The hackathon kick-off event will be September 24, 2024, with project submissions due on October 11. There will be a preliminary round of judging the week of October 14 from the investor community to choose the top 3 finalists from each division (Professional and Academic). The Closing event will take place on the afternoon of October 30 where the division finalists present their solutions to a C-suite panel of five judges from the provider organization community.

September 18, 2024

For this hackathon, the focus is on solving problems in the healthcare industry facing nursing professionals by leveraging AI.

We recommend having a team of 4 – 6 people to work together on the hackathon and build out your product. Once you have a team and an idea for what your project will be, you can sign up at the link here.

As part of signing up for the hackathon, your team is will create and submit the following materials:

• The Deck: PowerPoint presentation highlighting your team, your product and the technology used. (Don’t worry, we will provide you with a template)
• A Video: A 5-minute (or less) video, detailing the features of your submission (Use this to build on your PowerPoint, not just to reiterate it. The two items work together.)
• A Brief Description: ​​​​​​​300 characters to describe your project.
• The Team Sheet: a spreadsheet of all the awesome people who worked on your solution.

Here is the judging criteria that we’ve come up with:

Impact​

• How well does the project address the specific challenges faced in the healthcare industry? ​
• How accurate are the generated outputs? Do they align with the expected standards in the healthcare domain? ​
• Are the generated outputs consistent and reliable across different scenarios?​
• What is the impact of the solution in resolving its problem statement, and how is it measurable?​
• Can users understand how the generative AI model arrived at its outputs? Is the process transparent and explainable? ​
• Does the project have the potential to improve patient care, outcomes, or the overall healthcare experience?

Innovation ​

• Is the approach unique and inventive? Does it stand out from existing solutions in the market?​
• Does the project showcase creative thinking in how generative AI is applied to the targeted area?​
• Is the solution user-friendly, easy to understand, and efficient to use?​
• How well does the solution integrate into existing workflows and systems?​
• How effectively did the team communicate the problem, solution, and its benefits during the presentation?​
• Was the functionality of the solution adequately demonstrated to showcase its capabilities?​
• Did the team collaborate effectively across multiple disciplines (AI, healthcare, user experience, etc.)?

Difficulty​

• How well-suited are the chosen algorithms and generative models for the problem at hand?​
• Does the project involve complex technical implementation, indicating a high level of skill and expertise?​
• Can the solution be scaled up to handle larger volumes of data and usage without significant degradation in performance?​
• Is the solution designed in a way that it can be maintained, updated, and adapted over time as needs evolve?​
• Are there measures in place to ensure patient data privacy and ethical considerations in the generative AI solution?

The official solution development start date for all teams commences on September 24 and concludes on October 11.

No, unfortunately, we are recommending you start the build of a new use case solution on September 24, to keep the playing field leveled and fair.

• Programming languages:
  • Python is recommended though any programming language can be used
• Generative AI Frameworks:
  • LangChain , AutoGen, LlamaIndex, and CrewAI are currently popular LLM chaining frameworks though any framework can be used.
• Integrated Development Environment (IDE):
  • Visual Studio Code is recommended though any IDE can be used
• Foundational Models (LLMs / SLMs):
  • Important: A foundational model must be used in the project you submit to the TN HIMSS AI Hackathon
  • Upon request, TN HIMSS can provide credits to access Azure’s OpenAI API
  • You can use your own open source or public foundational model on any platform but you must take careful precaution to ensure data is deidentified or synthetic. Please note that TN HIMSS’ support and office hours will be focused on the Microsoft Azure AI tech stack, including the OpenAI API.
  • LLM Transparency: If you choose not to use Azure’s OpenAI API, you should provide an explanation of the reasoning that led you to a different solution. This should be done in your final presentation deck.
  • In the spirit of transparency, consider providing a “model card” for your foundational model.
  • Note: NTT Data and Microsoft will be available for technical support during open office hours to help address potential technical difficult with OpenAI functionality
• Hosting:
  • You can host your application anywhere
  • Your application demo must be accessible to the judges
    • You must make your application accessible through a public URL
    • You can choose to implement authentication and share credentials in your submission deck
  • Data:
    • You must source your own data sets
      • Here is an option to leverage if need be – Synthea
    • Patient data must be anonymized in alignment with HIPAA regulations
    • Optional: Consider data transparency and traceability by making available a “data card” revealing the source, intent and origins of any available data.

Considerations for the hacking teams:

• Nurses are often not technical so the teams should ensure they have the right blend of business/tech to translate solutions, guide through design thinking, etc.
• Have a clear PoV on how they will action their solution and make it real
• Have a strong business plan and understanding of the competitive landscape (e.g. stay away from common/emerging solutions like ambient scribe)
• Have sponsorship and/or perspective from senior nursing and clinical leaders in their respective organizations
• Understand demographics of their target users (e.g. there are big differences in generations of nursing workforce and how they view tech)

Three key areas where nursing needs innovation:

• Efficiency: areas in nursing workflow that can be simplified or automated
  • Patient Triage and Prioritization: AI can assist in triaging patients based on the severity of their conditions, ensuring that those who need urgent care are attended to promptly. This improves patient outcomes and reduces the stress of decision-making during high-pressure situations.
  • Administrative Tasks and Documentation: AI can automate routine administrative tasks, such as scheduling, managing patient records, and processing billing. This reduces the time nurses spend on paperwork, allowing them to focus more on patient care.
  • Clinical Decision Support: AI tools can assist nurses by providing evidence-based recommendations and decision support in real-time. For example, AI algorithms can analyze patient data to suggest potential diagnoses, treatment plans, or medication adjustments, which can improve the speed and accuracy of care delivery.
  • Patient Education and Communication: AI-driven chatbots or virtual assistants can help in educating patients about their conditions, medications, and discharge instructions. They can also handle routine inquiries, freeing up nurses to focus on more complex patient interactions.
  • Workflow Optimization: AI can analyze patterns in nursing workflows and suggest optimizations, such as the most efficient way to manage patient rounds or the best allocation of resources during shifts. This can lead to better time management and reduced burnout.
  • Predictive Analytics for Resource Management: AI can predict patient admission rates, lengths of stay, and resource needs (like staffing or supplies). This helps in better planning and ensures that the nursing staff is neither overworked nor underutilized.
  • Remote Patient Monitoring and Telehealth: AI can enhance telehealth services by analyzing data from remote monitoring devices and providing nurses with actionable insights. This is particularly useful in managing chronic diseases or post-discharge care, where continuous monitoring is crucial.

• Competency and Orientation: high staffing turnover rates create burden, inconsistency, and strain on tenured staff responsible for training
  • Personalized Learning Paths: AI can analyze individual nurses’ strengths and weaknesses to create customized learning plans. These plans can target specific areas where a nurse needs improvement, ensuring that competency is developed efficiently and effectively.
  • Competency Tracking: AI systems can continuously track and assess nurses’ skills and competencies through simulations, quizzes, and practical assessments. This allows for real-time feedback and the identification of areas that need further development.
  • Adaptive Learning Platforms: AI-driven platforms can adjust the difficulty and content of training materials based on the nurse’s progress. This ensures that learning is neither too easy nor too challenging, keeping nurses engaged and helping them achieve mastery in specific competencies.
  • Virtual Orientation Assistants: AI-powered virtual assistants can guide new nurses through their orientation process, answering questions, providing information, and helping them navigate the organization’s systems and procedures. This can reduce the burden on human trainers and ensure that all nurses receive consistent information.
  • Simulation-Based Training: AI-driven simulation tools can create realistic scenarios that new nurses might face in their roles. These simulations help in practicing decision-making, clinical skills, and teamwork in a safe environment, enhancing their readiness for real-world situations.
  • Onboarding Analytics: AI can analyze data from the orientation process to identify patterns and areas where new nurses may struggle. This allows organizations to adjust their orientation programs to address common challenges and improve the overall onboarding experience.
  • Continuous Support and Check-Ins: AI can facilitate regular check-ins with new nurses during their orientation period, gathering feedback, and identifying any issues they might be facing. This proactive approach helps in addressing concerns early and supports a smoother transition into the role.

• Wellbeing: AI can play a crucial role in supporting nurse well-being by reducing workload stress, improving work-life balance, and providing tools for mental and emotional health support. Here are some areas where AI could make a significant impact:
  • Automated Task Prioritization: AI can help nurses manage their workload by automatically prioritizing tasks based on urgency, patient needs, and available resources. This ensures that nurses can focus on the most critical tasks without feeling overwhelmed by everything that needs to be done.
  • Smart Scheduling: AI-driven scheduling tools can create more balanced work schedules, taking into account nurses’ preferences, past shifts, and the need for adequate rest. This reduces burnout by ensuring that nurses have sufficient time off between shifts and that work is distributed evenly across the team.
  • Task Automation: Routine and repetitive tasks, such as documentation or medication administration tracking, can be automated with AI, freeing up time for nurses to focus on more meaningful patient interactions and reducing the overall cognitive load.
  • AI-Powered Mental Health Chatbots: AI-driven chatbots can provide nurses with immediate support by offering a safe space to discuss their feelings, providing coping strategies, and even connecting them with professional help if needed. These tools can be accessible 24/7, offering support during or after stressful shifts.
  • Predictive Burnout Detection: AI can analyze data from various sources, such as shift patterns, workload, and self-reported stress levels, to predict when a nurse might be at risk of burnout. Early detection allows for timely interventions, such as counseling, adjustments to workload, or time off.
  • AI-Driven Peer Support Platforms: AI can facilitate the creation of peer support networks by matching nurses based on shared experiences, challenges, or goals. These platforms can provide a space for nurses to share their experiences, seek advice, and offer emotional support to each other.
  • Sentiment Analysis and Feedback: AI can analyze communications (such as emails, messages, or feedback) to detect signs of stress, frustration, or emotional exhaustion. This analysis can trigger supportive interventions, such as a check-in from a manager or a recommendation for mental health resources.
  • Resilience Training: AI-powered platforms can offer resilience training programs that help nurses build coping strategies and mental toughness. These programs can be tailored to the individual’s experiences and stressors, enhancing their ability to handle challenging situations.

We will be offering both technical and clinical office hours to teams. Microsoft and NTT DATA will provide experts in AI to support technical related questions. In addition, nursing staff office hours will be provided from area hospitals so teams can bounce their use cases off of the professional who are doing the work in real life! 

Yes, see below for a breakdown.

• Professional Division: This division is open to teams from provider and payer organizations.
• Academic Division: This division is open to student-led teams from local universities.

Yes, see below for more details on the prizes and awards.

Each division will have a winning team and runner-up. The overall best solution and team will be chosen across all three divisions. A breakdown of prizes is listed below (Note: There may be additional prizes included in each division from sponsors that sign-on to the event, leading up to the Kick-Off event).

• • Professional Division Winner
    • 1^st Place Prizes:
      • Trophy for all team members
      • Recognition on all TN HIMSS social media channels
      • Recognition and highlights on TN HIMSS Chapter Blog Recap post
      • Recognition on local news publication Hackathon recap (Nashville Biz Journal, Nashville Post, etc.)
      • FREE tickets to all team members for upcoming Summit ’24 TN HIMSS Conference
      • $1000 restaurant gift card to local, Greater Nashville restaurant for the team
    • Runner-Up Prizes:
      • Plaque for all team members
      • Recognition on all TN HIMSS social media channels
      • Recognition and highlights on TN HIMSS Chapter Blog Recap post
    • Academic Division Winner
      • 1^st Place Prizes:
        • Trophy for all team members
        • Recognition on all TN HIMSS social media channels
        • Recognition and highlights on TN HIMSS Chapter Blog Recap post
        • Recognition on local news publication Hackathon recap (Nashville Biz Journal, Nashville Post, etc.)
        • FREE tickets to all team members for upcoming Summit ’24 TN HIMSS Conference
        • $1000 restaurant gift card to local, Greater Nashville restaurant for the team
      • Runner-Up Prizes:
        • Plaque for all team members
        • Recognition on all TN HIMSS social media channels
        • Recognition and highlights on TN HIMSS Chapter Blog Recap post