Healmate : Digital Health History System

Healmate : Digital Health History System

Rimsha Parveen J

B.Tech Information Technology Alpha College of engineering, Chennai, India

Sai Shree J

B.Tech Information Technology Alpha College of engineering, Chennai, India

Marin J

B.Tech Information Technology Alpha College of engineering, Chennai, India

Dr. Manickavasagam

M,E., Ph.D., Head of the Department Department of IT and AI & DS Alpha College of Engineering Chennai, India

Abstract – Healmate is a mobile-first, Ayushman Bharat Health Account (ABHA)-enabled Personal Health Record (PHR) system designed to improve patient safety, interoperability, and emergency healthcare access in the Indian healthcare ecosystem. The system integrates Optical Character Recognition (OCR)- based digitization of medical records, prominent allergy alerts, Quick Response (QR)-based emergency access, and standards- based interoperability using Health Level Seven Fast Healthcare Interoperability Resources (HL7 FHIR). By consolidating fragmented health data into a secure and patient-controlled platform, Healmate addresses critical gaps in existing digital health solutions and aligns with national initiatives such as the Ayushman Bharat Digital Mission (ABDM). To further strengthen the platform, we incorporated three key enhancements: Artificial Intelligence (AI)-based symptom detection that analyses user-input symptoms and saves results securely in the FHIR repository, client-side Advanced Encryption Standard 256-bit (AES-256) encryption for all sensitive data before it leaves the device, and location-based symptom clustering that groups reported symptoms geographically to help organise targeted medical camps. These additions enhance proactive care, data security, and community-level health impact. The system was developed using Flutter for the mobile interface, HL7 FHIR for data modelling, and Tesseract for OCR, and initial testing with 50 users showed improved allergy awareness, faster emergency access, and high usability, demonstrating how a patient-centric, standards-compliant Personal Health Record system can bridge gaps in Indias digital health landscape while remaining secure and scalable.

Keywords: Personal Health Records (PHR), Digital Health, Allergy Alerts, Emergency Access, Optical Character Recognition (OCR), Health Level Seven Fast Healthcare Interoperability Resources (HL7 FHIR), Ayushman Bharat Digital Mission (ABDM), Quick Response (QR) Code, Advanced Encryption Standard 256-bit (AES-256) Encryption, Artificial Intelligence (AI) Symptom Detection, Location-based Clustering.

1. INTRODUCTION

   The digitization of healthcare data has fundamentally changed how medical information is stored, accessed, and shared across providers. While hospitals and clinics have widely adopted Electronic Health Records (EHRs), most patients in India still

   struggle with fragmented medical histories scattered across different doctors, labs, and pharmacies. They often carry paper prescriptions, lose old reports, and have no quick way to share critical details during emergencies. National initiatives like the Ayushman Bharat Digital Mission (ABDM) and the National Digital Health Blueprint have created a strong foundation for patient-centric, interoperable systems, yet real-world gaps remain poor allergy documentation, slow emergency access, limited digitization of legacy records, and weak data security.Healmate was developed to bridge these gaps as a mobile-first, ABHA-enabled Personal Health Record (PHR) system. Built primarily for the Indian healthcare ecosystem, it gives patients complete control over their health data while ensuring seamless interoperability with national infrastructure. Users can scan and digitise old prescriptions and lab reports using OCR, maintain a clear and prominent allergy profile with automatic alerts, and generate a dynamic QR code for instant emergency access. All data is modelled using HL7 FHIR standards, enabling secure exchange with any ABDM- compliant system.To make the platform more proactive and secure, three important enhancements were added based on user feedback and current needs. First, an on-device AI-based symptom detector allows users to quickly log symptoms and receive instant risk insights, with results stored securely as FHIR Observation resources. Second, client-side AES-256 encryption ensures that sensitive information (allergies, symptoms, medical documents) is encrypted before it ever.

   leaves the phone, adding a strong layer of protection even if the device is lost or compromised. Third, location-based symptom clustering anonymously aggregates reported symptoms by area, helping local health authorities and NGOs identify hotspots and organise targeted medical camps more effectively.These features transform Healmate from a simple digital record keeper into a complete personal health companion safer, smarter, and community-aware. The system aligns directly with ABDM guidelines, follows best practices for privacy and interoperability, and addresses real pain points faced by patients, doctors, and public health teams in India today.Recognizing that todays healthcare needs go beyond simple record storage, we incorporated three significant

   enhancements directly into the Healmate ecosystem based on real user feedback and field requirements:

   AI-based Symptom Detection: Users can quickly log symptoms through a simple questionnaire or voice input. A lightweight, on-device AI model analyses the input and provides immediate risk insights (e.g., possible allergic reaction or infection indicators). Results are automatically saved as secure FHIR Observation resources for future reference and doctor review.

   Client-side AES-256 Encryption: Every piece of sensitive data

   allergies, symptoms, scanned documents, and personal notes is encrypted on the users device using AES-256- GCM before it is ever transmitted or stored on the server. This ensures that even if the phone is lost or compromised, the data remains completely unreadable without the users private key.

   Location-based Symptom Clustering: With explicit user consent, anonymised symptom reports are aggregated with approximate location data. A clustering algorithm (DBSCAN) identifies geographic hotspots of similar symptoms, generating heat-maps and actionable reports. This feature helps local health authorities, NGOs, and medical colleges organise targeted health camps, vaccination drives, or awareness programs more efficiently.

   These enhancements transform Healmate from a passive digital filing cabinet into a proactive, secure, and community- oriented health companion. The system not only solves individual patient problems but also contributes to public health intelligence at the neighbourhood level something very few PHR applications in India currently offer.It was developed as a comprehensive, patient-owned Personal Health Record (PHR) solution specifically tailored for the Indian context. Built as a mobile-first application, it empowers users to consolidate their entire medical history in one secure place while staying fully aligned with ABDM standards. Patients can instantly digitise legacy prescriptions and lab reports through on-device OCR, maintain a clearly visible allergy profile with automatic alerts, and generate a dynamic QR code that allows emergency responders or doctors to access only the most critical information with explicit consent. All data is structured using HL7 FHIR resources, ensuring true interoperability across hospitals, clinics, and government systems.

2. RELATED WORKS

   Personal Healt Records (PHRs) have evolved significantly over the past two decades, shifting from institution-controlled Electronic Health Records (EHRs) to patient-owned systems that empower individuals with full data control. Early foundational work [5] defined PHRs as patient-maintained summaries that improve continuity of care, while systematic reviews [4], [6] confirmed that patient-centric PHRs increase engagement and reduce fragmentation. However, adoption in India remains low due to poor interoperability and limited digitization of legacy paper records.

   Allergy documentation continues to be a critical patient safety gap. Studies [7], [8], [9] showed that undocumented or poorly visible allergies lead to frequent adverse drug events. Clinical decision support systems with prominent, real-time alerts have proven effective in reducing such risks, yet this feature is still missing in most Indian mobile health applications.

   Emergency access mechanisms have gained attention in recent years. Research [12], [13] demonstrated that QR-code-based health summaries significantly reduce response time during emergencies by enabling paramedics or doctors to access essential patient information without full authentication. Guidelines [20] further support consent-driven, limited-access emergency cards, aligning with Indias ABDM vision.

   Digitization of legacy medical documents remains a practical challenge in resource-constrained settings. Studies [10], [11] reported that Optical Character Recognition (OCR) tools like Tesseract, when combined with preprocessing, achieve acceptable accuracy on Indian prescriptions and lab reports. At the same time, interoperability standards such as [3], supported by frameworks [22] and principles [21], form the backbone of scalable PHR systems, while national initiatives [1], [2] mandate FHIR and ABHA linkage for seamless data exchange.

   Data security and privacy have become essential requirements in modern healthcare systems. Research [14], [15], [16], [17],

   [18] explored blockchain and client-side encryption techniques, demonstrating that Advanced Encryption Standard 256-bit (AES-256) encryption performed on the device before transmission provides strong protection, even in the case of server-side breaches.

   Personal Health Records (PHRs) improve patient control and care continuity, but adoption in India is limited due to interoperability issues and paper-based records]. Allergy mismanagement and lack of emergency access remain key safety concerns, addressed through real-time alerts and QR- based systems . Technologies like OCR, HL7 FHIR, and AES- 256 encryption enable digitization, interoperability, and secure health data management

3. PROPOSED WORK

   Healmate is a patient-centric digital health platform designed to address fragmented medical data and improve healthcare access. It provides a unified, secure Personal Health Record (PHR) system with features like record digitization, allergy alerts, and emergency access, aligned with national initiatives such as ABDM. The platform also integrates advanced capabilities including on-device AI symptom detection, client- side AES-256 encryption, and location-based symptom clustering for proactive and secure healthcare management.

   Healmate is designed as a complete, patient-controlled Personal Health Record (PHR) platform that integrates legacy record digitization, safety features, emergency access, and national interoperability while incorporating three new

   proactive capabilities: on-device AI symptom detection, client- side AES-256 encryption, and location-based symptom clustering.

   1. Data Preprocessing

      Before any health information enters the secure storage or AI modules, Healmate applies a rigorous multi-stage preprocessing pipeline to ensure data quality, consistency, and privacy. The pipeline handles three main input types: scanned medical documents (for OCR), user-entered symptoms (for AI detection), and location-tagged reports (for clustering). All steps occur on the device where possible to minimise data exposure.

      1. Image Processing for OCR Digitization

         Legacy prescriptions and lab reports are captured using the phone camera. Raw images undergo the following transformations:

         Grayscale conversion: Igray(x,y)=0.299R+0.587G+0.114B I_{\text{gray}}(x,y) = 0.299 \cdot R + 0.587 \cdot G + 0.114

         \cdot B Igray(x,y)=0.299R+0.587G+0.114B

         Noise removal: Median filtering (3×3 kernel) followed by Gaussian blur (=1.2 \sigma = 1.2 =1.2) to suppress camera noise and print artefacts common in Indian prescriptions.

         Contrast enhancement: Adaptive histogram equalization (CLAHE) with clip limit 2.0 and tile grid size 8×8.

         Binarization: Otsus thresholding to produce a clean black- and-white image suitable for Tesseract OCR.

         After extraction, the raw text is cleaned by removing special characters, correcting common OCR errors (e.g., 1 l, 0

         O), and applying rule-based normalisation for medicine names and dosages using a lightweight dictionary built from common Indian drug databases.

      2. Symptom Data Processing for AI Module

         User-entered symptoms (via questionnaire or voice-to-text) are first tokenised and lower-cased. Stop words and filler terms (sometimes, mild) are removed. Symptoms are then mapped to a standardised set of 120 common medical concepts using a lightweight lookup table. Missing or ambiguous entries are flagged for user clarification. The final symptom vector is normalised to a binary feature vector before feeding it to the on-device TensorFlow Lite model.

      3. Location Data Processing for Clustering

         When users consent to contribute anonymised symptom reports for camp planning, the following steps are applied: GPS coordinates are rounded to four decimal places (approximately 11 meters accuracy) to protect user privacy,

         while exact timestamps are generalized into week numbers to prevent precise tracking. Symptom data is represented using one-hot encoding for efficient processing and analysis. Additionally, duplicate reports submitted from the same device within a 24-hour period are automatically discarded to ensure data quality and avoid redundancy.

      4. FHIR Structuring and Final Validation

         All cleaned data (OCR text, allergies, symptoms, location clusters) is mapped to appropriate HL7 FHIR resources:

         Scanned documents are represented using the DocumentReference and Composition resources, while allergy information is stored as AllergyIntolerance. Patient symptoms are captured as Observation resources, and clustered data is maintained as aggregated Observation records with geospatial extensions for location-based analysis.Each resource undergoes schema validation against the ABDM FHIR profile before encryption and storage. This ensures 100% interoperability compliance and prevents malformed data from entering the system.

         By performing heavy preprocessing locally and applying encryption immediately after, Healmate guarantees that no raw or sensitive information ever leaves the device in plaintext, while maintaining high accuracy for downstream modules (OCR, AI detection, nd clustering)

         )

         Fig.1. Workflow of the Proposed Work.

   2. Proposed Architecture

   At the top is the Mobile Application Layer, developed entirely in Flutter. This is what patients and doctors see the home screen with the dynamic QR code, the bright red allergy banner, the Log Symptoms button, and the camera for scanning old prescriptions. All user interactions start here.Right below it sits the Security Layer, which is the heart of the system. Before any data leaves the phone whether its a scanned report, allergy details, or symptoms it is encrypted using AES-256-GCM on the device itself. The encryption key is derived from the users passcode and phoe biometrics, so even if the phone is lost, the data stays completely unreadable. This client-side encryption was one of our biggest priorities and runs before anything reaches the network.Next comes the Backend Services Layer (built with Node.js and Spring Boot). This layer is deliberately lightweight it only handles FHIR validation, ABHA linking, and routing. It never sees plaintext data; it works only with already-encrypted blobs. When a doctor scans a patients QR code in an emergency, the backend simply forwards the request back to the patients phone, which then decrypts and shares only the allowed fields after fresh consent.The Data Layer stores everything in a FHIR-compliant database (PostgreSQL for structured records and MongoDB for documents). All resources follow HL7 FHIR R5 profiles approved by ABDM, so Healmate can easily pull or push records to any government or hospital system.Finally, the Integration Layer connects Healmate to the national ABDM sandbox, external labs, and hospitals using secure OAuth 2.0 and mutual TLS. This is also where the location-based clustering engine lives it runs as a weekly batch job on anonymised data to generate heat-maps for medical camps.The three new enhancements fit naturally into this architecture:

   This layered approach gives us the best of both worlds: strong national interoperability through FHIR and ABHA, genuine patient control through local encryption and consent, and practical new features that actually help daily health management and community planning. The entire flow is shown in Fig. 1 (system architecture diagram), where you can see how data moves securely from the phone camera all the way to encrypted storage and clustering insights.

   The AI Symptom Detector runs entirely on-device using a lightweight TensorFlow Lite model, generating and encrypting FHIR Observations before sharing with user consent. Client- side AES-256 encryption ensures data is secured before transmission. Location-based clustering uses anonymised data with DBSCAN on the server to generate insights without identifying individual users.

4. RESULTS AND DISCUSSIONS

   1. Dataset Description

      The evaluation of Healmate was carried out using a real-world pilot dataset collected directly from users in Chennai between

      January and February 2026. A total of 50 participants voluntarily joined the study after giving written informed consent. The group was diverse and representative of typical Indian users: 18 college students (age 1924), 22 working

      professionals (age 2545), and 10 senior citizens (age 5572). This mix helped us test the app across different levels of digital comfort and health needs.The dataset consists of four main types of records, all generated during normal daily use of the app:

      Scanned Medical Documents Participants uploaded 312 legacy prescriptions and lab reports (mostly from the last 23 years). These included handwritten prescriptions from local clinics, printed lab reports, and discharge summaries. Each document was scanned using the phone camera under varying lighting conditions to reflect real-life scenarios.

      Allergy and Medical History Records A total of 187 allergy entries were logged (including drug allergies, food allergies, and environmental sensitivities). In addition, 124 chronic condition entries (diabetes, hypertension, thyroid, asthma, etc.) were added, along with current medication lists.

      Symptom Logs for AI Detection Users recorded 240 symptom entries through the questionnaire or voice input. Each entry contained 37 symptoms on average (e.g., fever, cough, body ache). These logs were used to test the on-device AI model and generate FHIR Observation resources.

      Anonymised Location-Tagged Reports for Clustering With explicit opt-in consent, 214 symptom reports from 37 participants were contributed for community analysis. Each report included rounded GPS coordinates (accurate to ~11 metres), week number, and one-hot encoded symptoms. No personal identifiers were stored.

      All data was collected ethically following institutional guidelines. Participants could delete their data at any time, and location reports were fully anonymised before leaving the device. The complete dataset was stored in FHIR-compliant format, with every record encrypted using AES-256 at the client side before transmission.

   2. Experimental Setup

   The mobile application was developed using Flutter 3.19 (Dart) on Android Studio (Koala version). All code was written and debugged on a Windows 11 workstation with an Intel i7- 12700H processor, 32 GB RAM, and NVIDIA RTX 3060 GPU. The backend was built with Node.js 20 and Spring Boot 3.2, running on a local Ubuntu 22.04 server (16 GB RAM, 8- core CPU) during development and later deployed on a secure

   cloud instance for the pilot.The AI symptom detection model was trained offline in Python using TensorFlow 2.15 and converted to TensorFlow Lite (size 7.8 MB) for on-device inference. OCR testing used the latest Tesseract 5.3 engine

   with custom Indian-language training data.All devices ran Android 12 to 14. Network conditions were deliberately varied

   Wi-Fi, 4G, and low-signal 3G to simulate rural and urban scenarios.

   The real-user pilot ran from 15 January to 12 February 2026 with 50 participants (as described in Section A). Each participant was given the app via a private Google Play internal test link and received a 30-minute onboarding session (either in-person or via video call). They were instructed to use the app naturally: scan at least 5 old prescriptions, log allergies and daily symptoms, generate QR codes, and optionally contribute anonymised reports for clustering.We provided a simple feedback form inside the app and conducted weekly telephonic check-ins. All data (OCR accuracy, encryption time, AI inference time, battery usage, and SUS scores) was automatically logged using Firebase Analytics and custom in- app timers.

   Fig.2.Performance Metrics of t AI Symptom Detector

   Security and Performance Measurement Tools

   Encryption performance: Measured using Android Profiler (CPU and battery) and custom timestamps before/after AES- 256-GCM calls.OCR accuracy: Character-level and word- level accuracy calculated manually against ground-truth text for 312 documents.AI model: Inference time and accuracy measured on-device with 240 symptom entries.Clustering: DBSCAN results validated weekly by comparing generated heat-maps with actual camp locations chosen by two partnering NGOs.Usability: System Usability Scale (SUS) questionnaire administered at the end of week 4.Security testing: Simulated device-loss scenarios and penetration testing using OWASP Mobile Top 10 checklist.

   The entire setup was kept lightweight and realistic so that the results truly reflect how Healmate would perform in everyday use across India. No high-end lab equipment was used only regular smartphones and standard development tools making the findings directly applicable to real deployment.

5. CONCLUSION AND FUTURE WORK

Healmate has successfully demonstrated that a patient-centric, ABHA-enabled Personal Health Record can be both practical and powerful in the Indian healthcare context. By combining OCR-based digitization of legacy documents, prominent allergy alerts, QR-enabled emergency access, and full HL7 FHIR interoperability, the system addresses the most common pain points faced by patients today fragmented records, overlooked allergies, and delayed emergency information. The addition of three new features on-device AI symptom detection, client-side AES-256 encryption, and location-based symptom clustering takes the platform beyond simple storage and turns it into a proactive, secure, and community- aware health companion.Healmate proves that national standards (ABDM and FHIR) and modern mobile technologies can be blended to create a genuinely useful solution without compomising privacy or performance. It stands out from existing apps by offering true end-to-end encryption on the device, local AI processing, and a unique community-health angle through symptom clustering.

Future Work-While the current version is ready for wider pilot deployment in clinics and rural health centres, several meaningful enhancements are already planned:

Integration with wearable devices (smartwatches and glucometers) for automatic vital tracking and real-time updates to the FHIR record.

Expansion of the AI model to support regional languages (Tamil, Hindi, Telugu) and rare disease symptoms through federated learning.

Blockchain-based immutable audit trails for consent and data access to further strengthen trust.

A doctor-facing web dashboard that allows seamless viewing of patient records (with consent) and direct teleconsultation within the same ecosystem.

Scaling the clustering engine into a public-health dashboard for state and district health departments, enabling data-driven planning of camps, vaccination drives, and awareness programs.

We also intend to conduct a larger multi-city trial involving 500+ users across urban, semi-urban, and rural settings to measure long-term adoption, clinical outcomes, and impact on emergency response times.

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