The global healthcare AI market is projected to grow from $32.34 billion in 2024 to $431 billion by 2032. It is evident that artificial intelligence (AI) is transforming the healthcare sector, one workflow at a time. Even so, hospitals and clinics struggle to successfully integrate the technology into their workflows, as real-world deployment is fraught with complexities and bottlenecks.
This blog post describes some of the major challenges and actionable practices that healthcare leaders, clinicians, and data scientists face when implementing and scaling complex AI models into clinical workflows.
The Promises and Pitfalls of Integrating AI Models in Clinical Workflows
The implications of adopting AI and ML-driven models to enhance different business aspects of the healthcare sector are already quite clear. Not only can AI help you analyze and generate insights from vast datasets, but it can also identify subtle clinical patterns and even help you with the automation of routine tasks and activities.
A recent study has even demonstrated AI’s success in oncology by boosting the serious illness conversation rates from 3.4% to 13.5% among high-risk patients. However, you should bear in mind that such successes are quite common in clinical trial settings and do not reflect AI’s capabilities in real-world applications.
These “generalization gaps” and performance declines result from a wide range of issues like misalignment of workflows, algorithmic bias, insufficient methodologies, and more. Let us now understand some of the key HealthTech software development challenges and bottlenecks in deploying AI models in real-world clinical workflows.
Data Quality and Security Issues
The effectiveness of AI depends on your access to large volumes of high-quality, representative clinical data. However, this data accessibility is slowed down by the fragmentation of healthcare data across incompatible systems as well as the consequent gaps and inconsistencies. Ultimately, improper data quality can cause security issues, with over 63% of healthcare stakeholders citing it as the biggest barrier to implementing AI.
While regulatory frameworks control most security risks and issues, technical vulnerabilities can still be very detrimental to your system. Using appropriate access controls and encryption can help you secure AI pipelines. You should also consider investing in a secure and interoperable data infrastructure to securely share data and train models.
Workflow Integration Bottlenecks
Seamless integration of AI into existing clinical workflows is easier said than done. Did you know that only 30% of the organizations have successfully and fully integrated AI into their daily workflows?
Some common obstacles in the process include workflow disruption, insufficient staff training, and lack of interoperability with legacy systems. Even in the cases where these issues don’t have a direct impact, the operational inefficiencies and clinician resistance resulting from them can immediately bring any kind of AI integration to a standstill. You can work around these issues by mapping existing workflows and engaging clinicians from the start.
Algorithmic Bias and Fairness
Working with skewed or unrepresentative data can often lead to bias in AI models, which can subsequently result in disparities in patient care. According to Statista, over 52% of healthcare providers in the United States worry that AI-based medical decisions could introduce bias in healthcare. Moreover, due to generalization and stringent inclusion criteria during clinical trials, AI models that perform well in certain trials and populations may not perform as well for others.
Training your AI models on diverse and multicenter datasets will ensure that it has broader applicability. You should then validate model performance across different clinical and demographic subgroups, so that you can note any limitations and report any biases in model documentation.
For instance, if a diagnostic AI is trained on biased training datasets, the model may be more accurate when tested on the European demographic than on the American population.
Ethical and Regulatory Control
The speed at which AI innovations and applications are taking over the world usually outpaces the development of ethical and regulatory frameworks. As a result, any AI-powered breakthrough comes with a lot of uncertainty around safety, fairness, and accountability. While most regulatory agencies require ongoing performance reporting, data quality readiness, and human-readable explanations to justify any AI-driven decisions, the global standards are pretty inconsistent.
At a clinical management level, it is best to develop clear internal policies that help you validate, monitor, and report AI models. You should also stay abreast of the current local and global regulatory requirements and deploy AI ethicists and legal experts within AI governance structures so that you can deploy advanced AI models seamlessly.
6 Best Practices to Consider When Deploying AI Models in Clinical Workflows
Since we have now understood the biggest challenges in deploying AI models for streamlining clinical workflows, let us learn about the best practices you should follow in the process:
1. Conduct a Problem-Solution Fit Analysis
Before introducing AI into any of your workflows, identify the specific clinical need or problem that you want to address with the AI solution. Several AI models and projects fail simply because they are designed to identify and address bottlenecks, instead of addressing a specific pain point or workflow issue.
For instance, one company’s medication safety AI was able to decrease drug events by 38% because it identified medical reconciliation as a significant bottleneck during stakeholder workshops.
Identify the most critical bottlenecks and issues by performing time-motion studies to quantify inefficiencies. Consider using BPMN 2.0 (Business Process Model and Notation) diagrams to map care pathways, identify AI insertion points, and engage clinicians for high-priority use cases through co-design sessions.
2. Implement Technical Validation in the Pre-Deployment Stage
Not implementing an AI model correctly or making mistakes before or during deployment can have major ramifications for your clinical workflows. To avoid any negative consequences, you must rigorously validate your AI models to ensure they are robust, safe, and generalizable. For best results, your validation measures should extend beyond retrospective accuracy metrics and include real-world testing.
Consider conducting silent trials to ensure that AI runs parallel with your existing workflows, check interoperability with existing infrastructure, and perform external clinical validation on large and diverse cohorts to establish generalization and unbiased performance.
Hospitals using synthetic test environments to test AI models instead of solely relying on lab validation can eliminate most post-deployment errors.
3. Consider Workflow Integration During Implementation
Seamlessly integrating AI into your workflows is critical for operational efficiency and clinician adoption. Improper integration of AI models can lead to workflow disruption, increased cognitive load, and elevated rates of ignored alerts. It is best to start with designing AI components that can directly integrate into existing clinical systems, such as EHR or PACS.
During integration of AI into your clinical workflows, ensure you cover necessary components like quality control, results database and processing, error correction, and image/data delivery. You should also consider utilizing context-aware alerting to minimize interruptions and piloting integration with a limited group of users.
4. Establish Change Management Policies and Mechanisms
Did you know that over 63% of AI projects fail simply due to staff resistance and inadequate change management?
Consider setting up AI stewardship committees with rotating clinician leadership and developing communication playbooks that can educate staff, boost staff engagement, and confidence. Develop a comprehensive change management plan that includes communication strategies, clear timelines, and stakeholder analysis.
To ensure the adoption of AI projects is accurate, consider conducting monthly AI town halls that can address any staff concerns.
5. Track Post-Deployment Success Through Real-Time Performance Dashboards
The most worrying aspect of deploying a full-scale AI project or integration is that it poses a wide range of risks to patient safety. By implementing real-time monitoring through performance dashboards, you can identify and eliminate any early performance decay post-deployment.
For instance, Nairobi Hospital tracked triage and clinician response times through performance dashboards to decrease average patient wait times by 35%.
Tracking metrics such as latency, patient wait times, and model accuracy in live and real-time dashboards can help you increase the accuracy of your AI model and decrease clinician overrides. You should also set actionable thresholds for each metric to help you make informed daily decisions and long-term strategies.
6. Conduct Continuous Feedback Loops and Regular Staff Training
Due to changes in clinical practice, patient populations, or data sources, your AI models could degrade over time. Without consistently monitoring and retaining your AI models, you can even experience a significant decline in accuracy and performance. Leverage your AI model to generate real-time performance alerts and trigger retraining workflows if you detect a drift.
Use the capabilities and features of your AI model to collect and incorporate user feedback through regular incident reporting and surveys. Lastly, consider conducting quarterly bias audits to ensure that your model is fair and free of bias across demographics and clinical subgroups.
Concluding Remarks
Healthcare organizations and clinicians are largely resistant to AI integration in their workflows because of the risks and challenges associated with the process. Right from potential disruptions resulting from sudden workflow changes to ensuring patient safety, there are plenty of considerations to keep in mind while implementing AI models. Be wary of the challenges it poses to your clinical workflows, and implement some of the best practices associated with AI integration.