Implementation of a Clinical Decision Support Tool for Postpartum Depression

Обзор

This study will evaluate the use of an automated process in the electronic health record (EHR) that will help providers to detect patients at risk of developing postpartum depression (PPD).

Подробное описание

The goal of this randomized clinical trial is to assess the implementation of a clinical decision support (CDS) tool. The tool is designed to assist providers in managing patients at risk of developing of postpartum depression.

Investigators hypothesize that this tool will be acceptable and feasible for use and improve the use of mental health services for postpartum depression.

Patients in the control arm will receive usual care, while those in the intervention arm will receive CDS.

Clinicians will manage patients per usual care, including initiating PPD preventive such as conducting referrals to nutrition and behavioral health, suggest educational readings through patient portals, or no actions. Clinicians in the intervention arm will refer patients based on the CDS.

Вмешательства

• Другое Clinical Decision Support Tool
  The CDS in and AI algorithm that will alert clinicians only if patients have high risk of developing PPD, and provide clinicians with risk score, risk factors, and anticipatory actions with an order set to assist with ordering. Clinicians will make the ultimate clinical judgement after receiving CDS aid, including taking no actions towards PPD prevention. Clinicians will manage patients per usual care, including initiating PPD prevention such as conducting referrals to nutrition and behavioral h

Первичные конечные точки

• Acceptability as measured by Unified Theory of Technology Acceptance Theory (UTAUT). [Срок оценки: One month after initiation of tool use and three months after use.]
• Appropriateness as measured by Appropriateness Measure (IAM). [Срок оценки: One month after initiation of tool use and three months after use.]
• Feasibility as measured by Feasibility of Intervention Measure (FIM). [Срок оценки: One month after initiation of tool use and three months after use.]
• Mental Health service utilization as measured by number of mental health visits [Срок оценки: one month and three month]

Критерии участия

Критерии включения

• 18 years or older
• being seen at one of participating clinics
• pregnancy at 20 week gestation or more

Критерии исключения

• Does not speak inclusion
• does not have internet access

Clinician eligibility:

• If they manage patients in the three clinics as a board-certified OBGYN clinician.
• No exclusion criteria apply for clinician based on their knowledge and experience in treating PPD

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Да

Дизайн исследования

Распределение

Рандомизированное

Модель

Параллельные группы

Маскирование

Открытое

Основная цель

Поддерживающая терапия

Центры проведения

США · 4 центра

• Center for Community Health: 515 6th street (3rd floor) — Brooklyn
• Ob GYN Lower Manhattan 156 William street, New York City — New York
• Weill Cornell Medicine OBGYN practice 72 street — New York
• Weill Cornell Medicine- Women's Health practice 505 East 70th street, New York City — New York

Публикации

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• Rabin R, de Charro F. EQ-5D: a measure of health status from the EuroQol Group. Ann Med. 2001 Jul;33(5):337-43. doi: 10.3109/07853890109002087. PMID 11491192
• Weiner BJ, Lewis CC, Stanick C, Powell BJ, Dorsey CN, Clary AS, Boynton MH, Halko H. Psychometric assessment of three newly developed implementation outcome measures. Implement Sci. 2017 Aug 29;12(1):108. doi: 10.1186/s13012-017-0635-3. PMID 28851459
• Zhang Y, Wang S, Hermann A, Joly R, Pathak J. Development and validation of a machine learning algorithm for predicting the risk of postpartum depression among pregnant women. J Affect Disord. 2021 Jan 15;279:1-8. doi: 10.1016/j.jad.2020.09.113. Epub 2020 Sep 30. PMID 33035748
• Wang S, Pathak J, Zhang Y. Using Electronic Health Records and Machine Learning to Predict Postpartum Depression. Stud Health Technol Inform. 2019 Aug 21;264:888-892. doi: 10.3233/SHTI190351. PMID 31438052
• Venkatesh KK, Nadel H, Blewett D, Freeman MP, Kaimal AJ, Riley LE. Implementation of universal screening for depression during pregnancy: feasibility and impact on obstetric care. Am J Obstet Gynecol. 2016 Oct;215(4):517.e1-8. doi: 10.1016/j.ajog.2016.05.024. Epub 2016 May 20. PMID 27210067
• Dunkel Schetter C, Tanner L. Anxiety, depression and stress in pregnancy: implications for mothers, children, research, and practice. Curr Opin Psychiatry. 2012 Mar;25(2):141-8. doi: 10.1097/YCO.0b013e3283503680. PMID 22262028
• Cox EQ, Sowa NA, Meltzer-Brody SE, Gaynes BN. The Perinatal Depression Treatment Cascade: Baby Steps Toward Improving Outcomes. J Clin Psychiatry. 2016 Sep;77(9):1189-1200. doi: 10.4088/JCP.15r10174. PMID 27780317

Идентификаторы

NCT: NCT06818734 · 24-03027259

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

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