Missingness patterns in a comprehensive instrument identifying psychosocial and substance use risk in antenatal care

Saving time, signaling trust: Using the PROMOTE self-report screening instrument to enhance prenatal care quality and therapeutic relationships

OBJECTIVES

Comprehensive screening of psychosocial vulnerabilities and substance use in prenatal care is critical to promote the health and well-being of pregnant patients. Effective implementation of new screening procedures and instruments should be accompanied by an in-depth investigation to assess their feasibility and impact on care delivery.

METHODS

In 2020, following implementation of the Profile for Maternal and Obstetric Treatment Effectiveness (PROMOTE) an innovative self-report screening instrument developed for outpatient prenatal clinics in the U.S., we conducted individual interviews and focus groups with twenty-two midwives, nurse practitioners, and obstetric residents focused on the PROMOTE and its impacts on care delivery. We used interpretive description for the qualitative analysis of the interviews.

RESULTS

Five themes were identified: Guiding Time Efficiently: "The Time I Don't Have," Preventing Missed Care, Signaling Trustworthiness, Establishing Trauma-Informed Foundations, and Promoting "Honest" Patient Disclosure.

CONCLUSION

Interviews suggest that patient completion of the PROMOTE before the medical encounter helps reduce previously reported barriers, is more time-effective, and makes history-taking easier. It also facilitates the patient-provider relationship.

INNOVATION

Findings offer insight into the breadth and depth of clinical impact resulting from the PROMOTE, and provide guidance for the implementation of such tools to optimize health outcomes.

Applying machine learning methods to psychosocial screening data to improve identification of prenatal depression: Implications for clinical practice and research

We utilized machine learning (ML) methods on data from the PROMOTE, a novel psychosocial screening tool, to quantify risk for prenatal depression for individual patients and identify contributing factors that impart greater risk for depression. Random forest algorithms were used to predict likelihood for being at high risk for prenatal depression (Edinburgh Postnatal Depression Scale; EPDS ≥ 13 and/or positive self-injury item) using data from 1715 patients who completed the PROMOTE. Performance matrices were calculated to assess the ability of the PROMOTE to accurately classify patients. Probability for depression was calculated for individual patients. Finally, recursive feature elimination was used to evaluate the importance of each PROMOTE item in the classification of depression risk. PROMOTE data were successfully used to predict depression with acceptable performance matrices (accuracy = 0.80; sensitivity = 0.75; specificity = 0.81; positive predictive value = 0.79; negative predictive value = 0.97). Perceived stress, emotional problems, family support, age, major life events, partner support, unplanned pregnancy, current employment, lifetime abuse, and financial state were the most important PROMOTE items in the classification of depression risk. Results affirm the value of the PROMOTE as a psychosocial screening tool for prenatal depression and the benefit of using it in conjunction with ML methods. Using such methods can help detect underreported outcomes and identify what in patients' lives makes them more vulnerable, thus paving the way for effective individually tailored precision medicine.