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Salesforce Success Story

Case Study: Healthcare Health Cloud Marketing Cloud Large Childrens Hospital

Large children’s hospital needs a usable data model and enhanced security to deliver excellent patient outcomes. Healthcare Health Cloud Marketing Cloud Large Childrens Hospital. Industry: Healthcare Client is a large children’s hospital with pediatric healthcare offering acute care. Problem: Implemented : Our solution? Results: In order to improve operations, provide physician-facing services, and move data—including PHI and PII—to the cloud, we have assisted healthcare providers in overcoming these obstacles. Salesforce offers all-inclusive solutions specifically designed to meet the demands of payers (insurance companies) and providers (healthcare organizations). Better health outcomes, more operational effectiveness, and increased patient engagement are the goals of these solutions. Salesforce solutions for the health and life sciences are tailored to the particular requirements of the medical industry. Salesforce offers digital transformation technology for health and life sciences industries. If you are considering a Salesforce healthcare implementation, contact Tectonic today. Like2 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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Exploring Google Vertex AI

Vertex AI

Exploring Google Vertex AI Conversation — Dialogflow CX with Generative AI, Data Stores, and Generators Vertex AI Conversation, built on Dialogflow and Vertex AI, introduces generative conversational features that utilize large language models (LLMs) for natural language understanding, crafting responses, and managing conversation flow. These advancements streamline agent design and enhance the quality of interactions. With Vertex AI Conversation, you can employ a state machine approach to develop sophisticated, generative AI-powered agents for dynamic conversation design and automation. In this insight, we’ll delve into the cutting-edge Dialogflow CX Generative AI technology, focusing on Data Stores and Generators. Data Stores: The Library of Information for Conversations Imagine Data Stores as an extensive library. When a question is asked, the virtual assistant acts as a librarian, locating relevant information. Dialogflow CX’s Data Store feature makes it easy to create conversations around stored information from various sources: For data preparation guidance, visit Google’s official documentation. Generators: LLM-Enhanced Dynamic Responses Dialogflow CX also enables Generators to use an LLM directly in Dialogflow CX without webhooks. Generators can perform tasks like summarization, parameter extraction, and data manipulation. Sourced from Vertex AI, they create real-time responses based on your prompts. For example, a Generator can be customized to summarize lengthy answers—an invaluable feature for simplifying conversations in chat or voice applications. You can find common Generator configurations in Google Cloud Platform (GCP) documentation. Creating a Chat Application with Vertex AI To start building, go to the Search and Conversation page in Google Cloud, agree to the terms, activate the API, and select “Chat.” Setting Up Your Agent After naming your agent and configuring data sources, like a Cloud Storage bucket with PDF documents, you’ll see your new chat app under Search & Conversation | Apps. Navigate to Dialogflow CX, where you can use your data store by setting up parameters for the agent and configuring responses. Once your agent is ready, you can test it in the Agent simulator. Adding a Generator for Summarization Using the Generator feature, you can further refine responses. Set parameters to target the Generator’s summarization feature, and link it to a specific page for summarized responses. This improves chat flow, providing concise answers for faster interactions. Integrating with Discord If you want to deploy your agent on platforms like Discord, follow Google’s integration guide for Dialogflow and adjust your code as needed. With the integration, responses will include hyperlinks for easy reference. Conclusion Vertex AI Conversation, with Dialogflow CX, enables powerful, human-like chat experiences by combining LLMs, Data Stores, and Generators. Ready to build your own dynamic conversational experiences? Now is the perfect time to experiment with this technology and see where it can take you. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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Salesforce Pro Suite

Salesforce Pro Suite

Revolutionizing CRM: Introducing Salesforce Pro Suite In today’s dynamic business technology landscape, Salesforce has established itself as a leader in customer relationship management (CRM) solutions. The launch of Salesforce Pro Suite marks a significant milestone in their mission to empower businesses with cutting-edge tools designed to optimize operations, enhance customer engagement, and drive growth. This article explores the features, benefits, and potential of Salesforce Pro Suite, showcasing why it stands out as a transformative solution for businesses of all sizes. What is Salesforce Pro Suite? Salesforce Pro Suite is a comprehensive collection of integrated tools and services designed to augment the capabilities of Salesforce’s CRM platform. Tailored for modern businesses—from startups to large enterprises—it incorporates advanced functionalities such as artificial intelligence (AI), automation, and data analytics to boost productivity, foster collaboration, and facilitate informed decision-making. Unlock growth and deepen customer relationships with Pro Suite—the all-in-one CRM suite with marketing, sales, service, and commerce tools that scale with your business. Get the flexibility to automate tasks and customize your CRM to fit your specific needs with Pro Suite. Key Features of Salesforce Pro Suite Benefits of Salesforce Pro Suite Use Cases of Salesforce Pro Suite What Can You Do with Pro Suite? Conclusion Salesforce Pro Suite represents a significant advancement in CRM technology, offering a comprehensive suite of tools that cater to the diverse needs of modern businesses. By harnessing AI, automation, and advanced analytics, Pro Suite empowers organizations to optimize operations, enhance customer engagement, and make informed, data-driven decisions. Whether you’re a small startup or a large enterprise, Salesforce Pro Suite provides the scalability, flexibility, and security required to thrive in today’s competitive landscape. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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Considerations When Implementing PHI

Considerations When Implementing PHI

Consumers today express heightened concerns about their data privacy, with 92% of Americans showing apprehension about online privacy. This apprehension extends beyond worries about the security of mobile phones, email, and browsers, particularly in the healthcare sector, where providers face increased scrutiny in safeguarding patients’ Protected Health Information (PHI). PHI is a prime target for cybercriminals due to its sensitivity, but securing it at scale poses significant challenges. Considerations When Implementing PHI. What is PHI and how is it protected? With certain exceptions, the Privacy Rule protects a subset of individually identifiable health information, known as protected health information or PHI, that is held or maintained by covered entities or their business associates acting for the covered entity. HIPAA mandates stringent rules for the protection of healthcare information qualifying as PHI, imposing severe financial and criminal penalties for non-compliance. The HIPAA Privacy Rule specifically oversees PHI, encompassing health or personal information that can identify an individual, including historical, present, or future data related to mental or physical health. Entities handling PHI must adhere to strict requirements for transmitting, storing, and disposing of this data, as patients inherently possess legal rights to the privacy and security of their PHI. Compliance is vital for the protection of PHI, not only to fulfill regulatory obligations but also to mitigate the substantial risks posed by cybercriminals who target this valuable information. The allure for cybercriminals lies in the lucrative market for healthcare data, with records selling for hundreds to thousands of dollars per record on the black market. Given the potential for compromising millions of patient records in a single breach, attackers stand to gain significant sums. In contrast, other personal identifiers like Social Security numbers and credit card information fetch considerably lower prices. What are some of the barriers to implementing HIPAA guidelines in health care organizations? The three main aspects of HIPAA that continue to be a challenge for organizations are privacy, security and breach notification. Ensuring compliance involves both technical and procedural considerations, and practices must implement updated training programs, access controls, secure data disposal methods, encryption measures, and regular security assessments. Compliance extends beyond internal practices, requiring thorough scrutiny of third-party vendors’ adherence to PHI protection regulations. In the broader context of system compliance with PHI regulations, including HIPAA, specific software requirements play a pivotal role. These requirements, such as data encryption, access controls, audit logs, data integrity measures, and breach notification capabilities, collectively ensure the confidentiality, integrity, and availability of PHI. Compliance necessitates an organizational commitment to privacy and security considerations, encompassing technical safeguards, administrative policies, and physical security measures. Various businesses, including hospitals, insurance providers, pharmacies, and psychologists, handle PHI, making its protection challenging yet imperative to adhere to HIPAA standards. Maintain documents containing PHI in locked cabinets or locked rooms when the documents are not in use and after working hours. Establish physical and/or procedural controls (e.g., key or combination access, access authorization levels) that limit access to only those persons who have a need for the information. What’s your responsibility in protecting PHI? This includes implementing HIPAA-required administrative , physical , and technical safeguards with regard to any person, process, application, service, or system used to collect, process, manage, analyze, or store PHI. Like1 Related Posts Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more CRM Cloud Salesforce What is a CRM Cloud Salesforce? Salesforce Service Cloud is a customer relationship management (CRM) platform for Salesforce clients to Read more Salesforce Data Studio Data Studio Overview Salesforce Data Studio is Salesforce’s premier solution for audience discovery, data acquisition, and data provisioning, offering access Read more

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Healthcare Cloud Marketplace

Healthcare Cloud Marketplace

Healthcare Cloud Computing Market: A Comprehensive Overview and Future Outlook Vantage Market Research Report: Insights into Healthcare Cloud Computing by 2030 WASHINGTON, D.C., February 6, 2024 /EINPresswire.com/ — The global Healthcare Cloud Marketplace was valued at USD 38.25 billion in 2022 and is projected to grow at a compound annual growth rate (CAGR) of 18.2% from 2023 to 2030, reaching approximately USD 145.86 billion by 2030, according to Vantage Market Research. This technology allows healthcare organizations to utilize cloud-based services for data storage, management, and analysis, providing numerous benefits such as cost efficiency, scalability, flexibility, security, and interoperability. It enhances healthcare delivery by enabling seamless data access and sharing across various locations, devices, and networks. Additionally, cloud computing supports the integration of advanced technologies like artificial intelligence, big data analytics, telehealth, and mobile health, driving progress in disease diagnosis, treatment, and prevention. Market Dynamics The market’s growth is fueled by several key factors, including the increasing demand for healthcare IT solutions, the rising prevalence of chronic diseases, the widespread adoption of electronic health records (EHRs), and evolving payment models and regulatory frameworks. The exponential increase in healthcare data, encompassing patient records, imaging scans, and research findings, necessitates scalable storage and analysis solutions. Cloud computing meets this need by providing flexible and scalable infrastructure, accommodating data growth without overburdening IT systems. The rise of telehealth and remote patient monitoring further boosts the demand for secure, cloud-based platforms that facilitate efficient data exchange. However, stringent data privacy regulations like HIPAA and GDPR require robust security measures, compelling healthcare organizations to seek cloud providers that offer strong compliance and access controls. This need for a balance between agility and security shapes the healthcare cloud computing market’s future trajectory. Leading Companies in the Global Healthcare Cloud Computing Market Market Segmentation By Product: By Deployment: By Component: By Pricing Model: By Service Model: Key Trends and Opportunities The healthcare cloud computing market is witnessing significant trends, including the adoption of hybrid and multi-cloud models, which combine the benefits of both public and private clouds. The integration of artificial intelligence (AI) and machine learning (ML) into cloud-based healthcare applications is opening new avenues for personalized medicine, clinical decision support, and drug discovery. Moreover, blockchain technology is emerging as a solution to enhance data security and patient privacy, addressing critical industry concerns. Key Findings: Opportunities: Healthcare Cloud Marketplace The healthcare cloud computing market is poised for robust growth, driven by the increasing demand for scalable and secure data management solutions. As healthcare organizations navigate challenges related to data privacy and security, robust cloud solutions and supportive government policies will be essential in unlocking the full potential of cloud computing in healthcare. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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AI Adoption Not Even Across the Board

AI Adoption Not Even Across the Board

Reflecting on AI’s potential and its challenges, McElheran calls for a balanced approach: “To fully harness AI’s benefits, we need a realistic, evidence-based approach that accounts for both the advantages and the societal costs associated with adoption.”

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2024 AI and Machine Learning Trends

2024 AI and Machine Learning Trends

In 2023, the AI landscape experienced transformative changes following the debut of ChatGPT in November 2022, a landmark event for artificial intelligence. 2024 AI and Machine Learning Trends ahead, AI is set to dramatically alter global business practices and drive significant advancements across various sectors. Organizations are shifting their focus from experimental initiatives to real-time applications, reflecting a more mature understanding of AI’s capabilities while still being intrigued by generative AI technologies. Key AI and Machine Learning Trends for 2024 Here are the top trends shaping the AI and machine learning landscape for 2024: 1. Agentic AIAgentic AI is evolving from reactive to proactive systems. Unlike traditional AI that primarily responds to user inputs, these advanced AI agents demonstrate autonomy, proactivity, and the ability to independently set and pursue goals. 2. Open-Source AIOpen-source AI is democratizing access to sophisticated AI models and tools by offering free, publicly accessible alternatives to proprietary solutions. This trend has seen significant growth, with notable competitors like Mistral AI’s Mixtral models and Meta’s Llama 2 making strides in 2023. 3. Multimodal AIMultimodal AI integrates various types of inputs—such as text, images, and audio—mimicking human sensory capabilities. Models like GPT-4 from OpenAI showcase this ability, enhancing applications in fields like healthcare by improving diagnostic precision. 4. Customized Enterprise Generative AI ModelsThere is a rising interest in bespoke generative AI models tailored to specific business needs. While broad tools like ChatGPT remain widely used, niche-specific models are increasingly popular for their efficiency in addressing specialized requirements. 5. Retrieval-Augmented Generation (RAG)RAG combines text generation with information retrieval to boost the accuracy and relevance of AI-generated content. By reducing model size and leveraging external data sources, RAG is well-suited for business applications that require up-to-date factual information. 6. Shadow AIShadow AI, which refers to user-friendly AI tools used without formal IT approval, is gaining traction among employees seeking quick solutions or exploring new technologies. While it fosters innovation, it also raises concerns about data privacy and security. Looking Ahead to 2024 These trends highlight AI and machine learning’s expanding role across industries in 2024. Organizations must adapt to these advancements to remain competitive, balancing innovation with strong governance frameworks to ensure security and compliance. Staying informed about these developments will be crucial for leveraging AI’s transformative potential in the coming year. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more

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Marketing Cloud Growth and Advanced Editions

Marketing Cloud Growth and Advanced Editions

While Growth Edition is tailored to small businesses looking to get started with robust marketing automation, Advanced Edition caters to companies that need more sophisticated tools to scale personalization efforts, improve customer engagement, and streamline workflows. It offers additional features, including real-time journey testing, predictive AI for customer scoring, and advanced SMS capabilities, allowing businesses to enhance every touchpoint with their customers.

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MC Personalization Tips and Tricks

MC Personalization Tips and Tricks

Salesforce Marketing Cloud Personalization, formerly Interaction Studio, offers incredible power for personalization. MC Personalization Tips and Tricks below will help you level up your game. Einstein Recipes: Enhancements and Challenges Multiple Dimensional Variations for Products in Einstein Recipes Einstein Recipes offer powerful and flexible tools for creating recommendations. However, the fourth step, Variations, falls short compared to other options. Currently, you can configure only a single Dimensional Variation. While multiple Item Types are available, once you select one, you cannot limit recommended products to specific numbers per category or brand. This limitation hinders control over product recommendations, especially for e-commerce sites with diverse catalogs. Unlike Dimensional Variations, multiple Boosters or Exclusions of the same type can be configured differently, which would be a valuable feature to add for Variations. Department Variation for Products in Einstein Recipes Einstein Recipes allow Dimensional Variations at the Category level, but only for primary categories. There is no option for Department (master category) level, which is limiting for e-commerce sites with broad category trees, such as: Recommendations with Category Variation set can still be dominated by similar products due to similar primary categories. Two solutions could address this: Price Reduction Ingredient in Einstein Recipes Triggered Campaigns in Journey Builder can target various events, including Catalog Triggers. Some triggers, like Product Expiring Soon, are available for Web with Einstein Recipes Ingredients. However, there is no Ingredient for the common e-commerce use case of Price Reduction. Marketing Cloud Personalization (Interaction Studio) has the required price and listPrice attributes for Triggered Campaigns. A workaround involves calculating price reductions externally and passing this information to a Related Catalog Object. More efficient solutions would be: Rating Count in Recipe’s Rating Exclusion Marketing Cloud Personalization offers Exclusions/Inclusions on Recipes to fine-tune recommendations. One option is to exclude/include items based on their rating, with an optional zero rating capture. It would be beneficial to include an option to filter based on rating count, allowing for: Currently, such filters can only be applied on the server side in the Template, which can limit recommendations. Having this feature at the recipe level would be more powerful. Abandoned Cart Retention Setting Marketing Cloud Personalization captures cart information for Einstein Recipes recommendations. However, cart content remains indefinitely unless managed proactively. A workaround involves a Web Campaign that checks cart age and pushes a clear cart action if necessary. A better solution would be a configurable option in MCP settings to automatically remove old cart data. Catalog Enhancements Full MCP Category Hierarchy Support for ETL Marketing Cloud Personalization can create a hierarchical tree of categories with automatic summing of views and revenue. However, this is currently possible only under specific conditions, such as having one Category per product and using a Sitemap format. This limitation is problematic, as ETL is often a better way to manage it. The Category ETL already provides detailed information using department and parentCategoryId attributes, but this data does not replicate the drill-down hierarchy in the Catalog UI or pass data from the bottom Category up. Ensuring feature parity between Sitemap and ETL would be beneficial. Segmentation Enhancements MCP Action Name Management Marketing Cloud Personalization captures actions from multiple sources but does not allow managing created actions. An option to view and remove unnecessary actions would improve user experience by reducing the number of options in the segmentation/targeting picklists. An even better solution would be to merge existing actions, preserving behavioral data after refactoring action names. MCP Hourly-Based Segmentation Rules Currently, segmentation rules in Marketing Cloud Personalization are based on days, limiting on-site campaign targeting. For example, to display an infobar for abandoned cart users, the current segmentation can only show users who have not performed a Cart Action today. Hourly-based segmentation rules would allow more precise targeting, showing users who have not performed a Cart Action in the last hour. Adding a picklist to choose between day or hour-based rules would enhance segmentation capabilities. Full MCP Catalog Export Marketing Cloud Personalization supports manual catalog export but only with limited data. The current export file lacks complete catalog data (e.g., promotable and archived attributes), making it unsuitable for ETL sources. An option to export the full catalog data, matching the ETL schema and including hidden items, would greatly benefit debugging and batch-modifying items for subsequent ETL import. Full MCP Catalog Metadata Visibility Marketing Cloud Personalization supports viewing custom attribute metadata in the Catalog but is limited to ETL updates. Extending this to built-in attributes and including origin and lastUpdated values for all sources (Sitemap, Mobile App, Manual update, API) would simplify debugging Catalog metadata issues, reducing admin/developer work and support tickets. ETL Enhancements External Email Campaign ETL Experience Name & ID External Email Campaign ETL allows passing behavioral data but is limited to Campaign ID and Campaign Name. To fully leverage this data in segmentation, it should also support Email ID and Email Name. Adding Experience ID and Experience Name fields to the ETL would enable targeted personalization, allowing segmentation on entire campaigns or specific emails within campaigns. External Email Campaign ETL Send Segmentation External Email Campaign ETL passes Send, Click, and Open data but does not support segmentation based on Send events. Enabling segmentation rules for Send events would unlock use cases like targeting Web or Push campaigns to users who received an email campaign but did not open it, fully leveraging cross-channel and real-time personalization. External Email Campaign ETL Unsubscription Event Type External Email Campaign ETL passes Send, Click, and Open data but cannot pass unsubscriptions. Including the Unsubscribe event would enable targeted campaigns like surveys about unsubscription reasons, win-back campaigns, or replacing email subscription prompts with other channel recommendations. By addressing these enhancements and challenges, Salesforce Marketing Cloud Personalization (Interaction Studio) can further improve its capabilities and provide more precise, effective, and user-friendly tools for personalized marketing. Reporting Enhancements: Direct Attribution at the MCP Campaign Level Current Reporting in Marketing Cloud Personalization (MCP) Marketing Cloud Personalization (Interaction Studio) offers various reports based on Activity, Results, and Visits. However, it

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EPHI and PHI Explained

EPHI and PHI Explained

Lately, there’s been a lot of buzz about Protected Health Information (PHI), especially with concerns arising over what’s permissible to disclose. (Think vaccine status, anyone?) Let’s delve into precisely what constitutes protected health information and what doesn’t. Additionally, as technology progresses and electronic medical records become prevalent, a new category called electronic PHI (ePHI) has emerged, warranting exploration. PHI: Under HIPAA regulations, PHI encompasses “any identifiable health information utilized, maintained, stored, or transmitted by a HIPAA-covered entity.” These entities typically include healthcare providers, insurance providers, or associates of HIPAA-covered entities, such as subcontracted services like medical coding companies. As a result, any data linked to your health—whether it’s test results, medical history, or personal identifiers like your name or social security number—is classified as PHI. The inclusion of one or more of these identifiers renders the information PHI, necessitating adherence to HIPAA Privacy Rules for its security. There are 18 specific categories of patient identifiers: ePHI: ePHI functions similarly to PHI but encompasses information created, stored, or transmitted electronically. This includes systems operating with cloud databases or transmitting patient information via email. To ensure protection, specialized security measures such as encryption and secure backup are imperative. Several high-profile breaches of ePHI in recent years have resulted in substantial financial penalties ranging from six to seven figures. Exceptions: Certain types of information do not fall under HIPAA rules as PHI or ePHI, and it’s crucial to recognize these exceptions. Sometimes, any medical-related information is erroneously grouped under PHI when it shouldn’t be. To ascertain whether information qualifies as PHI, consider the following guidelines: The healthcare landscape relies heavily on information—comprising records, histories, forms, demographics, and reports. Managing HIPAA-compliant electronic forms can be a daunting task without the right partner. With virtual and telehealth communications becoming increasingly common, the electronic handling of sensitive ePHI is more vital than ever. Tectonic works with our health and life sciences customers to ensure that such data is safeguarded, user-friendly, and consistently secure. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more

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Communicating With Machines

Communicating With Machines

For as long as machines have existed, humans have struggled to communicate effectively with them. The rise of large language models (LLMs) has transformed this dynamic, making “prompting” the bridge between our intentions and AI’s actions. By providing pre-trained models with clear instructions and context, we can ensure they understand and respond correctly. As UX practitioners, we now play a key role in facilitating this interaction, helping humans and machines truly connect. The UX discipline was born alongside graphical user interfaces (GUIs), offering a way for the average person to interact with computers without needing to write code. We introduced familiar concepts like desktops, trash cans, and save icons to align with users’ mental models, while complex code ran behind the scenes. Now, with the power of AI and the transformer architecture, a new form of interaction has emerged—natural language communication. This shift has changed the design landscape, moving us from pure graphical interfaces to an era where text-based interactions dominate. As designers, we must reconsider where our focus should lie in this evolving environment. A Mental Shift In the era of command-based design, we focused on breaking down complex user problems, mapping out customer journeys, and creating deterministic flows. Now, with AI at the forefront, our challenge is to provide models with the right context for optimal output and refine the responses through iteration. Shifting Complexity to the Edges Successful communication, whether with a person or a machine, hinges on context. Just as you would clearly explain your needs to a salesperson to get the right product, AI models also need clear instructions. Expecting users to input all the necessary information in their prompts won’t lead to widespread adoption of these models. Here, UX practitioners play a critical role. We can design user experiences that integrate context—some visible to users, others hidden—shaping how AI interacts with them. This ensures that users can seamlessly communicate with machines without the burden of detailed, manual prompts. The Craft of Prompting As designers, our role in crafting prompts falls into three main areas: Even if your team isn’t building custom models, there’s still plenty of work to be done. You can help select pre-trained models that align with user goals and design a seamless experience around them. Understanding the Context Window A key concept for UX designers to understand is the “context window“—the information a model can process to generate an output. Think of it as the amount of memory the model retains during a conversation. Companies can use this to include hidden prompts, helping guide AI responses to align with brand values and user intent. Context windows are measured in tokens, not time, so even if you return to a conversation weeks later, the model remembers previous interactions, provided they fit within the token limit. With innovations like Gemini’s 2-million-token context window, AI models are moving toward infinite memory, which will bring new design challenges for UX practitioners. How to Approach Prompting Prompting is an iterative process where you craft an instruction, test it with the model, and refine it based on the results. Some effective techniques include: Depending on the scenario, you’ll either use direct, simple prompts (for user-facing interactions) or broader, more structured system prompts (for behind-the-scenes guidance). Get Organized As prompting becomes more common, teams need a unified approach to avoid conflicting instructions. Proper documentation on system prompting is crucial, especially in larger teams. This helps prevent errors and hallucinations in model responses. Prompt experimentation may reveal limitations in AI models, and there are several ways to address these: Looking Ahead The UX landscape is evolving rapidly. Many organizations, particularly smaller ones, have yet to realize the importance of UX in AI prompting. Others may not allocate enough resources, underestimating the complexity and importance of UX in shaping AI interactions. As John Culkin said, “We shape our tools, and thereafter, our tools shape us.” The responsibility of integrating UX into AI development goes beyond just individual organizations—it’s shaping the future of human-computer interaction. This is a pivotal moment for UX, and how we adapt will define the next generation of design. Content updated October 2024. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more

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Audience Builder Marketing Cloud

Marketing Cloud Audience Builder dynamically generates targeted audiences from contacts stored in your account based on attribute and behavioral values. These audiences can be used to target or exclude contacts from your marketing activities. In today’s world, where a staggering 347.3 billion emails are sent globally every day, email inboxes have become increasingly cluttered. In your specific niche, you’re not the only one trying to reach your target audience; numerous others are vying for their attention. With consumers having a multitude of options, marketers bear the responsibility of positioning themselves in a way that makes it impossible for potential customers to overlook them. Achieving this requires embracing customer-centricity, which involves deeply engaging with different buyer personas by segmenting your contact list based on various parameters such as age, gender, location, interests, preferences, past purchases, browsing history, and position in the sales funnel. However, manually managing this segmentation, especially with a large contact list, can be overwhelming. This is where a dependable tool like Salesforce Marketing Cloud’s Audience Builder proves invaluable. The SFMC Audience Builder empowers marketers to create granular segmentation frameworks based on demographic and behavioral data, making the execution of targeted campaigns effortless. It dynamically generates targeted audiences by utilizing contacts in your account and leveraging behavioral values and stored attributes as guiding parameters. In this overview, we aim to provide a comprehensive understanding of SFMC’s Audience Builder. Key Entities and Terminologies: Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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einstein discovery dictionary

Einstein Discovery Dictionary

Familiarize yourself with terminology that is commonly associated with Einstein Discovery. Actionable VariableAn actionable variable is an explanatory variable that people can control, such as deciding which marketing campaign to use for a particular customer. Contrast these variables with explanatory variables that can’t be controlled, such as a customer’s street address or a person’s age. If a variable is designated as actionable, the model uses prescriptive analytics to suggest actions (improvements) the user can take to improve the predicted outcome. Actual OutcomeAn actual outcome is the real-world value of an observation’s outcome variable after the outcome has occurred. Einstein Discovery calculates model performance by comparing how closely predicted outcomes come to actual outcomes. An actual outcome is sometimes called an observed outcome. AlgorithmSee modeling algorithm. Analytics DatasetAn Analytics dataset is a collection of related data that is stored in a denormalized, yet highly compressed, form. The data is optimized for analysis and interactive exploration. AttributeSee variable. AverageIn Einstein Discovery, the average represents the statistical mean for a variable. BiasIf Einstein Discovery detects bias in your data, it means that variables are being treated unequally in your model. Removing bias from your model can produce more ethical and accountable models and, therefore, predictions. See disparate impact. Binary Classification Use CaseThe binary classification use case applies to business outcomes that are binary: categorical (text) fields with only two possible values, such as win-lose, pass-fail, public-private, retain-churn, and so on. These outcomes separate your data into two distinct groups. For analysis purposes, Einstein Discovery converts the two values into Boolean true and false. Einstein Discovery uses logistic regression to analyze binary outcomes. Binary classification is one of the main use cases that Einstein Discovery supports. Compare with multiclass classification. CardinalityCardinality is the number of distinct values in a category. Variables with high cardinality (too many distinct values) can result in complex visualizations that are difficult to read and interpret. Einstein Discovery supports up to 100 categories per variable. You can optionally consolidate the remaining categories (categories with fewer than 25 observations) into a category called Other. Null values are put into a category called Unspecified. Categorical VariableA categorical variable is a type of variable that represents qualitative values (categories). A model that represents a binary or multiclass classification use case has a categorical variable as its outcome. See category. CategoryA category is a qualitative value that usually contains categorical (text) data, such as Product Category, Lead Status, and Case Subject. Categories are handy for grouping and filtering your data. Unlike measures, you can’t perform math on categories. In Salesforce Help for Analytics datasets, categories are referred to as dimensions. CausationCausation describes a cause-and-effect relationship between things. In Einstein Discovery, causality refers to the degree to which variables influence each other (or not), such as between explanatory variables and an outcome variable. Some variables can have an obvious, direct effect on each other (for example, how price and discount affect the sales margin). Other variables can have a weaker, less obvious effect (for example, how weather can affect on-time delivery). Many variables have no effect on each other: they are independent and mutually exclusive (for example, win-loss records of soccer teams and currency exchange rates). It’s important to remember that you can’t presume a causal relationship between variables based simply on a statistical correlation between them. In fact, correlation provides you with a hint that indicates further investigation into the association between those variables. Only with more exploration can you determine whether a causal link between them really exists and, if so, how significant that effect is .CoefficientA coefficient is a numeric value that represents the impact that an explanatory variable (or a pair of explanatory variables) has on the outcome variable. The coefficient quantifies the change in the mean of the outcome variable when there’s a one-unit shift in the explanatory variable, assuming all other variables in the model remain constant. Comparative InsightComparative insights are insights derived from a model. Comparative insights reveal information about the relationships between explanatory variables and the outcome variable in your story. With comparative insights, you isolate factors (categories or buckets) and compare their impact with other factors or with global averages. Einstein Discovery shows waterfall charts to help you visualize these comparisons. CorrelationA correlation is simply the association—or “co-relationship”—between two or more things. In Einstein Discovery, correlation describes the statistical association between variables, typically between explanatory variables and an outcome variable. The strength of the correlation is quantified as a percentage. The higher the percentage, the stronger the correlation. However, keep in mind that correlation is not causation. Correlation merely describes the strength of association between variables, not whether they causally affect each other. CountA count is the number of observations (rows) associated with an analysis. The count can represent all observations in the dataset, or the subset of observations that meet associated filter criteria.DatasetSee Analytics dataset. Date VariableA date variable is a type of variable that contains date/time (temporal) data.Dependent VariableSee outcome variable. Deployment WizardThe Deployment Wizard is the Einstein Discovery tool used to deploy models into your Salesforce org. Descriptive InsightsDescriptive insights are insights derived from historical data using descriptive analytics. Descriptive insights show what happened in your data. For example, Einstein Discovery in Reports produces descriptive insights for reports. Diagnostic InsightsDiagnostic insights are insights derived from a model. Whereas descriptive insights show what happened in your data, diagnostic insights show why it happened. Diagnostic insights drill deeper into correlations to help you understand which variables most significantly impacted the business outcome you’re analyzing. The term why refers to a high statistical correlation, not necessarily a causal relationship. Disparate ImpactIf Einstein Discovery detects disparate impact in your data, it means that the data reflects discriminatory practices toward a particular demographic. For example, your data can reveal gender disparities in starting salaries. Removing disparate impact from your model can produce more accountable and ethical insights and, therefore, predictions that are fair and equitable. Dominant ValuesIf Einstein Discovery detects dominant values in a variable, it means that the data is unbalanced. Most values are in the same category, which can limit the value of the analysis. DriftOver time, a deployed model’s performance can drift, becoming less accurate in predicting outcomes. Drift can occur due to changing factors in the data or in your business environment. Drift also results from now-obsolete assumptions built into the story

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Salesforce Certified Healthcare Technology

Salesforce Certified Healthcare Technology

As a Salesforce Certified healthcare technology consultant, Tectonic brings extensive experience across both large and small healthcare settings, witnessing firsthand the transformative impact of Salesforce Health Cloud. Having contributed to the development of an EHR and RCM application on Salesforce, Tectonic understands the substantial benefits this platform offers when integrated with existing technologies such as Electronic Health Records (EHR), claims data sources, and Patient Population Health Management systems. Enhanced Patient Population Health Management: Salesforce Health Cloud equips healthcare providers with tools to effectively manage Patient Population Health by gathering, analyzing, and acting on health data. Providers can leverage these insights to identify at-risk populations, design targeted interventions, and improve patient outcomes. Optimized Provider Referral Patterning: By integrating Salesforce Health Cloud with claims data, Tectonic enables providers to analyze referral patterns. This helps healthcare organizations collaborate more effectively with physicians based on their specialties, insurance networks, and common CPT codes, ultimately enhancing patient care and strengthening provider networks. AI-Enabled Contact Service Centers for Better Patient Care: Integrating Generative AI with Salesforce Health Cloud allows healthcare providers to deliver personalized, responsive services. By connecting Billing, EHR, and Patient Population Health Management platforms, Tectonic reduces administrative burdens, streamlines communication, and improves patient satisfaction in contact service centers. Business Development Liaison Route Planning: Using Salesforce Maps combined with 1st and 3rd party claims data, Tectonic enables business development teams to generate optimized, daily route plans within seconds. These plans allow healthcare representatives to visit the right providers based on location, specialty, referral volumes, and other key preferences. Strategic Territory Expansion and Planning: Salesforce Health Cloud empowers healthcare organizations to strategically plan for territory expansion. Through analyzing geographic claims data and patient demographics, Tectonic helps organizations make data-driven decisions on resource allocation and service growth. Extending Functionality with Salesforce AppExchange and Salesforce Health Cloud: The Salesforce AppExchange offers a wide array of applications that expand Health Cloud’s core capabilities. These apps can boost patient engagement, care coordination, and advanced analytics. In addition, Tectonic leverages multiple health and life sciences process, which integrates Revenue Cycle Management (RCM) and EHR functionalities, enabling providers to manage billing, claims, and clinical data seamlessly within one platform, among many other features. Conclusion: Salesforce Health Cloud, when combined with Tectonic‘s expertise and solutions like payer and provider processes, becomes a powerful asset for healthcare providers. By integrating Health Cloud into existing technology stacks and utilizing AppExchange apps, healthcare organizations can improve patient care, streamline operations, and strategically plan for growth in the ever-evolving healthcare landscape. Content updated September 2024. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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Big Data and Data Visualization

Big Data and Data Visualization Explained

Data Visualization: Turning Complex Data into Clear Insights Data visualization is the practice of converting information into visual formats, such as maps or graphs, to make data more accessible and understandable. The primary purpose of data visualization is to highlight patterns, trends, and outliers within large data sets, allowing users to quickly glean insights. The term is often used interchangeably with information graphics, information visualization, and statistical graphics. The Role of Data Visualization in Data Science Data visualization is a crucial step in the data science process. After data is collected, processed, and modeled, it must be visualized to draw meaningful conclusions. It’s also a key component of data presentation architecture, a discipline focused on efficiently identifying, manipulating, formatting, and delivering data. Importance Across Professions Data visualization is essential across various fields. Teachers use it to display student performance, computer scientists to explore AI advancements, and executives to communicate information to stakeholders. In big data projects, visualization tools are vital for quickly summarizing large datasets, helping businesses make informed decisions. In advanced analytics, visualization is equally important. Data scientists use it to monitor and ensure the accuracy of predictive models and machine learning algorithms. Visual representations of complex algorithms are often easier to interpret than numerical outputs. Historical Context of Data Visualization Data visualization has evolved significantly over the centuries, long before the advent of modern technology. Today, its importance is more pronounced, as it enables quick and effective communication of information in a universally understandable manner. Why Data Visualization Matters Data visualization provides a straightforward way to communicate information, regardless of the viewer’s expertise. This universality makes it easier for employees to make decisions based on visual insights. Visualization offers numerous benefits for businesses, including: Advantages of Data Visualization Key benefits include: Challenges and Disadvantages Despite its advantages, data visualization has some challenges: Data Visualization in the Era of Big Data With the rise of big data, visualization has become more critical. Companies leverage machine learning to analyze vast amounts of data, and visualization tools help present this data in a comprehensible way. Big data visualization often employs advanced techniques, such as heat maps and fever charts, beyond the standard pie charts and graphs. However, challenges remain, including: Examples of Data Visualization Techniques Early computer-based data visualizations often relied on Microsoft Excel to create tables, bar charts, or pie charts. Today, more advanced techniques include: Common Use Cases for Data Visualization Data visualization is widely used across various industries, including: The Science Behind Data Visualization The effectiveness of data visualization is rooted in how humans process information. Daniel Kahneman and Amos Tversky’s research identified two methods of information processing: Visualization Tools and Vendors Data visualization tools are widely used for business intelligence reporting. These tools generate interactive dashboards that track performance across key metrics. Users can manipulate these visualizations to explore data in greater depth, and indicators alert them to data updates or important events. Businesses might use visualization of data software to monitor marketing campaigns or track KPIs. As tools evolve, they increasingly serve as front ends for sophisticated big data environments, assisting data engineers and scientists in exploratory analysis. Popular data visualization tools include Domo, Klipfolio, Looker, Microsoft Power BI, Qlik Sense, Tableau, and Zoho Analytics. While Microsoft Excel remains widely used, newer tools offer more advanced capabilities. Data visualization is a vital subset of the broader field of data analytics, offering powerful tools for understanding and leveraging business data across all sectors. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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