How Open mHealth Designed a Popular Standard (Part 3 of 3)

The first section of this article introduced the common schemas for mobile health designed by Open mHealth, and the second section covered the first two design principles driving their schemas. We’ll finish off the design principles in this section.

Balancing permissiveness and constraints

Here, the ideal is to get accurate measurements to the precision needed by users and researchers. But many devices are known to give fuzzy results, or results that are internally consistent but out of line with absolute measurements.

The goal adopted by Open mHealth is to firm up the things that are simple to get right and also critical to accuracy, such as units of measurement discussed earlier. They also require care in reporting the time interval that a measurement covers: day, week, month. There’s no excuse if you add up the walks recorded for the day and the sum doesn’t match the total steps that the device reports for that day.

Some participants suggested putting in checks, such as whether the BMI is wildly out of range. The problem (in terms of public health as well as technology) is that there are often outlier cases in health care, and the range of what’s a “normal” BMI can change. The concept of a maximum BMI is therefore too strict and ultimately unhelpful.

Designing for data liquidity

Provenance is the big challenge here: where does data come from, how was it collected, and what algorithm was used to manipulate it? Open mHealth expects data to go far and wide among researchers and solution providers, so the schema must keep a trail of all the things done to it from its origin.

Dr. Sim said the ecosystem is not yet ready to ensure quality. For instance, a small error introduced at each step of data collection and processing can add up to a yawning gap between the reported measure and the truth. This can make a difference not only to researchers, but to the device’s consumers. Think, for instance, of a payer basing the consumer’s insurance premium on analytics performed on data from the device over time.

Alignment with clinical data standards

Electronic health records are starting to accept medical device data. Eventually, all EHRs will need to do this so that monitoring and connected health can become mainstream. Open mHealth adopted widespread medical ontologies such as SNOMED, which may seem like an obvious choice but is not at all what the devices do. Luckily, Open mHealth’s schemas come pre-labelled with appropriate terminology codes, so device developers don’t need to get into the painful weeds of medical coding.

Modeling of Time

A seemingly simple matter, time is quite challenging. The Open mHealth schema can represent both points in time and time intervals. There are still subtleties that must be handled properly, as when a measurement for one day is reported on the next day because the device was offline. These concerns feed into provenance, discussed under “Designing for data liquidity.”

Preliminary adoption is looking good. The schema will certainly evolve, hopefully allowing for diversity while not splintering into incompatible standards. This is the same balance that FHIR must strike under much more difficult circumstances. From a distance, it appears like Open mHealth, by keeping a clear eye on the goal and a firm hand on the development process, have avoided some of the pitfalls that the FHIR team has encountered.