Real-time Analytics and Decision Support: Driving Clinical Insights in Modern EHRs

One of the biggest problems the US healthcare system is facing right now is the growing shortage of healthcare professionals. According to a report from the Bureau of Labor Statistics, US healthcare will face a shortage of up to 195,400 nurses by 2031. The projections are similar for physicians, with an estimated shortage of up to 124,000 physicians by 2030, according to a report from the Association of American Medical Colleges. A combination of a larger aging population, an aging healthcare workforce and burnout among providers are the key factors for widening the gap in numbers.

While this is a concerning development, an unlikely hero has appeared in the form of advancements in real-time analytics and decision support systems (DSSs) for modern EHRs. These systems act like smart assistants who keep track of all the data a provider requires to make the right decisions for each patient. This could reduce the strain on the staff and improve operational efficiency. One of the top reasons for physician burnout is an increase in bureaucratic tasks like billing, approving health insurance coverage and overseeing medical personnel, according to the 2023 Medcape Physician Compensation Report. But operational efficiency is only one of the many utilities of real-time analytics and decision support systems that could cut costs significantly and improve outcomes across all parameters.

Integration with EHR systems: 

EHR modernization by implementing data analytics and decision support systems can offer healthcare providers real-time information and suggestions to help them make better decisions, from diagnosis to resource management.

• Operational efficiency: 

One of the most crucial use factors of decision support systems is in identifying trends and patterns in data that could help track performance metrics. Leaders at institutions can easily identify areas that require improvement initiatives on their dashboards. This could lead to efficient workflows and reduced healthcare costs, improving the overall operational efficiency. In a paper published by Intel, they address a system that identifies key patterns from each institution’s admission records to determine the patient inflow for any given time.

• Evidence-based decision-making: 

Decision support systems can help healthcare professionals navigate complex scenarios by leveraging real-time analytics and AI to extract useful insights instantly from the latest in medical literature, clinical trial data and patient records. This can promote evidence-based medicine and improve patient outcomes.

• Patient care: 

Real-time analytics can help healthcare providers make informed decisions by providing real-time information on patient health, predicting outcomes, and identifying potential risks. For instance, the Sepsis Early Risk Assessment (SERA) algorithm published in 2021 has the potential to increase the number of early sepsis detections by up to 32% compared to relying only on hospital physicians’ assessments. This is a significant development considering how, on average, 270,000 Americans die of Sepsis annually. Real-time analytics in patient care can help deliver better treatment plans and optimized care plans, resulting in improved patient satisfaction.

• Inventory management: 

Real-time analytics can also help providers manage their resources, and remind them to stock inventories based on past data. During the COVID pandemic, the Kinetica Active Analytics Platform was used to create a real-time analytics program for aggregating and tracking data on test kit quantities, PPE availability, and hospital capacity to aid emergency responders. This could take a significant administrative burden off healthcare professionals, allowing them to do what they do best – save lives.

Conclusion:

EHR modernization of the kinds we have discussed are already being implemented in swift response to crises. A significant example of analytics being deployed rapidly was InferRead, a software launched by the EU in 2020 to analyze CT scans and identify instantly if the lungs are damaged due to a COVID infection. This software helped diagnose patients much faster during a time of emergency when institutions were working overtime. It is safe to say that we must now invest our efforts into health tech innovations across the country that could significantly reduce the burden on providers and also improve overall patient care, from diagnosis and prescriptions to aftercare. This would boost confidence in the system, reduce the attrition of professionals due to burnout and help build foundational infrastructure for the future of health tech.

About us:

Reveal HealthTech provides specialized engineering, clinical model, and strategy support to healthcare organizations. With decades of expertise in healthcare management and AI, we work with facilities to create custom decision support systems and analytics algorithms that work for their respective specialties.