The widespread introduction of artificial intelligence (AI) in healthcare promises significant advancements, especially in the transformation of value-based care journeys. AI can enhance quality performance and improve risk adjustment accuracy, leading to better health and financial outcomes. Despite its enormous potential, healthcare C-suites remain skeptical and cautious about investing in AI tools. This is largely driven by their concerns about the practical applicability, financial return on investment (ROI), and overall impact on clinical outcomes, which makes the task of securing buy-in from executive leadership quite challenging. Dr. Michael S. Barr, president and founder of Medis, a healthcare consulting firm, proposes the SBAR communication framework to garner support from the C-suites. Here’s an in-depth look into his approach.
1. Current Situation
The first step in the SBAR framework is to clearly introduce the problem that an AI system aims to address. This is a crucial phase where one must identify specific issues that hold substantial weight with the C-suite. For many healthcare organizations, the current practice of risk adjustment, especially Hierarchical Condition Categories (HCC) coding, and quality performance measures to achieve high Medicare Star Ratings present significant inefficiencies. Highlighting these pain points is essential for establishing relevance.
Presenting concrete data that demonstrates the financial strain these inefficiencies impose on the organization can be quite impactful. For instance, bringing attention to the cost associated with inaccurate risk adjustments or the missed revenue resulting from lower Medicare Star Ratings due to sub-optimal quality performance can underscore the immediate need for a solution. Drawing a direct line from these inefficiencies to their financial repercussions can make the need for AI more poignant and compel the C-suite to pay attention.
2. Context and Background
Building on the identified issues, the next step is to provide a thorough context and background. This involves furnishing additional details, both quantitative and qualitative, about the limitations of the current technology, the various initiatives that have been undertaken in the past to address these problems, and why those efforts have yielded limited success. An in-depth analysis of the status quo helps set the stage for why a change, especially an AI-driven one, is necessary.
At this juncture, it is also essential to address the C-suite’s common concerns about adopting AI. These concerns often revolve around the system’s accuracy, reliability, transparency, and compatibility with existing platforms. Incorporating data that demonstrates AI’s precision and robustness can help alleviate fears regarding its reliability. Mentioning its potential seamless integration with current systems can also reduce anxieties related to compatibility. Additionally, addressing the issue of transparency by discussing how AI decisions can be traced and explained will further help in building trust.
3. Evaluation and Analysis
The third step dives deeper into the potential of the AI system being proposed. This is the Assessment stage, where one outlines how the AI system functions, its key capabilities, and the various use cases it addresses. A critical part of this analysis is to articulate the direct and quantifiable benefits the AI system can bring to the organization. Discussing specific examples, such as how AI can streamline HCC coding or enhance quality performance, makes the benefits more tangible.
Providing specific and quantifiable predictions of the system’s benefits can be very persuasive. Detailing metrics such as anticipated improvement in risk adjustment accuracy, reduction in manual errors, and eventual uplift in Medicare Star Ratings can help paint a picture of clear, measurable outcomes. Moreover, presenting these benefits not just in an optimistic scenario but also in a conservative estimate helps to demonstrate that even less ideal outcomes will still deliver sufficient ROI, which is crucial for easing financial concerns and gaining C-suite buy-in.
4. Proposal and Recommendation
The final step in the SBAR framework is the Recommendation stage, where one clearly outlines the proposed actions. This step involves offering concrete suggestions, such as launching a pilot program for an AI system. A pilot program allows for a controlled, limited implementation, enabling the organization to assess the effectiveness of the AI system without committing to a full-scale rollout immediately. This approach minimizes risk while showcasing the AI’s potential benefits.
In this phase, balancing specificity, clarity, and simplicity is crucial. Recommendations should be presented in a way that is easily understandable to the C-suite, including executives without a clinical background. To achieve this, avoid using jargon and opt for straightforward language. Additionally, it is helpful to define objectives using SMART goals—Specific, Measurable, Attainable, Relevant, and Time-based. This method offers a clear understanding of the targets, success metrics, feasibility, and timeline for the proposed actions.
Building confidence among healthcare leaders regarding AI investments requires presenting a logical, methodical, yet flexible framework. The SBAR approach enables advocates to create compelling arguments that connect identified problems to justified recommendations, backed by factual and data-driven narratives. Adapting the presentation to reflect the organization’s specific realities and priorities can further strengthen the persuasiveness of the proposal.
The SBAR framework has proven effective in facilitating critical discussions with the C-suite, addressing their concerns, and gaining their approval for AI investments. As AI technology continues to deliver promising results, it is essential to communicate its advantages effectively to healthcare leaders. By using a framework that addresses their worries and priorities, advocates can demonstrate that AI is not just a tech innovation but a strategic investment that offers significant returns and enhances clinical outcomes.