With the ubiquity of patient data and clinical knowledge, GenAI can play a significant role in enhancing the quality of decision-making at hospitals.
Clinical decision-making is a complex process where efficacy is determined, not only by biomedical knowledge, but also by experience, patient-centric processes, and completeness of data. It is an iterative process where early interventions can make the difference between life and death for patients.
The good news is that diagnostic data and patient history are now ubiquitous and available to doctors. Where digitization is already underway, this data may already be available through Electronic Medical Records (EMRs). By leveraging Generative AI (GenAI), doctors can now exploit this data to improve the accuracy and timeliness of clinical decisions, thus equalizing the quality of care across the healthcare ecosystem.
Clinical decision making: key challenges
Some of the most pressing issues in clinical decision-making include diagnostic errors or delays in identifying fast-progressing conditions. These issues become more challenging to address when doctors treat patients with multiple ailments, where it becomes difficult to attribute symptoms to a particular condition.
Moreover, doctors may find it difficult to correctly diagnose patients with a long and complex medical history. In such scenarios, overlooking important information may result in suboptimal decisions or erroneous prescriptions – which lead to poor patient outcomes.
How GenAI can improve clinical decision outcomes
In the past, AI has already been applied in Clinical Decision Support (CDS). However, the applicability of such systems is limited, and their black-box output emerges as an impediment in the decision-making process.
More recently, GenAI has shown significant promise in CDS. It can help clinicians analyze structured and unstructured data, summarize information, and identify similar cases to facilitate early interventions. Two-thirds of clinicians already see the technology as beneficial, and 40% are ready to use it this year.[1] Consider the following ways in which GenAI can enhance the quality of clinical decisions at scale.
Devising optimal treatment plans
By training Generative AI on large datasets comprising medical history, genetics, and lifestyle, clinicians can exploit the technology to infer how a patient will respond to the prescribed treatment. By accounting for minute details in troves of data, GenAI can spot correlations that evade doctors’ judgment.
This can help clinicians create personalized treatment plans that work the best for a particular patient. For instance, GenAI systems could be applied to anticipate the side effects of a blood pressure medication or various classes of statins on people based on their lifestyle and genetics.
Enhancing the scope and precision of CDS systems
GenAI can augment the accuracy of medical diagnoses in two ways. First, it can sharpen the accuracy of existing tools like lesion segmentation or diagnosis networks by enriching their training data. For this, GenAI can be applied to generate synthetic medical image data, which can be used to offset issues like overfitting or bias in existing CDS systems.
Flow-based models show promise in generating high-quality images, making them apt for completing deficient samples. This can help expand the applicability of CDS systems beyond a particular race or demographic – a shortcoming that has been noted in AI-based CDS solutions.
Optimizing care journeys for patients
Secondly, GenAI can infer patterns from complex medical histories of patients with multiple conditions. By making sense of seemingly unrelated details, such systems can provide clinicians with the complete picture of a patient’s health, enabling doctors to make more accurate calls when devising their care journey.
Making CDS systems more explainable and transparent
Hospitals are already leveraging AI-based CDS systems to help doctors arrive at more optimal diagnosis and treatment decisions. While these have been improving over time, GenAI can make them more effective and transparent. For instance, ML-based CDS models can be integrated with GenAI to make their outputs explainable to doctors. This can be achieved with LLMs that are trained in medical research, which can then contextualize CDS outputs for clinicians.
Final words
As hospitals pursue digital transformation, it is important to focus on the quality of clinical decisioning, as it significantly impacts patient outcomes. GenAI, especially multimodal models will be of special importance in this regard, as vast volumes of patient data become available to doctors.
However, it will be crucial to build well-tested systems as erroneous outputs could do irreversible damage to patients. That’s why, when implementing GenAI for CDS, it is important to address issues like response variability, which is characteristic of GenAI models. With a trusted technology leader, healthcare providers will be better equipped to effectively preempt such issues, and enhance clinical decision quality at scale with GenAI.
[1] https://www.wolterskluwer.com/en/news/gen-ai-clincian-survey-press-release
