Tailoring Learning to the Learner

In EMS education, one lesson doesn’t fit all.

There is a growing concern about the state of EMS refresher and recertification education. Many worry it’s too basic, inadequately meets the needs of professionals across diverse work environments, and discourages our people from pursuing education opportunities beyond minimum requirements. A greater tailoring of EMS education to individual needs can address these concerns and help us realize many of the new opportunities facing us. We can achieve this through individual paramedic learning plans based on three central pillars.

Pillar 1: Data-Driven Education

EMS agencies now collect an enormous amount of dispatch and patient care data in sophisticated programs that can intelligently analyze it for patient care trends, protocol adherence and community events. I envision automated analysis based on specific biological events or patient outcome measures.

For example, many EMS agencies provide annual training about influenza and droplet precautions. Because we don’t know exactly when influenza will arrive, we usually deliver the training in early fall, well before it’s actually needed. By the time influenza arrives, if at all, the training is forgotten or poorly remembered.

In a data-driven education system, an automated analysis of CAD and PCR data would recognize the arrival of influenza based on patient complaints, symptoms reported and provider diagnoses. That surveillance marker, when triggered, would automatically assign an influenza training program to all system paramedics. That program would be immediately relevant and useful, and could be completed before the peak of influenza patients.

Other potential uses for data-driven education include:

• Protocol adherence

EtCO2 monitoring is the standard for any intubated patient, but we know it is often not used. Rapid analysis of PCR data could immediately check for intubations performed and EtCO2 monitoring. If capnography was not performed, a series of automated actions might include a template e-mail from the medical director with a link to a form asking the paramedic to explain why EtCO2 was not used and how airway placement was confirmed. The same e-mail could include a link to a PDF of the airway management protocol, an instructional video of adding EtCO2 monitoring to the airway circuit, and an assignment to complete an online CE module on interpreting capnography waveforms.

• Skills competency

We’ve all heard complaints that certain providers seems to always magically have patients with blood pressures of 120/80—regardless of the patient’s age, condition or medical history. An automated analysis of PCR data could search for documentation anomalies by analyzing the last 100 blood pressures measured by each provider and then alert a QI manager of providers who have 90% of documented systolic blood pressures between 115–125 mmHg. Suspicion and time-consuming investigation are removed from the process because the data is always being analyzed for every provider.

• Correct diagnosis and treatment

Correctly assessed heart failure treated with CPAP and nitroglycerin is known to reduce the need for intubation, length of hospitalization and risk of hospital-acquired infections. Ongoing data analysis could always be looking for the proper assessment and treatment steps for heart failure. If, on the last five heart failure encounters, a paramedic only applied CPAP two times, that could be a trigger for automatic assignment of a CE module on heart failure, an invitation to skills lab to practice assembling the CPAP device, and an appointment at the simulator lab to practice and be checked off on assessment and treatment of heart failure.

Conversely, if a paramedic has correctly assessed and treated all their recent heart failure patients, continuing education on heart failure, at least at the same level as the underperformer, is not only unnecessary but a waste of time and resources. Instead, help high performers continue to broaden and deepen their knowledge of heart failure and other problems.

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