By Tim Patton, Computation and Informatics in Biology and Medicine (CIBM) Trainee, Project HealthDesign NPO, University of Wisconsin-Madison
As we have posted (They can't do it alone - ONC needs everyone's input), we at Project HealthDesign want to make meaningful use really useful for patients. At the same time, we want to facilitate communication between patients and physicians, nurses, public health workers and other clinicians.ONC has stated that the goals of Stage 2 and Stage 3 are as follows:
- By 2013, meaningful use of HIT should guide and support care processes and care coordination.
- By 2015, meaningful use of HIT should yield measurable improvements in quality, safety and outcome of care processes, including enhanced quality measures and extensive decision support at the individual and population level and enriched tools for patient self -management.
For Stage 2, it will be important to focus on the best use of resources. Given the short time horizon, we would be best served to focus our efforts on:
- Generalized data structures that support physical activity, diet, and sleep. These are emerging as ubiquitous ODLs and represent the best value proposition as we begin to integrate support.
- Flexible tools that allow individuals to view the data as I see fit. As we gradually come to understand the underlying ODLs, how we decide to view the underlying data will grow with our understanding. This might take the form of tools that will allow anyone who accesses that data to form a “view” that highlights the meaningful aspects while removing the noise. Furthermore, if a lay person opts to share his/her view, others can understand the personal importance of ODLs (which may be very different from a professional’s view).
For Stage 3, it will be important to continue to build upon previous efforts to make ODLs more useful to everyone involved by:
- Expanding support to other ODLs that fall outside the “big three” mentioned above.
- Supporting disambiguation by combining multiple methods of capture (coding accordingly so others reviewing the data will know how it was captured). For automated capture, calibration and algorithms can influence the data collected. Self report suffers from a large array of influences. To strengthen the information that can be derived from the data, it might be beneficial to combine collection methods (e.g., if automated collection agrees with self report, then we might increase our confidence that the data represents an accurate picture).
- Recording the ambient context (reality mining) by connecting ODLs with other forms of data, such as GPS camera images, allergen counts, sound pressure levels, etc. This might lead to new insights into how ODLs are related to contextual or environmental factors.
- Creating observation standards that serve to identify the original source of the data, such as automated or self-report. This might play a role analogous to LOINC Codes, which can identify the specific lab test (with a set of standards and assumptions) that produced the value in the database. As new devices and techniques come to market, they can be certified against such standards, thus insuring a certain level of consistency across vendors.