Evaluate your present professional practice

My current professional practice involves providing expertise in the field of healthcare technology. My role entails leveraging my clinical expertise to implement, develop, and optimize technology solutions in healthcare settings. I am constantly engaged in research and quality improvement initiatives aimed at evaluating the impact of healthcare technology on patient outcomes and satisfaction. Notably, I am also involved in training and educating the healthcare staff to make sure they understand how to leverage tools to enhance their patient care and improve efficiency.

One Surfacing Technological Tool/Advance/Innovation Used In a Healthcare System

The selected technological tool is artificial intelligence, which entails the use of computational technologies to emulate human intelligence, such as adaptation, thought, deep learning, sensory understanding, and engagement. AI is transforming healthcare, and it has been used to develop diagnostic tools and individualized treatment plans. A study indicates that the use of deep learning technology improves breast cancer risk prediction (Yala et al., 2019).

Workflow Implementation

AI can be implemented into healthcare workflow in the following way:

  1. Identification for specific use in which AI can add value: for example, the use of AI for detection of diabetic retinopathy
  2. Data collection and preparation for training the AI model: for example, by collecting large data sets with retinal images labeled with diagnostic information (Hwang et al., 2019). Data collection should comply with HIPAA regulations.
  3. Model development and training: This is done in collaboration with machine learning experts and data scientists who develop the AI model using the appropriate algorithms and train using the labeled data set.
  4. Integration into clinical workflow: Themodel is integrated with the existing technologies such as EHR
  5. Patient screening and triage: At this phase, the AI tool is used to screen patients for diabetic neuropathy, whereby the tool analyzes retinal images and provides the risk score indicating the likelihood of diabetic neuropathy
  6. Monitoring and evaluation: Feedback is collected from HCPs in regard to the AI tool usability, accuracy, and impact on patient care
  7. Quality improvement: Usingthe feedback data, the AI is improved over time with additional parameters.


  1. Data-driven insights- utilizes a large volume of data to identify patterns and trends
  2. Increased efficiency: automation of processes reduces the workload on HCPs
  3. Consistency: utilizes standardized tools that ensure uniformity in diagnostic decisions


  1. Cost: Developing, integrating, and training AI requires a significant financial investment.
  2. Integration challenges:Integration of the AI tool with the existing workflows may pose technical challenges (Dikici et al., 2020).

Impact on Patient Care Delivery

  1. Early detection and intervention: The tool can detect early signs of diabetic retinopathy
  2. Enhanced accuracy and consistency: AI tool ensures uniformity in care delivery
  3. Improved efficiency: allows HCPs to focus on other aspects of
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