# India NMC Doctor Registry Scraper (`jungle_synthesizer/india-nmc-doctor-registry-scraper`) Actor Scrapes the Indian Medical Register (IMR) from the National Medical Commission of India — the authoritative registry of 1.4M+ registered doctors. Filter by name, year, SMC, or registration number. Returns registration, qualification, university, address, and year. - **URL**: https://apify.com/jungle\_synthesizer/india-nmc-doctor-registry-scraper.md - **Developed by:** [BowTiedRaccoon](https://apify.com/jungle_synthesizer) (community) - **Categories:** Lead generation, Business - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing Pay per event This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## India NMC Doctor Registry Scraper Scrape doctor records from the [Indian Medical Register](https://www.nmc.org.in/information-desk/indian-medical-register/) maintained by the National Medical Commission (NMC) of India. Returns 1.4M+ registered doctors with registration number, State Medical Council, qualification, university, permanent address, and year of registration — the authoritative government source, not a directory aggregator pretending to be one. --- ### India NMC Doctor Registry Scraper Features - Pulls records straight from the NMC's own REST API. No HTML parsing, no rendered browsers. - Filters by doctor name, year of registration, State Medical Council, or registration number. - Enumerates the full 1.4M+ registry when you need it. A safety cap catches accidental unfiltered runs. - Returns 17 fields per doctor, including the SMC metadata you'd otherwise have to look up separately. - Handles the NMC's incomplete TLS chain and DataTables-shaped pagination envelope without any configuration on your end. --- ### Who Needs This Data? - **Healthcare credentialing teams** — Verify a doctor's registration against the authoritative source before onboarding. - **Medical recruiters** — Build targeted outreach lists filtered by state, specialty year, or qualifying university. - **Pharmaceutical sales ops** — Segment India's prescribing physician universe by SMC and qualification. - **Insurance and claims verification** — Confirm a provider's registration status is active before paying out. - **Health policy researchers** — Aggregate registration trends by state, university, or decade. - **Legal and compliance due diligence** — Run identity checks against the government registry rather than a third-party aggregator. --- ### How India NMC Doctor Registry Scraper Works 1. You supply a filter — a name substring, a year, a State Medical Council ID, a registration number, or nothing at all. 2. The scraper queries the NMC's paginated listing API in batches of 500 records. 3. For each hit, it issues a follow-up detail fetch to pull the full record — qualification, university, permanent address, additional qualifications. 4. Records are streamed to the dataset as they arrive. If a detail lookup fails, the listing data is still saved so nothing is dropped silently. --- ### Input ```json { "maxItems": 50, "name": "Sharma", "year": 2020, "smc_id": 6, "registration_no": "", "allow_full_enumeration": false } ```` | Field | Type | Default | Description | |-------|------|---------|-------------| | `maxItems` | integer | 50 | Maximum records to return across the full crawl. | | `name` | string | "" | Case-insensitive substring match against the doctor's full name. "Sharma" alone returns ~20k records. | | `year` | integer | — | Calendar year of registration. The registry goes back to 1925. | | `smc_id` | integer | — | Numeric ID of the State Medical Council (see table below). | | `registration_no` | string | "" | Exact registration number. Usually returns a single record. | | `allow_full_enumeration` | boolean | false | Required to exceed 1,000 records when no filter is provided. Keeps accidental billion-record runs in check. | #### Common State Medical Council IDs | ID | Council | |----|---------| | 1 | Andhra Pradesh Medical Council | | 4 | Bihar Medical Council | | 6 | Delhi Medical Council | | 8 | Gujarat Medical Council | | 13 | Karnataka Medical Council | | 16 | Maharashtra Medical Council | | 21 | Tamil Nadu Medical Council | | 23 | Uttar Pradesh Medical Council | | 25 | West Bengal Medical Council | Use the NMC portal's dropdown for the full list of 30+ councils. *** ### India NMC Doctor Registry Scraper Output Fields ```json { "doctor_id": "12437767", "registration_number": "10087", "doctor_name": "AYUSHI SHARMA", "father_name": "Shri Rakesh Sharma", "state_medical_council": "Uttarakhand Medical Council", "smc_id": 24, "qualification": "MBBS", "qualification_year": "2020", "university": "U.HNB, Uttarakhand Medical Education", "additional_qualifications": [], "year_of_registration": 2020, "date_of_registration": "03/07/2020", "permanent_address": "Sharma House, Vishesh Township, Kamaluaganja, P.O - Katghariya, Haldwani, Distt - Nainital.", "uprn_number": "", "date_of_birth": "16/01/1996", "profile_url": "https://www.nmc.org.in/information-desk/indian-medical-register/#doctorId=12437767®No=10087", "scraped_at": "2026-04-23T12:19:07.018Z" } ``` | Field | Type | Description | |-------|------|-------------| | `doctor_id` | string | Internal NMC doctor identifier. Used internally for detail lookups; included for your own joins. | | `registration_number` | string | Registration number issued by the State Medical Council. | | `doctor_name` | string | Full name (first + middle + last, whitespace-normalized). | | `father_name` | string | Father's or husband's name as listed on the source record. Frequently empty for pre-1971 entries. | | `state_medical_council` | string | Name of the SMC that issued the registration. | | `smc_id` | number | Numeric ID of the issuing council. | | `qualification` | string | Primary medical degree. | | `qualification_year` | string | Year the primary qualification was earned. Kept as a string because older records contain free-form values. | | `university` | string | Qualifying university name. | | `additional_qualifications` | array | Up to three extra qualifications, each formatted as `degree | year | university`. Empty slots omitted. | | `year_of_registration` | number | Calendar year the doctor entered the IMR. | | `date_of_registration` | string | Full registration date in DD/MM/YYYY. May be blank on very old records. | | `permanent_address` | string | Permanent correspondence address as reported to the SMC. | | `uprn_number` | string | Unique Permanent Registration Number when issued. | | `date_of_birth` | string | Date of birth in DD/MM/YYYY. Often blank on older records; sentinel value 01/01/1900 is normalized to empty. | | `profile_url` | string | Deep link back to the IMR portal with the doctor ID preserved for manual lookup. | | `scraped_at` | string | ISO-8601 timestamp of when the record was scraped. | *** ### FAQ #### Is this the official National Medical Commission registry? Yes. India NMC Doctor Registry Scraper reads directly from the NMC's public REST API at `nmc.org.in/MCIRest/`. The same endpoints back the registry portal's DataTables UI. #### How many doctors are in the registry? 1,421,853 as of April 2026. The count grows as State Medical Councils post new registrations. The oldest record on file is from 1925. #### Do I need a proxy? No. India NMC Doctor Registry Scraper runs without a proxy. The NMC API is public and has no rate limiting worth naming. #### What's the deal with the `allow_full_enumeration` flag? A safety fuse. Without a filter the scraper is pointed at 1.4M+ records, which is usually more than you intended. When no filter is provided and this flag is false, the run caps at 1,000 records. #### Why is `qualification_year` a string instead of a number? Older records contain free-form values like `""` or non-numeric text. Keeping it as a string means you get the raw value intact. Cast it downstream if needed. #### What happens when a detail fetch fails? The record is still saved using the fields available from the listing response — name, registration number, SMC, year. You lose the richer detail fields but never drop a record silently. #### Can I enumerate one SMC at a time? Yes. Set `smc_id` to the target council's ID and leave other filters blank. A single SMC returns anywhere from a few thousand to several hundred thousand records depending on the state. #### How fast is it? Sequential pagination at ~500 records per listing page plus one POST per detail fetch, with a 150 ms courtesy delay. In practice, a 10-record test run completes in under 25 seconds on Apify's default 512 MB worker. *** ### Need More Features? Open an issue in the [OrbLabs support tracker](https://apify.com/bowtiedraccoon/india-nmc-doctor-registry-scraper/issues) and describe what you need. ### Why Use India NMC Doctor Registry Scraper? - **Authoritative source** — Reads the government registry, not a commercial aggregator's cached copy. - **Structured output** — 17 flat fields per record, no nested objects to flatten downstream. - **Handles the quirks** — TLS chain, DataTables envelope, and two-phase detail fetch are all invisible to you. - **Safe by default** — Unfiltered runs cap at 1,000 records unless you explicitly opt in to full enumeration. # Actor input Schema ## `sp_intended_usage` (type: `string`): Please describe how you plan to use the data extracted by this crawler. ## `sp_improvement_suggestions` (type: `string`): Provide any feedback or suggestions for improvements. ## `sp_contact` (type: `string`): Provide your email address so we can get in touch with you. ## `maxItems` (type: `integer`): Maximum number of doctor records to return. Default 50 keeps initial runs responsive; set higher for bulk enumeration. ## `name` (type: `string`): Match doctors whose full name contains this text. Case-insensitive, server-side substring match (e.g. "Sharma" returns 20k+ records). ## `year` (type: `integer`): Return only doctors registered in this calendar year (e.g. 2020). Leave blank for any year. Registry goes back to 1925. ## `smc_id` (type: `integer`): Filter by State Medical Council. Common IDs — 1 Andhra Pradesh, 4 Bihar, 6 Delhi, 8 Gujarat, 13 Karnataka, 16 Maharashtra, 21 Tamil Nadu, 23 Uttar Pradesh, 25 West Bengal. See the NMC portal for the full list. ## `registration_no` (type: `string`): Exact registration number to look up. When provided, typically returns a single record. ## `allow_full_enumeration` (type: `boolean`): When false (default) and no filter is provided, the run is capped at 1,000 records as a safety guard. Set to true to enumerate large swaths of the 1.4M+ registry without filters. ## Actor input object example ```json { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 50, "allow_full_enumeration": false } ``` # Actor output Schema ## `results` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 50, "name": "", "registration_no": "" }; // Run the Actor and wait for it to finish const run = await client.actor("jungle_synthesizer/india-nmc-doctor-registry-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 50, "name": "", "registration_no": "", } # Run the Actor and wait for it to finish run = client.actor("jungle_synthesizer/india-nmc-doctor-registry-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 50, "name": "", "registration_no": "" }' | apify call jungle_synthesizer/india-nmc-doctor-registry-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jungle_synthesizer/india-nmc-doctor-registry-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "India NMC Doctor Registry Scraper", "description": "Scrapes the Indian Medical Register (IMR) from the National Medical Commission of India — the authoritative registry of 1.4M+ registered doctors. Filter by name, year, SMC, or registration number. Returns registration, qualification, university, address, and year.", "version": "0.1", "x-build-id": "FeaTLEOiaoZW2zrMf" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jungle_synthesizer~india-nmc-doctor-registry-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jungle_synthesizer-india-nmc-doctor-registry-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/jungle_synthesizer~india-nmc-doctor-registry-scraper/runs": { "post": { "operationId": "runs-sync-jungle_synthesizer-india-nmc-doctor-registry-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/jungle_synthesizer~india-nmc-doctor-registry-scraper/run-sync": { "post": { "operationId": "run-sync-jungle_synthesizer-india-nmc-doctor-registry-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "maxItems" ], "properties": { "sp_intended_usage": { "title": "What is the intended usage of this data?", "minLength": 1, "type": "string", "description": "Please describe how you plan to use the data extracted by this crawler." }, "sp_improvement_suggestions": { "title": "How can we improve this crawler for you?", "minLength": 1, "type": "string", "description": "Provide any feedback or suggestions for improvements." }, "sp_contact": { "title": "Contact Email", "minLength": 1, "type": "string", "description": "Provide your email address so we can get in touch with you." }, "maxItems": { "title": "Max Items", "minimum": 1, "maximum": 1000000, "type": "integer", "description": "Maximum number of doctor records to return. Default 50 keeps initial runs responsive; set higher for bulk enumeration.", "default": 50 }, "name": { "title": "Doctor Name (substring)", "type": "string", "description": "Match doctors whose full name contains this text. Case-insensitive, server-side substring match (e.g. \"Sharma\" returns 20k+ records)." }, "year": { "title": "Year of Registration", "minimum": 1925, "maximum": 2100, "type": "integer", "description": "Return only doctors registered in this calendar year (e.g. 2020). Leave blank for any year. Registry goes back to 1925." }, "smc_id": { "title": "State Medical Council ID", "minimum": 1, "maximum": 100, "type": "integer", "description": "Filter by State Medical Council. Common IDs — 1 Andhra Pradesh, 4 Bihar, 6 Delhi, 8 Gujarat, 13 Karnataka, 16 Maharashtra, 21 Tamil Nadu, 23 Uttar Pradesh, 25 West Bengal. See the NMC portal for the full list." }, "registration_no": { "title": "Registration Number", "type": "string", "description": "Exact registration number to look up. When provided, typically returns a single record." }, "allow_full_enumeration": { "title": "Allow Full Enumeration", "type": "boolean", "description": "When false (default) and no filter is provided, the run is capped at 1,000 records as a safety guard. Set to true to enumerate large swaths of the 1.4M+ registry without filters.", "default": false } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```