Global Healthcare Leadership

Since ancient medical practice, physicians have faced increased pressure to service more accurate and complete documentation while at the same time maintaining their focus on patient care. However, without complete documentation processes and the appropriate technology, doctors risk receiving inaccurate figures ad lower reimbursement rates. Till today, the use of data science to medical care has been under used compared to other disciplines. Moreover, the burgeoning interest in its use to medical practice may have herald a period which might be true in imaging (Andrew Lin, 2020).

What I would create and the problem it solves

Application of Artificial Intelligence in cardiovascular imaging- Artificial intelligence refers to the use of technological methods that imitate humanoid brain function. In health care, it includes bulky medical data quantity used to forecast a diagnosis, detect novel illness phenotypes, genotypes, and control treatment measures (Andrew Lin, 2020).

Cardiovascular treatment is prioritized for accessible Artificial intelligence applications that would infer large volumes of medical and imaging datasets in larger scales than before. Artificial intelligence -improved clinical systems have the possibility to provide reproducible and objective qualitative and quantitative outcomes that can enables medical decisions and enhance workflow. In the future, Artificial intelligence might be used in the cardiac image analytics software and periodic clinical summaries, enabling timely diagnosis and risk classification, and automatically collecting data (Andrew Lin, 2020).

The development of Artificial Intelligence has brought about chances in healthcare to achieve more complex details from imaging, and identify partners in existing data sources that are too sophisticated for the human brain. Today, it remains a question of when the Artificial Intelligence algorithms will prove fundamental for the cardiologist, and the imaging specialists.

How the innovation impact value-based-healthcare.

It improves workflow and efficiency as it analyzes data as well as aids doctors screen, access, and diagnoses patients. This ensures that the patients’ needs are valued and catered for during the check-ups and treatment. It also limits delays in detecting and addressing anomalous cardiovascular images as they read images in less time, thus showing both patients time value and those of their doctors. It increases levels of accuracy in identifying any cardio-related disorders as its algorithms evaluate comprehensively resulting in high precision. The innovation of Artificial Intelligence tools with vast imaging database will ensure accurate treatment administration and intensify the efficacy of imaging tests for the patients. (Andrew Lin, 2020).

How it will enhance my leadership capacity.

Artificial Intelligence analyzes medical images to access health and forecast any risk of disease thus the ability to apply risk prevention measures beforehand, it also manages huge workloads as it includes machine learning because they speed up scan time and make a more accurate diagnosis, enabling efficiency. Artificial Intelligence has the probability to afford a chance to expand, timely and quality of acquisitions, allow prompt diagnoses, reduce measurement time, that results in improved workflow and better patient care.

The barrier in the implementation of the innovation and how overcome it.

Artificial Intelligence has been widely spoke of in the recent past and yielded promising results. However, the use of the technology in the preset day cardiovascular treatment has been limited. Integrating Artificial Intelligence expertise within an organization can present some challenges. Bodies responsible for its regulation and approval have been reluctant with the software. Health systems cannot adequately fund data science resources, even with in-house data science talent, scaling that expertise to the broader analytics teams is difficult. Teaching data science concepts and skills to existing teams is slow, requires substantial cash outlays, and unsustainable process. (Topol, 2019).

Although Artificial Intelligence theoretically predicts outcomes, improved efficiency of managerial decisions seems undesirable compared to human neural network engagement with the clinical, individual, environmental, and social concepts of individual patients. Shortly, therefore, the Artificial Intelligence practical potential likely to be as an applicable medicinal tool for imaging by specialists and medical practitioners (Ziad Obermeyer, 2016).

How the five-stage innovation-decision process would overcome the barrier:

· Knowledge- getting medical teams in the participating organization to generate realistic ideas that could help fundraise and finance data science resources.

· Persuasion- educating the involved about Artificial Intelligence in cardiovascular imaging practice through an effective learning schedule promotes a fast inexpensive and sustainable process in the organization.

· Decision-approaching medically recognized experts before the implementation of Artificial Intelligence helps in successfully demonstrating the cardiovascular Imaging process which could also involve holding a referendum with the national medical body thus broadening the artificial intelligence performance and scalability.

· Implementation- introducing Artificial Intelligence in cardiovascular imaging in day-to-day medical practices would ensure the practitioners are diligent in demonstrating its benefits to their patients. Soon, as Machine Learning algorithms merged with medical routine for image capturing, analysis and forecast of patients results, customized medicines should help come up with correct and adequate for their patients whose "lives and medical background define the systems (Ziad Obermeyer, 2016).

· Confirmation promotion of artificial Intelligence in cardiovascular imaging through media nationally and internationally would confirm its practice and benefits to those that have experienced it physically and to the public.

References

Topol, E. J. (7/1/2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 44-56.

Ziad Obermeyer, M. a. (29/9/2016). Predicting the Future — Big Data, Machine Learning, and Clinical Medicine. The New England Journal of Medicine, 1216-1219.