When it comes to deciding which “observations of daily living” should be collected, stored and reviewed, clinicians and patients have (surprise, surprise) different views. And the overlap isn’t always that large. This insight was a recurring them at last week’s meeting of Project HealthDesign teams. So what’s to be done about that?
If you’re coming from a patient power or quantified self perspective, you might say that it doesn’t matter – people should track whatever they want, right? Of course, but on the other hand, suppose you have limited patience for self-tracking and you really want your ODL data to influence how your clinical team understands your condition and how they make treatment decisions, then you probably want to make sure that some of your energy is devoted to collecting ODLs to which they might actually pay attention. At the same time, it’s an opportunity for the patient to explain that ODLs she believes are relevant to her condition – either as triggers/exacerbators, or as symptoms of how her condition is manifesting itself in her life – are important to how she understands and manages her conditions. So, for example, while a clinician might not be interested in a particular patient’s sleep pattern, that patient might feel strongly that sleep is inextricably linked to her health.
All this suggests a negotiation between person/patient and provider, which is the solution that the Crohnology.MD team is proposing. They’ve mocked up an ODL prescription, which is, in effect, the outcome of the negotiation. The prescription specifies the ODLs to be collected and the periods or durations during which they are to be collected.
We’re also discovering that there is a third party to the negotiation – the system developer. In exploratory research project like the Project HealthDesign grants, this participation is explicit: the developer talks with patients, talks with providers and ultimately builds a system around their requirements. The developer’s design choices – from which ODLs to include to how they are defined to when they are collected – shape the patient’s participation in collecting them. For example, the developers of FitBaby, a Project HealthDesign grant to investigate the use of ODLs to assist in the care of prematurely born infants coming home from the hospital, learned that understandably anxious parents in this situation often feel compelled to weigh the infants frequently. However, weighing the baby too frequently not only lacks clinical value (as fluctuations are natural), but it’s also not healthy for the parents – particularly if the weighing becomes obsessive. So the system developers set up the weight tracking routine to accept only one measurement of weight per week. You can agree or disagree with that choice, but the point is that the developers shape the negotiation. Outside of the research context, as developers build products for the marketplace, they are unwitting participants in the negotiation insofar as they are defining the solution space within which the negotiation between patient and clinician takes place.
From a policy perspective, this discrepancy between patient and clinician perspective on which ODLs matter has the effect of complicating the concept of “patient-generated” data, which appears in both the HIT Policy Committee’s proposed Stage 3 Meaningful Use criteria and the ONC’s recently released strategic plan. There are at least three types of “patient-generated” data:
- Data, such as blood pressure readings, already collected in a health care setting and typically found in a medical record. The only difference is that the patient is collecting the reading.
- Data, such as those found in a food diary, that are self-reported by the patient, but are not typically collected in a health care setting or documented in a medical record
- Data, such as sensor data – or interpretations of sensor data, that are not self-reported by the patient but that are typically not found in the medical record.
As we go down the road of integrating “patient-generated” data – both traditional medical data and observations of daily living – into electronic medical records, then we’ll need to understand what types of data are really meant and which, under what circumstances, should be incorporated into one’s medical record. To these questions, we hope to have more to say as the Project HealthDesign teams conduct their studies and experiment with different ways of providing all three of these data types to clinicians.