By 2030, the healthcare artificial intelligence market is expected to be close to being worth it $188 billion.
The Institute of Electrical and Electronic Engineers, the world’s largest technical nonprofit dedicated to advancing technology for the benefit of humanity, keeps a close eye on AI – both the benefits and challenges of the technology that has exploded in health care.
Therefore Healthcare IT news recently spoke with IEEE Fellow Chenyang Lu. We asked him how AI is being used by healthcare professionals to help improve patient outcomes, what challenges there are in implementing AI in healthcare and how these challenges can be overcome, and how he thinks the future of AI in healthcare healthcare looks like. He gave some very insightful answers.
Q. What are your thoughts on how AI can be used by healthcare professionals to help improve patient outcomes?
A. AI will effectively become co-pilots for our doctors and enable timely, accurate and efficient treatment for each individual patient. AI models can make personalized predictions of a patient’s clinical outcomes, risk factors, and response to different treatments. Here are three examples of AI in healthcare with great promise to improve patient outcomes.
First, screening for depression. According to the WHO, more than 280 million people suffer from depression. Among them, more than 50% are not diagnosed or treated. The problem of underdiagnosis stems from the significant time and expense required to obtain the diagnosis by psychiatrists. A recent study has shown that deep learning models can detect depression and anxiety disorders using data collected from wearable devices, opening a new avenue to unobtrusively screen for depression.
This AI-based screening tool will enable physicians to deliver selective prevention programs to individuals in a targeted and timely manner (a critical evidence gap in depression prevention identified by the United States Preventive Services Task Force).
Secondly, cancer care. Cancer patients are at high risk of clinical deterioration: 6.4% of oncology patients are transferred to intensive care at least once, and 2.7% of them die in hospital wards, according to a study. recent research. Machine learning models can generate early warnings of the clinical deterioration of inpatient oncology patients by integrating heterogeneous data into the electronic medical records.
AI-generated early warnings, in addition to risk factors associated with the predictions, allow physicians to pre-identify patients at risk and provide early interventions to prevent deterioration. Physicians also face challenges in deciding how to discharge patients from oncology units. Prolonged stay reduces the availability of hospital access for patients with cancer. Machine learning models can be used to determine when a patient hospitalized with cancer is clinically stable for hospital discharge, improving the efficiency of cancer care and ensuring patient safety.
And third, perioperative care. Surgery poses significant risks and costs to patients. Early identification of risk factors can be crucial for early intervention and better outcomes. For example, resection of the pancreas is the only cure for pancreatic cancer, but is often associated with a high rate of serious complications. Using data collected with certain fitness wristbands, Machine learning models can predict a patient’s risk of serious complications before surgery.
If the risk to a patient is high, he/she may be included in prehabilitation programs to increase readiness for surgery. Machine learning models have also been developed using EHR data identify risks during surgery and to predict complications after surgeryto improve patient safety and outcomes in perioperative care.
Q: What do you see as the biggest challenges in implementing AI in healthcare and how can hospitals and healthcare systems overcome these challenges?
A. Integration of AI models with the EHR and clinical workflow is essential for implementing AI in healthcare. However, there are significant challenges in implementing AI models on today’s EHR platforms, as opposed to commercial cloud platforms that have made it much easier to build and deploy AI.
We currently have numerous AI models in pilot phase, but few have been implemented in EHRs. We are still in the early stages of AI in healthcare. Looking ahead, it is imperative to lower the barriers to deploying AI models into our infrastructure.
Additionally, we need to reimagine our workflows and protocols so that doctors and AI can work together effectively. The experience of recent years has shown that AI and doctors offer complementary capabilities. AI will be co-pilots working with doctors to arrive at the best decisions and treatments together. Significant research is needed to develop effective human-in-the-loop AI in clinical settings.
Q. What do you see as the future of AI in healthcare? What is the next step and where is this expected to go in the coming years?
A. We are seeing early adoption of generative AI to improve operational efficiency by automating clinical documentation and patient communications. Despite implementation challenges, we will see increased adoption of AI-based clinical decision support, driven by its great potential to improve patient outcomes and healthcare efficiency.
Importantly, we need to generate evidence for the efficacy and benefits of AI in healthcare in terms of patient outcomes and cost-effectiveness so that we can incrementally build AI capabilities into our healthcare systems. In the meantime, we must ensure fairness, safety, security, privacy and access to AI in healthcare through both policies and technologies.
This is another area where significant research is needed to enable sustainable growth of AI in healthcare.
Follow Bill’s HIT coverage on LinkedIn: Bill Siwicki
Email him: bsiwicki@himss.org
Healthcare IT News is a HIMSS Media publication.