Mac Clinical Database with OCR
Budget: £250 – £750 GBP
I need a Mac-native desktop (potentially working on cloud or backing up to cloud and can input data to from mobile or windows) application that will house a clinical database for a defined cohort of patients and their related diseases. The data model itself is straightforward—basic demographics, diagnosis codes, treatment timelines, and outcome notes—but a few smart features are critical to daily use:
• Multiple input channels. Clinicians must be able to type directly into clean, well-labelled forms, import legacy records from CSV or Excel, and drop scanned documents that the system will pass through OCR, automatically mapping recognised values to the correct fields.
• On-the-fly calculations. Once records are saved, the app should detect recurring patterns, run predefined calculations, and surface the results on a simple dashboard so staff can spot trends at a glance.
• Sustainable codebase. I’ll need full editable source code, clear build instructions, and a modular structure so future fields or logic can be added without rewriting the core.
Apple-centric technologies are welcome—Swift, SwiftUI, Core Data, and reliable OCR libraries such as Vision, Tesseract, or comparable frameworks—so long as the final package runs smoothly on recent macOS versions.
For acceptance I’ll review:
1. An installable .app bundle that opens on macOS, saves and searches records locally, and performs the OCR workflow and connects to the drive (cloud)
2. A small sample dataset that demonstrates manual entry, file import, and scanned-document capture feeding the same tables. create episodes for same patient with ability to track tasks and what is pending
3. A lightweight dashboard view showing at least two calculated metrics updating in real time.
4. Complete source code and a brief README explaining how to modify field definitions and compiled builds.
If parts of the workflow can be streamlined further, I’m open to suggestions; just keep the interface intuitive for clinical staff with minimal technical background.
• Multiple input channels. Clinicians must be able to type directly into clean, well-labelled forms, import legacy records from CSV or Excel, and drop scanned documents that the system will pass through OCR, automatically mapping recognised values to the correct fields.
• On-the-fly calculations. Once records are saved, the app should detect recurring patterns, run predefined calculations, and surface the results on a simple dashboard so staff can spot trends at a glance.
• Sustainable codebase. I’ll need full editable source code, clear build instructions, and a modular structure so future fields or logic can be added without rewriting the core.
Apple-centric technologies are welcome—Swift, SwiftUI, Core Data, and reliable OCR libraries such as Vision, Tesseract, or comparable frameworks—so long as the final package runs smoothly on recent macOS versions.
For acceptance I’ll review:
1. An installable .app bundle that opens on macOS, saves and searches records locally, and performs the OCR workflow and connects to the drive (cloud)
2. A small sample dataset that demonstrates manual entry, file import, and scanned-document capture feeding the same tables. create episodes for same patient with ability to track tasks and what is pending
3. A lightweight dashboard view showing at least two calculated metrics updating in real time.
4. Complete source code and a brief README explaining how to modify field definitions and compiled builds.
If parts of the workflow can be streamlined further, I’m open to suggestions; just keep the interface intuitive for clinical staff with minimal technical background.
Related categories:
Data Entry
Cloud Computing
OCR
Database Development
Swift
Data Visualization
Data Management
Desktop Application