Beyond IT Governance: Why Clinical Technology Requires Its Own Operating Model

The following is a guest article by Tanya M. Martin, MBA-HCM, Sr. Director, Clinical Technology Services at Tufts University

The Governance Gap Healthcare Can No Longer Ignore

Healthcare technology was once judged largely by availability: Was the system up, the project delivered, and the ticket closed? Those measures still matter, but clinical technology now sits where patient care, education, cybersecurity, data, artificial intelligence, finance, facilities, compliance, and operations meet.

This convergence has created a structural gap. Organizations have IT and capital governance, clinical councils, privacy and cybersecurity review, and procurement controls. Yet an investment can clear every committee without one accountable mechanism for redesigning workflow, preparing users, managing dependencies, measuring value, and planning retirement.

The answer is not another committee, but an operating model connecting strategy, decision rights, roles, workflows, capabilities, and measures across the lifecycle. Governance determines what is authorized and under which guardrails. The operating model determines how it will work—and who remains accountable after go-live.

Technology Adoption is Not Technology Value

Recent evidence makes this gap visible. In a 2024 federal data brief, 71 percent of hospitals reported routine access to necessary outside clinical information, but only 42 percent said clinicians often used it. That difference separates implementation from operational value.

Federal data also show that roughly nine in ten hospitals enabled patient access through an application programming interface, while many third-party integrations still used proprietary or non-API approaches. Connectivity can exist while workflow, data stewardship, support, and architecture remain fragmented.

Cybersecurity and AI increase the stakes. HHS treats healthcare cybersecurity as a resilience and patient-safety issue. WHO guidance emphasizes AI governance, transparency, accountability, and post-deployment oversight, while research calls for recurrent local performance monitoring. These responsibilities require named owners, multidisciplinary decisions, evidence review, and authority to change or retire technology.

A Practical Model: A.P.P.L.I.

Across three decades in healthcare technology leadership, I have developed a five-part model for disciplined execution. A.P.P.L.I. Align, Prepare, Partner, Lifecycle Management, and Improve applies before purchase, through implementation, and throughout the technology’s service life.

Figure 1. The Clinical Technology Operating Model

Align

Define the clinical or operational problem before selecting a product. Connect investment to strategy, architecture, interoperability, cybersecurity, regulatory obligations, financial capacity, and explicit success measures. Decide whether the technology belongs in the portfolio.

Prepare

Redesign workflow and establish readiness across people, process, data, infrastructure, training, testing, communication, support, and downtime response. AHRQ-sponsored guidance recommends mapping workflows with health system representatives before implementation. Preparation is a safety and value activity, not a late-stage training plan.

Partner

Assign shared ownership. Clinical and operational leaders own the outcome; technology leaders own secure, reliable, integrated enablement. Finance, compliance, privacy, facilities, education, and procurement participate according to risk and reach. Partnerships make decisions explicit.

Lifecycle Management

Treating technology as an enterprise asset from concept through retirement, including contracting, implementation, integration, validation, maintenance, upgrades, vendor performance, technical debt, replacement, and decommissioning. A contract begins a multi-year obligation; it does not complete a purchase.

Improve

Measure the outcomes used to justify the investment: adoption, safety, experience, workflow, service quality, cybersecurity, equity, financial return, and vendor performance. Evidence should drive optimization, additional investment, restriction, or retirement.

What the Model Changes in Practice

In one multi-site healthcare organization, an enterprise imaging replacement initially appeared to be an application project. Review revealed dependencies across clinical workflows, network capacity, device integration, cybersecurity, facilities, procurement, data migration, and support. Stakeholders who normally entered late joined early to make decisions before commitments were made. The initiative became an enterprise transformation with a shared readiness plan.

In an academic clinical environment, a specialized platform request was framed as a departmental need. Review showed effects on patient-facing processes, learner workflows, faculty practice, revenue, identity and access, reporting, and support. The question changed from “Can we buy it?” to “What capability are we creating, who owns each outcome, and what makes it sustainable?” That reframing surfaced readiness work early and strengthened executive prioritization.

Four Design Choices Leaders Must Make

First, establish one accountable clinical technology capability. Its authority to coordinate decisions across boundaries matters more than whether it sits in IT, clinical operations, or digital health.

Second, define lifecycle decision rights: who recommends, approves, owns outcomes, accepts risk, funds operations, and can stop or retire a solution. A committee without decision rights is a discussion forum.

Third, fund the lifecycle. Business cases must include integration, conversion, workflow redesign, training, support, cybersecurity, upgrades, optimization, and retirement—not only licenses and project labor.

Fourth, use a balanced scorecard. Availability and schedule should sit beside safety, adoption, experience, workflow, equity, stewardship, resilience, and educational or research value. Live, but unused technology is not successful.

From Framework to Operating Practice

Begin with one clinically significant investment and map it from intake through retirement. At each stage ask: What decision is required? Who has authority? What evidence is needed? Who owns the outcome? What triggers review? This reveals duplicate approvals, unowned decisions, and post-project gaps in operational, vendor, data, and optimization ownership.

Create three durable tools: an intake defining the problem, population, fit, value, dependencies, and risk; a readiness assessment covering workflow, people, data, integration, infrastructure, security, downtime, and support; and a lifecycle record naming owners, vendor obligations, measures, reviews, upgrades, and replacement horizon.

Set a few baselines and targets, such as workflow time, rework, adoption, interruptions, support demand, revenue leakage, data quality, model drift, or security findings. Assign each to an owner and review it routinely. State safety, compliance, experience, or mission value when benefits cannot be monetized.

Existing forums retain their authority. The operating model supplies connective tissue: a common lifecycle, shared evidence, decision rights, and escalation paths. Each material decision reaches the right owner at the right time and remains traceable to an outcome.

The Leadership Imperative

Healthcare will continue to absorb ambient documentation, predictive analytics, connected devices, automation, patient platforms, and AI-enabled decision support. The strategic question is not the pace of innovation, but whether organizations can evaluate, integrate, support, measure, and retire it responsibly.

The strongest organizations will not necessarily adopt every tool first. They will repeatedly convert technology into a safe, sustainable capability. That requires enterprise governance—and an operating model connecting decisions to execution and implementation to outcomes.

Clinical technology is no longer a collection of applications supported by IT. It is an enterprise capability shaping care delivery, learning, workforce experience, financial performance, resilience, and public trust. It should be managed accordingly.

About Tanya M. Martin

Tanya M. Martin, MBA-HCM, is a senior clinical technology executive and graduate educator with more than 30 years of experience spanning healthcare technology vendors, health systems, ambulatory care, and academic clinical environments. She has led multimillion-dollar technology portfolios, enterprise transformations, governance strategies, and modernization initiatives that align technology with clinical operations, education, cybersecurity, and organizational performance. Her leadership is grounded in translating complex clinical and operational needs into sustainable technology strategies that improve outcomes and advance institutional goals. She is the creator of the Clinical Technology Operating Model and its A.P.P.L.I. framework.