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Background: Electronic health records (EHRs) are a key tool for primary care practice. However, EHR functionality is not keeping pace with the evolving informational and decision-support needs of behavioral health clinicians (BHCs) working on integrated teams., Objective: Describe workflows and tasks of BHCs working with integrated teams, identify their health information technology needs, and develop EHR tools to address them., Method: A mixed-methods, comparative-case study of six community health centers (CHCs) in Oregon, each with at least one BHC integrated in their primary care team. We observed clinical work and conducted interviews to understand workflows and clinical tasks, aiming to identify how effectively current EHRs supported integrated care delivery, including transitions, documentation, information sharing, and decision making. We analyzed these data and employed a user-centered design process to develop EHR tools addressing the identified needs., Results: BHCs used the primary care EHR for documentation and communication with other team members, but the EHR lacked the functionality to fully support integrated care. Needs include the ability to: (1) automate and track paper-based screening; (2) document behavioral health history; (3) access patient social and medical history relevant to behavioral health issues, and (4) rapidly document and track progress on goals. To meet these needs, we engaged users and developed a set of EHR tools called the BH e-Suite., Conclusion: Integrated primary care teams, and particularly BHCs, have unique information needs, workflows and tasks. These needs can be met and supported by the EHR with a moderate level of modification.

Objective: Screening, brief intervention, and referral for treatment (SBIRT) for behavioral health (BH) is a key clinical process. SBIRT tools in electronic health records (EHR) are infrequent and rarely studied. Our goals were 1) to design and implement SBIRT using clinical decision support (CDS) in a commercial EHR; and 2) to conduct a pragmatic evaluation of the impact of the tools on clinical outcomes., Methods: A multidisciplinary team designed SBIRT workflows and CDS tools. We analyzed the outcomes using a retrospective descriptive convenience cohort with age-matched comparison group. Data extracted from the EHR were evaluated using descriptive statistics., Results: There were 2 outcomes studied: 1) development and use of new BH screening tools and workflows; and 2) the results of use of those tools by a convenience sample of 866 encounters. The EHR tools developed included a flowsheet for documenting screens for 3 domains (depression, alcohol use, and prescription misuse); and 5 alerts with clinical recommendations based on screening; and reminders for annual screening. Positive screen rate was 21% (≥1 domain) with 60% of those positive for depression. Screening was rarely positive in 2 domains (11%), and never positive in 3 domains. Positive and negative screens led to higher rates of documentation of brief intervention (BI) compared with a matched sample who did not receive screening, including changes in psychotropic medications, updated BH terms on the problem list, or referral for BH intervention. Clinical process outcomes changed even when screening was negative., Conclusions: Modified workflows for BH screening and CDS tools with clinical recommendations can be deployed in the EHR. Using SBIRT tools changed clinical process metrics even when screening was negative, perhaps due to conversations about BH not captured in the screening flowsheet. Although there are limitations to the study, results support ongoing investigation.

The detailed collection of family history information is becoming increasingly important for patient care and biomedical research. Recent reports have highlighted the need for efforts to better understand collection and use of this information in resources such as the Electronic Health Record (EHR). This two-part study involved characterizing the use and contents of free-text comments within the family history section of an EHR. Based on a manual review of a subset of 11,456 cancer-related family history entries, 20 "reasons for use" were identified and the distribution across these reasons determined. A semi-automated analysis of the 3,358 unique comments associated with these entries was then performed to identify and quantify key categories of information. Implications of this study include guiding efforts for the improved use, collection, and subsequent analysis of family history information in the EHR.