AI in Healthcare: How Cellma Powers Smarter Care
Table of Contents Help Others Discover – Click to Share! Facebook Twitter LinkedIn Your browser does not support the audio tag. Table of Contents Electronic Health Records (EHRs) and Electronic Medical Records (EMRs) were initially developed to substitute for physical files, render patient information traceable, and accelerate access to medical history. Today, however, healthcare has moved past information technology systems that simply house information, healthcare needs digital platforms that can think, assist, and act. That shift is already well underway. Globally, 86% of healthcare organisations report that they are already using or actively planning AI-driven capabilities within their EHR or EMR systems, while more than 73% are piloting or deploying AI in core functions such as clinical decision support, risk prediction, automation, or patient communication. At the leadership level, over 80% of health executives say AI, including generative AI, will significantly reshape care delivery by the end of 2025, and 84% specifically believe AI will influence clinical decision-making in the near term. Meanwhile, 80% of organisations expect AI to reduce manual administrative labour, signalling a shift from data entry systems to true clinical assistants. The scale of data behind this transformation is immense. Healthcare already generates around 30% of the world’s total data, and the volume is growing at 36% per year, making AI not just an advantage, but the only practical way to extract real-time insights from rapidly expanding patient datasets. Areas such as Europe are now among the leading global adopters as a result of various national digital health mandates, while newer health systems are leveraging AI-enabled EHRs to address staffing challenges, automate triage and increase access to care. Early adopters have already seen impact: 40% report measurable operational improvements due to AI-enabled EHR workflows, ranging from faster coding to shorter wait for diagnoses. The UK is rapidly progressing towards an AI-enabled healthcare model. The NHS England 10-Year Plan clarifies the critical need for AI and data to help reduce pressure on staff, improve clinical accuracy and provide care that is more predictive and personalised. Across the system, more than half of NHS Trusts are currently piloting or rolling out AI tools, with an additional hundreds of trials supported by the NHS. An NHS report of productivity also recently highlighted that staff could save up to 43 minutes each person each day with the use of AI tools, demonstrating the real impact on the day-to-day operation in care delivery. Artificial Intelligence is no longer an optional add-on to health records, it is redefining digital healthcare. Instead of passive repositories, AI enables digital healthcare to become active clinical partners that predict risk, guide decision-making, prevent errors, automate repetitive workflows, and personalise care at scale. As NHS Trusts, hospitals, and healthcare providers move toward fully connected care ecosystems, the question is no longer “Do we need AI in healthcare?” but “How intelligently is AI being built into our EHR and EMR systems, and is it truly improving care?” How AI Is Transforming the Healthcare Landscape AI is bringing a wide range of capabilities into modern EHR and EMR systems — and the examples below are just a few of the many ways it is reshaping digital healthcare. All of them focus on reducing manual work, improving clinical accuracy, and turning raw data into meaningful, real-time insight for care teams. AI Capability What It Means in Real Care Settings Predictive Analytics Identifies high-risk patients, readmission probability, disease progression Clinical Decision Support Suggests evidence-based interventions and flags risks before they occur NLP (Natural Language Processing) Converts free-text notes into structured, usable data Workflow Automation Auto-handles referrals, reminders, coding, authorisations Patient Engagement Intelligence Sends smart alerts, follow-up prompts, remote check-ins Clinical Documentation – Ambient Voice AI Listens during consultations and automatically generates structured clinical notes, dramatically reducing documentation time AI-Enabled Revenue Cycle Management Auto-captures charges, predicts potential claim denials, validates coding accuracy, and supports faster billing cycles This shift is no longer futuristic. It’s already visible in radiology reporting, oncology treatment pathways, early diabetes risk modelling, population health dashboards, etc. Real-World Benefits of AI-Enabled EHRs and EMRs Faster Diagnosis – AI supports clinicians by analysing patient history, vitals, and patterns instantly Reduced Clinician Burnout – Less typing, less duplication, less admin Smarter Population Health – Detects risk clusters before they escalate Better Patient Experience – Personalised follow-ups, reminders, clear digital pathways Operational Efficiency – Predicts bed demand, resource use, and workforce gaps In other words, AI is not replacing clinicians, it’s removing everything that gets in their way. Where Traditional EHRs Still Fall Short Even some of the most widely used EHRs today suffer from: High admin load – clinicians spend more time typing than treating Reactive workflows – action happens after something goes wrong Zero intelligence – systems store data but don’t interpret it Poor interoperability – fragmented systems, fragmented care Low user experience – forcing clinicians to “work for the software” instead of the other way around This is the exact gap AI-filled EHRs are built to solve, and it’s the space where platforms like Cellma are already delivering results. How Cellma Uses AI to Create a Smarter Digital Healthcare Ecosystem Cellma isn’t just a digital record system, it is an AI-enabled healthcare platform built to support clinicians, engage patients, and help organisations move toward predictive and personalised care. This is how AI operates within Cellma: AI-Based Clinical Decision Support Cellma supports clinicians by: Recommending evidence-based treatment plans Identifying drug interactions or contraindications Identifying abnormal lab trends or missed follow-ups The outcome? Safer care, fewer clinical errors, and quicker decision-making. Predictive Analytics for Risk & Population Health Cellma uses past and real-time data to help identify: Patients that are at risk of readmission Population level trends (e.g. elevated hypertension or diabetes risk) Early detection of deteriorating chronic conditions This enables both preventative care and future NHS long-term planning. NLP for Clinical Notes Natural Language Processing (NLP) is one of the most impactful AI capabilities built into modern EHR and EMR systems. Instead of forcing clinicians to rely on heavy typing, rigid templates, or endless dropdowns, NLP allows them to document in the way that feels most natural, while the system does the structuring in the background. How it works: Clinicians may type, dictate, or upload free-text notes. AI instantly extracts key clinical information, converts it into structured data, and places it in the correct fields within the record. The result? Less typing. Fewer dropdowns. No repeated coding. Automated Workflows Cellma applies AI logic to route: Referrals Appointment