Clinical inertia is defined as lack of treatment intensification in a patient not at evidence-based goals for care. Clinical inertia is a major factor that contributes to inadequate chronic disease care in patients with diabetes mellitus, hypertension, dyslipidemias, depression, coronary heart disease, and other conditions. Recent work suggests that clinical inertia related to the management of diabetes, hypertension, and lipid disorders may contribute to up to 80 percent of heart attacks and strokes. Clinical inertia is, therefore, a leading cause of potentially preventable adverse events, disability, death, and excess medical care costs. This paper addresses three specific objectives: (1) to present a conceptual model of clinical inertia that takes into account recent developments in human factors research, cognitive science, and organizational behavior; (2) to operationally define clinical inertia and propose simple clinical protocols that can be used to identify and map its incidence across populations of patients and physicians; and (3) to propose future research to reduce clinical inertia by specifically targeting the root causes of the problem. Ultimately, a better understanding of clinical inertia and the development of specific interventions to reduce it may be a productive strategy to reduce passive errors that contribute to hundreds of thousands of adverse events and tens of thousands of premature deaths annually in the United States.

Clinical inertia is defined as lack of treatment intensification in a patient not at evidence-based goals for care. Clinical inertia is a major factor that contributes to inadequate chronic disease care in patients with diabetes mellitus, hypertension, dyslipidemias, depression, coronary heart disease and other conditions. Recent work suggests that clinical inertia related to the management of diabetes, hypertension and lipid disorders may contribute to up to 80 percent of heart attacks and strokes. Clinical inertia is, therefore, a leading cause of potentially preventable adverse events, disability, death and excess medical care costs. This paper addresses three specific objectives: (1) to present a conceptual model of clinical inertia that takes into account recent developments in human factors research, cognitive science and organizational behavior; (2) to operationally define clinical inertia and propose simple clinical protocols that can be used to identify and map its incidence across populations of patients and physicians; and (3) to propose future research to reduce clinical inertia by specifically targeting the root causes of the problem. Ultimately, a better understanding of clinical inertia and the development of specific interventions to reduce it may be a productive strategy to reduce passive errors that contribute to hundreds of thousands of adverse events and tens of thousands of premature deaths annually in the United States.

BackgroundCurrently, there are few studies on the cardiovascular and fatigue effects of commercially available energy drinks. This study investigated the effects of Monster energy drink (Monster Beverage Corporation, Corona, California), on resting heart rate (HR), heart rate variability (HRV), ride time-to-exhaustion, peak exercise HR, respiratory exchange ratio (RER), and peak rating of perceived exertion (RPE).MethodsThe study used a double-blind, randomized, placebo controlled, crossover design. After an 8-hr fast, 15 subjects consumed Monster Energy Drink (ED standardized to 2.0 mg * kg-1 caffeine) or a flavor-matched placebo preexercise. Resting HR and HRV were determined. After an initial submaximal workload for 30 minutes, subjects completed 10 min at 80% ventilatory threshold (VT) and rode until volitional fatigue at 100% VT.ResultsResting HR was significantly different (ED: 65+/-10 bpm vs. placebo: 58+/-8 bpm, p = 0.02), but resting HRV was not different between the energy drink and placebo trials. Ride time-to-exhaustion was not significantly different between trials (ED: 45.5+/- 9.8 vs. placebo: 43.8+/-9.3 min, p = 0.62). No difference in peak RPE (ED: 9.1 +/- 0.5 vs. placebo: 9.0 +/- 0.8, p = 1.00) nor peak HR (ED: 177 +/- 11 vs. placebo: 175 +/- 12, p = 0.73) was seen. The RER at 30% of VT was significantly different (ED: 0.94 +/- 0.06 vs. placebo: 0.91 +/- 0.05, p = 0.046), but no difference between the two conditions were seen at the other intensities.ConclusionAlthough preexercise ingestion of the energy drink does increase resting HR there was no alteration in HRV parameters. Ride time-to-exhaustion was not enhanced.

OBJECTIVE -To assess two physician learning interventions designed to improve safety and quality of diabetes care delivered by primary care physicians (PCPs).RESEARCH DESIGN AND METHODS -This group randomized clinical trial included 57 consenting PCPs and their 2,020 eligible adult patients with diabetes. Physicians were randomized to no intervention (group A), a simulated case-based physician learning intervention (group B), or the same simulated case-based learning intervention with physician opinion leader feedback (group C). Dependent variables included A1C values, LDL cholesterol values, pharmacotherapy intensification rates in patients not at clinical goals, and risky prescribing events.RESULTS -Groups B and C had substantial reductions in risky prescribing of metformin in patients with renal impairment (P ϭ 0.03). Compared with groups A and C, physicians in group B achieved slightly better glycemic control (P ϭ 0.04), but physician intensification of oral glucose-lowering medications was not affected by interventions (P ϭ 0.41). Lipid management improved over time (P Ͻ 0.001) but did not differ across study groups (P ϭ 0.67).CONCLUSIONS -A simulated, case-based learning intervention for physicians significantly reduced risky prescribing events and marginally improved glycemic control in actual patients. The addition of opinion leader feedback did not improve the learning intervention. Refinement and further development of this approach is warranted. Diabetes Care 32:585-590, 2009