RESUMEN
Background: Screening for risk behaviors is a routine and essential component of adolescent preventive health visits. Early identification of risks can inform targeted counseling and care. If stored in discrete fields in the electronic health record (EHR), adolescent screening data can also be used to understand risk behaviors across a clinic or health system or to support quality improvement projects. Objective: Goals of this pilot study were to adapt and implement an existing paper adolescent risk behavior screening tool for use as an electronic data capture tool (the eTeenQ), to evaluate acceptance of the eTeenQ, and to describe the prevalence of the selected risk behaviors reported through the eTeenQ. Methods: The multidisciplinary project team applied an iterative process to develop the 29-item eTeenQ. Two unique data entry forms were created with attention to (1) user interface and user experience, (2) the need to maintain patient privacy, and (3) the potential to transmit and store data for future use in clinical care and research. Three primary care clinics within a large health system piloted the eTeenQ from August 17, 2020, to August 27, 2021. During preventive health visits for adolescents aged 12 to 18 years, the eTeenQ was completed on tablets and responses were converted to a provider display for teens and providers to review together. Responses to the eTeenQ were stored in a REDCap (Research Electronic Data Capture; Vanderbilt University) database, and for patients who agreed, responses were transferred to an EHR flowsheet. Responses to selected eTeenQ questions are reported for those consenting to research. At the conclusion of the pilot, the study team conducted semistructured interviews with providers and staff regarding their experience using the eTeenQ. Results: Among 2816 adolescents with well visits, 2098 (74.5%) completed the eTeenQ. Of these, 1811 (86.3%) agreed to store responses in the EHR. Of 1632 adolescents (77.8% of those completing the eTeenQ) who consented for research and remained eligible, 1472 (90.2%) reported having an adult they can really talk to and 1510 (92.5%) reported feeling safe in their community, yet 401 (24.6%) reported someone they lived with had a gun and 172 (10.5%) reported having had a stressful or scary event that still bothered them. In addition, 157 (9.6%) adolescents reported they were or wondered if they were gay, lesbian, bisexual, pansexual, asexual, or other, and 43 (2.6%) reported they were or wondered if they were transgender or gender diverse. Of 11 staff and 7 providers completing interviews, all felt that the eTeenQ improved confidentiality and willingness among adolescents to answer sensitive questions. All 7 providers preferred the eTeenQ over the paper screening tool. Conclusions: Electronic capture of adolescent risk behaviors is feasible in a busy clinic setting and well accepted among staff and clinicians. Most adolescents agreed for their responses to risk behavior screening to be stored in the EHR.
RESUMEN
OBJECTIVE: The goal was to examine the current abilities and future potential of electronic health record (EHR) systems to measure childhood developmental screening and follow-up rates in primary care settings. METHODS: A group of pediatric clinicians and health informatics experts was convened to develop quality indicators reflecting different aspects of the developmental screening process. These indicators included the administration of a standardized, validated instrument to screen children for developmental delays, the documentation of abnormal screening results, and the provision of follow-up care. Six integrated provider systems across the United States, with fully implemented EHR systems, were evaluated to determine the feasibility of implementing these measures within each system. Barriers related to measure implementation were identified. RESULTS: The EHR systems of all 6 health care organizations could implement measures examining developmental screening rates and could identify and track children with abnormal screening results. However, most of the systems did not have the ability to capture data for more-complex EHR-based measures. In particular, data elements based on workflow actions could not be captured with current EHR system designs. CONCLUSIONS: This study identified 2 main barriers to the implementation of developmental quality measures: concerns about data reliability and the tracking of care coordination within patient records. Potential solutions to these problems, including terminology standardization, patient portal use, and use of a single developmental screening instrument, are discussed.