Multi-Source Pharmacy Price Data Collection Across 14 US Platforms
Discover how DataSOS built an automated, multi-source pharmacy pricing pipeline collecting structured drug price data across 14 platforms, 1,000+ generic drugs, and 8 US zip codes to power real-time healthcare cost intelligence.
Client
GlobalRetail Inc.
Industry
Healthcare & Pharma
Region
United States
Challenges
Prescription drug pricing in the United States is one of the most fragmented and opaque data landscapes in the healthcare industry. The same generic drug can vary in price by hundreds of percent across pharmacies within the same zip code yet this pricing data is scattered across dozens of consumer-facing platforms, each with its own API structure, data format, naming conventions, and access rules.
Our client, a healthcare data intelligence firm, needed to build a comprehensive, structured pharmacy price dataset spanning 14 platforms, over 1,000 generic drugs, and representative zip codes across the United States. Their existing approach relied on manual lookups and inconsistent one-off data pulls that could not be scaled, standardized, or refreshed at the frequency their product required.
- Drug price data fragmented across 14 separate pharmacy platforms like GoodRx, WellRx, SingleCare, BlinkHealth, VisoryHealth, Hippo, BuzzRx, TrumpRx, ParamountRx, ScriptCycle, PerksOptum, SavingsSeekerRx, DiRXHealth, and HealthWarehouse with no unified access point
- Each platform returned data in different formats, field names, and structures requiring platform-specific extraction and normalization logic
- Drug catalog of 1,000+ generic drugs spanning varied forms tablets, capsules, creams, sprays, and injectables each requiring precise parameterization for accurate API querying
- Inconsistent drug naming across platforms making cross-source price comparison unreliable without a standardized mapping layer
- Zip code formatting inconsistencies missing leading zeros, non-standard entries causing failed lookups and missing data for entire geographic regions
- Pharmacy name variations across platforms (e.g., 'Giant Eagle' vs. 'Giant Eagle Pharmacy') breaking automated matching and deduplication
- No scalable process to refresh pricing data at regular intervals prices change frequently and stale data reduced the reliability of the client's output
- Manual handling of edge cases drugs with missing form, dosage, or quantity fields creating gaps and inconsistencies in the final dataset
Goals & Objectives
The client required a fully automated, production-ready data pipeline capable of querying all 14 pharmacy platforms systematically, normalizing the results into a single 17-column structured schema, and delivering a clean, query-ready dataset that could power pricing comparisons, market analysis, and cost intelligence tools across the US.
Automate Multi-Platform Price Collection
Build a scalable pipeline that queries all 14 pharmacy platforms programmatically covering 1,000+ generic drugs across 8 representative US zip codes on a repeatable, scheduled basis.
Standardize Drug & Pharmacy Naming
Implement a drug name mapping layer and pharmacy alternate-name matching system to resolve inconsistencies across platforms and ensure accurate cross-source price comparisons.
Deliver a Clean 17-Column Structured Dataset
Normalize all extracted records into a consistent output schema capturing drug identity, pharmacy details, pricing, location data, and GPS coordinates with no blank mandatory fields.
Handle Edge Cases Without Data Loss
Identify and separately log drugs with missing form, dosage, or quantity fields ensuring they are flagged for offline handling rather than silently dropped from the dataset.
Ensure Geographic Accuracy
Apply zip code validation and zero-padding logic across all 8 target zip codes to guarantee complete coverage and eliminate failed API lookups due to formatting errors.
Build for Refresh & Scale
Design the pipeline to support scheduled re-runs and easy expansion to additional pharmacy platforms or drug catalogs without requiring structural changes to the core system.
Solution
DataSOS delivered an end-to-end pharmacy price data collection system engineered for precision in every parameter, field, and edge case accounted for across all 14 sources.
- Platform-specific extraction modules handle individual API structures, authentication, and response schemas for each pharmacy source independently.
- A drug parameterization layer constructs accurate API queries for 1,000+ generic drugs encoding names, forms, dosages, quantities, and day-supply ranges per platform.
- A zip code validation engine applies leading-zero padding before every query eliminating missed-coverage failures across target geographic areas.
- A pharmacy name resolution system matches API responses against a master list and alternate-name table resolving variations like 'HEB' to 'H-E-B Pharmacy' for consistent output.
- All records are normalized into a unified 17-column schema with mandatory field validation applied before write.
- Drugs with missing or 'NA' fields are auto-flagged to a separate exception log keeping the primary dataset clean without losing any records.
Results
14
Pharmacy Platforms Integrated
1000+
Generic Drugs Covered
100%
Automated Data Collection
17
Structured Output Fields
Technologies Used
DataSOS solved every single one of those problems systematically
We had tried to build this internally and kept running into the same walls naming mismatches, zip code failures, drugs that broke the API. DataSOS solved every single one of those problems systematically. The dataset we received was cleaner and more complete than anything we had produced manually in two years of trying.