Common Reasons Data May Appear Inaccurate
- Overview
- Claims Coverage & Missing Fields
- Incomplete Or Missing Pharmacy Data
- Referral Matching Limitations
- NPI Profile & Data
- Patient Exclusions
- Frequently Asked Questions
- Important To Remember
- Related Articles
Overview
Jiro sources data from national claims databases and third-party provider registries. Because this data flows through multiple external systems, there are several common reasons why metrics in Jiro may differ from your internal records or other systems you use.
Consult the Time & Refresh Cadence page for specific lag windows by claim type.
Claims Coverage & Missing Fields
Jiro's real-world data includes approximately 60-70% of U.S. medical encounters and 50-60% of pharmacy encounters in any given year. Coverage varies by payer participation. Patients with insurance coverage may not appear in Jiro if no claims were submitted for them during a given period.
Financial fields including payment amounts and patient costs may be incomplete on both open and closed claims. Open medical claims carry a charge amount while closed claims are allowed or paid. Closed claim financials are integrated into open record during data depuplication. Pharmacy claims follow a different cost model including gross-due, paid, and patient-pay.
Common Observations
Patient counts in Metrics may be lower than your internal records reflect. Payment and cost values may be missing for recent or open claims.
Incomplete Or Missing Pharmacy Data
Pharmacy claims are linked to clinical information through National Drug Code (NDC) mappings. These mappings associate NDCs with drug classes, generic and brand designations, and adherence calculations. NDC databases have inherent gaps. Some prescriptions map incorrectly, and out-of-pocket payers do not appear in pharmacy claims data.
How This Shows Up
Generic vs. Brand Share Metrics may appear lower than expected. Specific drug classes or prescriptions may be absent. Medication Adherence Rates may show unexpected values.
Referral Matching Limitations
Referral patterns in Jiro are directional and inferred from claims data by matching a claim from one provider (the referring provider) to a claim from another provider (the receiving provider). A referral does not appear in Jiro if either provider did not generate a claims record, or if the referring claim was not billed under your NPI.
Expected Data Patterns
Referral counts may be lower than expected. Certain providers or facilities may not appear in your referral network. Transit times between referral initiation and completion may be missing for specific pathways.
NPI Profile & Data
NPI registry data is self-reported and updated at varying intervals by individual providers. Address, specialty code, and practice location fields may lag behind real-world changes. If a claim was billed under an NPI other than the one associated with your Jiro account, those encounters will not be attributed to you.
For technical details on how claims are attributed to your NPI and what types of claims are included, see How Jiro Attributes Data To Your Practice.
Metrics are calculated based on patients attributed to your NPI. A smaller attributed patient panel can produce Metric values that appear more extreme than they would in a larger cohort.
Impact On Your Metrics
Patient counts in Metrics may be lower than expected. Spotlights may highlight patterns that seem unrepresentative of your practice. Encounters in your internal records may not appear in Jiro. Referral patterns may not align with your actual referral behavior.
Patient Exclusions
Certain patients are excluded from Metric calculations by design. For example, patients who died during the measurement period or were hospitalized for specific conditions may be excluded to ensure Metrics reflect clinically appropriate care standards.
How This Affects Denominators
Metric denominators may be smaller than expected. Metrics may differ from manual calculations based on your internal data. Cohort breakdowns may not sum to your total patient count.
Frequently Asked Questions
Why does my data from last month look incomplete? Claims require time for submission, processing, and finalization. Data from the past six months (commercial) or nine or more months (Medicare/Medicaid) continues to accumulate. Check your Metrics after the relevant lag period has passed; data completeness typically improves significantly.
My data looks substantially different from my records. What should I do? First, review the "N" value on the Metric to see the number of patients included in the calculation. Then examine the drill-down view to confirm the Metric's definition and patient exclusion criteria. If a discrepancy remains, use the help button to report it with specific details: the Metric name, date range, and the values you expected.
Can Jiro correct NPI attribution errors? In some cases, yes. If you believe your NPI is misconfigured or that patients are missing from your attributed panel, contact support for investigation.
Why do different Metrics update on different schedules? Metrics refresh on different cadences depending on the claims data they depend on. Pharmacy-based Metrics typically refresh faster than Metrics based on inpatient or specialist claims.
Is my data in Jiro used for credentialing or performance evaluation? No. Data in Jiro is for your personal clinical and operational use only. It is not shared with employers, insurers, or credentialing bodies.
Why is the patient count ("N") low on some Metrics? The "N" reflects the number of patients included in a Metric's calculation. A low N is expected with a smaller panel, a specialized patient population, or when the Metric's defined exclusion criteria remove patients from the denominator.
Important To Remember
- Claims lag times vary significantly by payer and claim status. Refer to Time & Refresh Cadence for the most current lag windows and information
- Report data accuracy concerns through the help button with specific details: Metric name, date range, and expected values.
- Data gaps do not always indicate errors. Many gaps are expected due to claims processing delays, coverage limitations, or intentional patient exclusions designed to ensure Metrics reflect appropriate care.