Artificial Intelligence in the Inpatient Pharmacy Setting.pptx

AI in Clinical Practice

1. Enhancing Clinical Practice and Operations Onyemauchechukwu Ijezie MedStar Montgomery Medical Center Advancing Pharmacy with AI: Applications in Inpatient Settings

2. Disclaimer The logos and images included in this presentation are the property of their respective owners. I do not hold any rights to these pictures or logos and have used them solely for educational and illustrative purposes. All trademarks and logos are acknowledged and credited to their respective companies.

3. Objectives Define and demystify Artificial Intelligence (AI) E xamine various AI tools and discuss their benefits within clinical p harmacy Introduce AI applications that can support and improve existing SOPs R ecommend clinical applications for specialties in the pharmaceutical practice Review AI applications used in pharmacy management and healthcare t eams Predict how AI will impact the future pharmacy and health informatics fields

4. Overview Imagine a future, where healthcare is not only faster but safer and tailored to meet the unique needs of each patient —This is the vision of AI in healthcare Artificial Intelligence (AI) has the potential to revolutionize pharmacy practice by: A  -  Artificial Intelligence : Advanced data analysis.  E  -  Enhancing Decision-Making : Real-time clinical insights.  I  -  Improving Outcomes : Personalized patient care.  O  -  Optimizing Efficiency : Streamlined pharmacy operations.  U  -  Utilization of Data : Pattern detection and trend prediction.  Y  -  Yielding Innovations : Accelerating research and development. Developers have built AI applications with various tools for streamlining processes, reducing errors, and providing data-driven insights.

5. What is AI?

6. Understanding AI in Pharmacy: Definition of AI AI simulates human intelligence for complex problem-solving. Distinguishing AI from Regular Software: Not all software is AI, but all AI are software Regular software follows predefined instructions without adaptation while AI systems exhibit learning , reasoning , and adaptability . Common AI Applications: AI-driven tools are more common than most assume. Examples include virtual assistants, health monitoring devices and apps, content recommendations and advertisements.

7. What are some b iases & assumptions about AI?

8. Common Biases and Assumptions AI Will Replace Jobs : will take over all human jobs, leading to massive unemployment. Reality:   While AI can automate certain tasks, it is more likely to transform jobs. Many new jobs are also expected to be created because of AI advancements. AI Will Achieve Human-Level Intelligence: surpass human intelligence and become uncontrollable. Reality:   Current AI technologies are far from achieving general intelligence, which would require understanding, reasoning, and learning in a human-like way. Most AI systems today are specialized and limited to specific tasks. AI Creates Pseudo-Humans “ i -Robots”: thinks and feels like humans, hoping to replace real people. Reality:   AI can mimic human behavior to a certain extent, but it does not possess consciousness, emotions, or understanding like humans. AI operates based on algorithms and data. AI is Costly and Inaccessible : Only large companies can afford and implement AI technologies. Reality :  While some AI technologies can be expensive, many tools and frameworks are becoming more accessible and affordable for small businesses and individuals.

9. Common Biases and Assumptions AI is Inherently Biased: biased and unfair. Reality :  AI systems can inherit biases present in the data they are trained on. However, with careful design and monitoring, these biases can be mitigated. Developers use diverse and representative data sets and continuously test AI systems for fairness. AI Leads to Cheating and Unethical Behavior: only used for cheating and generating fake content or misinformation. Reality :  While AI can be used for unethical purposes, it can also be a powerful tool for detecting and preventing fraud and misinformation. Responsible use and regulation are key. AI is Never Wrong : always accurate and make no mistakes. Reality:   AI systems can make errors, especially if they are trained on poor-quality data or encounter situations they haven't been trained to handle. Human oversight is necessary to ensure AI outputs are reliable. AI is a Universal Solution : solves any problem, regardless of complexity. Reality :  AI is a powerful tool, but it is not suitable for every problem. It requires careful consideration of the specific use case, data quality, and ethical implications.

10. Understanding AI in Pharmacy: Core Technologies Machine Learning (ML): Learns patterns from data and improves over time. Examples: Google search, Google ads, and Spotify In Pharmacy: Enables predictive analytics and personalized medicine. Natural Language Processing (NLP): Interprets and processes human language. Example: Google translate and ChatGPT In Pharmacy: Facilitates communication between healthcare professionals and patients. Robotics: Automates repetitive or complex physical tasks. Examples: Roomba and Automated Dispensing Machines in Pharmacies In Pharmacy: Enhances efficiency and accuracy in medication dispensing.

11. Clinical Decision Support (CDSS): Provides real-time, evidence-based guidance. Integrates patient data with the latest research and guidelines for informed decisions. Operational Efficiency: Automates routine tasks. Streamlines processes like scheduling, inventory management, and patient data handling. Safety and Accuracy: Reduces medication errors. Enhances drug dispensing accuracy and safety checks to prevent adverse interactions. Personalized Care: Customizes medication regimens. Tailors treatments to individual patient profiles, improving outcomes and reducing side effects. AI in Clinical Pharmacy Practice: Benefits of AI in Inpatient Pharmacy

12. What are examples of AI used in Pharmacy?

13. Purpose: Offers evidence-based guidance. Provides real-time, accurate information at the point of care. Leverages current medical knowledge and research for decision-making. Analyzes patient-specific data against clinical guidelines to recommend optimal interventions. Essential for managing complex cases with multiple treatment options. Integration: Works with EHRs for optimal treatment suggestions. Effectively integrates with Electronic Health Records (EHRs) to access comprehensive patient data. Ensures contextual advice tailored to each patient’s health profile. Can alert clinicians about potential drug allergies or interactions, enhancing patient safety. Example: Cerner Multum:  Drug interaction checks and alerts. AI in Clinical Pharmacy Practice: Clinical Decision Support Systems (CDSS)

14. Intro: Cerner Multum is part of the Clinical Decision Support Systems (CDSS) integrated within the Cerner healthcare technology framework Functionality: Utilizes advanced AI algorithms. Detects potential drug interactions in real-time. Generates detailed alerts on interaction severity and involved drugs. Risk Mitigation : Ideal for managing complex medication regimens. Proactively prevents adverse drug interactions, enhancing patient safety. Integration with EHRs : Fully integrated with Cerner EHR systems for seamless data access. Supports up-to-date, data-driven decision-making. Educational Support : Offers educational content on drug interactions. Provides evidence-based alternatives to manage interactions effectively. AI in Clinical Pharmacy Practice: Cerner Multum

15. Purpose: Automates medication review during care transitions. Uses AI to streamline the review and reconciliation of medications as patients transition between care settings. Ensures accurate, consistent medication records to prevent errors and omissions. Benefits: Reduces adverse drug events. Automatically detects potential drug interactions, incorrect dosages, or contraindications. Increases patient safety by ensuring continuity and appropriateness of medication regimes. Example: MedAware :  Detects prescribing anomalies. AI in Clinical Pharmacy Practice: AI-Enhanced Medication Reconciliation

16. Intro: MedAware can be added to popular EHR systems like Cerner, Epic, or any other EHR systems and health IT platforms, although it is not exclusively part of any single platform. Functionality: Utilizes AI to monitor and analyze prescribing patterns. Identifies and prevents potential medication errors and adverse drug events. Risk Mitigation : R ecognizes unusual prescribing patterns, such as incorrect dosages or inappropriate medications. Generates immediate alerts to prescribers when potential errors are detected. Integration with EHRs : Seamlessly integrates with existing EHRs. Provides alerts and recommendations within clinical workflows. Benefits Over Conventional Systems : Ensures comprehensive patient safety by monitoring for a wide range of prescribing errors, beyond just drug interactions. Continuously learns from new data, improving its ability to identify unrecognized or unique errors. AI in Clinical Pharmacy Practice: MedAware

17. Purpose: Ensures accurate medication dispensing. Reduces human errors and enhances patient safety. A nalyzes usage patterns and patient data to predict future medication needs Integration: E nsures that medication dispensing is updated in real-time and integrated into the overall patient care plan within EHRs. Utilizes data from Pyxis to maintain ideal medication stock levels, preventing shortages. Syncs with Pyxis workflows to reduce manual entry and increase dispensing speed. Example: Pyxis MedStation : Utilizes robotics for precise medication dispensing and integrates AI for managing inventory and patient safety alerts. . Omnicell’s XT Series : These cabinets utilize combine robotics for handling medications with AI-driven software that optimizes inventory management and tracks medication usage patterns AI in Inpatient Pharmacy Operations: Automated Dispensing Systems

18. Pyxis MedStation System: Robotics AI Integration Omnicell XT Series: Robotics AI-Driven Software AI in Inpatient Pharmacy Operations : Omnicell vs Pyxis Integration Opportunities: Complement Existing Systems : Enhances capabilities where Pyxis may have limitations. Shared Data Insights : Integrates data for comprehensive medication management overview. Merges Pyxis reporting capabilities with Omnicell’s advanced analytics. Workflow Optimization : Distributes tasks based on system strengths, streamlining operations.

19. Purpose: Efficient Tracking : Automates inventory tracking to maintain optimal stock levels. Enhanced Forecasting : Uses AI to forecast inventory needs and prevent overstocking. AI capabilities Demand Prediction : Analyzes trends to predict future medication needs, aiding effective stock management. Error Reduction : Automates data entry, reducing errors in inventory management Examples: Kit Check:  RFID and AI for efficient inventory management. McKesson: Supply management software with AI reporting tools. AI in Inpatient Pharmacy Operations: Inventory Management

20. Intro: Kit Check is a leading provider of automated medication management solutions, specializing in pharmacy inventory tracking. Utilizes advanced RFID technology combined with AI to optimize the inventory process within hospital pharmacies. RFID Technology: Utilizes Radio Frequency Identification (RFID) to scan and track medications efficiently, eliminating the need for manual data entry. Dramatically reduces the time required for inventory audits, enhancing operational efficiency. AI Integration: Analyzes RFID data to gain deep insights into medication usage patterns. Provides highly accurate forecasts of future inventory needs based on historical data and usage trends. Helps pharmacists make informed decisions about stock levels, order timing, and medication management. Benefits of AI in Kit Check: S treamline pharmacy operations, reducing time spent on manual tasks. Minimizes human errors in inventory management, ensuring accuracy and safety. M anages inventory more effectively, potentially reducing wastage and associated costs. AI in Inpatient Pharmacy Operations: Kit Check

21. Intro: McKesson streamlines the supply chain for pharmaceuticals and medical supplies . Recognized as the forefront of healthcare services and technology, providing solutions that enhance clinical decision-making, improve patient care, and optimize operations through advanced data analytics and software solutions . Advanced Software Solutions: Utilizes AI to dynamically manage stock levels, ensuring pharmacies are neither overstocked nor understocked. A nalyzes medication usage patterns to enhance accuracy in stock predictions. Automates the reordering process based on predictive analytics, reducing manual workload and minimizing supply gaps. Delivers in-depth analytics and reports on inventory trends, usage rates, and potential shortages. Integration: Integrates smoothly with other systems like Kit Check, enhancing the utility and scope of inventory management. Highlighting AI Benefits: Improves overall operational efficiency, reducing waste and optimizing resource allocation. Reduces errors in inventory and order processing, increasing reliability and safety. Optimizes inventory levels and ordering processes, leading to significant cost savings. AI in Inpatient Pharmacy Operations: McKesson

22. AI Application: Personalized Dosing : AI analyzes patient-specific data, including genetic information, health status, and treatment responses, to tailor medication dosing accurately. Predictive Analytics : Predicts drug efficacy and patient responses to inform critical medication management decisions in dynamic care settings Benefits: Optimized Medication Protocols : Determines effective medication regimens for individual patients by analyzing real-time clinical data, crucial in critical care settings. Enhanced Patient Safety : Minimizes adverse drug reactions by ensuring precise dosage calculations based on each patient's unique health parameters. Increased Efficiency : Streamlines the process for dose adjustments and clinical assessments, essential in environments where time-sensitive decisions impact patient outcomes. Example: InsightRX :  Tailored treatment recommendations. AI in Pharmacy Specialty Areas: Intensive Care Unit and Other Critical Care Areas

23. Intro: InsightRX incorporates cutting-edge artificial intelligence to personalize medication dosing in clinical settings. InsightRX utilizes Bayesian forecasting models, which incorporate both population pharmacokinetic/pharmacodynamic (PK/PD) models and individual patient data. This approach allows the system to predict how different patients will respond to the same drug, adjusting dosing recommendations based on real-time data. Treatment Recommendations: Uses predictive analytics to forecast outcomes based on various treatment regimens. This capability is particularly useful in predicting therapeutic levels and potential toxicities. I ntegrates real-time clinical data (e.g., renal function, liver enzymes, age, weight) to continuously refine drug dosage calculations and conduct these complex calculations quickly and accurately. AI in Pharmacy Specialty Areas: InsightRX Nova

24. AI Application: Utilizes AI to analyze vast amounts of clinical data, genetic information, and current research to support highly personalized cancer treatments. P redict treatment outcomes, helping oncologists make more informed decisions about patient care. Benefits: Identifies effective treatment protocols based on individual patient profiles. Reduces the time needed for oncologists to evaluate multiple treatment options by providing evidence-based recommendations quickly. Example: IBM Watson for Oncology:  Tailored treatment recommendations. AI in Pharmacy Specialty Areas: Oncology Pharmacy

25. Intro: IBM Watson for Oncology is an advanced artificial intelligence system developed by IBM that assists oncologists in identifying tailored cancer treatments. Using Watson, IBM's cognitive computing platform, to analyze large volumes of medical data, including patient medical records, clinical trial data, and scholarly articles. Treatment Recommendations: Patient-Specific Analysis : Analyzes individual patient medical records alongside vast arrays of clinical trials and medical literature. Tailored Options : Offers personalized treatment recommendations based on a comprehensive understanding of each patient's unique health profile. Data-Driven Insights: Precision in Oncology : Leverages data from thousands of similar cases and research outcomes to inform treatment decisions, enhancing the accuracy and appropriateness of care. Support for Oncologists : Aids oncologists by providing well-rounded, evidence-based options, reducing uncertainty in treatment planning. Continuous Learning: Dynamic Updates : Regularly updates its database with new research findings, treatment results, and clinical practices to stay at the forefront of oncology care. Adaptive Recommendations : Continuously refines its algorithms based on new data, ensuring that the treatment suggestions remain aligned with the latest medical standards and discoveries. AI in Pharmacy Specialty Areas: IBM Watson for Oncology

26. AI Application: Utilizes AI to analyze patient data, infection patterns, and antimicrobial resistance trends to select the most effective therapies. Tailors antimicrobial therapy based on specific pathogens and individual patient factors, enhancing treatment efficacy and reducing the risk of resistance. Benefits: Supports pharmacists and infectious disease specialists in making faster, more accurate treatment decisions. Increases the likelihood of successful treatment outcomes by choosing antimicrobials suited to the specific infectious agent and patient health status. Example: Aidoc :  Analyzes infection sources and therapies. AI in Pharmacy Specialty Areas: Infectious Disease Pharmacy

27. Intro: Aidoc was established with the goal of bringing AI-driven solutions to the medical imaging field. Recognized for developing AI solutions that support rapid and accurate diagnosis across various medical fields, including radiology, neurology, and now extending to infectious disease management.. Treatment Recommendations: Analyzes extensive data from similar cases and integrates findings from the latest medical research and clinical trials. Recommends antimicrobial therapies that are specifically tailored to the identified pathogens and patient-specific factors. Minimizes Antibiotic Misuse : Promotes the precise use of antimicrobials to treat infections effectively. Helps reduce the spread of antibiotic resistance by avoiding unnecessary or inappropriate use of broad-spectrum antibiotics. AI in Pharmacy Specialty Areas: Aidoc

28. Purpose: Automates Scheduling and Task Allocation A nalyzes demand and adjust resource allocation in real-time to meet changing needs without overburdening staff. Benefits: Enhances staff productivity. Optimizes patient flow through precise scheduling, staff communications and task management, decreasing wait times and improving patient experiences. Example: Qventus :   Streamlines workflows and patient flow by automating the scheduling of staff and allocation of resources. Uses predictive models to forecast patient admissions, discharges, and transfers Microsoft AI: AI-enhanced communication tools (like Microsoft Teams integrated with healthcare plugins) to improve coordination among healthcare staff AI in Pharmacy Management: Workflow Optimization

29. Microsoft Teams: Cortana Integration : Cortana, Microsoft’s virtual assistant, uses AI to help users join meetings, make calls, and send messages through voice commands, improving accessibility and efficiency Meeting Insights : AI algorithms generate insights from meeting content, helping users find important documents or notes and summarizing key points discussed. Live Captions and Translations : Uses AI for real-time captioning and translation during meetings, making them more accessible to participants who speak different languages or are hearing impaired. Microsoft Outlook (Emails): Focused Inbox : AI helps sort emails by importance, moving less critical messages to an "Other" inbox and highlighting important emails in the "Focused" inbox, thus optimizing email management. Smart Reply Suggestions : AI analyzes the content of the email and provides quick, contextual responses users can select to reply faster. Email Insights : Uses AI to search and find relevant emails quickly based on contextual understanding of user queries. AI in Pharmacy Management: Microsoft AI in Teams and Emails

30. Purpose: Utilizes data analytics to provide comprehensive insights into pharmacy operations. AI Capabilities: Analyzes performance metrics such as medication turnaround time, staff productivity, and patient medication adherence. I dentifies trends and patterns in pharmacy operations, enabling proactive adjustments to enhance efficiency and effectiveness. Benefits: Informed Decision-Making : Data-driven insights allow pharmacy managers to make better-informed decisions regarding resource allocation, staff scheduling, and inventory management. Quality Improvement : Continuous analysis of operational data helps in maintaining high standards of pharmacy service and patient care. Example: Health Catalyst:  Data-driven pharmacy insights. AI in Pharmacy Management: Data Analytics and Reporting

31. Intro Health Catalyst specializes in healthcare data and analytics technology . I ntegrates data from across healthcare systems to provide comprehensive insights into clinical, financial, and operational performance to deliver actionable insights, used to improve patient care, reduce waste, and optimize operations . Purpose: Utilizes data analytics to provide comprehensive insights into pharmacy operations. AI Capabilities: Analyzes performance metrics such as medication turnaround time, staff productivity, and patient medication adherence. I dentifies trends and patterns in pharmacy operations, enabling proactive adjustments to enhance efficiency and effectiveness. Benefits: Outcome Improvement : Health Catalyst clients have seen improvements in patient outcomes and operational efficiency because of features like customized data analytics solutions. Innovation in Care Models : Assists hospitals and clinics in developing innovative care models and optimizing existing processes through data-driven insights AI in Pharmacy Management: Health Catalyst

32. Innovative Device Integrations: Automated Compounding Hoods : These devices automatically clean themselves and prepare medications. Robotic Medication Delivery : Robots are being used in hospitals to deliver medications directly to patients. Medical Drones : Drones equipped with automation technology are used for rapid transport of medical supplies, including medications, to remote or difficult-to-reach areas. Examples of Technological Integration in Other H ospital Settings: Da Vinci Surgical Robots : Perform complex surgeries with high precision, controlled by surgeons but enhanced by AI for improved outcomes. Smart Inhalers : Use AI to monitor usage and patient adherence, providing data that helps in managing respiratory conditions more effectively. Potential Advancements: Integration with Wearables : Increasing synergy between AI and wearable technologies for continuous health monitoring, providing real-time data that can be used to adjust treatments and monitor patient health remotely. Remote Monitoring Technologies : Extends the reach of healthcare providers, allowing for patient monitoring outside traditional clinical settings, which is crucial for chronic disease management and elderly care. Future of AI in Pharmacy: Technological Advancements

33. Broadening Scope AI is a key driver in developing personalized healthcare solutions utilizing vast amounts of data to tailor treatments and improve patient outcomes. Both established and startup tech companies are increasingly venturing into healthcare to leverage data management and AI. These entities are pioneering solutions that enhance personalized services and medicine. Emerging Trends: Predictive Analytics : Utilizes historical and real-time data to forecast health events, enhancing preventive care and personalized medicine. Personalized Medicine : Leverages genetic, environmental, and lifestyle data to tailor medical treatments to individual patient needs. Future of AI in Pharmacy: Catalyst in Healthcare

34. Ethical and Regulatory Concerns: Data Privacy and Security : Prioritize HIPAA compliance to protect patient information. Implement robust security measures to safeguard data integrity. Bias and Fairness : Proactively identify and mitigate biases in AI models to ensure equitable treatment across all patient demographics. Develop protocols to regularly assess AI decisions for fairness and accuracy. Transparency and Explainability : Design AI systems that are not just effective but also understandable to users. Ensure that AI-driven decisions can be explained in clinical contexts to foster trust among healthcare providers and patients. Call to Action: Collaborative Innovation : Encourage active collaboration across various disciplines—medical professionals, technologists, AI researchers—to leverage diverse insights and expertise in enhancing AI development. Cross-Sector Partnerships : Advocate for stronger partnerships between technology companies, healthcare institutions, and regulatory bodies. Aim to create a unified framework that drives the development of AI applications that are safe, effective, and patient-centered. Ethical and Regulatory Considerations: A Call to Action

35. Embracing AI in Healthcare AI simulates human intelligence for tasks like learning and problem-solving. It enhances SOPs by automating scheduling, analytics, and decision-making, letting healthcare professionals focus more on patient care. AI tools in pharmacy include predictive analytics, robotic dispensing, and diagnostic tools, improving efficiency and accuracy but facing challenges with integration and training. AI improves diagnostics in oncology and infectious diseases and optimizes drug interactions, adherence, and treatment monitoring. AI will advance health informatics and personalize healthcare, requiring cross-disciplinary collaboration. Collaboration among healthcare professionals, technologists, and researchers, as well as partnerships between tech companies, healthcare providers, and educational institutions, is essential for fostering innovation. Key Summary Points

36. Discussion and Feedback? Questions and Answers

37. References IBM Watson Health. Overview of IBM's AI applications in healthcare. Available from: https://www.ibm.com/watson-health. Accessed August 8, 2024. Health Catalyst. Detailed insights into their data analytics solutions for healthcare. Available from: https://www.healthcatalyst.com . Accessed August 8, 2024. Microsoft AI. Information about Microsoft's AI initiatives and tools. Available from: https://www.microsoft.com/ai . Accessed August 8, 2024. HIPAA Journal. Understanding compliance and data privacy in healthcare applications. Available from: https://www.hipaajournal.com . Accessed August 8, 2024. American Medical Association - AI Ethics. Discussion on ethical considerations in AI applications in healthcare. Available from: https://www.ama-assn.org/delivering-care/public-health/ai-ethics. Accessed August 8, 2024. Pharmacy Times - AI in Pharmacy. Exploration of AI applications in pharmacy settings. Available from: https://www.pharmacytimes.com . Accessed August 8, 2024. Healthcare IT News. Articles and case studies on how predictive analytics are transforming healthcare. Available from: https://www.healthcareitnews.com . Accessed August 8, 2024. National Institutes of Health (NIH) - AI in Healthcare. Insights on AI's applications in medical research and public health. Available from: https://www.nih.gov/ai-healthcare . Accessed August 8, 2024. Harvard Business Review - AI in Healthcare. A critical analysis of AI integration in healthcare operations and its future potential. Available from: https://hbr.org/ai-healthcare . Accessed August 8, 2024. U.S. Food and Drug Administration. FDA's Role in AI and Machine Learning. Guidelines and frameworks for the use of AI tools in healthcare. Available from: https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-and-machine-learning-software-medical-devices. Accessed August 8, 2024. Nature - AI in Pharmaceutical Research and Development. A detailed look at how AI is revolutionizing drug discovery and pharmaceutical R&D. Available from: https://www.nature.com/articles/ai-pharma . Accessed August 8, 2024. Future of Life Institute - Ethical AI in Health. Discussions about ethical considerations when implementing AI in health settings. Available from: https://futureoflife.org/ethical-ai-health. Accessed August 8, 2024. JAMA Dermatology. Machine Learning and Health Care Disparities in Dermatology. Available from: https://jamanetwork.com/journals/jamadermatology. Accessed August 8, 2024. Medical News Today - Wearable Technology in Healthcare. How wearable devices integrated with AI are transforming patient monitoring and care. Available from: https://www.medicalnewstoday.com/wearable-technology-healthcare. Accessed August 8, 2024. Pharmacy Times - Robotics and AI in Pharmacy. Review of robotics and AI applications in improving pharmacy operations. Available from: https://www.pharmacytimes.com/robotics-ai-pharmacy. Accessed August 8, 2024. OpenAI. ChatGPT Interaction. Personal communication. Accessed August 8, 2024.

38. Enhancing Clinical Practice and Operations Advancing Pharmacy with AI: Applications in Inpatient Settings Onyemauchechukwu Ogundare-Ijezie PharmD. Candidate/ Health Analyst University of Maryland School of Pharmacy MedStar Montgomery Medical Center August 2024 Thank You