Latest news with #SomnathBanerjee
Forbes
31-07-2025
- Health
- Forbes
Why AI In Healthcare Requires Real-Time Data Updates
Somnath Banerjee is an IT leader and an enterprise MDM architect at a Fortune 50 Health Insurance Company. Healthcare is rapidly adopting AI, with large language models (LLMs)—AI systems trained on massive datasets to understand and generate human-like language—now supporting clinical documentation, medical research and patient education. From AI-powered symptom checkers to decision support tools embedded in electronic health records (EHRs), these innovations promise to streamline care. Yet, the risk is equally real. Without real-time data pipelines, these tools can become dangerously outdated. As guidelines evolve and new diseases emerge, static models fall short, leading to inaccurate diagnoses and obsolete recommendations. For AI to be effective in medicine, it must be continuously updated. To address this, retrieval-augmented generation (RAG) offers a promising solution to keeping AI outputs grounded in current, evidence-based medical knowledge. Clinical Risks And Legal Implications Of Stale AI When AI operates on outdated information, the consequences can be serious. Static AI models, for instance, may recommend recalled medications, ignore updated clinical guidelines or miss emerging medical conditions if trained before their onset. LLMs, especially those detached from current data, can also hallucinate—confidently delivering fabricated 'facts.' One chatbot offered unsafe dieting advice to an eating disorder patient due to flawed training. Other AI systems, intended as therapeutic aids, have been linked to tragic consequences when users followed misguided recommendations. This raises significant legal and ethical concerns. If an AI system offers outdated medical advice that leads to harm, responsibility becomes murky. Lawsuits over healthcare AI errors are already surfacing, and regulators like the FTC could step in more stringently. Industry analysis warns that outdated LLMs can erode customer trust, damage reputations and pose legal risks. In healthcare, the cost is not just reputational—it can be measured in lives. The Rise Of Knowledge-Augmented AI In Healthcare RAG—a technique designed to keep AI answers grounded in up-to-date, domain-specific information—offers a lot of potential in keeping AI data current. RAG combines a retrieval system with generative AI, meaning the chatbot or assistant doesn't just rely on its generic training data. It actively pulls in relevant facts from approved databases or documents, then generates a response. This approach helps avoid the generic or incorrect replies that a pure LLM might give, and significantly reduces dangerous hallucinations by tethering answers to evidence. Real-world applications are expanding: • Smart EHR assistants help clinicians summarize notes, suggest diagnoses and stratify patient risk by analyzing recent medical knowledge. • Patient-facing chatbots manage chronic care, send medication reminders and triage symptoms while integrating with health devices. • Drug safety alerts powered by RAG systems can identify real-time contraindications and recalls, preventing harmful prescriptions. • Remote care assistants support elderly patients and those with chronic illnesses, conducting daily check-ins and transmitting vitals remotely to providers via IoT devices. These applications highlight the power of RAG-enabled AI in delivering safe, relevant and timely guidance—provided the underlying data remains current. This makes real-time RAG updates imperative, rather than relying on delayed batch processes. Avoiding Medical Errors With Real-Time AI Updates A constantly updated RAG architecture mitigates the risks of a stale AI system by ensuring AI outputs reflect the latest knowledge. These systems use continuously refreshed vector databases or semantic graphs to pull in updated journal articles, test results or FDA advisories at the time of query. For example, a chatbot aware of a same-day drug recall can alert patients immediately, while a clinician assistant might surface a new cancer study relevant to a patient's treatment. Building a real-time RAG system in healthcare requires a robust data infrastructure: • Integration with EHRs via HL7 FHIR standards allows secure access to real-time lab results, vitals and prescriptions. • Streaming technologies like Apache Kafka support instant updates to AI knowledge bases. • Strategic partnerships with credible data providers ensure continuous ingestion of up-to-date drug data, clinical guidelines and medical research. A well-architected, live-updating RAG system operates as a dynamic service, constantly learning and adjusting in response to new information. Charting A Smarter, Safer Path For Healthcare AI Real-time AI in healthcare holds great promise, but it must be used wisely. These tools are most effective when they support—not replace—humans. Human expertise provides the ethical reasoning, empathy and judgment that AI lacks. As such, transparency is key. Clinicians and patients alike must understand the capabilities and limitations of AI. Knowing when to question an AI's recommendation and how to trace its data sources is essential to safe adoption. If deployed responsibly, LLMs enhanced with real-time RAG can reduce administrative burdens, close knowledge gaps and transform care delivery. By keeping the data pipeline flowing and a human in the loop, we move further along the path to ensuring that AI heals, not harms. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
Forbes
24-06-2025
- Health
- Forbes
The Cybersecurity Gap: Ignoring MDM In A Breach-Prone Healthcare Era
Somnath Banerjee is an IT leader and an enterprise MDM architect at a Fortune 50 Health Insurance Company. getty Cyberattacks on healthcare organizations are increasingly common, with 725 data breaches reported in 2023 alone, compromising over 133 million patient records. Firewalls are strengthened and staff get phishing training. Yet an often neglected cornerstone of cybersecurity is the integrity of the underlying data itself. Without clear data lineage and accurate patient identity matching, breach response becomes chaotic. Master data management (MDM) offers a critical, often invisible, layer of defense, helping unify, govern and secure healthcare data across systems. The Data Disarray: Silos, Lineage And Identity Resolution Challenges Data Proliferation And Siloed Systems Healthcare's data volume is growing faster than any other sector, expected to increase by 36% annually through 2025. This data explosion, caused by EHRs, labs, wearables and apps, often leads to fragmented systems with inconsistent formats and identifiers. A single patient may have multiple records across disparate platforms. The fragmented data increases the chance of data loss or breach when systems fail to communicate effectively. The Pitfall Of Poor Data Lineage Without robust data lineage and understanding how data is created, modified and moved, security teams are hampered during breaches. It becomes nearly impossible to track compromised records or assess exposure, delaying both containment and compliance. In healthcare, where regulatory timelines for breach notification are strict, the inability to trace records quickly can lead to fines and loss of trust. The Identity Matching Crisis Duplicate and mismatched records are a major issue across healthcare systems. Merging errors or fragmented identifiers can lead to incorrect breach notifications or medical identity theft. If patient A's and patient B's records are entangled, the consequences during an incident—miscommunications, privacy violations or even legal liability—can escalate significantly. The High Cost Of Neglect: The Strategic Risk Of Poor MDM Operational Breakdowns In a ransomware scenario, if physicians are listed under inconsistent names across systems, such as 'Dr. A. Smith' versus 'Smith, A.B.,' alerts and recovery efforts may be delayed. What should have been a contained 48-hour downtime stretches into days of chaos. Downtime in healthcare operations can cost as much as $1.9 million per day. A lack of unified provider records transforms technical disruptions into care delivery crises. Breach Amplification Poor MDM can worsen breaches. For example, if insurance claims are compromised and patient identities are mismatched, organizations may inadvertently disclose PHI to the wrong party. Under HIPAA, even accidental disclosure is penalized, with fines up to $50,000 per violation. Regulatory And Legal Impacts Healthcare breach notification rules demand that organizations notify affected individuals (and authorities) within 60 days of discovering a breach. If data is poorly managed, it might be unclear exactly who was affected or what was stolen, making it difficult to meet this deadline. Organizations have been penalized for delaying breach notifications deemed unreasonably slow. In addition to penalties, breaches also erode patient trust. Financial Fallout According to the 2024 IBM Cost of Data Breach study, healthcare tops all industries in breach-related expenses, averaging $10.93 million per incident. These costs include forensic investigations, legal defense, regulatory fines and reputational damage. Poor MDM compounds these costs by slowing incident resolution and increasing remediation efforts. Master Data Management: The Cybersecurity Backbone Unifying Core Identities MDM provides a single source of truth for patients and providers, creating clean, validated records across platforms. This centralized consistency enhances access control, streamlines audits and reduces false positives in breach monitoring. Faster Breach Response During a security incident, MDM enables faster breach response as affected records can be instantly identified by cross-referencing compromised data with harmonized master datasets. Real-time lineage maps help isolate vulnerabilities and reduce response times. Preparedness And Recovery MDM supports breach simulations and post-attack validation. When it comes to recovery after a breach, MDM ensures that once systems are secured, the data put back into production is trustworthy. MDM acts as a backbone for resilience, allowing a return to normal operations with confidence in the data's accuracy. Strategic Integration Given the security benefits, leading healthcare organizations are elevating MDM to a strategic security priority. MDM, in conjunction with zero-trust principles and rigorous IAM, becomes a powerful triad to protect sensitive health data from both inadvertent leaks and malicious attacks. MDM Is Cybersecurity: Integrate It Or Invite The Consequences It's time to dispel the notion that master data management is merely a back-office IT function. In today's threat landscape, MDM is a frontline defender and an essential component of healthcare cybersecurity strategy. Healthcare organizations should thus champion MDM as vigorously as they do firewalls and antivirus software. It should be integrated into risk assessments, breach response playbooks and strategic planning. The hidden risks of ignoring MDM—from prolonged downtime to long regulatory wrath—are simply too great to tolerate in a data breach era. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
