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The increasing complexity of chronic disease management and the accelerating growth of big data in healthcare present a challenge and an opportunity.
Although more and more clinical data is available in routine care, this data is not being applied to its full potential. Leveraging this dataset in a format that provides connectivity in the delivery of patient care contributes to the need to adopt Clinical Decision Support (CDS).
The goal of CDS is to improve healthcare delivery through improved medical decisions based on clinical knowledge, patient data, and other health-related information.
Diagnosis, treatment and follow-up
Integrated 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 path. While empowering patients by placing them at the center of decision-making about their care, CDS gives physicians greater confidence in the adoption and use of digital applications to improve patient care while maintaining patient outcomes. standards of care. Healthcare leaders are better positioned to manage healthcare systems where interoperability and data integration ensure improved quality and patient experience.
This does not mean that CDS applications are designed to replace clinician judgment. Rather, the CDS supports decision-makers in the diagnosis, treatment and follow-up of patients for more personalized and precise care. It’s about making the right diagnosis and targeting the right treatment on the right patient at the right time.
The many variations and use cases of CDS in clinical settings range from guideline-guided decision support to advanced techniques incorporating artificial intelligence (AI). These strategies could include evidence-based decision support for complex disease management, guideline-guided decision support for appropriate image ordering, or advanced decision support for reading and reading. interpretation of images.
CDS in clinical practice
The use of CDS in the management of complex diseases, such as cancer care, has shown promise in improving clinical, operational and financial value. Unjustified variations in care, such as overtreatment and failure to follow directions, can adversely affect results. However, CDS applications that use evidence-based guidelines and AI are designed to ensure personalized and standardized care, leading to optimal results.
The amount of data associated with complex disease management cases is enormous, so the ability of a CDS application to apply relevant information from multiple data source systems for accurate diagnostic and treatment decisions. is imperative.
A multifaceted CDS application could integrate relevant data – imaging, clinical, pathology, genomics – with AI learning capabilities and risk models for predictive analysis and recommended treatment pathways.
Use of 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 along with other clinical findings to determine the best course of action. Imaging allows the phenotypic profile of a patient to be mapped throughout the course of the disease. The ability to quickly detect critical outcomes for chronically ill patients requires a highly efficient x-ray image analysis workflow for faster turnaround times.
Using AI solutions in the imaging interpretation and reporting process often leads to improvements in workflow efficiency and, for novice radiologists, increased confidence in interpretation. Cognitive factors can contribute to diagnostic errors 75% of the time, but using AI solutions can significantly reduce the likelihood of these errors.
Incorporating a fully integrated AI component into the imaging workflow could reduce errors, increase efficiency, and achieve image interpretation goals. Such a process could be carried out with the least effort by providing automated preselected images with segmentation and detection for the radiologist to review.
Reduce medical imaging orders
The rising cost of care is a major challenge in health care delivery systems. Delays in diagnosing or overtreating chronic disease patients have raised concerns. Inappropriate use of advanced imaging not only impacts patient care by delaying treatment and potentially exposing patients to unnecessary radiation, but it also leads to the misuse of valuable resources and unnecessary costs. .
Using CDS applications helps vendors manage and determine the need for imaging. CDS acts as an assistant in selecting the most appropriate imaging based on the patient’s unique clinical condition, while taking into account local standards of care and current evidence-based recommendations.
We are in the midst of a paradigm shift, with healthcare moving towards digital transformation and implementing sustainable systems that enable optimal care delivery. As the need for decision support continues to increase in line with the demand of the world’s population, pandemic preparedness, the exponential rise of big data, interoperability and the advancement of the IA, the future of CDS looks bright.
Click here to learn more about digitizing informed decision making throughout the patient journey.
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