Are We Using Electronic Health Records System (EHR) for Our Greatest Good?

Are We Using Electronic Health Records System (EHR) for Our Greatest Good?

In the age of increased threats to patient safety and our global embarrassment of 80% preventable maternal deaths, we have all this glorious technology that we’re not taking full advantage of.

In 2012, after leaving clinical practice because of burnout, I began my advanced clinical informatics training at the Johns Hopkins School of Medicine. I had been working on an app that would reduce maternal mortality and stillbirths, and I thought understanding clinical informatics would improve my product. EHRs were slowly being deployed, and physicians were encouraged to learn about clinical informatics to improve their colleagues’ acceptance. In retrospect, I don’t think we succeeded, but I digress.

One of the first things Hopkins taught us was that data is the ground level of knowledge. From data comes knowledge, and from knowledge comes wisdom. I hoped that through understanding decision trees and random forests, I could help bridge the gap between clinical practice and the emerging technologies meant to enhance it.

The curriculum had an ambitious agenda of integrating the intersection of Information Technology (IT) with clinical decision support (CDS) and clinical applications.

This was in 2012, and EHRs were slowly being deployed.

The Early Days of EHRs: A Missed Opportunity

In the early days of EHR integration, the focus was primarily on workflow optimization — digitizing patient records, streamlining administrative tasks, and ensuring compliance with federal mandates like the Health Information Technology for Economic and Clinical Health (HITECH) Act. While these were necessary steps, it’s clear in hindsight that we missed a critical opportunity: the potential to use EHRs for true clinical decision-making and patient care improvement.

Many of the early systems were clunky and not user-friendly, leading to frustration among physicians. Instead of facilitating better patient outcomes, EHRs often became a source of burnout. The clinical data we gathered was stored, but it wasn’t harnessed to generate insights that could change the trajectory of care. This was a crucial mistake.

The gap between IT professionals who built the systems and the clinicians who used them widened. Many systems were designed without a deep understanding of clinical workflows and patient care complexities, leaving us with tools that digitized paperwork but didn’t significantly impact clinical decision-making.

Where We Are Today: Progress But Room for Improvement

Fast-forward to today and EHRs have become ubiquitous. However, while workflows have been optimized and compliance with documentation improved, the real promise of EHRs — improving patient outcomes through data — remains largely untapped. Chief Medical Informatics Officers (CMIOs) continue to focus on workflows, but there is an insufficient focus on using the data within EHR systems to improve clinical decision-making and enhance patient care.

The Role of AI in Transforming EHR Systems

This is where artificial intelligence (AI) comes into play. AI has the potential to do what the early days of EHRs failed to accomplish — transform raw clinical data into actionable insights that can guide treatment decisions and improve patient outcomes. Imagine a system where AI-driven algorithms could analyze patient data in real time and predict complications before they occur, particularly in high-risk pregnancies.

By applying AI to EHRs, we can move beyond static patient records to dynamic, data-driven clinical decision support. Predictive analytics can help obstetricians identify early signs of preeclampsia, gestational diabetes, or other complications, allowing for timely interventions that could save lives.

This shift from simply digitizing data to using it for predictive insights is significant for maternal health. The United States has one of the highest rates of maternal mortality in the developed world, and it is estimated that up to 80% of these deaths are preventable. Stillbirths, another tragic outcome, often come with warning signs that go unrecognized.

The Future: AI, EHRs, and Risk Stratification

AI’s integration into EHR systems could benefit clinicians. It can also revolutionize how health insurance companies conduct risk stratification, allowing for more personalized and preventive care. Insurers could use AI to better predict whichpatients are at higher risk of complications and allocate resources accordingly. This could significantly reduce healthcare costs and improve overall patient outcomes.

Additionally, software developers and C-suite decision-makers in healthcare technology must rethink their approach. The tools we build and the technologies we implement need to be clinician-friendly and data-driven, not just focused on administrative efficiency. Incorporating AI into EHR systems can give healthcare professionals the tools they need to make smarter, faster and clinically competent decisions.

Conclusion: AI as a Catalyst for Reducing Maternal Mortality, Morbidity, and Stillbirths

With AI integrated into our EHR systems, we could finally harness the data we’ve been collecting for years to improve patient safety and outcomes. For obstetrics and gynecology, this means reducing maternal mortality, morbidity, and stillbirths. The technology is already here — we need the vision and commitment to use it for the greatest good.

As an AI consultant with over three decades of clinical expertise, I’m committed to helping healthcare organizations and technology developers realize this potential. By bridging the gap between data and clinical decision-making, we can transform the future of maternal care and beyond.