Gatwiri Mwiti
Learn · Adapt · Partner

Connected infrastructure for healthcare's next decade.

A growing practice working on protocol-layer thinking, AI-ready health systems, and the architectural decisions that separate fragmentation from flow.

Siloed systems fracture every pillar of the Quadruple Aim — margin, quality, clinician experience, patient outcomes. CMS 2026 enforcement turned the cost of that fragmentation from theoretical to operational. The infrastructure required to fix it already exists.
The Cost of Silos
$5.3T
U.S. national health expenditure — roughly 18% of GDP.1
~$266B
Annual U.S. administrative-complexity waste — the single largest waste domain.2
10
Distinct AI use-case categories now active across health systems — most still struggling to scale.3
Interactive Model

Same stakeholders. Same data. One protocol layer changes everything.

A live, interactive visualization of the fragmented and connected states of healthcare data. Toggle between them to see the operational transformation infrastructure investment produces.

EHR Lab Payer Pharma Remote Analytics MCP PROTOCOL LAYER INTERACTIVE · CLICK TO EXPLORE Experience the interactive model
The Practice

Principles drawn from a decade of implementation across U.S. academic and integrated health systems.

01
Interoperability as a foundation

Connected data is the prerequisite. Lab-to-EHR consolidations, FHIR APIs, MCP server architecture — every meaningful AI deployment rests on whether systems can speak to one another in a governed, secure way.

02
Analytics as a network effect

Enterprise analytics scale when infrastructure is shared. A platform introduced into one operational setting at scale tends to spread organically — an early demonstration of the network effects that well-sequenced infrastructure decisions produce.

03
Agentic AI as thought partner

The third phase is not another tool. It is action taken on top of connected data — AI agents operating across clinical and operational workflows with the context required to deliver value rather than noise.

Perspectives

Field essays on the operational layer of healthcare.

Biweekly, on infrastructure-first AI, public health practice, and the conversations the trade press doesn’t cover.

Latest · May 31, 2026
The Layer Beneath the Model: A 3-Arc Framework
Most AI pilots in health systems fail not at the model layer but at the infrastructure layer beneath it. The order matters because patients are downstream.
Read the series
Contact

For conversations about healthcare infrastructure, connected systems, and the protocol layer underneath both.

Connect on LinkedIn.

Sources
  1. Centers for Medicare & Medicaid Services. National Health Expenditure Data, NHE Fact Sheet. cms.gov
  2. Shrank WH, Rogstad TL, Parekh N. Waste in the US Health Care System: Estimated Costs and Potential for Savings. JAMA. 2019;322(15):1501–1509. pubmed.ncbi.nlm.nih.gov
  3. Reviewed AI use-case categories across clinical documentation, chart review, risk stratification, diagnosis, patient engagement, patient access, revenue cycle, business operations, analytics automation, and research support. pmc.ncbi.nlm.nih.gov