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TIPS-Connect

TIPS-Connect

Improving screening for depression in prenatal and postpartum care

Project status

Implementation

Collaborators

Ian Bennett, MD, PhD
Janet Rocchio RN, MBA
Regina Howard
Rebecca Henderson
Marian Moseley, MSS, MLSP
Jabina Coleman, MSW, CLC
C. Neill Epperson, MD
Liisa Hantsoo, PhD
Becky Marlow, RN, BSN, MBA
The SPIRIT Group

Innovation leads

Funding

Innovation Accelerator Program

Opportunity 

According to The American Congress of Obstetricians and Gynecologists, up to 23 percent of women struggle with depression symptoms during pregnancy. Depression can increase a woman's risk behaviors during pregnancy, such as poor nutrition and substance and alcohol use, leading to premature birth, low birth weight, and development problems. And women who experience postpartum depression are more likely to develop major depression and other psychiatric disorders.

At the start of this project, providers struggled to ensure consistent depression screening for prenatal and postpartum patients at the Helen O. Dickens Center for Women.

Intervention

TIPS-Connect is a tablet-based depression screening model for use in prenatal and postpartum care. The intervention, co-designed with patients and providers, implements privacy-centric, evidence-based screening in the clinic setting. 

Patients presenting for new obstetrics visits, 28-week prenatal visits, and initial postpartum visits are required to complete the screening as part of the check-in process. Results are uploaded immediately to the electronic health record (EHR) for provider reference, and decision support is offered based on raw patient scores.

Impact

At the initial pilot site, TIPS-Connect increased the standardized screening rate for prenatal depression from approximately 0 to 70 percent. Lessons learned from this project informed an enterprise-wide redesign of depression screening at Penn Medicine. 

TIPS-Connect is now a standard part of care at the Helen O. Dickens Center for Women.

Innovation Methods

Fake back end

It is essential to validate feasibility and understand user needs before investing in the design and development of a product or service. A fake back end is a temporary, usually unsustainable, structure that presents...

Fake back end

We developed a prototype iPad application to conduct PHQ-2 and PHQ-9 screenings for all eligible patients at three points during their prenatal and postpartum journey. The practice provided the iPad to patients at check-in to complete screening before they saw the provider. The data flowed into a secure database monitored by social workers at...

Fake back end

It is essential to validate feasibility and understand user needs before investing in the design and development of a product or service.

A fake back end is a temporary, usually unsustainable, structure that presents as a real service to users but is not fully developed on the back end.

Fake back ends can help you answer the questions, "What happens if people use this?" and "Does this move the needle?"

As opposed to fake front ends, fake back ends can produce a real outcome for target users on a small scale. For example, suppose you pretend to be the automated back end of a two-way texting service during a pilot. In that case, the user will receive answers from the service, just ones generated by you instead of automation.

Fake back end

We developed a prototype iPad application to conduct PHQ-2 and PHQ-9 screenings for all eligible patients at three points during their prenatal and postpartum journey.

The practice provided the iPad to patients at check-in to complete screening before they saw the provider. The data flowed into a secure database monitored by social workers at the practice, who would work with the patient and their provider based on survey results to facilitate follow-up evaluation, care, and referrals as required.

Based on the pilot's success, the workflow was translated into a scalable, sustainable process in Epic using the Welcome module to provide tablet screenings and best practice alerts to enable real-time clinical decision support.