Introduction:
Information regarding medicines must be accurate in order for the e-pharmacy and health tech products to be successful. Bad information about medications can lead to issues with inventory, the loss of regulatory compliance, pricing errors, and ultimately the loss of trust from patients and partners. This article helps to avoid data risks and build systems that can be scaled in a practical manner through the author’s experiences. This article is for founders, product, compliance, and health tech IT managers.
Why the Cost of Mistakes with Drug Data is Significant
Problems with drug data affect businesses, the law, and the medical field.
- Operational Cost. Fulfilling, returning, and shipping inventory is the result of mismatched SKU’s (stock keeping unit), or inconsistent dosage. This loss of revenue creates a bandwidth issue for operations.
- Regulatory Risk. Regulatory risks occur when there are pricing errors and gaps in price control. In addition to fines and mandatory price corrections, compliance teams are tasked with demonstrating control the gaps in regulation and where the data leads.
- Clinical Safety. Patient safety and the risk of liability increases when product metadata is wrong regarding the formulation or concentration.
- Commercial Impact. With bad product metadata, search performance, recommendations, and decision-making models suffer. With a loss of data, there are detrimental effects to conversion and retention.
Working with various Indian digital pharmacies, we noticed a specific pattern: a single error during mapping of supplier feeds with the canonical catalogue caused a week-long shipment delay, and an immediate decline of 6-8% in fulfilment accuracy. These issues are entirely preventable with data practices.
Failure patterns (and how to recognize them)
1. Identifier Chaos
Brand names, trade names, generic names, as well as internal SKUs and drug codes are utilized differently across various systems. The absence of normalized identifiers leads to duplicated entries and misdirected orders.
How to detect: Perform duplicate detection using a combination of normalized name + strength + pack size. Flag entries where several suppliers map to different identifiers.
2. Inconsistency in Dosage and Form
“Metformin 500 mg tablets” vs “Metformin 500mg tab” or inconsistent units (mg vs mcg) lead to the wrong items being shipped.
How to detect: Use standardized templates and check for gaps in strength, units, and forms. Validation can be done using parsers and human spot checks.
3. Stale Pricing and Coverage
Outdated drug databases can lead to undercharging or incurring fines due to the frequent changes in pricing, rebates, and regulatory price ceilings.
How to detect: automate and measure the lag to gaps in the reconciliation of supplier feeds, your Indian Medicine database, and the price lists.
4. Problems with mapping the datasets
Errors in the merging process, particularly with suppliers’ feeds and your master catalog, are both deeply rooted and widespread.
How to detect: keep track of mapping confidence scores and assign a manual review to the low-confidence mapping.
5. Incomplete API contracts and Error Management
With integration issues surrounding the Medicine database API and third-party providers, these failures remain unaddressed until the issues are exceptionally obvious.
How to detect: keep track of the API’s uptime, the rate of errors, and the variations in the schema of the payload.
Practical steps to prevent mistakes
The following actions should help to reduce risk.
Governance and ownership
- Designate a data owner for the medicine metadata, mapping rules, and reconciliation.
- Maintain a documented data dictionary with canonical fields (e.g., generic_name, brand_name, strength, form, pack_size, hsn_code, mrp, max_retail_price_flag).
Canonical identifiers and normalization
- Designate one canonical identifier for each medicine and ensure all other identifiers (supplier SKU, barcode, national code) are mapped to this single canonical key.
- Establish normalization rules for all document names, and units so that any mixture of punctuation, whitespace, and unit-abbreviation variations will be disregarded.
Source hierarchy and trust scoring
- Establish a source hierarchy such as: regulatory lists > manufacturer master files > certified Drug Data Provider India feeds > crowdsourced data.
- Assign every source a score for freshness and trust; utilize the score for automated vs manual updates.
Validation rules & business logic
- Create rules of validation for imperative attributes: ranges of strength, accepted forms, logic of pack-size, and ceilings of price.
- Before an SKU goes live, apply strict rules (prevent bad records).
Reconciliation & audit
- Daily automated reconciliations between the live catalogue, supplier feeds and your master Indian Medicine Data set.
- Immutable change logs: document who made changes, what the changes were, when, and the reason behind the changes.
API and integration best practices
- Consider the Medicine Database API as a contract. Version your internal consumers to match API versions.
- Use exponential backoff, retries, circuit breakers, and caching for external API calls.
- Incoming payloads should be validated against the applicable JSON schema and an alert should be triggered for schema drift.
Testing & Staging
- Conduct end-to-end tests to mimic real order workflows, including the edge cases of backorders, pack substitutions, and price changes.
- Provide a staging environment using masked, realistic data to exercise integration paths.
Monitoring & KPIs
- Set and track key metrics: API error rate, catalogue accuracy, price mismatch incidents, mapping failure rate, and mean time to resolution (MTTR) for data incidents.
Draft key performance indicator breach notifications and automated alert systems.
Integration of Resilient Medicine Database APIs
When exposing and consuming APIs for Medicine Databases, ensure some stability and observability in the system.
- APIs should utilize version control; thus, using semantic versioning is recommended. When deprecating fields, do so in a responsible manner.
- Provide sufficiently detailed documentation and tooling such as schemas, for each of the API contracts.
- The API provider should be equipped such that the receiving system should provide the capability to adhere to the API Idempotency rules.
- In any API service, rate limiting, and Service Level Agreements should be present and defined from both the consuming and providing system’s perspectives.
- For APIs, provide a mechanism to filter notifications by change. Additionally, provide a way to subscribe to notifications which document the logical domain events associated with the changes to facilitate the downstream systems’ domain objects to quickly reconcile the changes.
These engineering practices reduce the number of unpredictable events that creating and maintaining systems may yield.
Evaluating Drug Data Provider India
When it comes to provider selection, all of the providers will not be the same.
- Coverage: number of SKUs and the breadth of the associated metadata (formulations, pack sizes, regulatory flags)
- Freshness: how often it is updated; retention of the history.
- Proven track record: uptime, SLAs and errors.
- Documentation and support: samples of data, guides to system integration, engineering support availability.
- Support of compliance: relation to the national price control lists and the regulatory identifiers.
- Relationship of Commerce: licensing, price per call, and the scheduled delivery of contracts.
“It’s all a matter of assessing providers the same way you assess third-party risks that are critical: proof of concept, reference checks, and a brief initial contract with straightforward exit terms.”
Actual workflow – from supplier feed to catalogue
Below is a workflow that has worked for most startups:
- Receive: raw feed hits a staging bucket.
- Parse and normalize: extract fields, normalize units and names.
- Match: map to canonical ID via deterministic rule then fuzzy match.
- Validate: apply business rules (strength ranges, price ceilings, etc)
- Enrich: append reg flags, HSN/HS codes, pricing policy tags, etc from your Indian Medicine database.
- Approve: low confidence items go to human review.
- Publish: push to live catalogue, emit change event for downstream systems
- Reconcile: daily process that compares live catalogue to source feeds and Drug Database updates.
This pattern limits unnecessary and redundant manual tasks. It also makes it easier to be audited.
Culture, compliance and governance
Tech won’t solve issues. Culture does. Build a culture of collective ownership of the problem across product, compliance and pharmacy ops. Empower customer-support and fulfilment to identify and escalate data issues. Reactive playbooks for price updates, product recalls, and mapping reversions help prepare your org for when things don’t go to plan.
Conclusion:
Startups employing data incident controls should focus on the most impactful: canonical identifiers, source hierarchy, automated validation, and daily reconciliation. When assessing potential partners, advocate for integrations within trial periods and measurable service-level agreements.
For teams aiming to accelerate while not sacrificing precision, Data Requisite has partnered with digital pharmacies and healthtech to implement these controls and lower the occurrence of data incidents. The data governance and engineering practices you’re about to adopt will protect your customers, margins, and reputation, and position your startup for scalable growth.
If you want a tailored checklist and a brief technical audit of your medicine data pipelines, Data Requisite can create a pragmatic roadmap to fit your product and regulatory needs.
Also Read: The Future of Drug Intelligence in India: From Data Repositories to Decision Engines
