It’s a term constantly inserted into technology conversations to make a product or action seem new and cutting edge. And while the term is undoubtedly over-used, it remains relevant because it showcases a significant truth: the way we access data is changing.

In our personal and professional lives, a few clicks can now summon a myriad of empirical evidence about our fitness, financial records, and even utilities usage. Far from being esoteric technology, real-time data retrieval is quickly becoming a foundational element of our relationship with technology.

As information technology has increasingly become a part of medical practice, the healthcare industry is no longer immune from conversations involving the words “real-time.”

Why Real-Time Analysis Matters for Medicine

The decreasing cost of this technology has made it readily available for many providers, either through their practice management software or their electronic health record system. In an industry that’s relied on historical data – past medical history, comorbidities, etc. – having access to up to date clinical information is a boon.

Clinical decision support systems built into EHRs can alert physicians when their treatment plans run afoul of medication lists before prescriptions are finalized. Integrated health systems can manage entire populations using predictive algorithms that adjust by the second to pinpoint high-risk patients.

Instead of relying on information gathered from patients (which can be unreliable), providers can leverage hard data to inform their treatment decisions.

It can be easy to slide into the “real-time” hype, but that’s largely due to the promise this technology for medicine.

But instead of focusing on cutting edge developments at the Mayo Clinic or Cleveland Clinic, let’s examine how everyday practices can benefit from real time analysis using systems already widely adopted: EHRs.

Common Applications

1. Clinical Decision Support

Perhaps the most important real time use-case comes at the point of care. As physicians chart and build their treatment plans, the EHR system will use IF-THEN logic to determine if any proposed treatments or medications would result in adverse events for the patient. This simple algorithm helps prevent misdiagnosis or prescriptions, and can improve outcomes.

As the market for wearable health technology grows, physicians will have access to patients’ biometric data. By accessing this data at the point of care, physicians can use empirical evidence rather than patient testimonials to make treatment decisions. This type of clinical decision support will also play a large role in identifying high-risk patients.

If providers have the resources, they can use biometric data and health wearables to create alert thresholds for such patients. If the patient’s vital signs pass a certain point, providers can reach out directly and advise that they come in for treatment or alter their behavior.

All in real-time, of course.

2. Clinical Workflow

The line between practice management software and EHR platforms becomes blurrier by the day. Many EHRs now offer patient intake and scheduling features as well as financial reports about a practice’s cash flow – jobs that were traditionally left to PM systems.

Regardless of where the analysis takes place though, there’s a lot of potential for providers to leverage real-time capabilities to improve the patient experience, along with satisfaction scores. Much of it has to do with process. As patients enter the office, a real-time system can automate exam room assignments, freeing up receptionists to interact with patients.

Another opportunity lies in financial reports, which both PM and EHR systems can generate. In private practices, physicians aren’t only healing patients, but are also running a business. By monitoring financial performance on a weekly or even daily basis, physicians can stay informed about what’s working business-wise without visiting their CPA every week.

How Electronic Health Records Improve Patient Care

Many EHRs also include Meaningful Use dashboards, which are modules that centralize the data collection of quality scores and other required stats. This feature tracks how well practice’s are meeting MU criteria, so that any gaps in data collection can be corrected.

3. Coding Support

Human error represents the most common reason for rejected claims. Demographic information or patient health information that entered incorrectly can end up significantly delaying reimbursement.

Enter claims scrubbing. Again, using “real-time” analysis, scrubbing software checks a practice’s coding against industry standards for each procedure and corrects any glaring errors. Scrubbing engines use real time analysis to identify and correct for any mistakes before claims get submitted, which helps practices avoid unnecessary impediments.

Certain billing software can also track claims throughout the revenue cycle by visualizing where each claim lies in terms of reimbursement or rejection. In some cases the software will identify which payers offer the best reimbursement options for specialties, or commonly performed procedures. This type of information more closely resembles the real time analysis found in clinical workflow and clinical decision support features.

There’s a great deal more to be said about real-time analysis, but moving into the realm of decision trees and abstract algorithms won’t directly benefit most providers. For all intents and purposes, real-time analysis simply means the ability to quickly access powerful information – which is a phenomenal resource for healthcare providers