HARP Population Health - Insights

Attribution Type

The source/methodology used to define a user’s attributed patients, and those patients' relationships to individual providers and/or organizations (i.e., attributed entities). For HARP Population Health, the attribution type will always be “Geography”, which attributes patients to specific geographic regions, based on their most recent, valid home address.

For more information, visit the Attribution Type methodology section.

Attribution Level

The category used to group similar patient populations to establish relationships among zip codes, counties, and public health districts identified in the selected Attribution Type.

For more information, visit the Attribution Level methodology section.

Attribution Entity

The name(s) of the geographic region corresponding to the selected Attribution Level to which patients are attributed for reporting purposes.

This filter is conditional based on a user’s Attribution Type and Attribution Level selections.

For more information, visit the Attribution Entity methodology section.

MaineCare Eligible Member

The indication of whether a user’s attributed patients are active, eligible MaineCare (Medicaid) members at the time of reporting per MaineCare’s daily member eligibility file.

Valid values include Yes (i.e., patients who are active, eligible MaineCare members) and No (i.e., patients who are not active, eligible MaineCare members).

For more information, visit the MaineCare Eligibility Criteria methodology section.

Condition

The indication of whether a user’s attributed patients have a chronic condition for the selected chronic condition or event of care.

For more information about the chronic conditions and events of care available for reporting purposes, visit the Patient Condition/Event Flags methodology section.

Risk

The identified risk class, per risk model of a user’s attributed patients for those models included in HARP’s Predictive Risk Modeling.

For more information about predictive risk, visit the Predictive Risk Modeling methodology section.

Sex

The sex categories of a user’s attributed patients based on their most recently reported legal/insurance sex values.

Valid values include Male, Female, X, and Unknown.

For more information, visit the Value-Add Field Logic methodology section.

Age Group

The age group categories of a user’s attributed patients based on their reported date of birth values.

Valid values include 0-4, 5-9, 10-14, 15-17, 18-24, 25-34, 35-44, 45-54, 55-64, 65-74, 75-84, 85+, Unknown.

For more information, visit the Value-Add Field Logic methodology section.

Race

The race categories of a user’s attributed patients based on their most recently reported race values.

Valid values include AIAN (American Indian or Alaska Native), Asian, Black (Black or African American), Multi (two or more races), NHPI (Native Hawaiian or Pacific Islander), White, and Unknown.

For more information, visit the Value-Add Field Logic methodology section.

Ethnicity

The ethnicity categories of a user’s attributed patients based on their most recently reported ethnicity values.

Valid values include Hispanic, Non-Hispanic and Unknown.

For more information, visit the Value-Add Field Logic methodology section.

ZIP

The 5-digit ZIP code associated with the primary residence of a user’s attributed patients based on their most recently reported complete address. Note: Filtering by ZIP code is only available for patients with a primary residence within the United States, excluding territories.

For more information, visit the Value-Add Field Logic methodology section.

County

The county associated with the primary residence of a user’s attributed patients based on their most recently reported complete address. Note: Filtering by ZIP code is only available for patients with a primary residence within the United States, excluding territories.

For more information, visit the Value-Add Field Logic methodology section.

Measure

The eCQM assessing the care received by a user’s attributed patients.

For more information about available eCQMs, visit the Quality Performance Measurementmethodology section.

Percent Patients in Compliance

The calculated result indicating the percentage of a user’s attributed patients who are in the selected measure’s denominator and who also meet the numerator criteria.

Patients in Denominator

The count of a user’s unique attributed patients who meet the selected measure’s denominator criteria.

Patients in Numerator

The count of a user’s unique attributed patients who meet the selected measure’s numerator criteria.

Patients with COVID Hospitalization

The count of a user’s unique attributed patients who have a history of COVID-19 related hospitalization.

For more information on Patient Condition/Event Flags related to COVID-19 Hospitalization, visit the History of COVID-19 Related Hospitalization Prevalence Flag section.

Patients with Elevated Risk of Chronic Condition or Event

The count of a user’s unique attributed patients who are at high or very high risk of having a new chronic condition or event diagnosis in the next 12 months.

For more information on Patient Risk methodology, visit the Predictive Risk Modeling section.

Patients with CAD

The count of a user’s unique attributed patients with a diagnosis of Coronary Artery Disease at any time in the last 3 years.

Patients with PAD

The count of a user’s unique attributed patients with a diagnosis of Peripheral Artery Disease at any time in the last 3 years.

Patients with TIA

The count of a user’s unique attributed patients with a diagnosis of Transient Ischemic Attack at any time in the last 3 years.

Patients with AMI

The count of a user’s unique attributed patients with a diagnosis of Miocardial Infarction at any time in the last 3 years.