Streamlining Healthcare with AI: Optimizing Contracts and Data Management
Healthcare organizations are increasingly turning to artificial intelligence (AI) to optimize contract generation, management, and review processes. This shift aims to enhance efficiency and accuracy in areas such as supply chain management and compliance. To effectively implement AI, companies must structure their data for AI-powered analysis and invest in employee upskilling, according to experts at EncompaaS and Rush University Medical Center.
Preparing Data for Accurate AI Implementation
Creating uniform data sets is crucial for organizations aiming to build large language models (LLMs) or deploy effective chatbots. David Gould, chief customer officer at EncompaaS, emphasizes that data used to train LLMs must be accurately classified and organized into databases. Structured data, such as customer ID numbers and diagnostic codes, is already classified. However, unstructured data, like PDF contracts, requires planning to ensure AI algorithms can extract relevant information.
Contracts from different vendors often lack a uniform structure, necessitating training for machine-learning AI to identify and extract specific data. Such as, AI software can identify a document but needs further training to determine if it’s a contract, amendment, or notice. Gould notes that the algorithm’s effectiveness depends on the data matching its expectations. Simply instructing a chatbot to search for information across an enterprise can be costly and lead to inaccuracies due to unprepared data.
Did you Know? According to a 2023 report by McKinsey, AI has the potential to generate between $350 billion and $410 billion in annual value for the healthcare and pharmaceutical industries.
Preparing data involves addressing ancient information that may not have been properly classified or recorded. Updating this data with correct metadata for AI-based analysis can be time-consuming. Data must be classified correctly, placed within the appropriate context, and stored in compliance with privacy regulations like HIPAA, which protects patient privacy.
Key Steps in Data Preparation:
- Classify data accurately.
- Update historical data with correct metadata.
- Ensure compliance with privacy regulations.
Impact on Healthcare Personnel
Automating manual tasks can substantially transform roles in procurement and compliance. Matt Parker and Jacob Thompson of SpendMend,an AI tool for pharmacy procurement,highlighted the transformative potential of AI integration during a recent webinar. In the pharmaceutical and healthcare sectors, highly educated and ambitious professionals frequently enough spend hours on tasks like cutting and pasting contract information into spreadsheets. AI implementation can reduce this workload.
Jeremy Strong, vice president of supply chain at Rush University Medical Center, emphasizes the need for retraining and additional training for employees as AI changes job functions. Acknowledging the importance of these changes and implementing AI upskilling programs can help manage the transition effectively.
Pro Tip: Encourage employees to ask precise questions to improve the accuracy of AI algorithms.
The better employees become at asking precise questions, the more AI algorithms can improve at providing accurate answers. For instance, determining how many contracts with a specific clause will expire in 30 days typically takes weeks or months. With AI,an employee can learn to ask a precise question that captures this information more quickly.
Benefits of AI in Healthcare Contract management
AI offers several benefits in healthcare contract management, including:
- Increased efficiency in contract generation and review.
- Improved data accuracy and compliance.
- Reduced workload for healthcare professionals.
- Faster access to critical contract information.
By leveraging AI, healthcare organizations can streamline their operations, reduce costs, and improve patient care.
How do you see AI impacting your organization’s contract management processes? What steps are you taking to prepare your data for AI implementation?
The Evolution of AI in Healthcare
The integration of AI in healthcare has evolved significantly over the past decade. Initially, AI applications where primarily focused on diagnostic imaging and drug finding. Though, recent advancements in machine learning and natural language processing have expanded AI’s role to include contract management, supply chain optimization, and personalized medicine. According to a 2024 report by MarketsandMarkets, the global AI in healthcare market is projected to reach $102.7 billion by 2028, growing at a CAGR of 38.1% from 2023.
This growth is driven by the increasing availability of healthcare data, advancements in AI technologies, and the need to improve efficiency and reduce costs in healthcare systems. As AI continues to evolve, it is indeed expected to play an even greater role in transforming healthcare delivery and improving patient outcomes.
Frequently Asked Questions About AI in Healthcare
- What are the main challenges in implementing AI in healthcare?
- Challenges include data privacy concerns,the need for accurate and reliable data,and the integration of AI systems with existing healthcare infrastructure.
- How can healthcare organizations ensure the ethical use of AI?
- Organizations can establish clear guidelines and protocols for AI development and deployment, focusing on transparency, fairness, and accountability.
- what types of AI technologies are commonly used in healthcare?
- common technologies include machine learning, natural language processing, and computer vision, which are used for tasks such as diagnosis, treatment planning, and drug discovery.
Disclaimer: This article provides general information and should not be considered medical or professional advice. consult with qualified professionals for specific guidance.
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