Oct 5, 2025
6
min read
Medically Reviewed
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The Limitations of the Status Quo: Static Templates and Disconnected Data
To understand the role of AI, we must first be honest about the limitations of our current tools. The standard EMR or PMS template for a care plan is essentially a digital piece of paper. It provides structure, but it offers no intelligence. When a GP decides to create a care plan, they are faced with a series of blank fields that they must populate by hand. They have to manually review the patient's history in one window, identify their current medications in another, check their latest lab results in a third, and then re-type all of this relevant information into the template. The goals, tasks, and review dates discussed during the consultation must be transcribed from memory or from scribbled notes.
This fragmented process is not just inefficient; it is a barrier to true personalisation. The time pressure of a 15-minute consult often means that the plan becomes a high-level summary rather than the detailed, specific roadmap it is intended to be. Furthermore, this model is completely disconnected from other modern tools. For example, a clinic might use a separate AI scribe tool to transcribe the consultation. While this might provide a text record of the conversation about the care plan, it is unstructured data. The GP still has to manually read through the transcript and extract the key information to populate the template. The AI scribe, as a standalone "point solution," cannot create the structured, actionable document itself. The workflow remains broken, and the administrative burden on the GP remains immense.
The Unified Platform Solution: From Manual Transcription to AI-Powered Generation
A unified platform like MediQo fundamentally re-architects this broken workflow. It is built on the core principle that every part of the system should be interconnected, allowing for a seamless flow of data from one module to the next. The creation of a personalised treatment plan is a perfect example of this "Platform Advantage" in action. It is a process that leverages the full power of the platform, from historical data synthesis to real-time conversation capture.
The process begins with automated context gathering. The platform's AI does not start with a blank slate. Through features like "History-at-a-Glance," it has already synthesised the patient's entire longitudinal record from the PMS into a single, unified clinical story. Before the GP even begins to think about the care plan, the AI has a deep, contextual understanding of the patient's chronic conditions, their medication history, their allergies, and any relevant past consultations or test results. This automated preparation is the foundational step that standalone tools completely miss.
During the consultation, as the GP and patient discuss the goals and strategies for the treatment plan, the Clinical Assistant module is working in the background. Using sophisticated ambient documentation, it is not just transcribing the conversation; it is capturing the key clinical concepts as structured, FHIR-aligned data. When the GP says, "Let's aim to get your HbA1c below 7% over the next six months," the AI understands this as a specific, measurable clinical goal with a timeline. This real-time, structured data capture is the second critical ingredient.
Now, with both the deep historical context and the live consultation data, the AI can perform its most powerful function. MediQo's Automated Care & Treatment Plans feature uses this combined intelligence to generate a draft, personalised care plan. It takes the clinic's own pre-approved template and intelligently populates it with the specific details of the patient sitting in the room. The goals discussed moments ago are already there. The relevant medications from the patient's history are pulled in. A suggested review schedule is proposed based on clinical best practice and the goals that were set.
Expert Tips
"True personalisation of a treatment plan comes from the synthesis of a patient's entire history with the specific goals of today's consultation. A unified AI platform is the only tool that can perform this synthesis automatically, transforming care planning from a manual chore into a powerful, data-driven workflow." - Arash Zohuri, CEO, MediQo
The Clinician is Always in Command
It is absolutely crucial to stress that the AI's role is to assist, not to dictate. The system generates a comprehensive, highly-personalised draft that is presented to the GP for review. The clinician is always in complete control. They can easily edit, add, or remove any element of the plan. The AI has performed the heavy administrative lifting—the data gathering, the synthesis, the transcription—leaving the GP to focus on the high-value clinical task of refining and validating the plan with their patient. This "clinician-in-the-loop" model is the only safe and effective way to deploy AI in a clinical setting. It augments the GP's intelligence; it does not attempt to replace it.
Once the GP has signed off, the platform's unified nature continues to add value. The finalised plan can be uploaded back to the PMS via HL7 FHIR, ensuring it becomes a permanent part of the patient's official record. But it does not stop there. The platform can then automatically generate a Patient Education Letter, a patient-friendly version of the plan that explains their goals and tasks in simple, clear language. This is a powerful tool for improving patient understanding and adherence, transforming the plan from a clinical document into a shared roadmap to better health.
Key Takeaways
Prioritizing Ethical AI Implementation
Optimizing Practice Efficiency and Revenue
The Power of Unified Platforms
Strategic Innovation for Sustainable Growth
To understand the role of AI, we must first be honest about the limitations of our current tools. The standard EMR or PMS template for a care plan is essentially a digital piece of paper. It provides structure, but it offers no intelligence. When a GP decides to create a care plan, they are faced with a series of blank fields that they must populate by hand. They have to manually review the patient's history in one window, identify their current medications in another, check their latest lab results in a third, and then re-type all of this relevant information into the template. The goals, tasks, and review dates discussed during the consultation must be transcribed from memory or from scribbled notes.
This fragmented process is not just inefficient; it is a barrier to true personalisation. The time pressure of a 15-minute consult often means that the plan becomes a high-level summary rather than the detailed, specific roadmap it is intended to be. Furthermore, this model is completely disconnected from other modern tools. For example, a clinic might use a separate AI scribe tool to transcribe the consultation. While this might provide a text record of the conversation about the care plan, it is unstructured data. The GP still has to manually read through the transcript and extract the key information to populate the template. The AI scribe, as a standalone "point solution," cannot create the structured, actionable document itself. The workflow remains broken, and the administrative burden on the GP remains immense.
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