My First Time In An Ambulance

I almost called out of work today. I was exhausted, worn down by the friction of juggling two jobs and two classes for my DIY pre-med. But I went in, and I am glad I did because tonight completed my full experience as a mental health worker. I performed the final duty of the role, which is transport. Acute psychiatric hospitals do not have emergency medical services. We are a closed loop designed for the mind, not the body. When a patient’s physiology fails, we have to ship them out to a partner hospital. My shift started at 11:00 PM, and by 11:15 PM, I was in the back of an ambulance.

The process began with the internist. Unlike the psychiatrists who diagnose based on speech and behavior, the internist looks at the blood. A patient had a swollen left leg. The blood work and vital signs raised a red flag, so the decision was made to transfer them to the ER. The ambulance crew arrived, took the vitals again, loaded the patient, and I sat in the back for the ride. It was my first time in an ambulance. The transition was sharp. We moved from the static, timeless air of the locked unit to the kinetic, high-stakes environment of emergency medicine.

Upon arrival at the ER, the difference in intake was immediately apparent. At our hospital, intake is an administrative drill of liability forms and property searches where the psychiatrist is often just a name assigned later. Here, the expert was waiting for us. The triage nurse demanded allergies, medications, and holds, even screening the patient for self-harm immediately. The doctor met the patient at the door before they were even transferred to the bed. He checked the heart rate and the legs immediately. There was no waiting for an assignment. The decision-maker was the first line of defense, scanning for immediate physiological threats before the patient even settled.

Then came the technology. In our psych unit, the only monitoring tools are human eyes and paper charts. In the ER, the room became a hub of sensors. First came the EKG technicians, hooking up cords to read the electrical rhythm of the heart. Then came the X-ray technician to image the chest. Then the phlebotomist for blood draw. Then the ultrasound technician for the leg. Then the CT scan. The patient was dehydrated and could not provide a urine sample, so they hung IV fluids immediately. It was a symphony of objective measurement. We were not guessing what was wrong; we were looking inside the machine.

As someone coming from tech, I recognized this setup immediately. In infrastructure engineering, we use dashboards like Grafana or Prometheus to visualize the health of systems, applications, and services in real-time. The ER is the biological equivalent. It is a high-fidelity observability stack. We were not guessing what was wrong; we were reading the logs. The diagnosis revealed the gap between signs and pathology. Everyone, including the nurses at the psych hospital, suspected a Deep Vein Thrombosis because the patient’s left calf was swollen, reddish, and warm. That was the sign. But the ultrasound revealed the truth. The actual blood clot was not in the swollen calf where we were all looking. It was high in the thigh, in a spot that looked perfectly normal from the outside. Furthermore, the chest X-ray and CT scan captured a Pulmonary Embolism in the lung. The patient had a life-threatening clot in their chest that was completely invisible to the naked eye. No amount of observation or 15-minute rounds would have found it. We needed the sensors.

However, I also observed that the Entropy Trap exists in medicine too, just in a different form. The ER doctor, armed with millions of dollars of diagnostic technology, was guilty of the same detachment I see on the psych floor. He spent forty seconds with the patient at the door. Later, he spent another thirty seconds to confirm the diagnosis and say nurses would bring meds an d take the patient to the ICU. This was not because he didn't care. It was because the system’s metrics determine funding and efficiency. The sensors provided the data, so the conversation was deemed redundant. This confirms that efficiency can become its own form of entropy. The more efficient the system, the less human the interaction. We have the data to save the life, but we lose the human in the process. This validates the mission of my current side projects, specifically Psykicks. By handling the administrative load, we can allow the human connection to return.

This experience clarified my future trajectory. Standard psychiatry often ignores the body, treating the mind in a vacuum. Standard medicine often ignores the mind, treating the organ in a vacuum. I intend to operate at the interface. Consultation-Liaison Psychiatry is the clinical discipline that bridges this gap, and it will serve as the primary domain for my computational research. By integrating labs, imaging, and psych history, it provides the high-fidelity signals my algorithms require. I view C-L not just as a medical specialty, but as the necessary hardware access to build the next generation of psychiatric software. In C-L psychiatry, you are forced to stratify constantly. You must decide if agitation is due to hypoxia, withdrawal, or fear. It is the role of the System Administrator for the hospital's behavioral health. We need the objective precision of the ultrasound combined with the phenomenological depth of the therapist. We need to see the clot in the brain, but we also need to hear the person in the bed.