Using Electronic Health Record–Based Tools To Screen for Bipolar Disorder in Primary Care Patients With Depression

Discussion

Our intent in this study was to examine the impact of introducing an efficient EHR-based screening tool for bipolar disorder into primary care practices. The screening strategy employed in this study could be labeled “secondary screening” because it was targeted at a high-risk group that already carried a potentially inaccurate diagnosis of depression. We felt that this strategy would minimize practice burden and at the same time increase the case-finding “yield.”

Our results generally supported the clinical utility of the screening tool. The tool was used by clinicians to screen nearly half of all patients with depression who did not already have a diagnosis of bipolar disorder. Although one could argue that screening rates should be even higher than that, having half of patients screened in a 6-month period is high when compared with rates of other screening behaviors that are considered “routine.”^21,31,32

Of those patients who were screened, 2.5% were found to be at high or very high risk for bipolar disorder according to their responses to the CIDI-based screening protocol. This proportion is lower than the rate of 3% to 24% reported in previous studies^7,8 as well as the rate of 28.4% seen in secondary screening of 269 consecutive primary care patients referred to a depression disease management program led by one of the authors (MK). Some of the differences may be because of differences in case-finding methods. Smith et al⁷ used a complex sampling strategy and administered the Hypomania Checklist, Bipolar Spectrum Diagnostic Scale, and Mini International Neuropsychiatric Interview to establish a diagnosis; on the basis of assumptions about rates of disorder in patients who did not complete screening, their case rate varied from 3.3% (most conservative) to 21.6% (least conservative). Tafalla et al⁸ screened primary care patients “with a current major depressive episode” with the Mood Disorder Questionnaire, did not conduct confirmatory interviews, employed a cutoff rate for “caseness” similar to that used in this study, and reported a higher rate of 24%. However, their sample had a high score on the Hamilton Rating Scale for Depression at entry, suggesting that this study enrolled severely and actively depressed patients. Finally, the high rate reported in our series of 269 depressed primary care patients was obtained from a sample of more severely and chronically depressed patients who were referred to a depression disease management program.³³

In contrast, this study included all patients with any current active diagnosis of depression, and therefore included the full spectrum of depression seen in primary care—from patients with minor depression to those with a single episode now in remission to those with recurrent major depressive disorder or chronic, treatment-resistant depression. This is a more representative sample of the full range of “depressed” patients seen in a typical primary care practice who would be subject to a screening protocol for bipolar disorder. Therefore, we believe that our “positive” screening rate of 2.5% is a more accurate estimate of the potential yield of this type of protocol than the higher estimates obtained by others. Although this yield seems high relative to the yield for other screening behaviors, it could be that some clinicians do not find this yield high enough to warrant spending their time on routine screening, especially for patients with more mild depression.

As expected, we found that use of the tool by clinicians resulted in a higher rate of new diagnoses of bipolar disorder; the rate of new diagnoses in the intervention group was almost triple that in the control group. The absolute rate of new diagnoses was still relatively low, however, with only 1.1% of intervention patients with depression being diagnosed with bipolar disorder. It is important to note that the formal diagnosis rate was lower than the rate of “positive” (high or very high risk) screens. We believe this reflects the reluctance of primary care clinicians to make a definitive diagnosis without further evaluation and confirmation by a mental health specialist, especially when the results were less than conclusive. Although almost two thirds of patients with very high risk scores were diagnosed with bipolar by their primary care physician, this was true for only 42% of patients with high risk scores and 24% of patients with moderate risk scores. It is likely that primary care physicians would be more likely to defer to a specialist for making a definitive diagnosis in patients with more uncertain scores.

The rate of new bipolar medication prescriptions also was higher in the intervention group, although neither the relative difference nor the absolute difference was as great as for bipolar diagnoses. This may reflect an additional reluctance on the part of primary care physicians to initiate treatment for bipolar disorder and to instead defer treatment to a psychiatrist.

These results must be interpreted in light of study limitations. The primary limitations stem from our reliance on data that could be extracted reliably from the EHR. All mental health diagnoses were extracted from the EHR problem list and were not clinically validated, so some of the depressed patients who were identified as “high risk” for bipolar disorder might in fact have an established bipolar diagnosis not known to the primary care clinician. This is particularly true when patients were referred to mental health specialists who then made the diagnosis of bipolar disorder; though clinicians could indicate a referral on the form, this was done in text comments that were not tracked. Also, we do not know for sure that “bipolar medications” were in fact used for bipolar disorder because some of these medications can be used for other purposes. Because these medication prescriptions were extracted from the record and could not be validated against pharmacy records, we were, in effect, assessing clinicians' initial treatment decisions. We could not explore the context of those diagnostic or treatment decisions, nor could we explore the nuanced management plans that clinicians may have used in uncertain clinical situations. In some cases, provisional diagnoses or use of sample medications would have been included in a text visit note but not in the data fields we could access. However, our primary aim was to assess the impact of the CDS tool on diagnosis and treatment decisions, and we believe that diagnoses and medications recorded in the EHR provide a reasonable and valid way to assess those decisions. Also, limitations in the validity of diagnoses or medications are unlikely to differ between the intervention and comparison physicians, and differences between the 2 groups are unlikely to be effected by the limitations of EHR data. We also acknowledge that the study included only clinicians who use one particular EHR, who participate in the CHURN PBRN, and who agreed to participate in this particular PBRN study. One study found that patients in the MQIC database used by CHURN to be similar to the general US population in terms of disease prevalence,³⁴ but studies have not examined differences between the CHURN or MQIC populations and other outpatient populations in terms of quality of care. It is not clear whether these results can be generalized to apply to clinicians practicing in different environments.