ODLs at multiple levels

By Edmond Ramly, Project HealthDesign PhD Student, University of Wisconsin-Madison

As we investigate the observations people make in their everyday life, let's take a quick step back and look at the big picture.

Biomedical sciences have been improving people's health for centuries, and exciting new advances keep coming. At the same time, biomedical practitioners only get snapshots of people's health states. A person might spend three hours per year with a clinician, and then self-manage their health and health observations during the remaining 8,757 hours of the year.

Developing tools to support self-observations and integrate them into clinical workflows is a good start. But observations of daily living (ODLs) are relevant not only at the levels of the individual and the clinician. As we are seeing with the meaningful use of health IT conversation (see Navigating the path to Meaningful Use goals and They can’t do it alone), thinking about ODLs is very important at the health policy level as well.

More systematically, we can think of six categories of potentially relevant levels.

1. Infrapersonal: Air composition (pollutants, pollen), electromagnetic waves, radioactive emissions, etc.
2. Intrapersonal: Molecules, biochemical pathways, genetics, physiology, psychology, etc.
3. Personal: Sensory stimuli, feelings, behaviors, moods, attitudes, thoughts, decisions, etc.
4. Interpersonal: Patient-clinician interactions, patient-peer interactions, patient-family context, etc.
5. Extrapersonal: Culture, socio-economic status, insurance coverage, environmental context, etc.
6. Suprapersonal: Population health, health policy, health economics, etc.

Let me explain what I mean when I say ODLs may be relevant at these levels. At any given level, ODLs may be relevant because:

• There are things at that level that can constitute ODLs (e.g., ambient pollen count at an  infrapersonal level or thoughts at the personal level).
• There are issues that need to be addressed at that level in order to achieve the potential of ODLs (e.g., meaningful use of health IT by patients at the health policy level, understanding the underlying phenomena behind ODLs at various intrapersonal levels).
• ODLs might be highly idiosyncratic at some levels but share common characteristics at other levels (e.g., multiple instances of a given ODL may be very different across individuals but have similarities among individuals with similar cultural backgrounds).

Now, keeping in mind that the list I suggested is more explorative than exhaustive or definitive, it looks like we still have plenty of uncharted territory to explore as we continue investigating ODLs.

What's more, another exciting challenge will soon emerge as well: how do we integrate ODLs and what we know about them within and across levels? Integrating ODLs in clinical workflows and in health policy is only a start.

We have an exciting journey ahead!