ABSTRACT
BACKGROUND: This article reviews the literature to date and reports on a new study that documented the frequency of manual code-requiring blood glucose (BG) meters that were miscoded at the time of the patient's initial appointment in a hospital-based outpatient diabetes education program. METHOD: Between January 1 and May 31, 2007, the type of BG meter and the accuracy of the patient's meter code (if required) and procedure for checking BG were checked during the initial appointment with the outpatient diabetes educator. If indicated, reeducation regarding the procedure for the BG meter code entry and/or BG test was provided. RESULTS: Of the 65 patients who brought their meter requiring manual entry of a code number or code chip to the initial appointment, 16 (25%) were miscoded at the time of the appointment. Two additional problems, one of dead batteries and one of improperly stored test strips, were identified and corrected at the first appointment. CONCLUSIONS: These findings underscore the importance of checking the patient's BG meter code (if required) and procedure for testing BG at each encounter with a health care professional or providing the patient with a meter that does not require manual entry of a code number or chip to match the container of test strips (i.e., an autocode meter).
ABSTRACT
OBJECTIVE: The objective of this study was to determine inaccuracies of miscoded blood glucose (BG) meters and potential errors in insulin dose based on values from these meters. RESEARCH DESIGN: Fasting diabetic subjects at three clinical centers participated in a 2-hour meal tolerance test. At various times subjects' blood was tested on five BG meters and on a Yellow Springs Instruments laboratory glucose analyzer. Some meters were purposely miscoded. Using the BG values from these meters, along with three insulin dose algorithms, Monte Carlo simulations were conducted to generate ideal and simulated-meter glucose values and subsequent probability of insulin dose errors based on normal and empirical distribution assumptions. RESULTS: Maximal median percentage biases of miscoded meters were +29% and -37%, while maximal median percentage biases of correctly coded meters were only +0.64% and -10.45% (p = 0.000, chi(2) test, df = 1). Using the low-dose algorithm and the normal distribution assumption, the combined data showed that the probability of insulin error of +/-1U, +/-2, +/-3, +/-4, and +/-5U for miscoded meters could be as high as 49.6, 50.0, 22.3, 1.4, and 0.04%, respectively. This is compared to manually, correctly coded meters where the probability of error of +/-1, +/-2, and +/-3U could be as high as 44.6, 7.1, and 0.49%, respectively. There was no instance of a +/-4 or +/-5U insulin dose error with a manually, correctly coded meter. For autocoded meters, the probability of +/-1 and +/-2U could be as high as 35.4 and 1.4%, respectively. For autocoded meters there were no calculated insulin dose errors above +/-2U. The probability of insulin misdosing with either manually, correctly coded or autocoded meters was significantly lower than that with miscoded meters. Results using empirical distributions showed similar trends of insulin dose errors. CONCLUSIONS: Blood glucose meter coding errors may result in significant insulin dosing errors. To avoid error, patients should be instructed to code their meters correctly or be advised to use an autocoded meter that showed superior performance over manually, correctly coded meters in this study.
