Abstract

Objectives: Diabetes-related medical errors in outpatient practice are common and costly. This study attempts to accurately identify, classify, and interpret patterns of diabetes-related medical errors in primary care settings using diagnostic, laboratory, and pharmacy data. Methods: Automated diagnostic, laboratory, and pharmacy data were used to evaluate outpatient care received from 5,729 adults with an established diagnosis of diabetes, who received care at a single large medical group over a 12-month period of time. A subset 4,152 adults with diabetes who were (a) younger than 80 years; (b) had a Charlson comorbidity score of 2 or lower; (c) had pharmacy coverage; and (d)were linked to a primary care physician, were classified as having a glycemic-control error, a lipid-control error, or a pharmacy error. Results: Among the subset of 4,152 patients, 62.3 percent had one or more medical errors during the 1-year study period. Errors related to glycemic-control care occurred in 22.1 percent; errors related to low-density lipoprotein-cholesterol (LDL) care occurred in 58.3 percent. Inappropriate or risky drug prescribing occurred in 9.9 percent of the 5,729 patients. Metformin was used by 27.2 percent of those with congestive heart failure, and by 16.6 percent of those with a measured serum creatinine value greater than 1.5 mg/dl. Conclusions: Medical errors in adults with diabetes are the norm rather than the exception. Passive surveillance for medical errors can be done accurately and inexpensively using automated data routinely available in many primary care settings. These observed patterns of medical error define specific clinical domains and patient subgroups for whom aggressive efforts to reduce medical errors are most urgently needed.

Introduction

In diabetes care, achieving recommended glucose, blood pressure, and lipid treatment goals significantly reduces the risk of subsequent adverse events including heart attacks, strokes, renal failure, and premature death. ^1–3 However, at present, less that 20 percent of adults with diabetes have simultaneously achieved glycated hemoglobin (A1c) less than the recommended level of 7 percent, systolic blood pressure (SBP) less than 130 mm Hg, and -cholesterol (LDL) less than 100 mg/dl. ⁴ It is noteworthy—and somewhat discouraging—that for SBP and A1c, there has been little improvement in levels of care in the past 10 years, despite a steady stream of more effective medications and monitoring technologies. ^{5, 6}

Recent work has suggested that medical error rates in outpatient care, especially for those who are elderly or on multiple medications, are substantial. ^{7, 8} Medical errors in outpatient care often involve drug therapy. Among the categories of drug-related medical errors are those related to drug-drug interactions, use of contraindicated drugs, or failure to obtain recommended safety laboratory monitoring tests. ⁹ In addition to medical errors related to misuse or overuse of drugs, errors related to the under use of drugs or procedures is also common in outpatient practice ¹⁰ Errors of omission in diabetes care include failure to provide evidence-based diabetes tests, procedures, or treatments in a timely fashion. ¹¹ In this paper, we focus on development of methods to identify, classify, and interpret medical errors related to diabetes care.

In outpatient settings, follow-up of patients is often incomplete, and most medical errors are not recognized as errors by either physicians or patients. Thus, error surveillance strategies that rely on active reporting of errors by physicians or patients are a poor fit for identifying medical errors in outpatient chronic disease care. In theory, the best way to obtain surveillance for medical errors related to outpatient chronic disease care would be to monitor and interpret ongoing streams of automated diagnostic, laboratory, and pharmacy data. ¹² This strategy—while conceptually simple—poses a number of technical challenges related to data availability, data accuracy, and creation of computer programs to appropriately acquire, process, and interpret available data.

Since the landmark report of the Institute of Medicine, To Err Is Human: Building a Safer Health System, ¹³ more attention has been given to inpatient than outpatient medical-errors research. However, patients with chronic diseases typically receive the vast majority of their care in outpatient office settings. Increasing availability of automated clinical data in outpatient settings enhances our ability to identify medical errors and reduce adverse events related to such errors. The magnitude of adverse events related to medical errors in outpatient chronic disease care is often underestimated. Recent clinical trials and other studies indicate that the rate of major cardiovascular events in adults with diabetes can be reduced more than 50 percent, and possibly as much as 80 percent, by aggressive and effective outpatient management of A1c, SBP, and lipids. ^{14, 15} Each heart attack or stroke preceded by uncontrolled chronic diseases, and each associated premature death, represents an adverse event related, in part, to outpatient medical error.

We conducted this study to estimate the frequency of selected medical errors in the outpatient care of adults with diabetes mellitus. To achieve this goal, we developed automated methods to passively identify and classify diabetes-related medical errors, including both errors of omission and errors of commission. Further, we validated this error identification method, and classified diabetes-related medical errors in a large and well-defined group of adults with diagnosed diabetes mellitus.

Subjects and methods

Results

Table 1 presents data on the demographics and health status of the 4,152 study subjects with an established diagnosis of diabetes mellitus who met all of the following criteria: (a) age 80-years or younger; (b) Charlson comorbidity score of 2 or lower; (c) pharmacy coverage; and (d) able to be linked to a primary care physician. These data indicate that current levels of A1c and LDL control of adults with diabetes at this medical group are quite good compared with the national diabetes care surveys. ^{19, 20} A relatively high proportion of diabetes patients also receive regular monitoring of A1c and LDL, which increases our ability to accurately classify patients' error status.

Table 1

Demographic and clinical characteristics of the 4,152 study subjects.

Table 2 shows the final classification of patients by glycemic-control error status. The error rates are based on the subset of 4,152 study subjects. Descriptive definitions of the type of errors are also provided in Table 2. We found that 33 percent (N=1,371) of all study subjects had A1c at the recommended level of less than 7 percent, and 27.2 percent (N=1,129) had A1c between 7 percent and 7.9 percent. We did not classify those in the latter group as having a glycemic-control medical error, because with A1c in this range, advice or educational interventions that emphasize physical activity or nutrition therapy only may be clinically appropriate for many patients—and our data could not provide information on lifestyle interventions or advice. Among those with A1c ≥ 8 percent, 491 of 1,225 (40.1 percent) failed to receive active management of A1c to move toward evidence-based goals. If the 379 subjects on insulin alone are removed from the denominator (because we had insufficient data to assess insulin intensification), then 491 of 846 subjects with A1c greater than 8 percent (58 percent) had a medical error.

Table 2

Classification of 4,152 adults with diabetes by A1c status and glycemic-control pharmacotherapy over a 12-month period of time.

Table 3 provides similar data on lipid management for the same group of study subjects. In those with diabetes, 32.6 percent had LDL less than 100 mg/dl. Among those with LDL ≥ 100 mg/dl, or triglycerides > 400 mg/dl, only 376 of 1,887 (19.9 percent) had active lipid therapy, while 1,511 of 1,887 (80.1 percent) had no changes in lipid drug therapy. In addition, 984 subjects, or 21.9 percent of all subjects, had no LDL test within a year; of these 73 LDL values were missing because the concurrent triglyceride value was greater than 400 mg/dl and LDL could not be calculated.

Table 3

Classification of 4,152 adults with diabetes by lipid levels and lipid-lowering drug therapy over a 12-month period of time.

Table 4 presents the frequency of medication-related errors related to glucose-lowering or lipid-lowering agents among all the 5,729 adults with diabetes. Two rates are estimated. Column C provides the error rate in the subgroup at risk, while column D provides the error rate in the total population of 5,729 adults with diabetes included in this analysis. In general, fewer medication-related errors were related to lipid care than to glucose care. Metformin or thiazolidinediones (TZDs) were frequently used by patients with relative contraindications to their use—like renal insufficiency, congestive heart failure, liver function abnormalities, or chronic lung disease.

Table 4

Frequency of selected medical errors or risky prescribing practices among 5,729 adults with diabetes.

When all the errors were considered simultaneously, we found that 3,571 of the 5,729 study subjects (62.3 percent) had 1 or more errors, and 38.7 percent were free of errors during the 1-year study period. Of those with errors, 1,796 had one error, 1,570 had 2 errors, 191 had 3 errors, and 14 had 4 errors in the 1-year period of time.

Table 5 provides data on the relationships among three differing types of errors for patients relative to glucose and lipid errors. Odds ratios indicate whether a patient with one type of error is more likely or less likely to have another type of error. Overall, patients with glucose errors are significantly more likely to have analogous lipid errors. Patients with missing A1c tests are 22 times more likely to be missing an LDL test also, relative to patients that have an A1c test. This clustering of errors within patients could be related to patient, physician, or patient-physician interaction factors.

Table 5

Interrelationships among glucose and lipid errors at the level of individual patients#.

Discussion

In this study, conducted at a medical group recognized by the American Diabetes Association for high-quality diabetes care, medical errors in adults with diabetes were the norm rather than the exception. Inadequate control of A1c or LDL was commonly observed and is especially pernicious because the impact of inadequate control may be delayed for years to decades. Inappropriate or risky medication use was also common and some medication errors had the potential to rapidly lead to adverse clinical events. For example, among the 451 subjects with serum creatinine greater than 1.5 mg/dl, 16.6 percent were taking metformin. Among the 408 subjects with CHF, 27.2 percent were on metformin, which may cause lactic acidosis; and 11.3 percent were on a TZD, which may exacerbate CHF, due to fluid retention or other factors. If error rates are similar at other practice sites, then common outpatient medical errors may contribute to many potentially preventable adverse clinical events each year in adults with diabetes.

Although our estimate of medical error rates seems high, it is likely to be a conservative estimate for several reasons:

• We examined only selected errors, namely, those related to management of blood glucose or blood lipids. Moreover, if we had more complete data on insulin therapy, additional errors would likely have been identified. Also, many of these patients have other diseases; if we had considered errors related to treatments for these conditions, error rates would inevitably be higher.
• Median A1c was 7.4 percent and median LDL was 104 mg/dl at the study medical group at the time of the study. If our error identification algorithms were applied to medical groups with higher A1c or LDL values, the proportion of patients with errors would likely be higher. Diabetes care in the study setting was substantially better than diabetes care described in several recent national studies. ^19–21
• Our error rates were computed over a 12-month period. If we had computed errors of omission based on a shorter window of time, such as the time between office visits, to the proportion of patients classified as having errors of omission over a 12-month period would have been substantially higher.

Error rates were higher with respect to lipid management (58.3 percent of all patients) than glucose management (22.1 percent) in these diabetes patients. A number of recent studies suggest that many adults with diabetes may derive more benefit from aggressive lipid-lowering therapy (a 20 percent to 25 percent reduction in mortality risk) than from aggressive glucose-lowering therapy. ³ Moreover, the cost-effectiveness of lipid treatment is better than glucose treatment for patients over age 50 years. ²² These data reinforce the need for clinicians to focus more attention on lipid control (and blood pressure control) in diabetes patients in order to maximize reduction in cardiovascular complications of diabetes. The importance of this is underscored by the fact that more than 70 percent of all deaths and the majority of excess health-care charges in adults with diabetes are related to heart attacks and strokes. ²³

Passive identification of errors can be done by searching integrated outpatient laboratory, pharmacy, and diagnostic databases. Such databases are already available to many medical groups or health plans, and will become more widely available as more health-care delivery organizations invest in better information systems. ²⁴ Passive identification of errors is simple, relatively cheap, can be done iteratively over time, and the thresholds for action can be easily modified as evidence-based clinical guidelines change, or to meet local needs or preferences. Passive identification of errors is likely to be more complete than provider-initiated error identification. ²⁵ Careful analysis of error information may ultimately suggest effective intervention strategies to reduce medical errors and improve the health outcomes of adults with diabetes. For example, we have now developed physician learning interventions that significantly reduce clinical inertia and drug-related errors for diabetes care. ²⁶

A significant limitation of the study is our inability to reliably capture errors related to insulin management, because automated data are insufficient to identify changes in insulin doses. Insulin prescriptions do not include dose information, and patients are advised to discard unused insulin one month after opening a vial. These factors make it difficult to systematically capture information on changes in insulin dose using pharmacy data. Such data may be difficult to obtain even from electronic medial records, because many well-educated patients with diabetes self-adjust insulin doses based on acute illness or on daily variations in exercise, eating, and home glucose test values.

Despite the limitations, the results of this study are interesting and important. In adults with diabetes, medical errors are the rule rather than the exception, even in medical groups recognized for excellence in diabetes care. Errors of omission and overuse of potentially risky medications in those with CHF or renal insufficiency are two errors of particular concern. Moreover, the occurrence of glucose errors is significantly correlated with lipid errors in these patients. These findings justify the urgent development of interventions designed to reduce high medical error rates experienced by adults with diabetes. An ongoing stream of passive surveillance data would likely provide additional important insights on the epidemiology of medical error in diabetes patients that may guide the development of novel and effective interventions to reduce errors in diabetes care.

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