Unwarranted variations. Among health care systems and payers—both government and private—this term implies unnecessary risk to patients, diminished quality of care, and increased health care costs. From the numerous studies and papers published on the topic, a common description emerges: unwarranted variations are not directly due to the inherent variations of a given patient; rather to the variations in the processes and conditions of how care is delivered, especially when it results in inconsistencies in the adherence to and administering of evidence-based guidelines of care.
Consider the variations that exist among complex disease pathways such as oncology. In lung cancer, over 75% of patients are at an advanced state of disease when diagnosed, making it the leading cause of cancer deaths among both men and women. Thus, early detection, either as a focused diagnosis or an incidental finding, is key for an optimal five-year survival rate, as is the rapid commencement of a treatment protocol.
At the other end of the spectrum is prostate cancer. Although incidence rates are high, most prostate cancer cases are detected when the cancer is confined to be within the prostate. Consequently, five-year survival rates are high at approximately 98% in the U.S. and 83% in Europe. Small, localized tumors, particular in older men, are often monitored via an active surveillance process. However, patients often undergo more aggressive and costly treatments resulting in unnecessary consequences that impact quality of life.
As different as these two oncology pathways are, so are the differences in the variations that occur from screening to diagnosis and therapy decision to treatment. But what if advances in digitalization and artificial intelligence (AI) techniques could standardize processes at the crucial steps of diagnosis and therapy decision making? What if providers had support to make diagnostic and therapeutic decisions at these two critical points? And what if providers could easily access and adhere to the latest evidence-based guidelines of care?
Leveraging our experience as a pioneer in AI development and our global leadership in AI patent applications, we at Siemens Healthineers have responded to these questions by developing digital solutions that support health care providers in their diagnostic and therapeutic decision making. These solutions enable providers to address variations in care by automating and standardizing processes and facilitating guideline adherence.
Designed to simplify ordering decision support for appropriate diagnostic imaging utilization, the Medicalis Clinical Decision Support makes it easy for referring health care providers to request imaging consultations with evidence-based support to guide them through “the right order, first time.”
The AI-Rad Companion, our growing family of AI-powered, cloud-based augmented workflows, provides multi-modality and multi-organ imaging decision support. The AI-Rad Companion extensions automate workflows and reduce the burden of basic repetitive tasks, which may increase diagnostic precision when interpreting medical images. It shall be noted that the AI-Rad Companion products and features mentioned are not yet commercially available in all countries. Their future availability is in the works.
Leveraging data integration and AI techniques, the AI-Pathway Companion, with its applications, enables personalized and standardized patient management by automating patient-specific mapping along disease-specific care pathways using evidence-based guidelines. A prerequisite to automating patient-specific mapping along disease-specific care pathways using evidence-based guidelines is that all data is available as required per guideline. In terms of guidelines supported, it shall be noted that AI-Pathway Companion Prostate Cancer VA10A supports NCCN guidelines. The support of EAU guidelines is currently under development. The AI-Pathway Companion facilitates objective decision making by multidisciplinary experts on correlated patient data and preferences and offers transparent insights into time to diagnosis and treatment. This particular function is supported by AI-Pathway Companion Analytics.
The sum of the above described features is supported by AI-Pathway Companion Prostate Cancer. It shall be noted that AI-Pathway Companion Prostate Cancer supports prostate cancer adenocarcinoma cases only. AI-Pathway Companion Prostate Cancer is not yet commercially available in all countries. Its future availability is in the works.
Combined, our portfolio of diagnostic and therapeutic decision-making digital solutions enables care-providers to respond to the call to action on reducing variations in care. Based on joint efforts we are confident patients stand to benefit the most.
Learn more about our portfolio of ordering and clinical decision-making solutions that support care-providers throughout a patient’s disease journey.
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September 15, 2020 at 10:05PM
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AI Can Help Health Care Providers Make Diagnostic and Therapeutic Decisions - SPONSOR CONTENT FROM SIEMENS HEALTHINEERS - Harvard Business Review
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