Evaluating AI in Healthcare: Implementing Approaches

By Sumona Bose

March 7, 2024

Introduction

Artificial intelligence (AI) holds great potential in healthcare by enhancing clinical decision-making and patient outcomes. However, a significant gap exists between the development of AI models and their successful integration into clinical practice. Despite the proliferation of AI-based clinical decision support systems (AICDSS), only a meager 2% of these models progress beyond the prototyping stage, leaving the actual clinical impact largely unexplored.

Evaluating Clinical Value through Rigorous Trials

The evaluation of AICDSS through randomised controlled trials (RCTs) stands as a critical step in determining their true clinical value. While some RCTs have been conducted, their outcomes paint a nuanced picture. Although these trials showcase promising statistical performance of AI, nearly half of them fail to demonstrate improved patient outcomes. This discrepancy underscores the complexity of assessing AI solely based on quantitative metrics like accuracy. This may not capture the practical utility of these systems in real-world healthcare settings. Table 1 unpacks the definitions associated with interpreting the patient outcomes. This helps clinicians and researchers shift from the arbitrariness that hinders real-world settings.

Reported in N (%)
Implementation outcomea Clinical explanation Implementation stage RCTs (N = 64) Guidelinesb (N = 5)
Appropriateness Is the AI compatible with the clinical workflow and is it useful? Early 5 (8) 0 (0)
Acceptability Is the AI acceptable, agreeable, or satisfactory for the users? Ongoing 10 (16) 0 (0)
Feasibility Can the AI be successfully used as intended by the manufacturer? Early 16 (25) 0 (0)
Adoption Do the users express the initial decision, or action to try or employ the AI? Ongoing 6 (9) 0 (0)
Fidelity Is the AI implemented as intended by the manufacturer? Ongoing 31 (48) 0 (0)
Implementation cost What is the cost impact of implementing the AI system? Late 4 (6) 0 (0)
Penetration Has the AI been adopted by all groups of trained users? Late 0 (0) 0 (0)
Sustainability Is the AI maintained within ongoing clinical operations over time? Late 1 (2) 0 (0)

Table 1: AI in RCTs, Definitions of implementation outcomes were adapted from the taxonomy of implementation outcomes by Proctor et al (2011). 

The Need for a Holistic Evaluation Approach

A comprehensive understanding of AI’s role in clinical practice necessitates a multi-faceted evaluation strategy. Current guidelines like Developmental and Exploratory Clinical Investigations of DEcision support systems driven by Artificial Intelligence (DECIDE-AI) and Consolidated Standards of Reporting Trials–Artificial Intelligence (CONSORT-AI), fall short in providing robust measures for assessing AI implementation success. To address this gap, a mixed-methods evaluation approach,  proves invaluable in dissecting the various dimensions of AICDSS implementation.

Bridging the Gap in Implementation Evaluation

Despite the increasing focus on RCTs evaluating AICDSS in clinical settings, a gap exists in the comprehensive evaluation of implementation outcomes.  While metrics like ‘fidelity’ are commonly reported using quantitative measures, aspects such as ‘acceptability’ and ‘appropriateness’ that demand qualitative scrutiny are often overlooked. This imbalance underscores the need for a more holistic approach towards evaluating the implementation of AICDSS, encompassing factors beyond statistical performance. Figure 1 reiterates the comprehensive value of integrating implementation outcomes in AI in healthcare, revealing an innovative future in the field.

figure 2
Figure 1: a In the current situation, AI-CDSS, are clinically deployed, after going through multiple preclinical validations (e.g., external and temporal algorithm validation) to assess their clinical utility and effectiveness. b To enhance comprehension of factors that contributed to successful implementation or failure at the bedside, implementation outcomes should be systematically integrated in future clinical trials evaluating AICDSS in real-world clinical settings. *Implementation outcomes as described by Proctor et al.

Conclusion

While the efficacy of AICDSS in healthcare settings is crucial, understanding the contextual nuances is imperative. Enhanced systematic reporting of implementation outcomes alongside effectiveness metrics can bridge the existing gap in comprehensively assessing the impact of clinical AI. Embracing an inclusive evaluation framework will not only validate the effectiveness of AICDSS but also shed light on the intricate interplay between AI technology and healthcare delivery.

Reference url

Recent Posts

oral cancer East Africa
   

Oral Cancer in East Africa: The Need for Early Detection

💡 Did you know that Toombak use is a leading risk factor for oral cancer in East Africa?

A recent scoping review sheds light on the shocking prevalence of oral cancer in the region, emphasising the urgent need for public health interventions and improved early detection strategies. Enhancing awareness around risk factors like Toombak, tobacco, and alcohol for tackling this growing health crisis.

Curious about the key insights and their implications for health economics? Look into the full article to find out more!

#SyenzaNews #HealthEconomics #Oncology #GlobalHealth

Novartis patent cliff layoffs
     

Engineering Resilience: Mastering Pharma Patent Expiration Strategy

🚨 Are you still reacting to pharmaceutical patent expirations with layoffs and litigation, or are you ready to engineer a strategy that turns the patent cliff into your next competitive edge?

Patent expirations don’t have to derail your pharma portfolio. Learn how to outmaneuver generics and transform challenges into advantages. Dive into our latest insights and take control today.

#SyenzaNews #pharmaceuticals #innovation #PharmaStrategy #patentcliffs

diabetes medicine access
               

Improving Diabetes Medicine Access: Key Changes in the Pharmaceutical Benefits Scheme

🚀 Are we on the verge of a breakthrough in diabetes medication accessibility?

The latest updates to the Pharmaceutical Benefits Scheme (PBS) are set to transform type 2 diabetes management by expanding access to essential medicines like empagliflozin and streamlining the prescribing process for glucagon-like peptide 1 receptor agonists (GLP-1 RAs). These changes not only prioritize equity for high-risk populations but also align with global trends in cost-effective healthcare.

Dive deeper into how these revisions could reshape diabetes care and promote better health outcomes for all.

#SyenzaNews #HealthcareInnovation #healthcare #MarketAccess

When you partner with Syenza, it’s like a Nuclear Fusion.

Our expertise are combined with yours, and we contribute clinical expertise and advanced degrees in health policy, health economics, systems analysis, public finance, business, and project management. You’ll also feel our high-impact global and local perspectives with cultural intelligence.

SPEAK WITH US

CORRESPONDENCE ADDRESS

1950 W. Corporate Way, Suite 95478
Anaheim, CA 92801, USA

© 2025 Syenza™. All rights reserved.