From Diagnosis to Claims: Where AI Actually Works in Healthcare
Key Takeaways:
- AI is critical to address growing global care demands and increasing skills shortages
- Transformation possible in diagnosis, proactive care delivery, and efficient administration
- Ethical, responsible AI is key to counter workforce resistance and wariness
- Small-scale, high value use cases are an excellent place to start
The pressures on healthcare are considerable and global. Healthcare systems all over the world are struggling with rising populations, budget and resource constraints, increasing care demands, and data complexity.
To help solve these challenges, the use of AI in healthcare is growing all the time, with transformation across diagnostics, administration and more delivering greater efficiency, accuracy and personalization. That’s why the global healthcare AI market is currently growing at more than 38% a year, and is expected to reach $190 billion by 2030, supported by its use in clinical validation settings such as radiology and sepsis detection.
This blog takes a detailed look at AI healthcare solutions, including the areas that stand to benefit the most, real-world stories of success, and how best to approach implementation.
Where is AI in Healthcare Making The Biggest Difference?
AI is coming into healthcare at the ideal time to address a global crisis. According to the World Economic Forum, more than half the world’s population - 4.5 billion people - don’t have access to essential healthcare services. And given that the health worker shortage is expected to reach 11 million by the end of the decade, AI will prove increasingly essential in solving these problems.
AI is already having a major impact in several key areas, including:
Revolutionizing Medical Diagnostics Through AI
AI algorithms can quickly and accurately analyze imagery like CT scans, MRI scans and X-rays, and spot issues and abnormalities that require further investigation. This technology can go beyond simply informing doctors with more information, and can actually demonstrate diagnostic accuracy above and beyond skilled human capabilities. Google’s AI system has been used to detect cases of breast cancer in mammograms better than radiologists could.
The same principle applies to other diagnostic areas, such as analyzing data at scale to identify possible diseases faster. This has the knock-on impact of improving diagnosis and treatment times, so that problems can be addressed before they have the chance to spread.
Streamlining Administrative Processes and Reducing Costs
Many healthcare bodies spend a lot of time, money and resources on admin behind the scenes, from scheduling and record-keeping to billing and claims processing. Many of these repetitive tasks can be automated by AI, freeing up resources to add value elsewhere. For example, doctors’ notes and reports that might take hours to type up annually can be transcribed by generative AI in moments, and at a much higher level of accuracy. Similarly, using automated blood analyzers has been found to reduce processing mistakes by 30%, and deliver a 98% accuracy rate.
Google has found that nearly three-quarters of organizations using AI in healthcare are already generating positive return on investment, and Microsoft research indicates that some hospitals are saving $3.20 for every dollar they invest into AI. This is thanks to streamlined workflows, reduced error rates and more informed decision-making.
Enhancing Patient Experiences and Outcomes
Just as in retail, the public increasingly wants and expects a personalized, tailored approach to their healthcare. AI enables this by customizing treatment plans and therapies according to individuals’ lifestyles, genetic profile and medical records, helping to improve outcomes and patient satisfaction simultaneously. This personalization extends to wearable technology, where patients generate healthcare data in real time - from blood sugar levels to heart rate - and this enables continuous monitoring, predictive analysis and personalized insights.
Nearly two-thirds of hospitals in the United States are now using AI-based predictive models to proactively identify patients who are at high risk of emergency hospitalization. These systems enable healthcare providers to conduct proactive outreach to high-risk patients, leading to early interventions that prevent costly hospitalizations and ER visits. This approach not only improves patient outcomes but also significantly reduces operational costs for healthcare systems. That way, care providers can take steps to mitigate that risk, helping keep people healthy and reduce pressure on busy hospital environments.
Transforming Insurance and Payer Operations
AI is also making a substantial impact in fraud detection for healthcare payers. Machine learning algorithms can analyze historical claims data to identify anomalies and outliers, helping combat healthcare fraud estimated at hundreds of billions of dollars annually in the US. By detecting fraudulent claims more effectively, AI helps reduce insurance premiums and out-of-pocket costs for patients.
Implementing AI in Healthcare: Challenges, Considerations, and Best Practices
So how are these benefits being put into practice? From our experience working with implementations of AI in healthcare, success starts with a carefully planned approach that puts all the right building blocks in place, such as:
Making AI in Healthcare Responsible
The ability to protect sensitive data and comply with regulations must be built in from the outset, with robust data governance frameworks and security protocols. At the same time, the ethical use and implementation of responsible AI is in the spotlight; diverse datasets and audited algorithms can ensure output doesn’t introduce bias, while Explainable AI (XAI) can add transparency and clarity to the information AI is generating.
From a human standpoint, clear lines of responsibility should be put in place for AI-driven decisions, so that there are measures and response mechanisms that can swing into action if and when an AI tool makes a mistake.
Overcoming Implementation Challenges
It can be difficult to prove the value of AI in real-world clinical settings. This is especially so given the constant evolution of regulations around AI in healthcare which creates wariness and uncertainty, including potential liability if AI recommendations lead to adverse outcomes. Worries about complex integration with legacy systems can only add to the skepticism and resistance.
Addressing this starts by demonstrating performance across diverse use cases, through rigorous studies and validation processes. Comprehensive data cleansing can help in this regard by ensuring results are reliable and consistent, especially as healthcare data can often be messy and incomplete.
Evaluating AI Performance and Reliability in Healthcare
Key performance metrics have a vital role to play in proving the value of AI in healthcare. This can include recall (quantifying how many diagnoses AI correctly identifies), which can help fine-tune the AI model’s level of sensitivity; and Explainable AI (XAI) tooling that can help clarify model decisions and help build clinicians’ trust in the technology.
These should come in conjunction with real-world validation against independent datasets from different patients and hospitals, and user feedback from clinicians that can uncover practical issues such as AI misrepresentation or interface problems.
How Ciklum is Transforming Healthcare Through AI Solutions
Ciklum has enjoyed extensive success with implementing AI in healthcare for organizations just like yours. This has included streamlining claims processing, automating admin processes, improving diagnostic accuracy and maximizing resource efficiency. Working with some of the world’s foremost healthcare organizations, we have helped them generate substantial ROI, and achieve better patient outcomes without stretching their resources.
Our comprehensive, framework-based approach to identifying the highest-value implementation opportunities makes us your perfect partner. Covering both primary activities and supporting functions, our methodology introduces AI innovation without the complexity, and demonstrates real-world benefits that can counter skepticism and resistance.
Find out more on how we can maximize the impact of your investment into AI in healthcare - contact the Ciklum team today to discuss your specific requirements.
You may also like
Subscribe to receive our exclusive newsletter with the latest news and trends
Subscribe to receive our exclusive newsletter with the latest news and trends
Want to reach out directly to us?
hello@ciklum.com
© Ciklum 2002-2023. All rights reserved