Database and Operations for Medical Device Yield Rates

Data sources primarily include the medical insurance bureau’s listed price database, hospital HIS system equipment treatment ledgers, manufacturer

What the data for this category looks like

Data sources primarily include the medical insurance bureau’s listed price database, hospital HIS system equipment treatment ledgers, manufacturer operation and maintenance service settlement documents, and medical insurance settlement details. Update schedules follow three cadences:

  • Listed price data is updated quarterly
  • Treatment volume and charging data is synced daily, used to generate that day’s yield rate daily report
  • Equipment depreciation and operation and maintenance costs are updated monthly

Each data record includes these fields: unique device code, purchased recorded cost, monthly consumable expenses, monthly treatment service volume, single service pricing, operation and maintenance service fees, and service life. Cost fields use yuan as the unit. Service volume uses patient visits as the unit. Service life uses years as the unit.

Constraints on database and operations workflows

Differences in sync frequencies across multiple data sources require layered scheduling tasks. Prioritize sync timeliness for daily yield rate data. This prevents high-frequency daily tasks from competing for server resources with low-frequency monthly tasks. Uniform unit requirements across multiple fields require format validation rules during data ingestion. This stops mixed units for cost and service volume data from different sources, which reduces the accuracy of daily reports. The unique device code as the association primary key requires a primary key conflict detection mechanism. This ensures no anomalies in cross-system data association, avoiding duplicate or missing data in daily reports. Monthly updated depreciation and operation and maintenance data requires a dedicated bulk write window. This avoids conflicts with daily sync tasks. Data scale grows with the stock of medical institution equipment. Extensible sharding rules must be configured in advance to support future data expansion and daily report query needs.

Configuration Settings

Configuration ItemRecommended ValueRationale
MAX_CONCURRENT_SYNC_TASKS3–5Balances execution efficiency of bulk sync tasks and server resource usage, matching the bulk processing requirements of medical device data
MONGO_VERSION5.0.24 or laterResolves known security vulnerabilities in version 5.0.18, ensuring database operational security
ENABLE_EXTERNAL_DB_ADAPTEREnable Hangao database adapterSupports integration with third-party databases to meet storage requirements for multi-source medical device data
TENANT_ISOLATION_CONFIGSpecify dedicated business tenantIsolates medical device yield rate data from other business data, facilitating subsequent data export and operation and maintenance management
DATA_EXPORT_API_ENABLEEnable knowledge base data export functionSupports exporting trained text data according to configuration, complying with operation and maintenance archiving requirements
SHARDING_KEYUse unique device codeAdapts to the association query characteristics of medical device data, improving cross-device data query efficiency

The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material form, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.

Three Common Misconfigurations

  • Phenomenon: ETIMEDOUT error occurs during bulk sync task execution, task execution times out. Cause: No reasonable concurrent task upper limit is configured. Excessive concurrent requests occupy server resources, leading to timeouts.
  • Phenomenon: Security alerts appear in database logs, or version compliance checks fail. Cause: MongoDB version 5.0.18 is used, and no upgrade has been performed to the version that fixes the corresponding vulnerability.
  • Phenomenon: DB_CONNECT_FAILED error occurs when attempting to connect to the Hangao database, and data initialization cannot be completed. Cause: External database adapter configuration is not enabled, or database connection parameters are not correctly configured.

How to Verify Successful Configuration

  • Review sync task scheduling logs to confirm daily and monthly tasks execute at preset times, with no resource competition.
  • Run a database version check to confirm the MongoDB version meets preset configuration requirements.
  • Attempt to connect to the Hangao database and run a single data write test to verify the external database adapter configuration is active.
  • Trigger a knowledge base data export operation to confirm trained text content under the specified tenant can be retrieved normally.

Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.