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As of 2022, 96% of hospitals were involved in some form of electronic public health data exchange, according to a blog post by the Office of the National Coordinator for Health Information Technology (ONC). Yet Barriers to Healthcare Data Exchange Despite this high engagement, barriers to effective data exchange remain. About three-quarters of hospitals reported facing at least one obstacle to public health reporting. The most frequent challenge was the perception that public health authorities (PHAs) could not receive information electronically. Additionally, hospitals cited technical complexities and exchange costs as significant barriers. ONC data also revealed lower rates of electronic public health reporting among small, rural, independent, and critical access hospitals. Similarly, office-based physicians encounter difficulties in public health data exchange due to limited EHR integration. The 2022 National Physician Health IT Survey indicated that less than half of primary care physicians (41%) used their EHR systems to access immunization data from outside their organizations. Several initiatives are underway to address these public health reporting challenges and improve interoperability between healthcare providers and PHAs: Additionally, a proposed rule from ONC seeks to enhance public health data exchange further. The Health Data, Technology, and Interoperability: Certification Program Updates, Algorithm Transparency, and Information Sharing (HTI-2) proposed rule builds on existing certification criteria by: According to the ONC blog, “Together, these efforts will help address persistent challenges to public health data sharing by investing in public health infrastructure, establishing a governing approach for nationwide health information exchange, and advancing standards to support seamless electronic exchange.” 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
Not all medical chatbots are created equal, as a recent JAMA Network Open study reveals. The study found that some chatbots are better at tailoring health information to patient health literacy than others. Chatbots in Healthcare may not be ready for prime time. The report compared the free and paid versions of ChatGPT, showing that while the paid version initially provided more readable health information, the difference was minimal once researchers asked the chatbots to explain things at a sixth-grade reading level. The findings suggest that both versions of ChatGPT could potentially widen health disparities in terms of information access and literacy. Chatbots like ChatGPT are becoming increasingly prominent in healthcare, showing potential in improving patient access to health information. However, their quality can vary. The study evaluated the free and paid versions of ChatGPT using the Flesch Reading Ease score for readability and the DISCERN instrument for consumer health information quality. Researchers tested both versions using the five most popular cancer-related queries from 2021 to 2023. They found that while the paid version had slightly higher readability scores (52.6) compared to the free version (62.48) on a 100-point scale, both scores were deemed suboptimal. The study revealed that prompting the free version of ChatGPT to explain concepts at a sixth-grade reading level improved its readability score to 71.55, outperforming the paid version under similar conditions. Even so, when both versions were asked to simplify answers to a sixth-grade reading level, the paid version scored slightly higher at 75.64. Despite these improvements, the overall readability of responses was still problematic. Without the simplification prompt, responses were roughly at a 12th-grade reading level. Even with the prompt, they remained closer to an eighth- or tenth-grade level, possibly due to chatbot confusion about the request. The study raises concerns about health equity. If the paid version of ChatGPT provides more accessible information, individuals with the means to purchase it might have a clear advantage. This disparity could exacerbate existing health inequities, especially for those using the free version. The researchers concluded that until chatbots consistently provide information at a lower reading level, clinicians should guide patients on how to effectively use these tools and encourage them to request information at simpler reading levels. Like 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 Guide to Creating a Working Sales Plan Creating a sales plan is a pivotal step in reaching your revenue objectives. To ensure its longevity and adaptability to Read more 50 Advantages of Salesforce Sales Cloud According to the Salesforce 2017 State of Service report, 85% of executives with service oversight identify customer service as a Read more
Post-Outage Recovery and Lessons from the CrowdStrike Incident Following the CrowdStrike outage on July 19, 2024, companies globally have been working to restore business continuity and enhance their resilience for future incidents. The outage, caused by a faulty content update, led to crashes on approximately 8.5 million Windows devices, affecting hospitals, airlines, and other businesses. Although less than 1% of all Windows machines were impacted, the incident caused significant disruptions, including appointment cancellations at hospitals. For instance, Mass General Brigham canceled all non-urgent visits on the day the outage began. Other healthcare organizations, such as Memorial Sloan Kettering Cancer Center, Cleveland Clinic, and Mount Sinai, also faced operational challenges. The cause of the outage was a defective content configuration update to CrowdStrike’s Falcon threat detection platform, not a cyberattack. A bug in the content validator allowed the faulty update to bypass validation, as noted in CrowdStrike’s preliminary post-incident review. David Finn, Executive Vice President of Governance, Risk, and Compliance at First Health Advisory, shared with TechTarget Editorial, “The recovery is well underway, and most healthcare organizations are back up and running. While the scope was smaller compared to other recent incidents in healthcare, the response was effective. There are valuable lessons to be learned.” Preparing for Future Incidents Finn, with 40 years of experience in health IT security, emphasized that incidents are inevitable. “The challenge is to plan, prepare, and be able to recover and stay resilient,” he stated. Whether facing a major cyberattack like the February 2024 Change Healthcare incident or an IT outage without malicious intent, healthcare organizations must be ready for various cyber incidents affecting critical systems. He highlighted the importance of thorough due diligence and incident response planning. Addressing potential operational challenges in advance and planning for cybersecurity events or IT failures will prove beneficial when an incident occurs. “We need to rethink how we deploy software,” Finn added. “Human errors will always happen, and it’s our job to protect against those mistakes.” Building Cyber-Resilience Cyber-resilience is crucial for quickly recovering and resuming operations. Organizations should anticipate incidents and focus on building resilience. Finn noted, “While I still trust CrowdStrike, trust does not guarantee perfection. Resilience and redundancy are vital.” Healthcare organizations responded swiftly to the CrowdStrike incident, with Mass General Brigham activating its incident command to manage the situation. The organization ensured that clinics and emergency departments remained open for urgent health concerns and resumed scheduled appointments and procedures by July 22. Evaluating Risk and Updating Protocols Erik Weinick, co-head of the privacy and cybersecurity practice at Otterbourg, urged organizations to use the CrowdStrike incident as an opportunity to reevaluate their risk management protocols. “Even if the incident was accidental, organizations should conduct information audits, penetration testing, update system mappings, and reinforce security practices like multifactor authentication and strong password policies.” Addressing Third-Party Risk The outage underscored the importance of managing third-party risks. The interconnectedness of healthcare systems amplifies these risks, as evidenced by some of the largest healthcare data breaches in recent years originating from third-party vendors. Finn suggested that while organizations may conduct risk analyses on vendors like CrowdStrike, they should also inquire about the tools used in software development. “We need standards and certifications for software used in critical infrastructure sectors,” he said. In response to the incident, CrowdStrike committed to enhancing its software resilience by adding more validation checks and conducting independent third-party security code reviews. Weinick advised reviewing vendor agreements, updating business disruption insurance coverage, and conducting tabletop exercises to rehearse business continuity and recovery procedures for all potential disruptions. Overall, the CrowdStrike outage highlighted critical IT and security considerations, emphasizing the need for resilience, effective third-party risk management, and robust incident response and recovery plans. 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
Data and Technology Strategies Enhance Payer Patient Education Analytics platforms, omnichannel engagement tools, telehealth, and other technological advancements have become essential in driving successful, enhanced payer patient education. Cathy Moffitt, MD, a pediatrician with 15 years of experience in the pediatric emergency department and now the senior vice president and Aetna chief medical officer at CVS Health, understands the critical role of patient education. “Education is empowerment. It is engagement. It is very critical to making patients more equipped to handle their healthcare journey,” Moffitt said in an episode of Healthcare Strategies. “Even overseeing a large payer like Aetna, I still believe tremendously in health education.” Enhance Payer Patient Education For large payers, effective patient education begins with data analytics and a deep understanding of their member population. Through data, payers can identify key insights, including when members are most receptive to educational materials. “People are more open to hear you and to be educated and empowered when they need help right then,” Moffitt explained. Timing is crucial—offering educational resources when they’re most relevant to a member’s immediate needs increases the likelihood that the information will be absorbed and acted upon. Aetna’s Next Best Action initiative, launched in 2018, exemplifies this approach. Through this program, Aetna employees reach out to members with specific conditions, offering guidance on the next best steps for managing their health. By providing education at a time when members are most open to it, the initiative ensures that patient education is both timely and impactful. In addition to timing, payer data can shape patient education by providing insights into a member’s demographics, including race, sexual orientation, gender identity, ethnicity, and location. Tailoring educational efforts to these factors ensures that communication is accessible and resonates with members. To better connect with a diverse member base, Aetna has integrated translator services into its customer support and trained representatives on sensitivity to sexual orientation and gender identity. Additionally, updating the provider directory to reflect demographic data is crucial. When members see providers who share their language, culture, and experiences, they are more likely to engage with and retain the educational materials provided. “Understanding, in a multicultural and multifactorial way, who our members are and trying to help understand what they need…as well as understanding both acute and chronic illness from an actionability standpoint, where we can best engage to good effect as we reach out to people—that’s the cornerstone of our intent and our philosophy around how we scrub data,” Moffitt shared. With over 20 years in the healthcare industry, both as a provider and now in a payer role, Moffitt has observed key trends and identified strengths and weaknesses in patient education efforts. She noted that the most successful patient education initiatives have been in mental health and preventive care, with technology playing a crucial role in both areas. Patient education has significantly reduced the stigma around mental healthcare and highlighted the importance of mental wellness. Telemedicine has vastly improved access to care, particularly in mental health, Moffitt noted. In preventive care, more people are now aware of the benefits of cancer screenings, vaccines, wellness visits, and other preventive measures. Moffitt suggested that the increased use of home health visits and retail clinics has contributed to these improvements, particularly among Aetna’s members. Looking ahead, Moffitt predicted that customized engagement is the next frontier for patient education. Members increasingly want educational materials delivered in a personalized and streamlined manner that suits their preferences. Omnichannel engagement solutions will be vital in meeting this demand. While significant progress has been made in enabling members to receive educational materials through various channels such as email, text, and phone calls, Moffitt anticipates even more advancements in the future. “I can’t tell you exactly where we’re going to be in 10 years because I wouldn’t have been able to tell you 10 years ago where we are now, but we will continue to respond and meet the demands with the technological commitments that we’re making,” Moffitt said. 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 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
Neo4j Runway and Healthcare Knowledge Graphs Recently, Neo4j Runway was introduced as a tool to simplify the migration of relational data into graph structures. LLMs Turn CSVs into Knowledge Graphs. According to its GitHub page, “Neo4j Runway is a Python library that simplifies the process of migrating your relational data into a graph. It provides tools that abstract communication with OpenAI to run discovery on your data and generate a data model, as well as tools to generate ingestion code and load your data into a Neo4j instance.” In essence, by uploading a CSV file, the LLM identifies the nodes and relationships, automatically generating a Knowledge Graph. Knowledge Graphs in healthcare are powerful tools for organizing and analyzing complex medical data. These graphs structure information to elucidate relationships between different entities, such as diseases, treatments, patients, and healthcare providers. Applications of Knowledge Graphs in Healthcare Integration of Diverse Data Sources Knowledge graphs can integrate data from various sources such as electronic health records (EHRs), medical research papers, clinical trial results, genomic data, and patient histories. Improving Clinical Decision Support By linking symptoms, diagnoses, treatments, and outcomes, knowledge graphs can enhance clinical decision support systems (CDSS). They provide a comprehensive view of interconnected medical knowledge, potentially improving diagnostic accuracy and treatment effectiveness. Personalized Medicine Knowledge graphs enable the development of personalized treatment plans by correlating patient-specific data with broader medical knowledge. This includes understanding relationships between genetic information, disease mechanisms, and therapeutic responses, leading to more tailored healthcare interventions. Drug Discovery and Development In pharmaceutical research, knowledge graphs can accelerate drug discovery by identifying potential drug targets and understanding the biological pathways involved in diseases. Public Health and Epidemiology Knowledge graphs are useful in public health for tracking disease outbreaks, understanding epidemiological trends, and planning interventions. They integrate data from various public health databases, social media, and other sources to provide real-time insights into public health threats. Neo4j Runway Library Neo4j Runway is an open-source library created by Alex Gilmore. The GitHub repository and a blog post describe its features and capabilities. Currently, the library supports OpenAI LLM for parsing CSVs and offers the following features: The library eliminates the need to write Cypher queries manually, as the LLM handles all CSV-to-Knowledge Graph conversions. Additionally, Langchain’s GraphCypherQAChain can be used to generate Cypher queries from prompts, allowing for querying the graph without writing a single line of Cypher code. Practical Implementation in Healthcare To test Neo4j Runway in a healthcare context, a simple dataset from Kaggle (Disease Symptoms and Patient Profile Dataset) was used. This dataset includes columns such as Disease, Fever, Cough, Fatigue, Difficulty Breathing, Age, Gender, Blood Pressure, Cholesterol Level, and Outcome Variable. The goal was to provide a medical report to the LLM to get diagnostic hypotheses. Libraries and Environment Setup pythonCopy code# Install necessary packages sudo apt install python3-pydot graphviz pip install neo4j-runway # Import necessary libraries import numpy as np import pandas as pd from neo4j_runway import Discovery, GraphDataModeler, IngestionGenerator, LLM, PyIngest from IPython.display import display, Markdown, Image Load Environment Variables pythonCopy codeload_dotenv() OPENAI_API_KEY = os.getenv(‘sk-openaiapikeyhere’) NEO4J_URL = os.getenv(‘neo4j+s://your.databases.neo4j.io’) NEO4J_PASSWORD = os.getenv(‘yourneo4jpassword’) Load and Prepare Medical Data pythonCopy codedisease_df = pd.read_csv(‘/home/user/Disease_symptom.csv’) disease_df.columns = disease_df.columns.str.strip() for i in disease_df.columns: disease_df[i] = disease_df[i].astype(str) disease_df.to_csv(‘/home/user/disease_prepared.csv’, index=False) Data Description for the LLM pythonCopy codeDATA_DESCRIPTION = { ‘Disease’: ‘The name of the disease or medical condition.’, ‘Fever’: ‘Indicates whether the patient has a fever (Yes/No).’, ‘Cough’: ‘Indicates whether the patient has a cough (Yes/No).’, ‘Fatigue’: ‘Indicates whether the patient experiences fatigue (Yes/No).’, ‘Difficulty Breathing’: ‘Indicates whether the patient has difficulty breathing (Yes/No).’, ‘Age’: ‘The age of the patient in years.’, ‘Gender’: ‘The gender of the patient (Male/Female).’, ‘Blood Pressure’: ‘The blood pressure level of the patient (Normal/High).’, ‘Cholesterol Level’: ‘The cholesterol level of the patient (Normal/High).’, ‘Outcome Variable’: ‘The outcome variable indicating the result of the diagnosis or assessment for the specific disease (Positive/Negative).’ } Data Analysis and Model Creation pythonCopy codedisc = Discovery(llm=llm, user_input=DATA_DESCRIPTION, data=disease_df) disc.run() # Instantiate and create initial graph data model gdm = GraphDataModeler(llm=llm, discovery=disc) gdm.create_initial_model() gdm.current_model.visualize() Adjust Relationships pythonCopy codegdm.iterate_model(user_corrections=”’ Let’s think step by step. Please make the following updates to the data model: 1. Remove the relationships between Patient and Disease, between Patient and Symptom and between Patient and Outcome. 2. Change the Patient node into Demographics. 3. Create a relationship HAS_DEMOGRAPHICS from Disease to Demographics. 4. Create a relationship HAS_SYMPTOM from Disease to Symptom. If the Symptom value is No, remove this relationship. 5. Create a relationship HAS_LAB from Disease to HealthIndicator. 6. Create a relationship HAS_OUTCOME from Disease to Outcome. ”’) # Visualize the updated model gdm.current_model.visualize().render(‘output’, format=’png’) img = Image(‘output.png’, width=1200) display(img) Generate Cypher Code and YAML File pythonCopy code# Instantiate ingestion generator gen = IngestionGenerator(data_model=gdm.current_model, username=”neo4j”, password=’yourneo4jpasswordhere’, uri=’neo4j+s://123654888.databases.neo4j.io’, database=”neo4j”, csv_dir=”/home/user/”, csv_name=”disease_prepared.csv”) # Create ingestion YAML pyingest_yaml = gen.generate_pyingest_yaml_string() gen.generate_pyingest_yaml_file(file_name=”disease_prepared”) # Load data into Neo4j instance PyIngest(yaml_string=pyingest_yaml, dataframe=disease_df) Querying the Graph Database cypherCopy codeMATCH (n) WHERE n:Demographics OR n:Disease OR n:Symptom OR n:Outcome OR n:HealthIndicator OPTIONAL MATCH (n)-[r]->(m) RETURN n, r, m Visualizing Specific Nodes and Relationships cypherCopy codeMATCH (n:Disease {name: ‘Diabetes’}) WHERE n:Demographics OR n:Disease OR n:Symptom OR n:Outcome OR n:HealthIndicator OPTIONAL MATCH (n)-[r]->(m) RETURN n, r, m MATCH (d:Disease) MATCH (d)-[r:HAS_LAB]->(l) MATCH (d)-[r2:HAS_OUTCOME]->(o) WHERE l.bloodPressure = ‘High’ AND o.result=’Positive’ RETURN d, properties(d) AS disease_properties, r, properties(r) AS relationship_properties, l, properties(l) AS lab_properties Automated Cypher Query Generation with Gemini-1.5-Flash To automatically generate a Cypher query via Langchain (GraphCypherQAChain) and retrieve possible diseases based on a patient’s symptoms and health indicators, the following setup was used: Initialize Vertex AI pythonCopy codeimport warnings import json from langchain_community.graphs import Neo4jGraph with warnings.catch_warnings(): warnings.simplefilter(‘ignore’) NEO4J_USERNAME = “neo4j” NEO4J_DATABASE = ‘neo4j’ NEO4J_URI = ‘neo4j+s://1236547.databases.neo4j.io’ NEO4J_PASSWORD = ‘yourneo4jdatabasepasswordhere’ # Get the Knowledge Graph from the instance and the schema kg = Neo4jGraph( url=NEO4J_URI, username=NEO4J_USERNAME, password=NEO4J_PASSWORD, database=NEO4J_DATABASE ) kg.refresh_schema() print(textwrap.fill(kg.schema, 60)) schema = kg.schema Initialize Vertex AI pythonCopy codefrom langchain.prompts.prompt import PromptTemplate from langchain.chains import GraphCypherQAChain from langchain.llms import VertexAI vertexai.init(project=”your-project”, location=”us-west4″) llm = VertexAI(model=”gemini-1.5-flash”) Create the Prompt Template pythonCopy codeprompt_template = “”” Let’s think step by
Accelerating Healthcare AI Development with Confidential Computing Can confidential computing accelerate the development of clinical algorithms by creating a secure, collaborative environment for data stewards and AI developers? The potential of AI to transform healthcare is immense. However, data privacy concerns and high costs often slow down AI advancements in this sector, even as other industries experience rapid progress in algorithm development. Confidential computing has emerged as a promising solution to address these challenges, offering secure data handling during AI projects. Although its use in healthcare was previously limited to research, recent collaborations are bringing it to the forefront of clinical AI development. In 2020, the University of California, San Francisco (UCSF) Center for Digital Health Innovation (CDHI), along with Fortanix, Intel, and Microsoft Azure, formed a partnership to create a privacy-preserving confidential computing platform. This collaboration, which later evolved into BeeKeeperAI, aimed to accelerate clinical algorithm development by providing a secure, zero-trust environment for healthcare data and intellectual property (IP), while facilitating streamlined workflows and collaboration. Mary Beth Chalk, co-founder and Chief Commercial Officer of BeeKeeperAI, shared insights with Healthtech Analytics on how confidential computing can address common hurdles in clinical AI development and how stakeholders can leverage this technology in real-world applications. Overcoming Challenges in Clinical AI Development Chalk highlighted the significant barriers that hinder AI development in healthcare: privacy, security, time, and cost. These challenges often prevent effective collaboration between the two key parties involved: data stewards, who manage patient data and privacy, and algorithm developers, who work to create healthcare AI solutions. Even when these parties belong to the same organization, workflows often remain inefficient and fragmented. Before BeeKeeperAI spun out of UCSF, the team realized how time-consuming and costly the process of algorithm development was. Regulatory approvals, data access agreements, and other administrative tasks could take months to complete, delaying projects that could be finished in a matter of weeks. Chalk noted, “It was taking nine months to 18 months just to get approvals for what was essentially a two-month computing project.” This delay and inefficiency are unsustainable in a fast-moving technology environment, especially given that software innovation outpaces the development of medical devices or drugs. Confidential computing can address this challenge by helping clinical algorithm developers “move at the speed of software.” By offering encryption protection for data and IP during computation, confidential computing ensures privacy and security at every stage of the development process. Confidential Computing: A New Frontier in Healthcare AI Confidential computing protects sensitive data not only at rest and in transit but also during computation, which sets it apart from other privacy technologies like federated learning. With federated learning, data and IP are protected during storage and transmission but remain exposed during computation. This exposure raises significant privacy concerns during AI development. In contrast, confidential computing ensures end-to-end encrypted protection, safeguarding both data and intellectual property throughout the entire process. This enables stakeholders to collaborate securely while maintaining privacy and data sovereignty. Chalk emphasized that with confidential computing, stakeholders can ensure that patient privacy is protected and intellectual property remains secure, even when multiple parties are involved in the development process. As a result, confidential computing becomes an enabling core competency that facilitates faster and more efficient clinical AI development. Streamlining Clinical AI Development with Confidential Computing Confidential computing environments provide a secure, automated platform that facilitates the development process, reducing the need for manual intervention. Chalk described healthcare AI development as a “well-worn goat path,” where multiple stakeholders know the steps required but are often bogged down by time-consuming administrative tasks. BeeKeeperAI’s platform streamlines this process by allowing AI developers to upload project protocols, which are then shared with data stewards. The data steward can determine if they have the necessary clinical data and curate it according to the AI developer’s specifications. This secure collaboration is built on automated workflows, but because the data and algorithms remain encrypted, privacy is never compromised. The BeeKeeperAI platform enables a collaborative, familiar interface for developers and data stewards, allowing them to work together in a secure environment. The software does not require extensive expertise in confidential computing, as BeeKeeperAI manages the infrastructure and ensures that the data never leaves the control of the data steward. Real-World Applications of Confidential Computing Confidential computing has the potential to revolutionize healthcare AI development, particularly by improving the precision of disease detection, predicting disease trajectories, and enabling personalized treatment recommendations. Chalk emphasized that the real promise of AI in healthcare lies in precision medicine—the ability to tailor interventions to individual patients, especially those on the “tails” of the bell curve who may respond differently to treatment. For instance, confidential computing can facilitate research into precision medicine by enabling AI developers to analyze patient data securely, without risking exposure of sensitive personal information. Chalk explained, “With confidential computing, I can drill into those tails and see what was unique about those patients without exposing their identities.” Currently, real-world data access remains a significant challenge for clinical AI development, especially as research moves from synthetic or de-identified data to high-quality, real-world clinical data. Chalk noted that for clinical AI to demonstrate efficacy, improve outcomes, or enhance safety, it must operate on real-world data. However, accessing this data while ensuring privacy has been a major obstacle for AI teams. Confidential computing can help bridge this “data cliff” by providing a secure environment for researchers to access and utilize real-world data without compromising privacy. Conclusion While the use of confidential computing in healthcare is still evolving, its potential is vast. By offering secure data handling throughout the development process, confidential computing enables AI developers and data stewards to collaborate more efficiently, overcome regulatory hurdles, and accelerate clinical AI advancements. This technology could help realize the promise of precision medicine, making personalized healthcare interventions safer, more effective, and more widely available. Chalk highlighted that many healthcare and life sciences organizations are exploring confidential computing use cases, particularly in neurology, oncology, mental health, and rare diseases—fields that require the use of
Salesforce Data Cloud and Snowflake are excited to announce that bidirectional data sharing between Snowflake, the Data Cloud company, and Salesforce Data Cloud is now generally available. In September, we introduced the ability for organizations to leverage Salesforce data directly in Snowflake via zero-ETL data sharing, enabling unified customer and business data, accelerating decision-making, and streamlining business processes. Today, we’re thrilled to share that customers can now also share Snowflake data into the Salesforce Data Cloud, using the same zero-ETL innovation to reduce friction and quickly surface powerful insights across sales, service, marketing, and commerce applications. Data Cloud and Snowflake Bidrectional Data Sharing. Data Cloud and Snowflake Bidrectional Data Sharing Enterprises generate valuable customer data within Salesforce applications, while increasingly relying on Snowflake as their preferred data platform for storing, modeling, and analyzing their full data estate. This integration between Salesforce and Snowflake minimizes friction, data latency, scale limitations, and data engineering costs associated with using these two leading platforms. The Snowflake Marketplace also offers customers the opportunity to acquire new data sets to enhance or fill gaps in their existing business data, driving innovation. By combining enterprise data and third-party data from Snowflake Marketplace with valuable customer data from Salesforce applications, organizations can unify their data and build powerful AI solutions to surface rich insights, driving superior and differentiated customer experiences. “Zero-ETL data sharing between Salesforce Data Cloud and Snowflake is game-changing. It has opened up new frontiers of data collaboration. We’re excited to see how customers are powering their customer data analytics and developing innovative AI solutions with near real-time data from Salesforce and Snowflake, generating incredible business value. Now that this integration is generally available, this kind of innovation will be broadly accessible,” says Christian Kleinerman, SVP of Product, Snowflake. Power Personalized Experiences with Salesforce and Snowflake Data sharing between Salesforce Data Cloud and Snowflake brings together holistic insights, empowering multiple customer-facing departments within any organization to create a truly robust customer 360. As Snowflake’s Chief Marketing Officer, Denise Persson, often states, a true, enterprise-wide customer 360 is the beating heart of a modern, customer-facing organization. The applicability of this integration spans various industries and unlocks new growth opportunities. For example: The bidirectional integration enables data sharing across business systems, Salesforce clouds, and operational systems, facilitating data set analysis and future action planning. This brings actionable insights and drives actions, unleashing a new level of customer experience and business productivity. 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
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
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
Salesforce has unveiled Life Sciences Cloud, a secure and trusted platform tailored for pharmaceutical (pharma) and medical technology (medtech) organizations. This innovative solution aims to expedite drug and device development, streamline patient enlistment and retention throughout the clinical trial journey, and harness AI capabilities to deliver personalized customer experiences. The significance of this announcement lies in the life sciences industry’s urgent need for accurate and accessible data to advance research and development efforts and enhance clinical trials. Despite this need, the industry has been slow to adopt digital tools, with a staggering 88% of healthcare and life sciences organizations yet to achieve their digital transformation objectives. Amit Khanna, SVP & GM of Health and Life Sciences at Salesforce, emphasized the necessity for integrated, compliant, and data-driven solutions in the life sciences industry. He highlighted Salesforce’s commitment to enhancing stakeholder engagement across the R&D and commercialization spectrum by leveraging data, AI, and CRM capabilities. The Salesforce solution encompasses: Commercial Operations, available now, provides insights into the commercial lifecycle, including contract compliance, pricing, and inventory management. AI-powered bots offer timely alerts to field representatives and forecasting insights to optimize sales strategies. Clinical Operations offers tools to set up and execute efficient trials, including Data Cloud for Health, Chain of Custody Management, and Participant Management features, aiming to enhance patient recruitment, safety, and engagement. Pharma CRM facilitates personalized engagement with stakeholders, managing interactions and digital content while ensuring compliance with regulations. Features like Healthcare Professional (HCP) Engagement and Einstein for Life Sciences enhance engagement and automate tasks for streamlined operations. Customer testimonials, such as from SI-BONE, highlight the tangible benefits of digitizing processes and improving efficiency with Salesforce solutions. Availability details for various features are provided, with some features already generally available and others set to roll out in the coming months and years. To learn more about Salesforce’s offerings for healthcare and life sciences, access industry insights, and explore the potential of CRM and AI in this sector, interested parties are encouraged to dig into the available resources or contact Tectonic today. Additionally, it’s noted that sales automation functionality for pharma/biotech customers will be available from mid-2025 onward. Learn about Salesforce for healthcare and life sciences Learn more about Salesforce Life Sciences Cloud 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
Healthcare marketers face the dual challenge of reducing costs and driving revenue, with the added responsibility of dealing with matters of life and death. Balancing these demands while prioritizing the well-being of patients and members requires a core focus on personalization in healthcare marketing strategies. Marketing Strategies for Healthcare can save lives and money. Despite the challenges, new technologies are ushering in stronger communications that benefit both healthcare organizations and those in need of care. Marketing Strategies for Healthcare Here are five ways a personalized healthcare marketing strategy can keep patients and members engaged and informed, spanning from acquisition to coordination: 1. Service Line Optimization: Service line optimization streamlines healthcare providers’ identification of the appropriate department for their patients. Thereby ensuring efficient and tailored care. This approach mirrors traditional multi-channel engagement and journey optimization. Simultaneously reaching patients through personalized messaging across various channels. Real-world Example: Ochsner Health utilized Salesforce Marketing Cloud to unify data sources and implement personalized email campaigns. Amazingly resulting in a 10% year-over-year increase in CRM-driven appointments and a 34% year-over-year increase in CRM-based revenue. Key takeaway: Personalizing healthcare marketing strategies enhances patient care and reduces administrative burdens. 2. Personalized Outreach for Appointment Adherence: Appointment adherence is a significant challenge for healthcare providers, with 18% of patients no-showing for appointments. Painfully costing providers over $150 billion annually. Personalized patient journeys, utilizing Salesforce Marketing Cloud, ensure patients are prepared for procedures through tailored communications, reducing no-shows. And increasing patient satisfaction. Real-world Example: Memorial Hermann replaced a generic 44-page guidebook with personalized checklists and timely reminders, resulting in improved patient preparation and engagement. Key takeaway: Personalized outreach increases appointment adherence and patient preparedness. 3. Complete Customer View for Medicare Providers and Payers: Finding suitable Medicare coverage is a complex task for those approaching retirement. Personalization helps healthcare payers build relationships. By providing plan recommendations based on medical history and sending updated recommendations when members change providers. Key takeaway: Personalized outreach helps retain members by offering tailored recommendations and proactive engagement. 4. Personalization Creates Increased Member Satisfaction and Better Star Ratings: Maintaining high Medicare star ratings requires a year-round effort to drive better member experiences. Personalization, utilizing customer data and AI, prevents message fatigue by delivering relevant content, meeting members’ expectations, and solving their problems. Key takeaway: Personalization contributes to higher member satisfaction and improved star ratings. In the challenging landscape of healthcare marketing, the integration of personalization technologies emerges as a key strategy to enhance patient care, streamline operations, and achieve organizational goals. 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 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 Guide to Creating a Working Sales Plan Creating a sales plan is a pivotal step in reaching your revenue objectives. To ensure its longevity and adaptability to 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 took a strategic approach to cater to specific industries a few years ago, introducing the ‘Industries’ Cloud products. These products aimed to offer tailored CRM frameworks that address the unique needs of organizations within those industries. By enabling businesses to leverage Salesforce efficiently without the need for extensive structural modifications. Account Engagement for Healthcare and Life Sciences is one of those industry specific products. Industry Specific Having worked with diverse businesses spanning various industries, one could observe the distinct structural variations in Sales Cloud from one industry to another. It became evident that Salesforce Marketing Cloud Account Engagement needed to adapt to seamlessly integrate into this framework, aligning with Salesforce records and processes rather than competing with them. Account Engagement for Healthcare Account Engagement, formerly known as Pardot, has responded by introducing industry-focused offerings, with specific emphasis on Salesforce Health Cloud and Financial Services Cloud. This discussion will look into Account Engagement (Pardot) for Healthcare & Life Sciences, highlighting popular healthcare marketing use cases within this vertical. Before exploring these use cases, let’s understand the unique features of Salesforce Health Cloud compared to the standard Sales Cloud. Salesforce Health Cloud: Key Differentiators Salesforce Health Cloud extends beyond marketing hype, presenting substantial differences beneath the surface compared to the standard Sales Cloud. Understanding the structured objects on the Salesforce side is crucial, as Account Engagement (Pardot) must seamlessly integrate into this data model, complementing Salesforce records and processes. Notable features of Salesforce Health Cloud include: Source: ‘The Health Cloud Data Model,’ developer.salesforce.com. Note: Not all objects listed are included. 3 Healthcare Marketing Use Cases for Marketing Cloud Account Engagement, formerly Pardot Given the complex web of relationships in Health Cloud, Account Engagement, primarily B2B, focuses on engaging healthcare professionals. Here are three use cases for different sub-verticals: Salesforce Account Engagement’s alignment with Salesforce’s industry-specific mission marks a positive step, offering tailored solutions for verticals with distinct CRM requirements. This alignment is expected to enhance the compatibility of Pardot-specific objects with Health Cloud objects, improving record synchronization, segmentation, and overall marketing operational efficiency. Tectonic looks forward to assisting your organization in uniting the marketing power of Account Engagement with Health Cloud. Contact us today. 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
PHI vs. ePHI: Navigating Healthcare Data Security Established in 1996, HIPAA predates the era of high-speed internet access, cloud computing, and ubiquitous smartphones. During the 90s, healthcare providers relied on fax, paper forms, and traditional mail to transmit Protected Health Information (PHI). In today’s digital landscape, providers leverage electronic means to transmit a greater volume of patient data more efficiently. Gone are the days of sending a fax with a cover page asking whoever picks it up on the receiver end not to read it. PHI vs ePHI have changed the way healthcare data is handled forever. Electronic Protected Health Information (ePHI) refers to digitized PHI transmitted, received, or stored electronically. This encompasses data in online patient records, applications, PDFs, emails, medical devices, flash drives, and other electronic formats. Despite the transition to electronic storage and transmission, the standards for safeguarding PHI and ePHI remain the same, differing only in the medium used by providers. While digital tools enhance healthcare convenience, they present a new, dual challenge. The digital format and storage and sharing of ePHI on company networks and the internet make it susceptible to cyber theft. Unlike traditional PHI, which can be physically secured, protecting ePHI poses greater challenges, particularly for large hospitals and distributed healthcare organizations. Given the various ways ePHI can be accessed, modified, and stolen, HIPAA mandates robust cybersecurity measures to safeguard digital patient information. The Security Rule, an extension of HIPAA, stipulates physical, administrative, and technical safeguards specifically tailored for ePHI. In an era where cybercriminals can exploit vulnerabilities with a few keystrokes, coupled with the growing trend toward decentralized healthcare delivery and data-driven practices, healthcare organizations must develop a distinct strategy for ePHI protection in collaboration with their cybersecurity teams. As the digitization of patient care increases, securely sharing ePHI emerges as the next frontier in healthcare compliance. If you work with PHI or ePHI contact Tectonic for assistance in keeping your data secure and compliant. 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 Salesforce Government Cloud: Ensuring Compliance and Security Salesforce Government Cloud public sector solutions offer dedicated instances known as Government Cloud Plus and Government Cloud Plus – Defense. Read more PII Explained Personal Identifiable Information (PII) is defined as: Any representation of information that permits the identity of an individual to whom Read more Case Study: Health Payer/Provider Onboarding/Network Growth After doing their initial Sales Cloud implementation and SAP integration over 12 years ago, this company was only leveraging Salesforce Read more
Understanding Cloud Analytics Cloud analytics refers to leveraging cloud computing resources to conduct data analysis more efficiently. It involves using advanced analytical tools to extract insights from vast datasets, presenting information in a user-friendly format accessible via web browsers. Core Concepts of Cloud Analytics Explained Cloud analytics shifts traditional data analytics operations, such as processing and storage, to public or private cloud environments. Similar to on-premises analytics, cloud solutions facilitate pattern identification, predictive modeling, and business intelligence (BI) insights. They leverage cloud technologies and algorithms, notably artificial intelligence (AI), including machine learning (ML) and deep learning (DL). Operational Framework of Cloud-Based Analytics Cloud analytics platforms offer capabilities to build, deploy, scale, and manage data analytics solutions in a cloud-based infrastructure. Examples include cloud enterprise data warehouses, data lakes, and on-demand BI and marketing analytics. Users can subscribe to services under flexible pricing models, alleviating concerns about scalability, performance, and maintenance. Types of Cloud Analytics Cloud-based analytics solutions vary by deployment model: Key Features and Benefits Cloud analytics offers several advantages: Applications and Use Cases Cloud analytics supports diverse applications, including: Comparing Cloud Analytics with Traditional Data Analytics Cloud analytics leverages cloud infrastructure for scalable and flexible data processing, contrasting with traditional analytics tools deployed on-premises. This shift enhances agility and accessibility while reducing operational complexities and costs. Why Cloud Analytics Matters Cloud analytics empowers organizations to harness actionable insights efficiently, driving informed decision-making and competitive advantage. It streamlines operations, fosters collaboration, and enhances data reliability and strategic planning capabilities. Adopting cloud-based analytics enables businesses to transform data into valuable intelligence, fueling innovation and growth. By leveraging cloud-based resources, organizations can achieve operational excellence, secure data-driven insights, and maintain a competitive edge in today’s dynamic business 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
