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Objective: We evaluated the impact of an automated decision support tool (DST) on clinicians' ability to identify glycemic abnormalities in structured self-monitoring of blood glucose (SMBG) data and then make appropriate therapeutic changes based on the glycemic patterns observed.Research Design and Methods: In this prospective, randomized, controlled, multicenter study, 288 clinicians (39.6% family practice physicians, 37.9% general internal medicine physicians, and 22.6% nurse practitioners) were randomized to structured SMBG alone (STG; n = 72); structured SMBG with DST (DST; n = 72); structured SMBG with an educational DVD (DVD; n = 72); and structured SMBG with DST and the educational DVD (DST+DVD; n = 72). Clinicians analyzed 30 patient cases (type 2 diabetes), identified the primary abnormality, and selected the most appropriate therapy.Results: A total of 222 clinicians completed all 30 patient cases with no major protocol deviations. Significantly more DST, DVD, and DST+DVD clinicians correctly identified the glycemic abnormality and selected the most appropriate therapeutic option compared with STG clinicians: 49, 51, and 55%, respectively, vs. 33% (all P < 0.0001) with no significant differences among DST, DVD, and DST+DVD clinicians.Conclusions: Use of structured SMBG, combined with the DST, the educational DVD, or both, enhances clinicians' ability to correctly identify significant glycemic patterns and make appropriate therapeutic decisions to address those patterns. Structured testing interventions using either the educational DVD or the DST are equally effective in improving data interpretation and utilization. The DST provides a viable alternative when comprehensive education is not feasible, and it may be integrated into medical practices with minimal training. [ABSTRACT FROM AUTHOR]

Abstract Background: We evaluated how a structured patient/physician self-monitoring of blood glucose (SMBG) intervention influenced the timing, frequency, and effectiveness of primary care physicians' treatment changes with type 2 diabetes mellitus (T2DM) patients over 12 months. Methods: The Structured Testing Program (STeP) study was a cluster-randomized, multicenter trial with 483 poorly controlled, insulin-naive T2DM subjects. Primary care practices were randomized to the Active Control Group (ACG) or the Structured Testing Group (STG), the latter of which included quarterly review of structured SMBG results. STG patients used a paper tool that graphs seven-point glucose profiles over 3 consecutive days; physicians received a treatment algorithm based on SMBG patterns. Impact of structured SMBG on physician treatment modification recommendations (TMRs) and glycemic outcomes was examined. Results: More STG than ACG patients received a TMR at each study visit (P < 0.0001). Of patients who received at least one TMR, STG patients demonstrated a greater reduction in glycated hemoglobin A1c (HbA1c) than ACG patients (-1.2% vs. -0.8%, P < 0.03). Patients with a baseline HbA1c >=8.5% who received a TMR at the Month 1 visit experienced greater reductions in HbA1c (P = 0.002) than patients without an initial TMR. More STG than ACG patients were started on incretins (P < 0.01) and on thiazolidinediones (P = 0.004). The number of visits with a TMR was unrelated to HbA1c change over time. Conclusions: Patient-provided SMBG data contribute to glycemic improvement when blood glucose patterns are easy to detect, and well-trained physicians take timely action. Collaborative use of structured SMBG data leads to earlier, more frequent, and more effective TMRs for poorly controlled, non-insulin-treated T2DM subjects. [ABSTRACT FROM AUTHOR]

Objective: To assess the effectiveness of structured blood glucose testing in poorly controlled, noninsulin-treated type 2 diabetes.Research Design and Methods: This 12-month, prospective, cluster-randomized, multicenter study recruited 483 poorly controlled (A1C ≥ 7.5%), insulin-naïve type 2 diabetic subjects from 34 primary care practices in the U.S. Practices were randomized to an active control group (ACG) with enhanced usual care or a structured testing group (STG) with enhanced usual care and at least quarterly use of structured self-monitoring of blood glucose (SMBG). STG patients and physicians were trained to use a paper tool to collect/interpret 7-point glucose profiles over 3 consecutive days. The primary end point was A1C level measured at 12 months.Results: The 12-month intent-to-treat analysis (ACG, n = 227; STG, n = 256) showed significantly greater reductions in mean (SE) A1C in the STG compared with the ACG: -1.2% (0.09) vs. -0.9% (0.10); Δ = -0.3%; P = 0.04. Per protocol analysis (ACG, n = 161; STG, n = 130) showed even greater mean (SE) A1C reductions in the STG compared with the ACG: -1.3% (0.11) vs. -0.8% (0.11); Δ = -0.5%; P < 0.003. Significantly more STG patients received a treatment change recommendation at the month 1 visit compared with ACG patients, regardless of the patient's initial baseline A1C level: 179 (75.5%) vs. 61 (28.0%); <0.0001. Both STG and ACG patients displayed significant (P < 0.0001) improvements in general well-being (GWB).Conclusions: Appropriate use of structured SMBG significantly improves glycemic control and facilitates more timely/aggressive treatment changes in noninsulin-treated type 2 diabetes without decreasing GWB. [ABSTRACT FROM AUTHOR]

Abstract Background: The purpose of this study was to determine if primary care physicians could utilize data collection tools to accurately identify glycemic abnormalities in structured, episodic self-monitoring of blood glucose (SMBG) data from patients with non-insulin-treated type 2 diabetes and whether use of these SMBG data would influence their therapeutic decisions. Methods: Twenty-three case studies demonstrating several glycemic states (normoglycemia, elevated fasting glucose, elevated postprandial glucose, all elevated glucose, and hypoglycemia) were presented to 61 primary care physicians who evaluated the cases based upon A1C data, alone and then in combination with SMBG data. SMBG data were presented in five formats. Participants were to identify the specific glucose pattern, determine the necessity for therapy change, and select specific therapeutic changes. Participant assessments were compared with assessments made by a panel of diabetes care specialists. Results: Most (78%) participants identified the same primary blood glucose feature identified by the diabetes specialists; 93.8% agreed with the diabetes care specialists regarding the need for therapy modification. The majority (77%) of participants changed the way they would manage the case after evaluating case studies with SMBG data made available to them. Eighty-six percent of participants considered the SMBG data to be of equal value or more valuable than an A1C test result; 71% of participants strongly agreed that they are now more likely to recommend structured, episodic SMBG to their non-insulin-treated type 2 diabetes mellitus patients. Conclusions: Primary care physicians can correctly identify glycemic abnormalities in SMBG data obtained through structured, episodic SMBG. Additional studies are needed to determine the clinical impact of similar testing regimens in primary care practice settings. [ABSTRACT FROM AUTHOR]