Electronic Health Records (EHRs) are widely used in obstetric care across the United States. They record patient details from prenatal visits to labor and delivery. These systems help keep track of clinical notes, improve communication between doctors and patients, and help coordinate care among different specialists.
The American College of Obstetricians and Gynecologists (ACOG) understands that improving EHR features can lead to better health for mothers and babies. EHR portals let pregnant women see lab results, schedule appointments, and contact their doctors directly. This connection helps women stay involved in their care, which is important for a healthy pregnancy.
Pregnancy care does not only focus on physical health, such as monitoring blood pressure or baby growth. Mental health is very important too. Problems like anxiety and depression during pregnancy and after birth can affect both the mother and baby. Right now, mental health screening in obstetric EHRs is not common or consistent.
ACOG has suggested adding new information to obstetric EHRs to better track mental health risks. These new items include checks for anxiety, depression, and the mother’s plans about breastfeeding. This helps make sure care covers both body and mind.
Adding mental health details in EHRs helps doctors find women who may need extra help early. For example, checking for depression during regular doctor visits can catch symptoms that might be missed otherwise. Finding these problems early can lower risks, help moms connect with their babies, and lessen the chance of serious mental health problems after birth.
Also, including mental health data in EHRs supports ongoing care. This information can be shared between doctors, mental health experts, and social workers. Working together lets care cover all parts of the mother’s health.
The Office of the National Coordinator for Health IT (ONC) works to make sure different EHR systems can share pregnancy and mental health data smoothly. This sharing is called interoperability.
ONC and ACOG support adding 14 new data items to obstetric EHRs. These include mental health checks and breastfeeding plans. These changes fit with the U.S. Department of Health and Human Services’ goal to improve care quality and fairness in pregnancy care.
Practice managers and IT staff need to get ready for these changes. Updating EHR systems to handle new screening information helps meet current rules and prepares clinics to take part in new care programs focused on value.
Artificial Intelligence (AI) is an important part of future obstetric care. AI helps doctors by giving extra information and support, not by replacing them.
AI is used in many ways, such as improving ultrasound images and predicting problems during pregnancy. For mental health, AI can make it easier to screen patients and speed up work in clinics.
AI systems can look at large amounts of health data, like questionnaires, doctor notes, and patient reports. They find signs of mental health disorders during pregnancy and after birth. Machine learning, a type of AI, finds patterns that might show depression or anxiety.
For instance, AI tools can automatically review depression scores collected during check-ups. If scores are unusual, the system alerts doctors so they can follow up or refer patients to specialists. This helps ensure no cases are missed and patients get help on time.
New AI chatbots and voice assistants are being tested to support pregnant women by offering quick answers, symptom checks, and basic counseling outside clinics. These tools add extra support between doctor visits.
AI can also handle many administrative tasks in obstetric clinics. This includes writing notes, scheduling appointments, and managing patient messages. Automated phone systems, like Simbo AI, can answer calls and handle questions. This frees up staff to work on harder tasks and reduces wait times.
Practice managers find AI helps keep patients happy by responding quickly and correctly. Simbo AI uses natural language processing to understand questions, book visits, send test results, and prioritize urgent calls.
AI can also remind patients to fill out mental health questionnaires before appointments and enter data directly into EHRs. This makes work easier for providers and staff, helping clinics run smoothly.
AI can analyze health data in real time to spot early warning signs. For mental health, this might include mood changes, sticking to treatment plans, or behavior tracked through apps or wearable devices.
Connecting AI with telehealth services helps doctors keep track of patients remotely. This is useful for women in rural areas or places where visiting clinics is hard.
Adding mental health screening and AI tools in obstetric EHRs takes careful planning. Practice owners and managers should think about these points:
ACOG’s Committee on Health Economics and Coding watches how AI changes obstetric practice management, paperwork, and payments. They work with the American Medical Association’s AI groups to create rules that help AI fit into clinical work smoothly.
The ONC keeps updating pregnancy data standards to improve EHR content and sharing. These national efforts shape how hospitals, clinics, and private practices use technology and care policies.
Clinic leaders and IT staff should stay updated on news from ACOG, ONC, and AMA. These groups provide frameworks that balance new technology use with patient safety, data privacy, and law compliance.
Obstetric care in the U.S. is moving toward EHR systems that track both physical and mental health. AI and automation will help improve diagnosis, make work easier, and support patients remotely.
Practice managers, owners, and IT staff should prepare by updating their EHRs to include mental health information, investing in AI tools to automate tasks, and encouraging teamwork among different health specialists. Doing this can help obstetric care keep track of all health needs of mothers during pregnancy and after birth.
Improving obstetric EHRs with professional guidelines and new technology will lead to safer and fairer care for mothers and babies. These upgrades can also make operations smoother in clinics providing obstetric care.
Health information technology (HIT) refers to electronic systems health care professionals use for processing, storing, retrieving, and sharing health information, including electronic health records (EHRs), imaging tools, decision support systems, telemedicine, and AI.
HIT enhances patient care through improved direct engagement via patient portals, remote monitoring, telehealth services, and mobile apps that enable tracking of health and fetal development, ultimately improving health outcomes.
AI assists in clinical decision-making, personalized medicine, improving maternal and fetal outcomes, and reducing administrative burdens, thereby enhancing diagnostic accuracy and patient health outcomes.
AI improves diagnostic accuracy in ultrasound and MRI, helping detect congenital abnormalities and distinguishing various fetal brain conditions, thus facilitating early and accurate diagnosis.
AI analyzes real-time data from cardiotocographs to monitor fetal heart rate and contractions, helping health care professionals identify patterns and deviations, and providing insights into fetal health.
AI evaluates vital signs and lab tests to detect early signs of complications like preeclampsia and gestational diabetes, enabling timely interventions through real-time monitoring and trend analysis.
AI models analyze biological markers and imaging data to identify high-risk pregnancies and predict complications like preterm birth, providing actionable insights for health care practitioners.
AI algorithms enhance the identification of cancerous cells, reduce diagnostic errors, and support personalized treatment plans, improving early detection and tailored therapies for cancer patients.
ACOG monitors AI’s impact on practice management and administrative burdens while collaborating with the AMA on regulatory discussions to ensure effective integration into clinical practice guidelines.
ACOG recommends adding new data elements, such as anxiety and depression screening and breastfeeding intention, to obstetric EHRs to enhance care quality and promote maternal health research.
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