The Journal
Working papers from the harness and papers published in journals.
Working paper
This paper argues that AI-prepared dashboards and digests add information faster than people can act on it, and proposes a Decisions & NBA Board: a short queue of decisions, each with cited evidence, options, an owner with authority and a dated check on the result. It matters because a report that changes no decision is a cost. No hypothesis has been tested.
Working paper
This paper proposes a field-testable operating model for CTOs: one traceable evidence base that executive peers can inspect for their own jobs without relying on the CTO's persuasion. It matters because senior leaders often judge engineering by remembered incidents and impressions. The six hypotheses have not been tested.
Working paper
This paper proposes Olive Button, a Blue Button-style FHIR implementation pattern for DD214 service data that helps meet the 30-, 120-, and 180-day mandates of Executive Order 14426 using existing systems and standards. It matters because the order now requires records sharing at separation. The FHIR mapping is an implementation hypothesis, and the order does not mandate FHIR.
Landscape review
This landscape review tells digital health executives how long an EHR integration takes and what shortens the wait. It gives a planning range of six to twelve months from kickoff to first customer live, most of it spent on customer and EHR-vendor steps rather than code. The range is a judgment from public sources, not a measured median.
Journal of Diabetes Science and Technology
An editorial arguing that biotech commercialization should be run the way engineering is run, with specifications, measurement, and repeatable process, rather than treated as a series of one-off translational events. The piece is indexed under artificial intelligence, biotechnology, commercializatio
Journal of Translational Medicine
Patients, clinicians, administrators, regulators, and AI developers each need a reason to trust medical AI, and diabetes shows what that takes. Trust requires moving beyond single deterministic outputs to clear standards for medical AI provenance and performance, with each stakeholder holding its ow
Journal of Diabetes Science and Technology
This article argues that sharing research code in an open access, version-controlled repository benefits science and individual researchers, and aligns with the NIH Gold Standard Science program and the FAIR and TRUST principles. Pairing a cited data snapshot with a versioned code release lets reade
Journal of Diabetes Science and Technology
This article reviews new methods of continuous glucose monitoring (CGM) data analysis, called CGM Data Analysis 2.0, that use functional data analysis and artificial intelligence. Compared with traditional CGM metrics, they can give a more detailed understanding of glucose fluctuations and trends an
Journal of Diabetes Science and Technology
This article describes the Diabetes Research Hub (DRH), a centralized data management system and repository for gathering, storing, analyzing, and using diabetes data for research. It matters because advanced analytics on large datasets can support a nuanced understanding of physiological patterns a
Journal of Diabetes Science and Technology
A meeting report from the iCoDE project, which is writing the standard for getting continuous glucose monitor data into the electronic health record. The June 22, 2022 steering committee reviewed progress from six working groups. Working Group 1 set out a conceptual framework with a definition of CG
Journal of Diabetes Science and Technology
This article reports the first meeting of the iCoDE project on integrating continuous glucose monitor data into the electronic health record. Diabetes Technology Society organized it virtually on January 27, 2022, with clinicians, government officials, data aggregators, attorneys, and standards expe