Implementing an Application Programming Interface for PROMIS Measures at Three Medical Centers

Engaging primary care clinicians in the selection of implementation strategies for toddler social-emotional health promotion in community health centers

BACKGROUND

Social-emotional risk for subsequent behavioral health problems can be identified at toddler age, a period where prevention has a heightened impact. This study aimed to meaningfully engage pediatric clinicians, given the emphasis on health promotion and broad reach of primary care, to prepare an Implementation Research Logic Model to guide the implementation of a screening and referral process for toddlers with elevated social-emotional risk.

METHOD

Using an adaptation of a previously published community partner engagement method, six pediatricians from community health centers (CHCs) comprised a Clinical Partner Work Group. The group was engaged in identifying determinants (barriers/facilitators), selecting and specifying strategies, strategy-determinant matching, a modified Delphi approach for strategy prioritization, and user-centered design methods. The data gathered from individual interviews, two group sessions, and a follow-up survey resulted in a completed Implementation Research Logic Model.

RESULTS

The Clinical Partner Work Group identified 16 determinants, including barriers (e.g., patient access to electronic devices) and facilitators (e.g., clinician buy-in). They then selected and specified 14 strategies, which were prioritized based on ratings of feasibility, effectiveness, and priority. The highest-rated strategies (e.g., integration of the screener into the electronic health record) provided coverage of all identified barriers and comprised the primary implementation strategy "package" to be used and tested.

CONCLUSIONS

Clinical partners provided important context and insights for implementation strategy selection and specification to support the implementation of social-emotional risk screening and referral in pediatric primary care. The methodology described herein can improve partner engagement in implementation efforts and increase the likelihood of success. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Seamless Integration of Computer-Adaptive Patient Reported Outcomes into an Electronic Health Record

BACKGROUND

Patient-reported outcome (PRO) measures have become an essential component of quality measurement, quality improvement, and capturing the voice of the patient in clinical care. In 2004, the National Institutes of Health endorsed the importance of PROs by initiating the Patient-Reported Outcomes Measurement Information System (PROMIS), which leverages computer-adaptive tests (CATs) to reduce patient burden while maintaining measurement precision. Historically, PROMIS CATs have been used in a large number of research studies outside the electronic health record (EHR), but growing demand for clinical use of PROs requires creative information technology solutions for integration into the EHR.

OBJECTIVES

This paper describes the introduction of PROMIS CATs into the Epic Systems EHR at a large academic medical center using a tight integration; we describe the process of creating a secure, automatic connection between the application programming interface (API) which scores and selects CAT items and Epic.

METHODS

The overarching strategy was to make CATs appear indistinguishable from conventional measures to clinical users, patients, and the EHR software itself. We implemented CATs in Epic without compromising patient data security by creating custom middleware software within the organization's existing middleware framework. This software communicated between the Assessment Center API for item selection and scoring and Epic for item presentation and results. The middleware software seamlessly administered CATs alongside fixed-length, conventional PROs while maintaining the display characteristics and functions of other Epic measures, including automatic display of PROMIS scores in the patient's chart. Pilot implementation revealed differing workflows for clinicians using the software.

RESULTS

The middleware software was adopted in 27 clinics across the hospital system. In the first 2 years of hospital-wide implementation, 793 providers collected 70,446 PROs from patients using this system.

CONCLUSION

This project demonstrated the importance of regular communication across interdisciplinary teams in the design and development of clinical software. It also demonstrated that implementation relies on buy-in from clinical partners as they integrate new tools into their existing clinical workflow.