How Jiro is Personalized for You

[SCREENSHOT — Practice showing personalized metrics and Spotlights for a physician, with peer benchmarks visible]

Overview

Jiro Health delivers practice intelligence specific to you through your existing real-world data — your patients, your clinical patterns, your specialty context. This article explains how that personalization works at each layer of the platform, from the data that anchors it to the signals it surfaces.

Your NPI as the Foundation

Every layer of personalization in Jiro Health begins with your NPI. When your account is created, Jiro Health links your NPI to the underlying real-world dataset and begins identifying the clinical activity associated with you as an individual practitioner. No manual data entry, EHR integration, or panel upload is required. Your NPI serves as the anchor that connects your account to the real-world data that powers the platform.

How Your Patients Are Identified

Your attributed patient panel is derived from real-world data in which your NPI appears as the ordering, performing, or rendering physician. The specific attribution logic varies by service context — professional and institutional records, for example, follow different attribution rules — but the principle is consistent: patients are attributed to you based on documented clinical responsibility as reflected in the underlying record.

Attribution is reassessed on a rolling basis as new data becomes available. Your attributed panel at any given time reflects activity within the active measurement period, typically the most recent 12 months.

Because attribution is based on how clinical activity is documented, patients you treated whose records were submitted under a group or facility NPI — without individual provider attribution — may not appear in your panel. This is a structural characteristic of how clinical data is submitted, not a limitation specific to Jiro Health.

How Metrics Are Selected for You

Jiro Health surfaces a default set of metrics relevant to your specialty. Specialty is determined from your NPI taxonomy at registration and used to select the metrics most likely to reflect the clinical, operational, and financial dimensions of your practice. A cardiologist and an emergency physician, for example, see different default metric sets reflecting the different care contexts and population patterns relevant to each specialty.

Within that default set, metrics are calculated from your attributed patient panel. You see your own numbers — not aggregate or population-wide figures — benchmarked against peers in comparable practice settings.

[SCREENSHOT — Practice showing the metric set for a specific specialty, with the physician's values and peer benchmarks displayed]

How Spotlights Are Generated

Spotlights surface meaningful changes and patterns in your practice data — automatically, without any configuration required. They are generated from your metric values and updated as new data becomes available.

The Spotlights algorithm evaluates your metrics across multiple dimensions — including patient demographic stratifications such as age, diagnosis cohort, and payer type — to identify where significant shifts or patterns exist. Spotlights are filtered to those most relevant to your active metric set and selected to provide variety across metric categories over time, so the same area of your practice is not surfaced repeatedly.

[SCREENSHOT — the Spotlights section of Practice, showing an example Spotlight with its headline, metric context, and peer comparison]

How Peer Benchmarks Are Constructed

Every metric in Jiro Health is accompanied by a peer benchmark — a reference value calculated from the same real-world data that powers your personal metrics. Benchmark cohorts are defined by specialty and, where data are sufficient, by practice location, so the comparison group reflects physicians practicing in similar clinical contexts.

Because benchmarks are derived from real-world data rather than survey responses or self-reported figures, they reflect actual practice patterns across the physician population. The benchmark for any given metric represents the distribution of values among your peers, giving your metrics context that would otherwise require external research to obtain.

Benchmarks are not evaluative. They are a reference point for your own interpretation.

How Personalization Adapts to Data Coverage

The depth of personalization Jiro Health provides depends on how much of your clinical activity is captured in the real-world dataset. Coverage varies based on your geography, payer mix, and practice setting.

When data coverage is high, Jiro Health delivers fully personalized metrics calculated from your attributed patient panel, with peer benchmarks specific to your specialty and location.

When coverage is lower — for physicians in markets where data is less complete, or for physicians whose historical activity is still being indexed — Jiro Health adjusts the level of personalization rather than displaying empty or potentially misleading data. In these cases, the platform falls back to specialty-level or location-level signals, which are less specific to your individual practice but still clinically relevant. [NEEDS CONTENT: confirm which confidence indicators are currently live in the product and describe how they appear alongside metrics]

For physicians with no data available — for example, residents or newly licensed practitioners — Jiro Health provides specialty benchmarks and non-personalized insights while data coverage builds over time.

How Consult Is Personalized

Consult draws on the same real-world data that powers the Practice tab to contextualize its responses. When relevant, Consult incorporates patterns from your attributed patient population — such as payer mix, diagnosis distribution, or treatment patterns — to provide responses grounded in data specific to your clinical context rather than generic population-level evidence alone.

The degree of personalization in Consult responses depends on data coverage in the same way as Practice metrics: where coverage is high, responses can draw on your specific panel; where coverage is limited, responses rely on specialty-level or broader population data, with appropriate caveats noted inline.

Important To Remember

  • Specialty is assigned at registration based on your NPI taxonomy. If your specialty is incorrectly assigned, or if you practice across multiple specialties, contact Jiro Health support to review your account configuration.
  • Jiro Health personalization does not use data you enter into any external system, including your EHR or practice management software. All personalization is derived from real-world data.
  • Attribution logic and benchmark methodology follow established industry standards, including HEDIS, CMS, and AHRQ frameworks, adapted for the data available in the Jiro Health dataset.

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Version History

Updated: 4/22/2026

Reviewed by: Claude, Help Scout Docs Reviewer

Approved by: Caitlyn Edwards, Head of Communications

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