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Leverage the various levels of AI – machine learning, generative and conversational – to improve efficiency, accuracy, and scalability of decision-making.
Are you ready for AI? Here are 5 Questions to Address AI Readiness.
Skypoint’s AI & Machine Learning solutions leverage master data management and data governance capabilities to accelerate delivery and reduce the complexities of deploying AI applications by making AI accessible and useful throughout your business.
By analyzing vast amounts of data and recognizing patterns, it can provide invaluable insights that enable organizations to take proactive steps to capitalize on opportunities or mitigate issues before they become major problems.
Artificial Intelligence (AI) and Machine Learning (ML) are two related but distinct technologies that form a harmonious relationship.
AI collects data like demographics, transactions, or health history, while ML algorithms process this massive amount of information to identify patterns and make recommendations based on predicted outcomes.
By continuously adapting ML models with highly curated data, organizations can make the right decisions to best serve their patients, residents, customers, or guests.
Skypoint’s AI and ML models provide the competitive edge to help you make rapid decisions at scale to deliver the best possible outcomes.
With a variety of options ranging from simple yes/no predictions with AI Builder, to bespoke machine learning models for complex scenarios – Skypoint provides the competitive edge to help you make rapid decisions at scale to deliver the best possible outcomes.
Import data from disparate sources into the Skypoint Lakehouse using Dataflow, Skypoint’s built-in ELT tool.
Data is then unified through a process known as master data management—creating “golden record” profiles for ML models.
Once Profiles are created, you can select from a library of built-in ML models, or upload your own custom model.
Visualize valuable BI and predictive insights, or export outputs and execute with 3rd-party applications.
Our pre-built AI and machine learning prediction models enable you to unlock advanced data potential across industries.
The Churn Model predicts who is most likely to cancel, un-enroll, move out, or switch to a competing alternative.
The Recommendation Model uses transaction data like purchases, returns, and donations as its foundation for smart recommendations.
The Lifetime Value Model predicts the potential lifetime value (LTV) of revenue or costs that individuals will bring to your organization.
Entity Resolution enables you to to deduplicate, manage, and improve core business entities or individuals, such as patients, providers, customers, guests, residents, products, and suppliers.
RFM Model (Recency, Frequency, and Monetary) uses values to create targeted segments audiences based on revenue generated or expenses incurred.
The Sentiment Analysis Model utilizes Natural Language Processing (NLP) and intelligent labeling to process feedback from reviews, social media posts, or surveys — unlocking insights into how people feel about everything from products to experiences.
Import your own custom AI and machine learning models deployed as web service endpoints to utilize unified tables.
An AI Copilot interface trained to answer questions in a conversational manner based on data that is up-to-date and relevant to specific industries.
Our AI & Machine learning models are revolutionizing the way industries operate, from retail and hospitality to value-based care and senior living. By combining unified data with the powerful potential of innovative AI solutions, we have enabled distinct use cases to unlock a world of possibilities across the organization.
Skypoint’s AI and ML models play a key role in improving all aspects of the Quadruple Aim healthcare framework by providing more personalized and efficient care, improving patient outcomes, reducing costs, and improving the well-being of healthcare providers.
Master Data Management forms the baseline for unified, accurate patient and provider records that AI and ML models rely on. These models enable you to accurately predict what will likely happen next, and proactively mitigate. By continuously adapting with new data available, these predictive analytics provide true insights into how best care can be provided.
Here are a few examples of Skypoint Cloud’s AI and ML models:
Entity Resolution Model
Entity resolution models accurately match patient and provider data across different systems and datasets, allowing for more accurate patient risk stratification and targeted interventions.
For example, a healthcare organization could use entity resolution to consolidate electronic health records, claims data, and social determinants of health data, to identify patients who are at high risk of hospitalization or readmission, and provide targeted interventions to improve their outcomes and reduce costs.
Generative AI:
Generative and conversational AI form a powerful combination for creating natural language interfaces to engage with people in more personalized ways, resembling real-world interactions. By applying both generative AI to craft the output text followed by conversational AI’s interpretation capacity – complemented with further refinement – users can access real-time data to answer questions and provide the most accurate information as simple and as quickly as possible.
Here are some examples:
Improve access to care: Patients can access care and support services quickly and easily, without needing to wait for an appointment or speak to a provider. This can improve the patient experience and satisfaction by providing on-demand support when and where it’s needed.
Personalize care delivery: Develop personalized treatment plans and recommendations based on patient data, such as medical history, preferences, and demographics. These models can be integrated into conversational AI interfaces, allowing staff to receive tailored advice and guidance on managing patient health, improving outcomes and experience.
Support care coordination: Provide patients with reminders and updates about appointments, medications, and care plans.
Reduce provider burnout: Offload routine tasks and support healthcare providers in managing patient care, like automating appointment scheduling or managing prescription refills.
Churn Prediction Model:
Churn prediction models can identify patients who are at risk of leaving the healthcare organization, allowing for early interventions to retain them as patients. For example, a healthcare organization could use a churn prediction model to identify patients who have missed appointments or not returned for follow-up care, and provide targeted interventions, like appointment reminders or follow-up calls.
Recommendation Model:
Recommendation models analyze health data of individuals to identify patterns and risk factors, then proposes personalized treatment plans through conversational AI interfaces. For example, unnecessary tests may be eliminated while optimizing scheduling procedures. Providers can also get real-time feedback on patients as well as identify areas of improvement which could lead to training initiatives or quality improvements.
Sentiment Analysis Model:
Sentiment analysis can be used to improve patient outcomes by identifying areas for improvement in healthcare delivery, improve the patient experience by addressing patient concerns and providing real-time feedback, reduce costs by improving patient outcomes and reducing the need for expensive interventions, and improve the well-being of healthcare providers by providing them with the support they need to provide high-quality care.
Here are some examples:
Improve patient experience: monitor patient feedback and provide real-time feedback to healthcare providers.
Reduced costs: identify patterns in patient feedback and proactively address issues that could lead to costly complications or readmissions.
Improved provider experience: monitor provider feedback and identify areas where providers need additional training or support.
With Skypoint, senior living operators can unlock a world of insights into resident experiences. Our Master Data Management forms the foundation for maintaining unified and accurate records on each individual, while machine learning models empower you to anticipate potential risks and opportunities—helping ensure that seniors have every opportunity to live life as comfortably and worry-free as possible.
Here are a few examples of Skypoint Cloud’s AI and ML models:
Entity Resolution:
This model uses advanced pattern recognition techniques, such as analyzing similarities between data points like names or addresses, to identify related records and consolidate them into one single record. It can improve accuracy when it comes to residents, but also provide valuable insights in areas like fraud detection and repetitive process automation. It is an essential part of Master Data Management (MDM) and is critical for organizations that rely on accurate and reliable data for their operations.
Generative AI:
This powerful model can be used to query information about residents from both the operator and resident perspective. Think of it as ChatGPT trained with industry-specific datasets.
Generative AI creates a comprehensive data set that covers different scenarios and questions that staff and residents may have, while conversational AI can be used to create a user-friendly interface to understand natural language and provide relevant information. With this combination of technologies, senior living operators can better understand their data and provide more efficient and effective care for their residents.
Staff members could ask questions using natural language, like: “What is the care plan for Mrs. Johnson?”, “Has Mr. Smith’s family been informed about the fall that occurred last night?”, or “What is the dietary plan for Ms. Brown?”. The conversational AI system then uses natural language processing to understand the question and retrieve the relevant information from the resident’s records.
Sentiment analysis & topic modeling:
Senior living facilities can use sentiment analysis to get a better understanding of how staff, residents and families are doing. By tracking changes in moods across reviews and online forums, you can identify areas where people may be dissatisfied with care or services, and make the necessary adjustments that result in improved experiences. Whether it’s facilities, medical care, meals, or staff conditions, providing an optimal level of quality is key when ensuring satisfaction.
Recommendation Models:
This model helps ensure seniors receive personalized recommendations and access to tailored services. By studying an individual’s medical record, lifestyle habits, and preferences – recommendation models can effectively determine appropriate products, services or facilities that best meet their needs.
By leveraging the power of Skypoint’s AI and machine learning models, Retail and Hospitality brands can better understand their audiences, predict outcomes, and proactively provide personalized experiences at scale. Master Data Management forms the baseline to enable unified & accurate customer 360 profiles, while AI and machine learning enables you to take the best course of action to earn the ever-coveted consumer loyalty.
Here are a few examples of Skypoint Cloud’s AI and ML models:
Entity Resolution:
Entity resolution optimizes the effectiveness of data governance and master data management. By connecting multiple isolated datasets and unifying customer profiles, we provide a streamlined view of customers’ information that can be used in pricing analysis as well as inventory tracking – allowing businesses to make quicker decisions with increased accuracy on both fronts. Plus, our matching process ensures consistency across product attributes globally — making it easier than ever before to manage large amounts of critical data.
Whether it’s internal or customer-facing, unstructured and structured data, it can all be transformed into meaningful intelligence that facilitates operational efficiency and business effectiveness.
Generative AI:
Generative AI, when paired with its conversational counterpart, can bring a whole new level of service to consumers. It allows retailers to provide both employees and customers human-like interaction and personalized product advice tailored for them – all while powering business operations like order fulfillment seamlessly in the background.
For example, a retailer can leverage generative AI to train a conversational AI interface that can assist customers with product recommendations based on their individual preferences and purchase history. The conversational AI interface might ask the customer questions about their preferences and interests, and then use the generative AI model to generate personalized product recommendations that meet the customer’s needs.
In addition to customer-facing applications, this model can also be used to improve business operations and automate processes such as inventory management and order fulfillment. By using generative AI to train conversational AI interfaces that can understand and respond to internal business inquiries, retailers can reduce the amount of manual effort required for these tasks and improve operational efficiency.
Product Recommendations:
Provide customers with highly personalized recommendations that reflect past behavior and interests. If a customer has purchased dresses in the past, or stayed at one of your hotels, they may receive tailored offers for new apparel items or properties recently added to your lineup – helping them discover something truly unique. Over time, these machine-learning powered predictions become more accurate as the algorithm is continually refined based on each individual’s response data.
Customer Churn:
Gain a deeper understanding of the factors driving churn. This empowers businesses to take proactive steps, such as launching targeted marketing campaigns or improving their support services, in order to retain customers who are at risk of leaving. Additionally, businesses have a valuable resource they can rely on – with each successive refinement based on results collected from past efforts, the model grows more precise over time for increasingly effective strategies reducing customer attrition rates.
RFM Model:
Determining which customers are most valuable to you can be easier than ever with the help of an RFM (recency, frequency, monetary value) model. Gain key insights by using customer purchase data to segment customers into various groups based on recency of their purchases, how often they buy something, and products that have a higher or lower financial impact.
Those who score high in all three areas – your ‘Champions’ – could warrant special offers or loyalty rewards. Those scoring low across the board may require re-engagement campaigns such as personalized promotions. Utilizing this type of analysis helps ensure each user receives tailored messaging that works best for them.
Customer Lifetime Value (LTV):
Leverage customer data to forecast the value customers will generate for your business over time. By taking into account purchase history, demographics and behavior, we can identify who are likely to be most valuable in terms of long-term profitability: those making frequent purchases of high-value items or less often but higher price points may become more profitable than low-priced one-off buyers. Using these insights, you have a tailored approach that allows maximizing retention efforts towards those highest yielding clients.
In healthcare for example, AI collects and analyzes large amounts of patient data, including demographic information, medical history, lab results, and imaging studies, while machine learning algorithms analyze this data to identify patterns that are predictive of patient outcomes.
The machine learning algorithms are trained on historical data to make predictions about future patient outcomes and continuously improve their performance as new data becomes available. This combination of AI and machine learning allows healthcare providers to make informed decisions about patient care and develop effective, evidence-based treatment plans.
This powerful machine learning technique can reveal hidden layers in large amounts of text, helping you find relationships between documents and identify key topics.
Two popular algorithms for achieving this are Latent Dirichlet Allocation (LDA) and its variants as well as Non-Negative Matrix Factorization (NMF).
With their help you’ll have all sorts insights into trends, classifications or sentiments within your texts – ready to enhance a variety of applications from document classification to sentiment analysis.
Yes! You can integrate your artificial intelligence and machine learning models deployed as web service endpoints to utilize unified tables. Bulk uploading allows you to take advantage of the Machine Learning and Artificial Intelligence technologies that send data to your Azure machine learning model and brings the output.
To configure the Custom model, the following prerequisites must be met:
Retrain the model: You can add new data to the existing training dataset and retrain the model with the combined dataset to make the model more accurate.
Fine-tuning: In this approach, you can take an existing pre-trained model and fine-tune it with new data. This approach is especially useful when you have limited new data.
Transfer learning: Transfer learning is similar to fine-tuning, but in this approach, you use a pre-trained model to extract relevant features from the new data and then train a new model on these features. This approach is especially useful when the new data is significantly different from the original data used to train the model.
Incremental learning: Incremental learning involves updating the model with new data while retaining the knowledge gained from the previous data. This approach is useful when the model needs to adapt to new data without forgetting the previously learned information.
For data to be fully utilized by AI and machine learning models, it needs to meet several requirements:
Quality: The data must be of high quality and free from errors, missing values, and inconsistencies. This will ensure that the models generate accurate results.
Quantity: The data needs to be sufficient in quantity, covering a diverse range of scenarios, in order to allow the models to generalize and make accurate predictions.
Relevance: The data must be relevant to the task the model is being used for. For example, in healthcare, the data must pertain to patient medical history, demographic information, and outcomes.
Format: The data must be in a format that can be easily processed by machine learning algorithms. This often involves cleaning, transforming, and normalizing the data to remove any biases or irrelevant information. This is why master data management is critical to leverage ML
Labeling: For supervised learning, the data must be labeled with clear and accurate ground truth information.
Skypoint Lakehouse is integrated with MLflow to provide a powerful combination for managing and deploying machine learning models. You can store and manage your data and models in the same place.
Skypoint’s Generative AI plugin is a trained model which interacts in a conversational way with prompts & meta prompts specific to the industries we serve (Healthcare, Senior Living, Retail & Hospitality).
The dialogue format makes it possible for the model to answer follow up questions, admit its mistakes, challenge incorrect premises, and reject inappropriate requests.
It is trained to follow an instruction in a prompt and provide a detailed response.
Ready to get the most value from your data? Skypoint connects unified data with generative AI so you can safely chat with your data and accelerate innovation.
