Jan Beger’s Post

In our hospital we deployed AI using a PDSA framework similar to this, but this infographic also comes with a link to published manuscripts, which is very handy. Lunit Cancer Screening DeepTek CARPL - Radiology AI Platform Synapxe

This is an interesting post. But we need to study measures in all three layers of an AI based technology: foundation layer pure model metrics interma of performance & bias etc, technical layer im preclinical studies and first in system studies. Then sociotechical layer and real world process measures and patient outcomes.

Healthcare AI Opportunities Available Now Artificial intelligence (AI) adoption in healthcare lags behind other sectors like finance and retail, generally paralleling the overall use of information technology. Challenges to using AI include the complexity of healthcare data, integration into clinical workflows, and the industry's focus on short-term financial goals. There are three key areas where AI can impact healthcare. AI's impact is most remarkable in revenue cycle management and claims processing, where it optimizes the financial interactions between providers and payers. AI can reduce the number of denied or delayed claims, thereby improving the financial health of healthcare organizations. It also enhances the patient experience by streamlining payment processes. AI can improve clinical operations by optimizing workflows, particularly in high-stakes environments like the operating theater. It helps reduce scheduling days, enhances the utilization of operating rooms, and assists physicians in documenting in the EHR. Organizations use AI to improve patient safety by developing algorithms to reduce adverse drug events, sepsis, and diagnostic errors. AI can integrate complex, unstructured data with measurable metrics to make predictive analyses that alert healthcare providers to potential risks, thereby improving patient safety and clinical outcomes. In summary, AI has the potential to revolutionize various aspects of healthcare, from financial transactions to clinical operations and patient safety. While the technology is still in the early stages of adoption in healthcare, its promise for improving efficiency and outcomes is significant.

Artificial Intelligence (AI) has gradually become a game-changer across various industries, and healthcare is no exception. In the pursuit of improving healthcare outcomes and controlling costs, value-based purchasing has gained significant traction. These words explore the potential applications of AI in value-based purchasing within the healthcare industry, highlighting how AI can enhance decision-making, optimize resource allocation, and improve patient care. - Enhanced Decision-making: AI technologies, such as machine learning algorithms and natural language processing, have the capability to analyze vast amounts of healthcare data quickly and efficiently. This analysis can assist healthcare providers and administrators in making informed decisions related to value-based purchasing. By leveraging AI, data-driven insights can be generated, which can inform the selection of cost-effective treatments and interventions and enhance long-term patient outcomes. - Optimal Resource Allocation: Efficient resource allocation is a crucial aspect of value-based purchasing, aiming to ensure that healthcare expenditures align with positive patient outcomes. AI can play a significant role in optimizing resource allocation by predicting disease patterns, analyzing medical trends, and identifying areas where resource allocation can be optimized. By considering variables such as patient demographics, disease severity, and treatment effectiveness, AI algorithms can assist in determining the most appropriate allocation of resources to maximize value. - Predictive Analytics: Value-based purchasing involves incentivizing healthcare providers to improve patient outcomes. Predictive analytics, powered by AI, can provide valuable insights into identifying high-cost patients who are at risk of poor outcomes. By integrating historical patient data, predictive analytics models can identify patterns and risk factors associated with certain conditions. This information can help healthcare providers proactively intervene and allocate resources where they will have the most significant impact, ultimately reducing costs and improving patient care. - Quality Monitoring and Fraud Detection: AI technologies can support value-based purchasing initiatives by continually monitoring and assessing healthcare providers' performance. By comparing treatment outcomes, costs, and adherence to quality measures, AI algorithms can detect patterns and anomalies that indicate potential fraud, waste, or abuse. This can help prevent unnecessary losses, ensuring that value-based purchasing programs achieve their goals while promoting fairness and transparency within the healthcare system. As AI technologies continue to advance, it is crucial for healthcare organizations, policymakers, and regulators to collaborate in developing ethical frameworks and guidelines to ensure the responsible and equitable implementation of AI in value-based purchasing.

Artificial Intelligence (AI) and General Artificial Intelligence (GAI) can significantly reduce costs in healthcare through various specific ways: AI in Diagnosis and Treatment: ·      Cost Savings: AI can lead to substantial cost savings in healthcare by addressing inefficiencies such as subpar vigilance delivery, overtreatment, and improper health care delivery   . ·      Efficiency: AI-based systems can reduce time in diagnosis and therapy, achieving high accuracy in a short period   . ·      Improved Accuracy: AI assists in improving diagnostic accuracy by eliminating bias and subjectivity, reducing the likelihood of incorrect examinations   . ·      Patient Comfort: Patients may feel more at ease when visiting a doctor due to AI technology   . ·      Data Analysis: AI examines vast amounts of data to determine the most effective treatments, maximizing returns on investments for healthcare organizations   . GAI Impact on Healthcare Costs: ·      Administrative Costs: Adoption of AI-enabled use cases focusing on administrative costs can help healthcare organizations reduce costs and improve labor productivity 2. ·      Savings Estimates: Broader adoption of AI could lead to savings between 5% and 10% in healthcare spending, amounting to roughly $200 billion to $360 billion annually   . Potential Savings Breakdown: Physician groups could save $20 to $60 billion annually. Private insurers could save $80 to $110 billion annually. o  Hospitals could save $60 to $120 billion annually   . In conclusion, the integration of AI and GAI technologies in healthcare can lead to significant cost reductions by improving efficiency, accuracy, patient comfort, and streamlining administrative processes.  Using The Innovation Strengths Preference Indicator® Executive Edition, (ISPI™ EXE) . www.innovatingedge.com to assemble AI & GAI will help greatly to increase the cost savings that are possible. Jerry Fisher, MBA, PhD…Artificial Intelligence Enthusiast Co-Founder and Chief Innovation Officer Innovating Edge, Inc. www.innovatingedge.com E: jfisher@innovatingedge.com Bio Gerard Fisher (innovatingedge.com) If you would be interested in taking the Innovation Strengths Preference Indicator® for no charge, just send me an email and I will send you a link. It takes about 15 minutes and you will immediately receive a 36-page report on your results. E: jfisher@innovatingedge.com

www.tririktechnology.com What are the challenges of implementing AI in the healthcare sector? - Implementing AI in the healthcare sector presents numerous challenges that must be addressed to ensure its effective and ethical deployment. Here are some key challenges: 1. Data Privacy and Security Patient Confidentiality: Ensuring AI systems comply with strict privacy regulations (e.g., HIPAA in the U.S.) to protect patient data. Data Breaches: Safeguarding sensitive health information from cyberattacks and unauthorized access. 2. Data Quality and Availability Data Standardization: Healthcare data often comes from various sources and formats, making it challenging to standardize and integrate. Incomplete or Inaccurate Data: Missing, inconsistent, or erroneous data can negatively impact the performance of AI models. 3. Regulatory and Legal Issues Approval and Compliance: Navigating the complex regulatory environment to get AI tools approved by health authorities (e.g., FDA). Liability and Accountability: Determining who is responsible when an AI system makes an error or a misdiagnosis. 4. Ethical Concerns Bias and Fairness: Addressing biases in AI algorithms that could lead to unfair treatment of certain patient groups. Informed Consent: Ensuring patients are adequately informed about the use of AI in their care and its implications. 5. Integration with Existing Systems Interoperability: Ensuring AI systems work seamlessly with existing Electronic Health Record (EHR) systems and other healthcare technologies. Workflow Disruption: Minimizing disruptions to healthcare providers’ workflows during the integration of new AI tools. 6. Clinical Validation Accuracy and Reliability: Demonstrating that AI systems can provide accurate and reliable results across diverse patient populations. Generalizability: Ensuring that AI models trained on specific datasets can generalize well to different clinical settings and populations. 7. Cost and Resource Allocation High Implementation Costs: The initial investment in AI technology and ongoing maintenance can be substantial. Training and Support: Providing adequate training for healthcare professionals to use AI tools effectively and understand their limitations. 8. Change Management Resistance to Change: Overcoming skepticism and resistance from healthcare professionals who may be wary of relying on AI. Cultural Shift: Promoting a culture that embraces technological advancements and continuous learning. 9. Technical Challenges Algorithm Complexity: Developing sophisticated algorithms that can handle the complexity and variability of medical data. Real-Time Processing: Ensuring AI systems can process data and provide insights in real time, which is crucial in many clinical scenarios. 10. Sustainability and Scalability Scalability: Developing AI solutions that can scale across different healthcare systems and regions.

The healthcare industry is on the cusp of a major transformation, driven by the rapid adoption of artificial intelligence (AI) and machine learning (ML) technologies. One of the most promising applications of AI in healthcare is the development of chatbots. Chatbots are computer programs that can simulate human conversation. They can be used to provide a variety of services, such as answering questions, scheduling appointments, and providing health information. Chatbots are becoming increasingly sophisticated, and they are now able to understand natural language and provide personalized responses. The market for healthcare chatbots is expected to reach USD 314.3 million by 2023, growing at a CAGR of 20.8%. This growth is being driven by the increasing demand for convenient and accessible healthcare services. Chatbots can be used to provide 24/7 support, which can be especially beneficial for patients in rural or underserved areas. Chatbots can also be used to reduce costs. Chatbots can be used to automate tasks that are currently performed by healthcare professionals, such as answering questions and scheduling appointments. This can free up professionals to focus on more complex tasks, such as providing medical care. In addition to reducing costs, chatbots can also improve patient satisfaction. Chatbots can provide patients with a more convenient and personalized experience. Patients can use chatbots to get answers to their questions, schedule appointments, and even receive medical advice. This can lead to improved patient satisfaction and loyalty. Overall, the future of healthcare chatbots is bright. Chatbots have the potential to revolutionize the healthcare industry by making it more convenient, accessible, and affordable for everyone.

One for all, and all for AI

How to continuously monitor and improve AI systems in healthcare? AI has the potential to greatly improve healthcare outcomes and efficiency, but only if it is used correctly and effectively. Continuous monitoring makes sure that AI tools are performing as expected, and identifies any issues for improvement. But how do we start monitoring and improving the AI systems? 1. Define Objectives and Metrics:  Clearly define what you want to achieve with the AI tools and how you will measure success. This could be improving patient outcomes, reducing costs, or increasing efficiency. 2. Implement AI Tools: Deploy AI tools in the healthcare system. This could involve integrating AI into existing systems or developing new tools. 3. Monitor Performance: Continuously track the performance of the AI tools against the defined metrics. This could involve regular reports or real-time dashboards. 4. Collect Feedback: Gather feedback from users of the AI tools, such as healthcare professionals and patients. This could involve surveys, interviews, or user testing sessions. 5. Analyze Results and Identify Areas for Improvement: Use the performance data and feedback to identify areas where the AI tools could be improved. 6. Iterate and Improve: Make improvements to the AI tools based on the analysis. This could involve tweaking algorithms, improving user interfaces, or providing additional training to users. 7. Repeat the Process: Continue the cycle of monitoring, feedback, analysis, and improvement. What steps do you take to monitor and improve your AI tools? 

The Crucial Role of Data Quality in the NHS and AI Integration Introduction. As a Principal Data Analyst in the NHS, I’ve witnessed how data quality directly impacts healthcare outcomes. High-quality data underpins effective decision-making, patient care, and the successful implementation of AI and machine learning (ML) technologies. This post explores the importance of six data quality dimensions - Accuracy, Completeness, Uniqueness, Consistency, Timeliness, and Validity and their influence on AI and ML applications within the NHS. Key Dimensions of Data Quality Accuracy: Essential for correct patient diagnosis and treatment. Errors can lead to serious health risks. Completeness: Critical for comprehensive care. Missing information can compromise patient safety. Uniqueness: Prevents duplicate records, ensuring a unified patient history across NHS services. Consistency: Ensures uniform data representation, crucial for clear communication and care coordination. Timeliness: Vital for current and relevant decision-making, reflecting the latest patient status. Validity: Guarantees data adherence to NHS standards, critical for interoperability and accurate care delivery. Impact on AI and ML. The advent of AI and ML offers transformative potential for the NHS, from improving predictive analytics to streamlining operations. However, these technologies demand high-quality data. Poor data quality can lead to inaccurate AI predictions, risking patient safety and resource wastage. Ensuring data integrity, through rigorous governance and involving healthcare professionals in curation, is fundamental to the reliability of AI applications in healthcare. Conclusion. Data quality is not merely a technical requirement but a cornerstone of patient-centred care in the NHS, especially with the integration of AI and ML technologies. Committing to excellence in data quality across its dimensions ensures a strong foundation for healthcare innovations, ultimately leading to better patient outcomes.