The rising complexities of chronic disease management and the accelerated growth of healthcare’s big data present a challenge—and an opportunity.
Although more and more clinical data is available in routine care, that data isn’t applied to its full potential. Leveraging that body of data in a format that ensures connectedness across patient-care delivery contributes to the need for adopting clinical decision support (CDS).
The objective of CDS is to improve healthcare delivery through the enhancement of medical decisions based on clinical knowledge, patient data, and other related health information.
Diagnosis, Treatment, and Follow-Up
Incorporated into clinical routine, CDS can help clinicians navigate the often complex web of decision-making, creating opportunities to optimize the patient journey along a care pathway. While empowering patients by putting them at the center of decision making about their care, CDS gives physicians more confidence in adopting and using digital applications to enhance patient treatment while maintaining standards of care. Health care leaders are better positioned to run health systems where interoperability and integration of data ensures improved quality and patient experience.
This doesn’t mean that CDS applications are designed to replace clinician judgment. Rather, CDS supports decision makers during diagnosis, treatment, and follow-up of patients for more personalized and precise care. It’s all about making the right diagnosis and targeting the right treatment to the right patient at the right time.
The many variations and use cases of CDS in clinical settings range from guideline-driven decision support to advanced techniques incorporating artificial intelligence (AI). These strategies could include evidence-based decision support for complex disease management, guideline-driven decision support for appropriate image ordering, or advanced decision support for image reading and interpretation.
CDS in Clinical Practice
Employing CDS in complex disease management, such as cancer care, has shown promise in improving clinical, operational, and financial value. Unwarranted variations in care, such as overtreatment and lack of adherence to guidelines, can harm outcomes. However, CDS applications that use evidence-based guidelines and AI are designed to ensure personalized and standardized care, leading to optimal outcomes.
The amount of data associated with complex disease-management cases is enormous, so a CDS application’s ability to apply relevant information from multiple data-source systems for precise diagnosis and treatment decisions is imperative.
A multifaceted CDS application could integrate relevant data—imaging, clinical, pathology, genomics—with AI learning functionalities and risk models for predictive analysis and recommended treatment pathways.
Using AI in Patient Care
Diagnostic imaging plays a central role in disease management, from prognostic workup to diagnosis, treatment, monitoring, and follow-up.
At each step, imaging is used with other clinical results to determine the best course of action. Imaging helps map out the phenotypic profile of a patient throughout the course of the disease. The ability to quickly detect critical findings for chronically ill patients requires a highly efficient radiology image-analysis workflow for faster turnaround times.
Using AI solutions in the imaging interpretation and reporting process often leads to improvements in workflow efficiencies and, for junior radiologists, an increase in interpretation confidence. Cognitive factors may contribute to diagnostic errors in 75% of cases, but using AI solutions can significantly reduce the likelihood of these errors.
Embedding a seamlessly integrated AI component in the imaging workflow could reduce errors, increase efficiency, and achieve image-interpretation objectives. Such a process could be conducted with the least effort by providing automated prescreened images with segmentation and detection for the radiologist to review.
Reducing Medical Imaging Orders
The rising cost of care is a major challenge across health care delivery systems. Delays in diagnosis or overtreatment of chronic disease patients has raised concerns. The inappropriate use of advanced imaging not only impacts patient care by delaying treatment and potentially exposing patients to unnecessary radiation, it also leads to the misuse of valuable resources and unnecessary costs.
Employing CDS applications helps providers manage and determine the necessity of imaging. CDS acts as an assistant in selecting the most appropriate imaging based on a patient’s unique clinical condition, while factoring in local standards of care and current evidence-based guideline recommendations.
We are in the midst of a paradigm shift, with healthcare moving toward digital transformation and implementing sustainable systems that enable optimal care delivery. As the need for decision support continues to increase in line with global population demands, pandemic preparedness, the exponential rise of big data, interoperability, and the advancement of AI, the future of CDS appears promising.
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