Population health, Big Data, predictive analytics, and all that massive computing power working to improve health. Well, it sounds great. But care still has to be delivered, and it will probably still be one patient and one caregiver at a time. Okay, it might be virtual in some cases, but it is still likely to involve a patient and a provider in some type of encounter; a population of two working together to improve health and outcomes one patient at a time.

This is significant because most current systems use templates for documentation, but individual patients can’t really be “templated.” Sure, they come in with a problem list but, the moment they bring up one of those “oh, by the way” new issues, well, you can toss that template away.

This is yet another reason a clinical engine that adapts to the changing situation in an encounter, enabling a population of two to focus on the unique aspects of each interaction, yet accommodating a physician’s thought process at the point of care, will be a critical part of the next generation EHR systems.

To accommodate a clinician’s thought process at the point of care, a system must be able to present, in less than one second and with just one click, a problem-oriented view of a patient’s chart. That view must let the physician see, at a glance, what is new, different or unchanged for any problem, whether it is an existing diagnosis, a new presentation of symptoms, or a constellation of findings.

This requires an underlying engine which diagnostically links all the clinical data points together so any problem, or set of problems, can be quickly analyzed and presented, on demand, at the point of care.

If clinical presentations were static and predictable, then static and predictable templates would suffice. But the moment you involve a real patient and a real physician at the point of care, what we like to call a population of two, then you need adaptable, clinically-oriented tools for documentation and patient care.