AI Resources to Advance Healthcare and Improve Patient Outcomes

Technology continues modernizing medicine by aiding with staffing shortages, enabling providers to reprioritize tasks, facilitating ‘patient-first’ outcomes, and reducing expenses negatively impacting the industry. Healthcare has transformed dramatically, from how providers build relationships with patients to how care is delivered.

Medical imaging is a critical resource healthcare providers utilize to diagnose patients and draft treatment plans. Despite positive advancements, the U.S. spends an astronomical $4.3 trillion in healthcare, with Centers for Medicare & Medicaid Services (CMS) reporting increased diagnostic medical imaging expenditures responsible for rising costs. However, significant provider and equipment costs continue vexing the healthcare industry as it seeks tech solutions to complex challenges.

A Tech-Based Approach to Complex Medical Challenges

Software specialists collaborating with the medical industry continue yielding positive results, but cost mitigation remains challenging for the industry, especially within the medical imaging vertical. Medical imaging enables healthcare providers to gain a unique insight into the human body, allowing for more proactive patient examinations, but this insight comes at a steep price.

The American College of Physicians reported overuse of medical imaging services, such as radiology and MRI tests, ultimately costing the healthcare system $200-$250 billion. Artificial Intelligence remains a versatile, cutting-edge resource that can be effectively implemented within several industries. When deployed within the medical industry, its accuracy of patient data diagnosis is unprecedented. Expert-level software developers have enhanced AI to serve most industries that seek to optimize tasks and navigate complex data.

Utilizing AI within medical imaging detects subtle patterns within patient diagnostics, some finite enough to be overlooked by medical personnel. Healthcare providers relying on AI to analyze data and generate predictive reports, such as what future disease a patient is susceptible to, enables healthcare to create and implement proactive patient care plans not imaginable under traditional care guidelines.

The U.S. Government Accountability Office (GAO) has lauded the benefits of Machine Learning, a subset of AI focusing more on algorithms and data to ‘learn’ like humans and complete assigned tasks. The GOA cited medical diagnostic errors affect 12 million patients annually, costing healthcare $100 billion. With continued innovations by expert-level software developers, the GOA believes ML is a valuable resource for solving complex challenges.

Automation to Facilitate Precision, Promote Patient Outcomes

Radiology, an umbrella term encompassing imaging treatments such as X-rays, MRIs, CT scans, ultrasounds, and PET scans, has evolved to incorporate automation technology. Studies have shown that processes such as directing patient positions for image scanning can be automated, reducing the repetition of daily tasks. Automation of patient data and analysis has resulted in accurate conclusions and diagnoses.

You have outcome-based healthcare models centered on a patient’s progress to recovery, tailored to individualized treatment plans, as opposed to value-based models that compare patient outcomes vs. cost to deliver patient care. Automation enables faster image screenings, resulting in streamlined workflows that efficiently handle larger tasks. Medical providers can shift priorities to patients and work to create further positive patient outcomes.

Modernizing Medicine for Predictive Patient Diagnosis

AI continues to be an invaluable and cutting-edge resource for modernizing healthcare. Software specialists can tailor AI to adapt to unique parameters, enabling more personalized outcome-based treatment plans.

AI can be implemented to limit human error during critical data analysis, as the technology enables precise and expedient processing of critical patient information. Implemented within radiology, this powerful resource can help radiologists accurately identify and diagnose diseases and medical conditions, leading to earlier detection and improved patient outcomes.

Perhaps the most significant progression of AI in medicine is ongoing development and collaboration with oncologists. One example is software-powered nanosensors programmed to study patient blood samples and proactively look to test for ovarian cancer. The American Cancer Society estimates over 13,000 women fall victim to ovarian cancer annually.

Modernizing medicine is by no means a finished product. As the healthcare system continues undergoing reevaluation due to new models being implemented, new regulations, and legislation, we see each subset, from radiology to oncology, working to transcend their respective challenges. The continued enhancements of AI by software specialists are designed to proactively diagnose and aid healthcare providers in efficiently treating patients while streamlining workflow and contributing to mitigating healthcare costs.

Technology has always been the driving force for change. When applied to healthcare, it can be a catalyst for improving the health of all communities while providing today’s healthcare providers with versatile resources to achieve this goal. Demands for more outcome-based healthcare models, reduced expenses, and optimized services can only be fully realized with carefully designed and implemented software-based resources.