancer, menopause, mental illness, advancing kidney disease; screening decisions for cancer, genetic testing). Four of the 28 studies included three comparator arms.  For decision coaching compared with usual care (n = 4 studies), we are uncertain if decision coaching compared with usual care improves any outcomes (i.e. preparation for decision making, decision self-confidence, knowledge, decision regret, anxiety) as the certainty of the evidence was very low.  For decision coaching compared with evidence-based information only (n = 4 studies), there is low certainty-evidence that participants exposed to decision coaching may have little or no change in knowledge (SMD -0.23, 95% CI: -0.50 to 0.04; 3 studies, 406 participants). There is low certainty-evidence that participants exposed to decision coaching may have little or no change in anxiety, compared with evidence-based information. We are uncertain if decision coaching compared with evidence-based information improves other outcomes (i.e. decision self-confidence, feeling uninformed) as the certainty of the evidence was very low. For decision coaching plus evidence-based information compared with usual care (n = 17 studies), there is low certainty-evidence that participants may have improved knowledge (SMD 9.3, 95% CI: 6.6 to 12.1; 5 studies, 1073 participants). We are uncertain if decision coaching plus evidence-based information compared with usual care improves other outcomes (i.e. preparation for decision making, decision self-confidence, feeling uninformed, unclear values, feeling unsupported, decision regret, anxiety) as the certainty of the evidence was very low. For decision coaching plus evidence-based information compared with evidence-based information only (n = 7 studies), we are uncertain if decision coaching plus evidence-based information compared with evidence-based information only improves any outcomes (i.e. feeling uninformed, unclear values, feeling unsupported, knowledge, anxiety) as the certainty of the evidence was very low.

AUTHORS' CONCLUSIONS

Decision coaching may improve participants' knowledge when used with evidence-based information. Our findings do not indicate any significant adverse effects (e.g. decision regret, anxiety) with the use of decision coaching. It is not possible to establish strong conclusions for other outcomes. It is unclear if decision coaching always needs to be paired with evidence-informed information. Further research is needed to establish the effectiveness of decision coaching for a broader range of outcomes.

The effects of clinical decision support systems on insulin use: A systematic review

BACKGROUND

A clinical decision support system (CDSS) is a computerized system using case-based reasoning to assist clinicians in assessing disease status, in selecting appropriate therapy or in making other clinical decisions. Previous randomized controlled trials (RCTs or trials) have shown that CDSSs have the potential to improve the insulin use, but the evidence was conflicting and uncertain. The purpose of our study was to determine whether a CDSS improves the use of insulin.

METHOD

PubMed, Embase, Cochrane Central Register of Controlled Trials, and ClinicalTrials.gov were searched from their inception to October 2018. The quality assessment was based on the risk of bias criteria of the Cochrane Handbook.

RESULTS

Twenty-four RCTs, involving 7653 participants, were included. Thirteen of those trials (54.2%) used a computerized algorithm or a computer-assisted insulin protocol for insulin dose and therapy adjustment, of which 30.8% (four of 13) found significant changes. Of 10 trials that measured mean blood glucose levels and the 11 trials reported HbA1c, the computerized insulin dose adjustment resulted in lower mean blood glucose levels in 70.0% (seven of 10) and 36.4% (four of 11) of RCTs, respectively. Additionally, a significant reduction of hyperglycaemia events was reported in three of six RCTs. The evidence in a majority of the 24 RCTs was of moderate quality.

CONCLUSIONS

CDSSs have the potential to improve the insulin use and blood glucose control in a clinical setting. The methodologies in these studies were of mixed quality. Better designed and longer-term studies are required to ensure a larger and more reliable evidence base on the effects of CDSS intervention on insulin use.

Model-Based Tool for Personalized Adjustment of Basal Insulin Supply in Patients With Intensified Conventional Insulin Therapy

BACKGROUND

The decisive factor in successful intensive insulin therapy is the ability to deliver need-based-adjusted nutrition-independent insulin dosages at the closest possible approximation to the physiological insulin level. Because this basal insulin requirement is strongly influenced by the patient's lifestyle, its subtlety is of great importance. This challenge is very different between patients with type 1 diabetes and those with insulin-dependent type 2 diabetes. Furthermore, it is more difficult to finetune a basal insulin dosage with intensified conventional insulin therapy (ICT), due to delayed insulin delivery, compared to insulin pump therapy, which provides continuous delivery of small doses of exclusively short-acting insulin. In all cases, the goal is to achieve an optimal basal delivery rate.

METHOD

We hypothesized that this goal could be achieved with a modeling tool that determined the optimal basal insulin supply based on the patient's anamnestic data and monitored glucose values. This type of modeling tool has been used in health insurance programs in Germany to improve insulin control in patients that receive ICT.

RESULTS

Our retrospective data analysis showed that this modeling tool provided a significant improvement in metabolic control, significant reductions in HbA1c and Q scores, and improved time-in-range values, with reduced daily insulin levels.

CONCLUSION

The model-based basal rate test could provide additional data of the actual effect of the basal insulin adjustment in intensified insulin treated diabetes to the physician or treatment team.

The role of the chronic care model in promoting the management of the patient with rare liver disease

INTRODUCTION

The chronic care model (CCM) provides a holistic approach for managing chronic illnesses. Patients with rare liver diseases (RLD) have complex needs, impaired quality of life and often life-threatening complications. Most RLD meet the criteria for a long-term chronic condition and should be viewed through the prism of CCM. We aimed to ascertain whether the CCM has been considered for the frequently-encountered RLD.

METHODS

MEDLINE®/PubMed®/Cochrane/EMBASE were searched to identify publications relating to the use of the CCM for the management of six RLD. We identified 33 articles eligible for inclusion.

RESULTS

Six, eleven, one, thirteen, two and zero studies, discussed individual components of the CCM for autoimmune hepatitis (AIH), primary biliary cholangitis (PBC), primary sclerosing cirrhosis (PSC), Wilsons disease (WD), alpha-1 antitrypsin deficiency (A1AD) and lysosomal acid lipase deficiency (LALd) respectively. We have not identified studies using the full CCM for any of the aforementioned RLD.

DISCUSSION

Unlike in common chronic conditions e.g. diabetes, there has been limited consideration of the use of CCM (or its components) for the management of RLD. This may reflect a reluctance of the clinical community to view these diseases as chronic or lack of healthcare policy investment in rare diseases in general.

Impact of Information Technology-Based Interventions for Type 2 Diabetes Mellitus on Glycemic Control: A Systematic Review and Meta-Analysis

BACKGROUND

Information technology-based interventions are increasingly being used to manage health care. However, there is conflicting evidence regarding whether these interventions improve outcomes in people with type 2 diabetes.

OBJECTIVE

The objective of this study was to conduct a systematic review and meta-analysis of clinical trials, assessing the impact of information technology on changes in the levels of hemoglobin A_1c (HbA_1c) and mapping the interventions with chronic care model (CCM) elements.

METHODS

Electronic databases PubMed and EMBASE were searched to identify relevant studies that were published up until July 2016, a method that was supplemented by identifying articles from the references of the articles already selected using the electronic search tools. The study search and selection were performed by independent reviewers. Of the 1082 articles retrieved, 32 trials (focusing on a total of 40,454 patients) were included. A random-effects model was applied to estimate the pooled results.

RESULTS

Information technology-based interventions were associated with a statistically significant reduction in HbA_1c levels (mean difference -0.33%, 95% CI -0.40 to -0.26, P<.001). Studies focusing on electronic self-management systems demonstrated the largest reduction in HbA_1c (0.50%), followed by those with electronic medical records (0.17%), an electronic decision support system (0.15%), and a diabetes registry (0.05%). In addition, the more CCM-incorporated the information technology-based interventions were, the more improvements there were in HbA_1c levels.

CONCLUSIONS

Information technology strategies combined with the other elements of chronic care models are associated with improved glycemic control in people with diabetes. No clinically relevant impact was observed on low-density lipoprotein levels and blood pressure, but there was evidence that the cost of care was lower.

Q-Score: development of a new metric for continuous glucose monitoring that enables stratification of antihyperglycaemic therapies

BACKGROUND

Continuous glucose monitoring (CGM) has revolutionised diabetes management. CGM enables complete visualisation of the glucose profile, and the uncovering of metabolic 'weak points'. A standardised procedure to evaluate the complex data acquired by CGM, and to create patient-tailored recommendations has not yet been developed. We aimed to develop a new patient-tailored approach for the routine clinical evaluation of CGM profiles. We developed a metric allowing screening for profiles that require therapeutic action and a method to identify the individual CGM parameters with improvement potential.

METHODS

Fifteen parameters frequently used to assess CGM profiles were calculated for 1,562 historic CGM profiles from subjects with type 1 or type 2 diabetes. Factor analysis and varimax rotation was performed to identify factors that accounted for the quality of the profiles.

RESULTS

We identified five primary factors that determined CGM profiles (central tendency, hyperglycaemia, hypoglycaemia, intra- and inter-daily variations). One parameter from each factor was selected for constructing the formula for the screening metric, (the 'Q-Score'). To derive Q-Score classifications, three diabetes specialists independently categorised 766 CGM profiles into groups of 'very good', 'good', 'satisfactory', 'fair', and 'poor' metabolic control. The Q-Score was then calculated for all profiles, and limits were defined based on the categorised groups (<4.0, very good; 4.0-5.9, good; 6.0-8.4, satisfactory; 8.5-11.9, fair; and ≥12.0, poor). Q-Scores increased significantly (P <0.01) with increasing antihyperglycaemic therapy complexity. Accordingly, the percentage of fair and poor profiles was higher in insulin-treated compared with diet-treated subjects (58.4% vs. 9.3%). In total, 90% of profiles categorised as fair or poor had at least three parameters that could potentially be optimised. The improvement potential of those parameters can be categorised as 'low', 'moderate' and 'high'.

CONCLUSIONS

The Q-Score is a new metric suitable to screen for CGM profiles that require therapeutic action. Moreover, because single components of the Q-Score formula respond to individual weak points in glycaemic control, parameters with improvement potential can be identified and used as targets for optimising patient-tailored therapies.

Glucose concentrations in blood and tissue - a pilot study on variable time lag

AIM

The aim of this pilot study was to acquire insight into the parameters of glycaemic control, especially, (1) the time delay (lag phase) between plasma and tissue glucose concentrations in relation to rise and fall in glucose levels and (2) the rate of glucose increase and decrease.

METHODS

Four healthy people (HP), 4 people with type 1diabetes (DM1) and 4 with type 2 diabetes (DM2) underwent concurrent glucose measurements by means of (1) the continuous glucose monitoring system (CGMS-Medtronic), Medtronic-Minimed, CA, USA, calibrated by the glucometer Calla, Wellion, Austria, and, (2) the Beckman II analyser to measure glucose concentrations in venous plasma. Samples were taken on 4 consecutive days in the fasting state and 4 times after consumption of 50 g glucose. Carelink Personal, MS Excel, Maple and Mat lab were applied to plot the evolution of glucose concentration and analyse the results. The time difference between increase and decrease was calculated for HP, DM 1 and DM 2.

RESULTS

In DM1and DM2, glucose tolerance testing (GTT) resulted in slower transport of glucose into subcutaneous tissue than in HP where the lag phase lasted up to 12 min. The maximum increase/decrease rates in DM1 and DM2 vs HP were 0.25 vs < 0.1 mmol/L/min.

CONCLUSION

CGMS is shown to provide reliable plasma glucose concentrations provided the system is calibrated during a steady state. The analysis of glucose change rates improves understanding of metabolic processes better than standard GTT.

How Do Interventions That Exemplify the Joint Principles of the Patient Centered Medical Home Affect Hemoglobin A1C in Patients With Diabetes: A Review

Objective:

To review the impact of the Joint Principle of the Patient Centered Medical Home (PCMH) on hemoglobin A1C (HbA1C) in primary care patients with diabetes.

Methods:

Systematic review of English articles using approximate terms for (1) the 7 principles of the PCMH, (2) primary care, and (3) HbA1C. We included experimental and observational studies. Three authors independently extracted data and obtained summary estimates for concepts with more than 2 high-quality studies.

Results:

Forty-three studies published between 1998 and 2012 met inclusion criteria, 33 randomized and 10 controlled before–after studies. A physician-directed medical practice (principle 2) lowered HbA1C values when utilizing nursing (mean difference [MD] −0.36, 95% confidence interval [CI] −0.43 to −0.28) or pharmacy care management (MD −0.76; 95% CI −0.93 to −0.59). Whole-person orientation (principle 3) also lowered HbA1C (MD −0.72, 95% CI −0.98 to −0.45). Studies of coordinated and integrated care (principle 4) and quality and safety interventions (principle 5) did not consistently lower HbA1C when reviewed in aggregate. We did not identify high-quality studies to make conclusions for personal physician (principle 1), enhanced access (principle 6), and payment (principle 7).

Conclusion:

Our review found individual interventions that reduced the HbA1C by up to 2.0% when they met the definitions set by of the Joint Principles of the PCMH. Two of the principles—physician-led team and whole-person orientation—consistently lowered the HbA1C. Other principles had limited data or made little to no impact. Based on current evidence, PCMH principles differentially influence the HbA1C, and there are opportunities for additional research.

Continuous glucose monitoring system: Is it really accurate, safe and clinically useful?

The continuous glucose monitoring system (CGM) has been used for constant checking of glucose level by measuring interstitial glucose concentrations, since the early days of the 21st century. It can potentially improve diabetes care if used carefully with the understanding of the characteristics of this system. Although there is a time lag of approximately 5–15 min between blood and interstitial glucose levels, the system is considered the most suitable device for meticulous glucose control and prevention of hypoglycemia. A large number of studies have examined its accuracy, safety and clinical effectiveness. The continuous glucose‐error grid analysis (CG‐EGA), designed by WL Clarke, evaluates the clinical accuracy of CGM. It examines ‘temporal’ characteristics of the data, analyzing pairs of reference and sensor readings as a process in time represented by a ‘bidimensional’ time series and taking into account inherent physiological time lags. Investment in CG‐EGA is clearly meaningful, even though there are other methodologies for evaluation. The use of each method complementarily is the most effective way to prove the accuracy of the device. The device has improved gradually, and real‐time CGM, which allows real‐time monitoring of blood glucose level, is already available commercially. The use of real‐time CGM could potentially lead to over‐ or undertreatment with insulin. Patient education through proper and effective handling of the new device is essential to improve diabetes care. (J Diabetes Invest, doi: 10.1111/j.2040‐1124.2012.00197.x, 2012)

Computerized advice on drug dosage to improve prescribing practice

BACKGROUND

Maintaining therapeutic concentrations of drugs with a narrow therapeutic window is a complex task. Several computer systems have been designed to help doctors determine optimum drug dosage. Significant improvements in health care could be achieved if computer advice improved health outcomes and could be implemented in routine practice in a cost-effective fashion. This is an updated version of an earlier Cochrane systematic review, first published in 2001 and updated in 2008.

OBJECTIVES

To assess whether computerized advice on drug dosage has beneficial effects on patient outcomes compared with routine care (empiric dosing without computer assistance).

SEARCH METHODS

The following databases were searched from 1996 to January 2012: EPOC Group Specialized Register, Reference Manager; Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, Ovid; EMBASE, Ovid; and CINAHL, EbscoHost. A "top up" search was conducted for the period January 2012 to January 2013; these results were screened by the authors and potentially relevant studies are listed in Studies Awaiting Classification. The review authors also searched reference lists of relevant studies and related reviews.

SELECTION CRITERIA

We included randomized controlled trials, non-randomized controlled trials, controlled before-and-after studies and interrupted time series analyses of computerized advice on drug dosage. The participants were healthcare professionals responsible for patient care. The outcomes were any objectively measured change in the health of patients resulting from computerized advice (such as therapeutic drug control, clinical improvement, adverse reactions).

DATA COLLECTION AND ANALYSIS

Two review authors independently extracted data and assessed study quality. We grouped the results from the included studies by drug used and the effect aimed at for aminoglycoside antibiotics, amitriptyline, anaesthetics, insulin, anticoagulants, ovarian stimulation, anti-rejection drugs and theophylline. We combined the effect sizes to give an overall effect for each subgroup of studies, using a random-effects model. We further grouped studies by type of outcome when appropriate (i.e. no evidence of heterogeneity).

MAIN RESULTS

Forty-six comparisons (from 42 trials) were included (as compared with 26 comparisons in the last update) including a wide range of drugs in inpatient and outpatient settings. All were randomized controlled trials except two studies. Interventions usually targeted doctors, although some studies attempted to influence prescriptions by pharmacists and nurses. Drugs evaluated were anticoagulants, insulin, aminoglycoside antibiotics, theophylline, anti-rejection drugs, anaesthetic agents, antidepressants and gonadotropins. Although all studies used reliable outcome measures, their quality was generally low.This update found similar results to the previous update and managed to identify specific therapeutic areas where the computerized advice on drug dosage was beneficial compared with routine care:1. it increased target peak serum concentrations (standardized mean difference (SMD) 0.79, 95% CI 0.46 to 1.13) and the proportion of people with plasma drug concentrations within the therapeutic range after two days (pooled risk ratio (RR) 4.44, 95% CI 1.94 to 10.13) for aminoglycoside antibiotics;2. it led to a physiological parameter more often within the desired range for oral anticoagulants (SMD for percentage of time spent in target international normalized ratio +0.19, 95% CI 0.06 to 0.33) and insulin (SMD for percentage of time in target glucose range: +1.27, 95% CI 0.56 to 1.98);3. it decreased the time to achieve stabilization for oral anticoagulants (SMD -0.56, 95% CI -1.07 to -0.04);4. it decreased the thromboembolism events (rate ratio 0.68, 95% CI 0.49 to 0.94) and tended to decrease bleeding events for anticoagulants although the difference was not significant (rate ratio 0.81, 95% CI 0.60 to 1.08). It tended to decrease unwanted effects for aminoglycoside antibiotics (nephrotoxicity: RR 0.67, 95% CI 0.42 to 1.06) and anti-rejection drugs (cytomegalovirus infections: RR 0.90, 95% CI 0.58 to 1.40);5. it tended to reduce the length of time spent in the hospital although the difference was not significant (SMD -0.15, 95% CI -0.33 to 0.02) and to achieve comparable or better cost-effectiveness ratios than usual care;6. there was no evidence of differences in mortality or other clinical adverse events for insulin (hypoglycaemia), anaesthetic agents, anti-rejection drugs and antidepressants.For all outcomes, statistical heterogeneity quantified by I(2) statistics was moderate to high.

AUTHORS' CONCLUSIONS

This review update suggests that computerized advice for drug dosage has some benefits: it increases the serum concentrations for aminoglycoside antibiotics and improves the proportion of people for which the plasma drug is within the therapeutic range for aminoglycoside antibiotics.It leads to a physiological parameter more often within the desired range for oral anticoagulants and insulin. It decreases the time to achieve stabilization for oral anticoagulants. It tends to decrease unwanted effects for aminoglycoside antibiotics and anti-rejection drugs, and it significantly decreases thromboembolism events for anticoagulants. It tends to reduce the length of hospital stay compared with routine care while comparable or better cost-effectiveness ratios were achieved.However, there was no evidence that decision support had an effect on mortality or other clinical adverse events for insulin (hypoglycaemia), anaesthetic agents, anti-rejection drugs and antidepressants. In addition, there was no evidence to suggest that some decision support technical features (such as its integration into a computer physician order entry system) or aspects of organization of care (such as the setting) could optimize the effect of computerized advice.Taking into account the high risk of bias of, and high heterogeneity between, studies, these results must be interpreted with caution.

Can computerized clinical decision support systems improve diabetes management? A systematic review and meta-analysis

AIMS

To systematically review randomized trials that assessed the effects of computerized clinical decision support systems in ambulatory diabetes management compared with a non-computerized clinical decision support system control.

METHODS

We included all diabetes trials from a comprehensive computerized clinical decision support system overview completed in January 2010, and searched EMBASE, MEDLINE, INSPEC/COMPENDEX and Evidence-Based Medicine Reviews (EBMR) from January 2010 to April 2012. Reference lists of related reviews, included articles and Clinicaltrials.gov were also searched. Randomized controlled trials of patients with diabetes in ambulatory care settings comparing a computerized clinical decision support system intervention with a non-computerized clinical decision support system control, measuring either a process of care or a patient outcome, were included. Screening of studies, data extraction, risk of bias and quality of evidence assessments were carried out independently by two reviewers, and discrepancies were resolved through consensus or third-party arbitration. Authors were contacted for any missing data.

RESULTS

Fifteen trials were included (13 from the previous review and two from the current search). Only one study was at low risk of bias, while the others were of moderate to high risk of bias because of methodological limitations. HbA1c (3 months' follow-up), quality of life and hospitalization (12 months' follow-up) were pooled and all favoured the computerized clinical decision support systems over the control, although none were statistically significant. Triglycerides and practitioner performance tended to favour computerized clinical decision support systems although results were too heterogeneous to pool.

CONCLUSIONS

Computerized clinical decision support systems in diabetes management may marginally improve clinical outcomes, but confidence in the evidence is low because of risk of bias, inconsistency and imprecision.

Impact of a computerized system for evidence-based diabetes care on completeness of records: a before-after study

Background

Physicians practicing in ambulatory care are adopting electronic health record (EHR) systems. Governments promote this adoption with financial incentives, some hinged on improvements in care. These systems can improve care but most demonstrations of successful systems come from a few highly computerized academic environments. Those findings may not be generalizable to typical ambulatory settings, where evidence of success is largely anecdotal, with little or no use of rigorous methods. The purpose of our pilot study was to evaluate the impact of a diabetes specific chronic disease management system (CDMS) on recording of information pertinent to guideline-concordant diabetes care and to plan for larger, more conclusive studies.

Methods

Using a before–after study design we analyzed the medical record of approximately 10 patients from each of 3 diabetes specialists (total = 31) who were seen both before and after the implementation of a CDMS. We used a checklist of key clinical data to compare the completeness of information recorded in the CDMS record to both the clinical note sent to the primary care physician based on that same encounter and the clinical note sent to the primary care physician based on the visit that occurred prior to the implementation of the CDMS, accounting for provider effects with Generalized Estimating Equations.

Results

The CDMS record outperformed by a substantial margin dictated notes created for the same encounter. Only 10.1% (95% CI, 7.7% to 12.3%) of the clinically important data were missing from the CDMS chart compared to 25.8% (95% CI, 20.5% to 31.1%) from the clinical note prepared at the time (p < 0.001) and 26.3% (95% CI, 19.5% to 33.0%) from the clinical note prepared before the CDMS was implemented (p < 0.001). There was no significant difference between dictated notes created for the CDMS-assisted encounter and those created for usual care encounters (absolute mean difference, 0.8%; 95% CI, −8.5% to 6.8%).

Conclusions

The CDMS chart captured information important for the management of diabetes more often than dictated notes created with or without its use but we were unable to detect a difference in completeness between notes dictated in CDMS-associated and usual-care encounters. Our sample of patients and providers was small, and completeness of records may not reflect quality of care.

Computerized clinical decision support systems for chronic disease management: a decision-maker-researcher partnership systematic review

Background

The use of computerized clinical decision support systems (CCDSSs) may improve chronic disease management, which requires recurrent visits to multiple health professionals, ongoing disease and treatment monitoring, and patient behavior modification. The objective of this review was to determine if CCDSSs improve the processes of chronic care (such as diagnosis, treatment, and monitoring of disease) and associated patient outcomes (such as effects on biomarkers and clinical exacerbations).

Methods

We conducted a decision-maker-researcher partnership systematic review. We searched MEDLINE, EMBASE, Ovid's EBM Reviews database, Inspec, and reference lists for potentially eligible articles published up to January 2010. We included randomized controlled trials that compared the use of CCDSSs to usual practice or non-CCDSS controls. Trials were eligible if at least one component of the CCDSS was designed to support chronic disease management. We considered studies 'positive' if they showed a statistically significant improvement in at least 50% of relevant outcomes.

Results

Of 55 included trials, 87% (n = 48) measured system impact on the process of care and 52% (n = 25) of those demonstrated statistically significant improvements. Sixty-five percent (36/55) of trials measured impact on, typically, non-major (surrogate) patient outcomes, and 31% (n = 11) of those demonstrated benefits. Factors of interest to decision makers, such as cost, user satisfaction, system interface and feature sets, unique design and deployment characteristics, and effects on user workflow were rarely investigated or reported.

Conclusions

A small majority (just over half) of CCDSSs improved care processes in chronic disease management and some improved patient health. Policy makers, healthcare administrators, and practitioners should be aware that the evidence of CCDSS effectiveness is limited, especially with respect to the small number and size of studies measuring patient outcomes.

Review of electronic decision-support tools for diabetes care: a viable option for low- and middle-income countries?

CONTEXT

Diabetes care is complex, requiring motivated patients, providers, and systems that enable guideline-based preventative care processes, intensive risk-factor control, and positive lifestyle choices. However, care delivery in low- and middle-income countries (LMIC) is hindered by a compendium of systemic and personal factors. While electronic medical records (EMR) and computerized clinical decision-support systems (CDSS) have held great promise as interventions that will overcome system-level challenges to improving evidence-based health care delivery, evaluation of these quality improvement interventions for diabetes care in LMICs is lacking. OBJECTIVE AND DATA SOURCES: We reviewed the published medical literature (systematic search of MEDLINE database supplemented by manual searches) to assess the quantifiable and qualitative impacts of combined EMR-CDSS tools on physician performance and patient outcomes and their applicability in LMICs.

STUDY SELECTION AND DATA EXTRACTION

Inclusion criteria prespecified the population (type 1 or 2 diabetes patients), intervention (clinical EMR-CDSS tools with enhanced functionalities), and outcomes (any process, self-care, or patient-level data) of interest. Case, review, or methods reports and studies focused on nondiabetes, nonclinical, or in-patient uses of EMR-CDSS were excluded. Quantitative and qualitative data were extracted from studies by separate single reviewers, respectively, and relevant data were synthesized.

RESULTS

Thirty-three studies met inclusion criteria, originating exclusively from high-income country settings. Among predominantly experimental study designs, process improvements were consistently observed along with small, variable improvements in risk-factor control, compared with baseline and/or control groups (where applicable). Intervention benefits varied by baseline patient characteristics, features of the EMR-CDSS interventions, motivation and access to technology among patients and providers, and whether EMR-CDSS tools were combined with other quality improvement strategies (e.g., workflow changes, case managers, algorithms, incentives). Patients shared experiences of feeling empowered and benefiting from increased provider attention and feedback but also frustration with technical difficulties of EMR-CDSS tools. Providers reported more efficient and standardized processes plus continuity of care but also role tensions and "mechanization" of care.

CONCLUSIONS

This narrative review supports EMR-CDSS tools as innovative conduits for structuring and standardizing care processes but also highlights setting and selection limitations of the evidence reviewed. In the context of limited resources, individual economic hardships, and lack of structured systems or trained human capital, this review reinforces the need for well-designed investigations evaluating the role and feasibility of technological interventions (customized to each LMIC's locality) in clinical decision making for diabetes care.

Model-based decision support in diabetes care

The model-based Karlsburg Diabetes Management System (KADIS®) has been developed as a patient-focused decision-support tool to provide evidence-based advice for physicians in their daily efforts to optimize metabolic control in diabetes care of their patients on an individualized basis. For this purpose, KADIS® was established in terms of a personalized, interactive in silico simulation procedure, implemented into a problem-related diabetes health care network and evaluated under different conditions by conducting open-label mono- and polycentric trials, and a case-control study, and last but not least, by application in routine diabetes outpatient care. The trial outcomes clearly show that the recommendations provided to the physicians by KADIS® lead to significant improvement of metabolic control. This model-based decision-support system provides an excellent tool to effectively guide physicians in personalized decision-making to achieve optimal metabolic control for their patients.

Smart telemedicine support for continuous glucose monitoring: the embryo of a future global agent for diabetes care

Although current systems for continuous glucose monitoring (CGM) are the result of progressive technological improvement, and although a beneficial effect on glucose control has been demonstrated, few patients are using them. Something similar has happened to telemedicine (TM); in spite of the long-term experience, which began in the early 1980s, no TM system has been widely adopted, and presential visits are still almost the only way diabetologists and patients communicate. The hypothesis developed in this article is that neither CGM nor TM will ever be routinely implemented separately, and their consideration as essential elements for standard diabetes care will one day come from their integration as parts of a telemedical monitoring platform. This platform, which should include artificial intelligence for giving decision support to patients and physicians, will represent the core of a more complex global agent for diabetes care, which will provide control algorithms and risk analysis among other essential functions.

The Karlsburg Diabetes Management System: translation from research to eHealth application

BACKGROUND

Several telemedicine-based eHealth programs exist, but patient-focused personalized decision support (PDS) is usually lacking. We evaluated the acceptance, efficiency, and cost-effectiveness of telemedicine-assisted PDS in routine outpatient diabetes care.

METHODS

Data are derived from the Diabetiva® program of the German health insurance company BKK TAUNUS. Diabetiva offers telemedicine-based outpatient health care in combination with PDS generated by the Karlsburg Diabetes Management System, KADIS®. This retrospective analysis is based on data from the first year of running KADIS-based PDS in routine diabetes care. Participants were insured persons diagnosed with diabetes and cardiovascular diseases. For final analysis, patients were grouped retrospectively as users or nonusers according to physician acceptance or not (based on questionnaires) of the KADIS-based PDS.

RESULTS

A total of 538 patients participated for more than one year in the Diabetiva program. Of these patients, 289 had complete data sets (two continuous glucose monitoring measurements, two or more hemoglobin A1c (HbA1c) values, and a signed questionnaire) and were included in the final data analysis. Of the physicians, 74% accepted KADIS-based PDS, a rate that was clearly related to HbA1c at the beginning of the observation. If KADIS-based PDS was accepted, HbA1c decreased by 0.4% (7.1% to 6.7%). In contrast, rejection of KADIS-based PDS resulted in an HbA1c increase of 0.5% (6.8% to 7.3%). The insurance company revealed an annual cost reduction of about 900 € per participant in the Diabetiva program.

CONCLUSIONS

KADIS-based PDS in combination with telemedicine has high potential to improve the outcome of routine outpatient diabetes care.

Translation of personalized decision support into routine diabetes care

OBJECTIVE

The aim of this study was to evaluate the impact of personalized decision support (PDS) on metabolic control in people with diabetes and cardiovascular disease.

RESEARCH DESIGN AND METHODS

The German health insurance fund BKK TAUNUS offers to its insured people with diabetes and cardiovascular disease the possibility to participate in the Diabetiva® program, which includes PDS. Personalized decision support is generated by the expert system KADIS® using self-control data and continuous glucose monitoring (CGM) as its data source. The physician of the participating person receives the PDS once a year, decides about use or nonuse, and reports his/her decision in a questionnaire. Metabolic control of participants treated by use or nonuse of PDS for one year and receiving CGM twice was analyzed in a retrospective observational study. The primary outcome was hemoglobin A1c (HbA1c); secondary outcomes were mean sensor glucose (MSG), glucose variability, and hypoglycemia.

RESULTS

A total of 323 subjects received CGM twice, 289 had complete data sets, 97% (280/289) were type 2 diabetes patients, and 74% (214/289) were treated using PDS, resulting in a decrease in HbA1c [7.10±1.06 to 6.73±0.82%; p<.01; change in HbA1ct0-t12 months -0.37 (95% confidence interval -0.46 to -0.28)] and MSG (7.7±1.6 versus 7.4±1.2 mmol/liter; p=.003) within one year. Glucose variability was also reduced, as indicated by lower high blood glucose index (p=.001), Glycemic Risk Assessment Diabetes Equation (p=.009), and time of hyper-glycemia (p=.003). Low blood glucose index and time spent in hypoglycemia were not affected. In contrast, nonuse of PDS (75/289) resulted in increased HbA1c (p<.001). Diabetiva outcome was strongly related to baseline HbA1c (HbA1ct0; p<.01) and use of PDS (p<.01). Acceptance of PDS was dependent on HbA1ct0 (p=.049).

CONCLUSIONS

Personalized decision support has potential to improve metabolic outcome in routine diabetes care.

Update on mathematical modeling research to support the development of automated insulin delivery systems

One year after its initial meeting, the Glycemia Modeling Working Group reconvened during the 2009 Diabetes Technology Meeting in San Francisco, CA. The discussion, involving 39 scientists, again focused on the need for individual investigators to have access to the clinical data required to develop and refine models of glucose metabolism, the need to understand the differences among the distinct models and control algorithms, and the significance of day-to-day subject variability. The key conclusion was that model-based comparisons of different control algorithms, or the models themselves, are limited by the inability to access individual model-patient parameters. It was widely agreed that these parameters, as opposed to the average parameters that are typically reported, are necessary to perform such comparisons. However, the prevailing view was that, if investigators were to make the parameters available, it would limit their ability (and that of their institution) to benefit from the invested work in developing their models. A general agreement was reached regarding the importance of each model having an insulin pharmacokinetic/pharmacodynamic profile that is not different from profiles reported in the literature (88% of the respondents agreed that the model should have similar curves or be analyzed separately) and the importance of capturing intraday variance in insulin sensitivity (91% of the respondents indicated that this could result in changes in fasting glucose of >or=15%, with 52% of the respondents believing that the variability could effect changes of >or=30%). Seventy-six percent of the participants indicated that high-fat meals were thought to effect changes in other model parameters in addition to gastric emptying. There was also widespread consensus as to how a closed-loop controller should respond to day-to-day changes in model parameters (with 76% of the participants indicating that fasting glucose should be within 15% of target, with 30% of the participants believing that it should be at target). The group was evenly divided as to whether the glucose sensor per se continues to be the major obstacle in achieving closed-loop control. Finally, virtually all participants agreed that a future two-day workshop should be organized to compare, contrast, and understand the differences among the different models and control algorithms.

Identification of intraday metabolic profiles during closed-loop glucose control in individuals with type 1 diabetes

BACKGROUND

Algorithms for closed-loop insulin delivery can be designed and tuned empirically; however, a metabolic model that is predictive of clinical study results can potentially accelerate the process.

METHODS

Using data from a previously conducted closed-loop insulin delivery study, existing models of meal carbohydrate appearance, insulin pharmacokinetics, and the effect on glucose metabolism were identified for each of the 10 subjects studied. Insulin's effects to increase glucose uptake and decrease endogenous glucose production were described by the Bergman minimal model, and compartmental models were used to describe the pharmacokinetics of subcutaneous insulin absorption and glucose appearance following meals. The composite model, comprised of only five equations and eight parameters, was identified with and without intraday variance in insulin sensitivity (S(I)), glucose effectiveness at zero insulin (GEZI), and endogenous glucose production (EGP) at zero insulin.

RESULTS

Substantial intraday variation in SI, GEZI and EGP was observed in 7 of 10 subjects (root mean square error in model fit greater than 25 mg/dl with fixed parameters and nadir and/or peak glucose levels differing more than 25 mg/dl from model predictions). With intraday variation in these three parameters, plasma glucose and insulin were well fit by the model (R(2) = 0.933 +/- 0.00971 [mean +/- standard error of the mean] ranging from 0.879-0.974 for glucose; R(2) = 0.879 +/- 0.0151, range 0.819-0.972 for insulin). Once subject parameters were identified, the original study could be reconstructed using only the initial glucose value and basal insulin rate at the time closed loop was initiated together with meal carbohydrate information (glucose, R(2) = 0.900 +/- 0.015; insulin delivery, R(2) = 0.640 +/- 0.034; and insulin concentration, R(2) = 0.717 +/- 0.041).

CONCLUSION

Metabolic models used in developing and comparing closed-loop insulin delivery algorithms will need to explicitly describe intraday variation in metabolic parameters, but the model itself need not be comprised by a large number of compartments or differential equations.

Patient-reported outcomes for an integrated real-time continuous glucose monitoring/insulin pump system

BACKGROUND

A 16-week, two-site study evaluated outcomes for a new device (the Paradigm 722 System, Medtronic MiniMed, Northridge, CA) that combines a "smart" continuous subcutaneous insulin infusion (CSII) pump with real-time (RT) continuous glucose monitoring (CGM) and CareLinktrade mark data management software (DMS).

METHODS

CSII-naive adults with type 1 diabetes in suboptimal control (mean glycosylated hemoglobin [A1C] = 8.6%) were randomized to the control arm, consisting of multiple daily injections (MDI) and self-monitoring of blood glucose (SMBG), or the study arm (CSII with RT-CGM as an adjunct to SMBG). Participants (n = 28) completed the validated Insulin Delivery System Rating Questionnaire (IDSRQ) and the parallel Blood Glucose (BG) Monitoring System Rating Questionnaire (BGMSRQ) at study start and end. Participants in the study arm (n = 14) also completed newly developed User Acceptance Questionnaires (UAQs) for CSII, RT-CGM, and DMS at study end.

RESULTS

A1C reduction from study start to end was significant (P < 0.05) in both arms (-1.7% for study arm;-1.0% for control arm); there was no significant change in weight in either arm. The IDSRQ showed significantly (P < 0.05) greater benefit for the study arm in convenience, acceptability of BG monitoring requirements, BG control efficacy, diabetes worries, and interpersonal hassles, as well as higher overall satisfaction/preference. The BGMSRQ showed significantly (P < 0.05) greater benefit for the study arm in the BG monitoring system's ability to help manage glycemic control and less interest in changing to another BG monitoring system. The Study Arm UAQs showed positive ratings of system features.

CONCLUSIONS

Several patient-reported outcomes were significantly more positive in the study arm than the control arm; none was significantly more positive in the control arm. The features of the integrated RT-CGM/CSII system were frequently used and highly rated by participants, with high user satisfaction.

Continuous glucose monitoring in type 2 diabetes

The utility and efficacy of self-monitoring of blood glucose (SMBG), using single capillary glucose determinations, in the management of non-insulin treated type 2 diabetes has been called into question. The use of continuous sub-cutaneous glucose monitoring (CGM) systems provides an answer for at least some of the inadequacies attributed to point capillary SMBG. The use of CGM adds information on postprandial glucose excursions, nocturnal hypoglycemia or hyperglycemia not previously detected by SMBG. This added information facilitates the tailoring of treatment regimens to the individual patient in order to achieve treatment targets without incurring an increased risk of hypoglycemia and provides a useful tool for patient self-management education.