Strategy and Oversight of AI Implementation in Medical Quality Control

This case study reviews a strategy to replace a human-intensive quality validation process at a medical analysis lab with AI-powered validation. We track specific implementation steps and show how the AI implementation was handled at the strategic level—through the introduction of necessary controls and alignment with stakeholder needs.

Strategy and Oversight of AI Implementation in Medical Quality Control

Company Profile

This case study examines a private laboratory specializing in medical analysis, with a national network of affiliated labs.

  • The laboratory processes approximately 80,000 tests per day across its network.
  • It operates its own IT system that connects various laboratory instruments—including those used for diagnostics and clinical testing.

Initial Analysis

The initial analysis included stakeholder identification, cost mapping, the definition of quality benchmarks, and the assessment of capability gaps.

Stakeholder Analysis

The starting point involved identifying stakeholders and their needs:

  • The quality validation challenge primarily affected internal quality specialists. Their needs were quantified in terms of average monthly hours spent on manual quality analysis.
  • Other stakeholders were identified due to legal obligations. Their interests included the continued existence of a documented and traceable validation process. Regarding AI processing, regulations required that medical data be processed within the country of operation.
  • Senior stakeholders expected increased speed, reduced costs, and error rates matching or improving upon current levels.

Cost Mapping

Following stakeholder analysis, direct and indirect costs were mapped. These included the salaries of quality specialists (based on time spent on validation) and related managerial overhead.

Scope of Implementation

The scope of implementation was defined to clearly distinguish areas where AI implementation was feasible, and where, by contrast, traditional software automation was the preferred choice.

Quality Benchmarks

To track improvement, quality benchmarks were defined. The baseline was the current error rate of human-led validation, to be compared with future AI-powered performance.

Capabilities and Infrastructure Gaps

Capability gaps were identified both in the development team and among the human quality controllers.

The existing IT infrastructure was reviewed and validated for its suitability to support AI-powered automation tasks.

Implementation Strategy

The identified challenges, success criteria, and action directions were mapped using a Balanced Scorecard-style strategy map.

Implementation

Platform for Strategic Oversight

Given the uncertainties of the new technology, the AI implementation followed a strategic, experimental approach rather than a fixed plan. The BSC Designer platform, already used for general strategy implementation, was adopted as the primary tool to track AI implementation success.

Definition of Safety Rules

A fundamental requirement of the AI validation system was the inclusion of safety rules that restricted AI from addressing topics that required human confirmation.

To validate basic AI functionality, self-tests using known cases were introduced.

Phases of Implementation

To ensure controlled value delivery to stakeholders, the implementation was divided into these steps.

Pilot Phase

  • Preparing data and anonymizing it; this involved converting existing threshold norms and measurement units into a structured JSON format.
  • Establishing an initial learning loop where developers compared AI validation (not visible to users) with human validation.
  • Designing controls to let human operators update AI instructions.
  • Creating a second learning loop, enabling direct prompt adjustments by human operators.

Scaling Phase

  • Expanding the data scope to allow AI to detect a broader range of anomalies.
  • Optimizing AI speed by identifying the task first and loading only task-relevant knowledge.
  • Refactoring processes with an AI-first mindset, shifting from current data analysis to include historical data.

AI Governance and Strategic Alignment

To ensure proper AI governance, several additional controls were introduced:

  • Quantified outputs from human oversight and automated tests were automatically routed to the AI dashboard.
  • Monthly reviews and refactoring of AI prompts modified by human operators.
  • Quarterly reviews of typical error patterns and misunderstandings to improve AI’s learning process.

Results

The AI system reduced the overall error rate by a factor of 10 compared to human validation.

Operational Outcomes

  • In 90% of cases, validation was nearly instant, eliminating an average 5-hour wait associated with human validation.
  • Approximately 5 full-time equivalent (FTE) doctors were freed from routine analysis at the main lab, and 2 FTEs at each branch.

Innovations

  • Expanding AI context with analytical and clinical history data enabled the detection of previously unidentifiable cases, some of which were later referenced in scientific literature.
  • The organization’s continuous learning efforts were supported by structured learning loops with measurable KPIs.

Fear of Job Loss

While some negative perception was anticipated due to fears of job loss, no actual cases occurred. This may be attributed to the routine nature of the validation task. Creative judgment and final decisions remained in human hands, as unclear cases still required human oversight.

AI Governance

  • Established controls helped quantify risks and ensured effective mitigation.
  • Performance reporting was automated through scheduled reports.
  • Stakeholders had clear visibility into the AI implementation and operations.

Strategic Alignment

Outputs from specific objectives were used as leading indicators in other scorecards. For example, error rate data was incorporated into quality assurance scorecards, while learning loop performance fed into HR scorecards.

Brand Impact

The successful implementation, strategic alignment, and AI-driven anomaly detection positioned the laboratory’s management as leaders in innovation within their field.

Training programSession: 'Strategic Oversight of AI Implementation' is available as part of BSC Designer's ongoing learning program, offered both as an online and on-site workshop. Learn more....

Conclusions

The implementation of AI is an example of digital transformation through the adoption of disruptive technology. Its success depends on deeply understanding stakeholder needs and setting up proper controls to ensure ongoing quality and learning monitoring.

  • The BSC Designer team added value by providing strategic advisory—aligning technical implementation requirements with best practices in AI governance and overall strategic alignment.
  • Specific oversight controls were automated through the BSC Designer platform, supporting continuous monitoring and learning.
Cite as: Alexis Savkín, "Strategy and Oversight of AI Implementation in Medical Quality Control," BSC Designer, May 29, 2025, https://bscdesigner.com/ai-strategy-for-quality-control.htm.

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