SIH26047: Patient Case-Taking Software — SIH 2026 | Wynit

SIH26047: Patient Case-Taking Software — SIH 2026 | Wynit

What they'll ask you

14

Generated from the brief's own wording and the competition's published rules, never from a guess about what this ministry prefers.

Exact list of regional languages required beyond Hindi and English?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Target accuracy threshold for handwritten prescription OCR?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Specific Hospital Information System (HIS) standard or mock interface to integrate with during the demo?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.

11 morethe ones a panel gets to if there is time

ABDM sandbox credentials or testing environment setup instructions?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Has any part of this been shown at a previous event, hackathon or college project?
The guidelines are explicit: your solution must not have appeared in any previous event or programme, of any sort. A recycled project is what a team under time pressure reaches for.
The brief asks for conversational history intake engine. How would you build that?
Decoded from SIH26047 itself: Conducts structured adaptive interviews using voice and touch in Indian languages, including AYUSH parameters and red-flag symptom alerts. The brief asks for it by name.
The brief asks for document scanning and extraction. How would you build that?
Decoded from SIH26047 itself: Performs OCR on handwritten or printed prescriptions and lab reports to extract clinical entities and build a chronological timeline. The brief asks for it by name.
The brief asks for physician summary generator. How would you build that?
Decoded from SIH26047 itself: Synthesizes patient conversation and extracted document data into an editable, standard clinical summary for the doctor's screen. The brief asks for it by name.
The brief asks for abdm and his integration. How would you build that?
Decoded from SIH26047 itself: Handles ABHA authentication, patient consent management compliant with DPDP rules, and FHIR-based data transfer to hospital systems. The brief asks for it by name.
Where does your data come from: a published source, one you collect, or one you generate?
No dataset is attached to this problem statement, so sourcing it is part of the work and nobody told you that.
Why not use what already exists? Name the closest thing to this that is already running.
A team that has not named the alternative themselves is answering this for the first time in the room.
Which single thing will you demonstrate end to end, start to finish, with nothing skipped?
Ours, not a rule: a narrow thing that fully works survives questioning better than a broad thing that half works. If nobody on the team can name it, that is the finding.
Show me this working: the evaluators will check for dual-mode voice and touch interaction in Indian languages suitable for low-literacy users, alongside AYUSH Dashavidha Pariksha history taking.
This is the evaluator read for your problem statement, decoded from the brief's own wording.
Show me this working: they will also look for accurate OCR processing of handwritten and printed medical documents, emergency red-flag symptom alerting, and compliance with ABDM FHIR standards and DPDP privacy rules.
This is the evaluator read for your problem statement, decoded from the brief's own wording.

The government's original description (1619 words)

Background: 1.1 The Clinical History-Taking Bottleneck in Indian Hospitals History taking â€” the structured elicitation of a patient's presenting complaints, history of present illness, past medical and surgical history, drug and allergy history, family and personal history, and a review of systems â€” is the single most important diagnostic activity in clinical medicine. Classical teaching holds that a well-conducted history yields the correct diagnosis in 70â€“80% of cases, even before examination or investigation. Yet in India's overburdened public hospital outpatient departments (OPDs), the time available for this critical interaction has collapsed to unsustainable levels. India operates one of the most patient-dense healthcare systems in the world. Tertiary government hospitals and apex institutions routinely register 4,000â€“10,000 OPD patients per day, with a doctor-to-patient consultation time frequently reported between 2 and 5 minutes â€” among the shortest globally (study published in BMJ Open, 2017, across 67 countries placed India's average primary-care consultation at just over 2 minutes). Within this window, the physician must simultaneously elicit history, examine the patient, review prior records, formulate a diagnosis, counsel, and prescribe. The result is systematic under-elicitation of history, missed comorbidities, repeated questioning across visits, and diagnostic error. AYUSH institutions face an additional layer of complexity. Ayurvedic history taking (Trividha, Ashtavidha, and Dashavidha Pariksha) requires detailed assessment of Prakriti (constitution), Vikriti (current imbalance), Agni (digestive capacity), Koshtha (bowel nature), Ahara-Vihara (diet and lifestyle), Nidana (causative factors), and Samprapti (pathogenesis) â€” a far more extensive history framework than allopathic intake. Capturing this depth manually within OPD time constraints is effectively impossible, forcing practitioners to abbreviate the very assessment that defines personalized Ayurvedic care. 1.2 The Documentation and Records Fragmentation Problem Compounding the time problem is the fragmentation of patient records. Patients in India typically carry physical paper prescriptions, laboratory reports, discharge summaries, and imaging films from multiple prior providers. During consultation, the physician must manually scan through these unstructured documents â€” often handwritten, in varying languages, and chronologically disordered â€” consuming a significant fraction of the already-scarce consultation time. There is no point-of-entry mechanism to digitize, structure, and chronologically organize a patient's prior medical documents before they reach the consultation room. The Ayushman Bharat Digital Mission (ABDM) has established the national digital health infrastructure â€” ABHA (Ayushman Bharat Health Account) IDs, the Health Information Exchange, and FHIR-based interoperability standards. However, the 'first-mile' problem remains unsolved: there is no efficient, patient-facing software platform that captures structured history and digitizes documents into the ABDM ecosystem before the clinical encounter begins. 1.3 The Opportunity: AI-Powered Digital Clinical Intake Platform Self-service kiosks have transformed high-throughput service industries â€” ATMs in banking, self-check-in terminals in aviation, and ordering kiosks in quick-service restaurants â€” by offloading structured data-entry tasks from human staff to the user, dramatically improving throughput and accuracy. In healthcare, patient check-in kiosks are now widespread in developed-country hospitals, but these are limited to administrative check-in. None perform deep, AI-driven, multimodal clinical history acquisition with medical document digitization. The convergence of mature enabling technologies â€” robust automatic speech recognition (ASR) for Indian languages and accents (Bhashini / AI4Bharat models), large language models for conversational clinical history elicitation, high-accuracy OCR for handwritten and printed medical documents, and ABDM's FHIR interoperability â€” now makes it feasible to build an AI-powered clinical history software platform. Description: 2.1 The Problem in Precise Terms There is no purpose-built, patient-facing software platform that enables patients to independently and comprehensively record their medical history â€” through both natural spoken conversation and guided touchscreen interaction â€” and simultaneously digitize their existing physical medical documents, generating a structured, physician-ready clinical history summary that integrates with the hospital information system and the ABDM ecosystem before the patient enters the consultation room. 2.2 Why Existing Solutions Fall Short • Existing hospital registration systems (currently deployed in some Indian hospitals) capture only demographic and appointment data â€” name, age, department, token number. They do not elicit any clinical history or process medical documents. • Mobile health apps and tele-triage chatbots require smartphone literacy, stable connectivity, and patient enrolment ahead of the visit â€” excluding the large elderly, rural, low-literacy, and first-visit patient populations who form the bulk of government hospital OPD load. • Manual nurse-led triage / history desks are themselves human-resource-limited, do not scale to 5,000+ daily patients, and reintroduce the same time and transcription bottleneck the system is trying to eliminate. • Generic document scanners digitize images but do not extract, structure, or chronologically organize clinical content, nor link it to a structured history or ABHA record. 2.3 Specific Challenges a Solution Must Overcome • Multilingual, multi-accent voice capture in noisy hospital environments across Hindi, English, and major regional languages, for patients of varying literacy and digital comfort. • Accessibility for low-literacy and elderly users through intuitive icon-driven UI, audio prompts, and conversational guidance â€” the software platform must be usable by a first-time, non-tech-savvy patient with zero training. • Accurate clinical history structuring converting free-form patient narration into a standardized, physician-readable history (chief complaint, HPI, past history, drug/allergy, family, personal, review of systems) â€” and, for AYUSH settings, Dashavidha Pariksha parameters. • Reliable medical document digitization OCR of handwritten and printed prescriptions, lab reports, and discharge summaries in multiple languages, with intelligent extraction of diagnoses, medications, and investigation values. • Privacy, consent, and data security compliance with the Digital Personal Data Protection Act 2023 and ABDM consent framework â€” handling sensitive health data within a secure software environment. Expected solution: 3.1 Solution Overview â€” 'MediKiosk' AI Clinical History Software Platform The proposed solution â€” tentatively designated MediKiosk â€” a software platform for an AI-powered clinical history software platform that allows any patient to record a comprehensive medical history through natural voice conversation and guided touchscreen interaction, scan and digitize their existing physical medical documents, and generate a structured, physician-ready clinical history summary that is pushed to the hospital information system (HIS) and linked to the patient's ABHA record â€” all completed before the consultation, with minimal staff assistance required. • Insert Table*3.2 3.3 Software & AI Stack (Integrated) Module A â€” Conversational Multimodal History Engine A conversational AI engine that conducts a structured clinical history interview through both voice and touch. The patient speaks naturally in their preferred language; the engine asks intelligent follow-up questions (e.g., on stating 'chest pain', it probes onset, character, radiation, aggravating/relieving factors â€” the SOCRATES framework) and simultaneously offers touch-based multiple-choice options for patients who prefer tapping. Built on Indian-language ASR, a dialogue manager constrained by a clinical history ontology, and text-to-speech for audio prompts. • Adaptive questioning: dynamically branches based on chief complaint and prior answers, mirroring a physician's clinical reasoning to elicit a complete HPI and review of systems • Dual-mode input: every question answerable by speaking OR tapping, ensuring usability across literacy and comfort levels • AYUSH history mode: for Ayurvedic OPDs, an extended interview capturing Dashavidha Pariksha (Prakriti, Vikriti, Sara, Samhanana, Pramana, Satmya, Sattva, Ahara Shakti, Vyayama Shakti, Vaya) and Ahara-Vihara assessment • Red-flag detection: AI flags emergency symptoms (e.g., acute chest pain with dyspnoea, stroke symptoms) and triggers immediate priority alert to triage staff rather than routine queueing Module B â€” Medical Document Digitization & Intelligence An integrated scanning and document-AI pipeline that allows the patient to upload prior prescriptions, lab reports, and discharge summaries. The system performs high-accuracy OCR (printed and handwritten, multilingual), then extract and structure clinical entities. • Intelligent extraction: diagnoses, prescribed medications with dosages, investigation results with values and reference ranges, and procedure/surgery history • Chronological organization: automatically dates and orders documents into a coherent medical timeline for the physician • Abnormal-value highlighting: flags out-of-range lab values and potential drug interactions for physician attention Module C â€” Structured History Summary Generator An AI summarization engine that synthesizes the conversational history and the digitized documents into a single, concise, physician-ready clinical summary in standard format â€” presented on the consultation screen the moment the patient enters the room. The physician reads a complete, structured history in seconds rather than spending minutes eliciting it, and can edit/confirm before saving. • Standard clinical format: Chief complaint ? HPI ? Past medical/surgical ? Drug & allergy ? Family ? Personal ? ROS ? Prior investigations summary • Editable & verifiable: physician retains full control â€” the summary is a draft to accept, amend, or reject, never an autonomous diagnosis • Bilingual output: patient-facing audio confirmation in local language; physician-facing summary in English/Hindi Module D â€” Consent, Privacy & ABDM Integration A robust consent and security layer compliant with the Digital Personal Data Protection Act 2023 and the ABDM consent framework. The patient authenticates via ABHA ID, grants explicit consent for data capture and sharing, and the structured history is pushed to the hospital HIS/EMR and linked to the ABHA Personal Health Record via FHIR APIs. • Secure processing: voice and document AI are processed securely within the software platform • Session termination: temporary session data is cleared immediately after submission • Consent-first design: granular, revocable consent with audio explanation for low-literacy patients 3.4 End-to-End Patient Journey • Step 1 â€” Identify: Patient logs into the software platform, enters/scans ABHA ID or Aadhaar details or registers as new; selects language; grants consent (audio-guided) • Step 2 â€” Converse: AI conducts adaptive voice + touch history interview, capturing chief complaint, HPI, and full history; red flags trigger priority triage • Step 3 â€” Scan: Patient uploads prior prescriptions, lab reports, and discharge summaries; AI digitizes, structures, and timelines them • Step 4 â€” Summarize & Route: AI generates structured history summary, links to ABHA, pushes to HIS, updates the patient's digital record; summary appears on physician's screen at consultation • Step 5 â€” Consult: Physician reviews complete history in seconds, edits/confirms, and devotes the full consultation to examination, reasoning, and counselling