Blackford Analysis
The first step in designing an AI strategy is to identify the most prolific and impactful problems and priorities in your organization, and then align potential AI initiatives accordingly. In the AI evaluation process, it can be appealing to seek out algorithms with distinct and specific use-cases that neatly align with singular departmental goals. For example, improving clinical report consistency within the radiology department is an explicit goal with a clear outcome that can be addressed with a singular AI algorithm. However, if you were to zoom out and look at the big picture, your organization’s overarching goal might be to grow referrals through increased patient and referrer satisfaction. This could lead you towards a series of algorithms that target the most common exams and pain points to decrease patient wait times, optimize exam throughput, and improve radiologist productivity and clinical confidence.
This approach to AI strategy development not only drives to your larger organizational objectives, but ultimately unburdens radiologists, enabling them to complete reports more efficiently and confidently. So, when designing your AI strategy, keep this big picture approach in mind and consider:- How your strategy can help achieve broader organizational goals while simultaneously delivering value across the entire medical imaging enterprise, and
- How to effectively measure and monitor a chosen solution to ensure it is meeting the objectives that were previously identified.
- Ensure your objectives are outcome-based: Outcomes Based Specifications (OBSs) focus on the results and value to be achieved according to target clinical, operational, and financial criteria rather than rigid prescribed feature sets.
- Consider intangible metrics: Outcomes such as improvements in radiologist confidence are important to track as they can have a big impact on the bottom line. While difficult to measure, they can often be quantified using downstream metrics such as productivity, satisfaction scores, and turnover.
- Set clear timelines and milestones: Interim goals can be used to track your progress, highlight successes, and course correct as necessary, which not only keeps the AI project on track, it also builds momentum and garners buy-in from radiologists and other stakeholders.
- Key performance indicators (KPIs) and the consideration towards the resources required to manage and track them.
- Input from multidisciplinary stakeholders who are best positioned to assess whether desired outcomes are being met.
- A mechanism to analyze AI's effectiveness in terms of quality and performance.