Table 3:
Potential clinical and research advantages of an open diabetes platform.
| Current State | Open Platform |
|---|---|
| Near one-to-one relationship between device and application | Any application can use data from any device |
| Mobile apps require manual data entry by the user, leading to user burden and transcription errors | Mobile apps automatically access and collect device data |
| A provider must learn to interpret data presented in each unique software application and visualization format from each device vendor (or choose only one device to prescribe to their patients) | A provider selects and gains expertise using one software application of choice while still allowing patients the freedom to choose to use whichever devices they prefer |
| Patients may use devices (e.g., one pump and one continuous glucose monitoring device) from different vendors but are forced to sacrifice data interoperability | Patients who choose any permutation of devices are able to use device data in an integrated fashion |
| Mobile app developers must each develop a unique vertical product stack including back-end features (e.g., secure and private data storage) | App developers can focus on developing front-end, user-facing apps that integrate with the back-end of the Tidepool Platform |
| Diabetes software only uses and shows data from diabetes specific hardware | Diabetes software can incorporate data from any source, including trackers for fitness/activity, heart rate, and food |
| Device companies must devote resources to software development | Device companies can focus efforts and resources on hardware, their area of expertise |
| Clinical research studies utilizing devices are restricted in the permutations of devices they can use | Clinical research studies can use any combination of devices |
| Developing new clinical decision algorithms in research studies requires many slow iterations of testing, refinement, and then pushing out the updated algorithm for testing again | Researchers can more efficiently study clinical algorithms, pushing the algorithms out to users through the cloud and rapidly getting feedback about efficacy |
| Researchers must manually collect data for each new retrospective study they conduct | Researchers can access and query a comprehensive clinical dataset that is already collected |