Abstract: The Impact of EHR Functionality on Patterns of Depression Treatment in Primary Care (Society for Social Work and Research 24th Annual Conference - Reducing Racial and Economic Inequality)

448P The Impact of EHR Functionality on Patterns of Depression Treatment in Primary Care

Saturday, January 18, 2020
Marquis BR Salon 6 (ML 2) (Marriott Marquis Washington DC)
* noted as presenting author
Elizabeth Matthews, PhD, Assistant Professor, Fordham University, New York, NY
Ayse Akincigil, PhD, Associate Professor, Rutgers University, NJ
Background: Although primary care is an opportune location to treat depression, many individuals exhibiting symptoms are still not offered antidepressants or referrals to mental health services by their physician. Provider identified barriers to addressing depression include time constraints, insufficient knowledge of mental health treatment, and poor access to mental health providers. Electronic health records (EHRs) have been shown to improve adherence to many clinical best practices in health care, however, the impact of these systems on depression treatment specifically has been mixed. Problematically, most studies treat EHR use as a binary term, failing to account for heterogeneity in capability and design of these systems. This leaves less known about whether specific EHR functions may be particularly beneficial to supporting depression treatment. For example, EHRs with prescribing supports and the capacity for health information exchange (HIE) with mental health providers may be particularly equipped to mitigate provider identified barriers to treatment. To examine this, the present study examined the impact of these particular EHR functions on patterns of depression treatment in primary care visits.

Methods: This study was completed using 2014-2015 data from the National Ambulatory Medical Care Survey (NAMCS). The sample included 2,865 visits where adults were identified as having depression by a primary care specialist. Visits were categorized in three groups relating to available EHR functionality: having EHR-based prescribing supports present, having prescribing supports and mental health HIE present, or having neither. Depression treatment included physician notation that antidepressants were prescribed and/or psychotherapy or mental health counseling were offered. Using three separate models, logistic regression examined the impact of EHR functionalities on the odds of receiving any depression treatment, antidepressants, or a mental health intervention. Patient level and clinical level covariates were included in the fully adjusted model.

Results: Of the study sample, 59% (N=1,700) received any depression treatment. The vast majority (58%, N=1,673) were prescribed antidepressant medications, while only 2.4% (n=69) were offered a mental health intervention.  Compared those without these functions, visits with only prescribing supports present (OR=1.6, p<.05) and visits with both prescribing supports and mental health HIE (OR=3.4, p<.001.) present increased the odds of receiving any type of depression treatment. Similarly, prescribing supports (OR=1.6, p<.05) and combined prescribing supports and mental health HIE (OR=3.6, p<.001) increased the likelihood of antidepressant prescribing. Neither EHR functionality significantly impacted linkages to mental health interventions.

Conclusion: Many patients experiencing depression were not linked to treatment, and were rarely offered mental health interventions. Visits where prescribing supports and mental health HIE were present increased the likelihood of receiving depression treatment. Importantly, the improvement in rates of depression treatment were primarily accounted for by increases in antidepressant prescribing, while EHR functionality did not impact the likelihood of being offered a mental health intervention. This suggests that while particular EHR capacities can increase rates of depression treatment, it also influences the type of depression care patients receive. This underscores the importance of ensuring EHR function and design is well aligned with best practices. Particular implications for interdisciplinary depression treatment will be discussed.