Electronic Health Record

Clinical documentation should preserve a coherent clinical narrative over time while enabling structured, interoperable data. An Electronic Medical Record (EMR) must therefore support continuity across encounters, link information to problems and episodes of care, and provide semantic anchors for meaning and reuse. This page provides a concise introduction to these principles and to a pragmatic baseline architecture for implementation in modern clinical systems.

Summary

Overview

This guide outlines the essential concepts and a baseline logical architecture for designing or evaluating an Electronic Medical Record (EMR). Drawing on the author’s experience of co-developing seven medical information systems, it synthesises two established approaches: the Problem-Oriented Medical Record (POMR) as described by Weed and the Episode-Oriented Medical Record as described by Solon. The core concepts and the baseline architecture are presented below:

Patient centricity

Each patient is represented by a single longitudinal medical record spanning organisational units, locations, and departments. Administrative information (e.g. cases, billing) is managed per organisational unit, while clinical information remains patient-centred.

Structuring and semantic referencing

The EMR combines two complementary dimensions to maximise reuse and interoperability:

• Structuring: clinical information is stored in defined categories (history, examination, diagnostics, therapy, etc.), organised via a three-layer dictionary model.
• Semantic referencing: dictionary entries are linked to standard terminologies and classifications (e.g. ICD, SNOMED CT, LOINC), enabling machine-interpretable and interoperable data.

Contact and detail entry

A contact (encounter) is the smallest logical unit on the timeline, capturing the patient, care provider, event, time, location, and type of stay. Each contact contains detail entries: structured, semantically referenced clinical statements linked to both the contact and an episode of care. Each entry records three timestamps: documentation time, event time, and validation time.

Problems, diagnoses, and SOAP

Health problems evolve from initial observation through suspected diagnosis to confirmed diagnosis and, ultimately, past history. A diagnosis/problem list (after Weed) provides a hierarchical, numbered index of current and past health issues. Progress notes follow the SOAP structure (Subjective, Objective, Assessment, Plan) to separate observations from clinical interpretation and planning.

Episode of care

An episode of care covers the full course of a single health problem—from the first to the last relevant contact—grouping all related detail entries. Episodes are represented linearly in an episode list and can also be reflected hierarchically within the problem list. The concept of a partial contact links each detail entry to the specific health problem addressed within a contact. Chronic episodes may remain open over long periods.

Data integrity and access control

The architecture incorporates rigorous governance mechanisms:

• Historisation: all create/change/delete actions are logged with user, timestamp, and content—preventing silent modification.
• Validation and sign-off: hierarchical review supports clinical accountability for new, modified, or removed entries.
• Range concept (privacy by default): access rights can be defined at the detail-entry level, from personal notes to organisation-wide visibility.
• Access logging: read and write access is recorded to support auditability and transparency.
  

Architecture and scalability

The logical architecture integrates contacts, episodes, detail entries, and semantic dictionaries into a coherent data model. It scales from free-text documentation by a single clinician, through partially referenced records, to fully structured and semantically anchored data enabling advanced capabilities such as decision support, guideline-driven workflows, AI-assisted documentation, chronic disease monitoring, and billing support via tariff-linked dictionary entries.

Key benefits

• Reuse of clinical data across views, reports, dashboards, and decision support without re-entry.
• Patient-centred perspectives: chronological, problem-oriented, episode-based, and specialty-specific.
• Comparable cost and quality analysis at episode level—supporting outcome measurement and cost-unit accounting.
• Strong support for privacy, retention, and legal traceability through explicit governance and auditability.
• Technology-independent persistence: high structure and semantic anchoring support future migration with minimal information loss.