Overview

Incremental Primary Care Views (iPCV) transforms the complex, highly normalised data held within primary care systems into a collection of curated, analyst-ready clinical data models. By shaping raw source tables into intuitive clinical concepts — such as medications, patient demographics, and coded clinical observations — iPCV removes the technical barriers typically associated with large-scale electronic health record analysis.

iPCV provides immediate access to the most frequently used patient attributes without requiring specialist knowledge of the underlying source database structures. To ensure high performance, iPCV is materialised daily, removing the heavy computational overhead of runtime processing and allowing even petabyte-scale workloads to be queried efficiently. The result is a comprehensive longitudinal record that significantly reduces development time while maintaining rigorous governance through built-in delta processing and configurable privacy controls.

Core Capabilities

• Analyst-Ready Clinical Models: Data is pre-joined and de-normalised into intuitive clinical concepts presented as data models, removing the need for specialist knowledge of complex underlying system schemas.
• High-Performance Materialisation: Models are materialised daily to eliminate runtime computational overhead, enabling fast query responses even at petabyte scale.
• Efficient Delta Processing: Built-in incremental views identify only the changes applied within the last 24 hours, allowing for streamlined data synchronisation.
• Integrated Governance: Native, configurable controls for de-identification and the restriction of sensitive codes ensure data access remains compliant with legal and contractual requirements.

Out of Scope

To ensure high-performance analytics and data consistency, iPCV focuses exclusively on structured clinical data. The following are not included:

• Unstructured Data: iPCV does not contain free-text clinician notes, comments, or narrative descriptions. It also excludes all media attachments, such as images, PDFs, and videos.
• Uncoded Observations: iPCV includes only clinical entries recorded with a valid clinical code (for example, SNOMED CT or Read codes). Any data entered as text-only or without a formal code is omitted.
• Real-Time Feeds: iPCV is an analytical tool refreshed every 24 hours and inherits latency from the clinical system. It is not intended for real-time clinical monitoring or immediate transactional updates.
• Raw Source Tables: iPCV is a curated, de-normalised layer and does not include the raw information held in underlying primary care source systems. If your use case requires access to raw source data or alternative Explorer schemas, contact your account director to discuss available options.

Target Audience

iPCV is designed for technical teams who require direct, high-performance access to large-scale electronic health records. It is specifically built for:

• Data Engineers and Developers: Building automated data pipelines, integrating primary care data into existing cloud stacks, or developing bespoke applications.
• BI and Data Analysts: Performing complex longitudinal research, population health analysis, or creating advanced reporting dashboards.

Common Use Cases

iPCV is typically utilised by organisations looking to perform deep-dive analysis across the full patient record, including:

• Longitudinal Research & Real-World Evidence (RWE): Tracking patient journeys over years or decades to understand disease progression, treatment pathways, and long-term clinical outcomes.
• Population Health Management: Identifying cohorts and risk-stratifying patient groups to improve preventative care and resource allocation across large geographies.
• Operational & Clinical Benchmarking: Analysing variations in care, prescribing habits, and clinical activity to drive efficiency and standardisation across primary care networks.
• Predictive Modelling & AI Development: Using cleaned, coded clinical data as a foundation for training machine learning models to predict health risks or identify early signs of chronic conditions.
• Service Evaluation: Measuring the impact of clinical interventions or new care models by analysing changes in patient activity and health markers over time.

Technical Requirements

Users interact with iPCV via a pull-based SQL interface using Trino SQL technology. To effectively utilise the models, your team should be proficient in:

• SQL: Writing and optimising queries to extract and analyse data.
• Data Integration: Connecting to the SQL endpoint via industry-standard ODBC or JDBC drivers to pull data into your own tools, languages, or platforms.

iPCV is not a file-based delivery service (for example, CSV or SFTP). It is a live analytical environment designed for teams who want to query data at source or manage their own incremental data extracts.