UDS Tables Explained for FQHCs: A Detailed Breakdown of Every Key Reporting Table

The Uniform Data System is HRSA’s annual reporting framework for Health Center Program-funded health facilities. UDS data includes patient demographics, services and visits, clinical quality metrics, staffing, costs, and revenues, resulting in uniform national statistics on health center performance. Each UDS table serves a specific purpose and is based on information obtained from the health center’s EHR/registration, billing, and finance systems, and HR/payroll.

Table 3A, for example, counts unduplicated patients by age and gender, whereas Table 9D keeps track of total charges and collections by payer. Careful attention to terminology and cross-table uniformity is essential.

Recent trends show that by 2022, health centers treated a record ~30.5 million patients, 90% of whom were ≤200% FPL, and by 2024, over 32.3 million patients. This guide walks through every key UDS table, detailing its purpose, data elements, sources, calculation rules, common pitfalls, and compliance tips – with an illustrative example row for each.

Purpose and Scope of UDS Reporting

The UDS is a standardized annual data set that every Federally Qualified Health Center awardee must submit. It is mandated by Section 330 of the PHS Act and covers all services in a health center’s project scope. The UDS captures unduplicated patient counts, visit counts, patient demographics, service mix, clinical quality measures, staffing, financial costs, and revenue sources. Its purpose is both operational and compliance/oversight. For example, HRSA’s published summaries show national trends in patient volume and health outcomes based on UDS data. Health centers must update these data annually.

In practice, UDS data are gathered from a variety of systems: patient registration and EHR databases provide demographics and visit/service records; billing/claims systems provide diagnosis and financial data; HR/payroll systems provide staff FTEs; and the accounting system provides cost and revenue information. The graph above depicts how clinic EHRs, billing, HR, and finance interact with the various UDS tables. Key points: Each patient or service should be tallied once, and health centers must adhere to the manual’s criteria. UDS data are publicly available at the aggregate level, and health centers can obtain their own previous UDS data through the HRSA EHB and data portal.

Related: What is UDS Reporting in Healthcare? Everything You Need to Know

Key UDS Reporting Tables

Patients by ZIP Code Table

Breaks down patients by their residence ZIP code and primary medical insurance. This table helps map the clinic’s service area and payer distribution. It lists each ZIP code in the service area, the count of patients residing there, and those patients’ insurance categories.

ZIP code, number of patients with that residence ZIP, segmented by insurance. Lines are also provided for “Other ZIP” and “Unknown” residences.

Patient registration address and insurance records. Patient ZIP is taken from the address on file at last encounter; primary insurance is as of last visit. Count each unduplicated patient by ZIP. The “Other ZIP” line must collect all ZIPs with ≤10 patients. Patients lacking a known ZIP are counted in “Unknown”. Total patients should equal the health center’s total patient count.

Verify that the sum of patients on this table matches Tables 3A, 3B, and 4. Ensure that “Other” and “Unknown” patients stay in small numbers; otherwise, data may be incomplete. For special populations: if a homeless patient does not have a ZIP on file, use the shelter/service site ZIP; if a migrant worker has a temporary housing ZIP, use it. Mock example row: If ZIP 12345 includes 150 Medicaid patients and 50 Private, the row would display ZIP=12345, Medicaid/Other Public=150, Private=50.

Table 3A: Patients by Age and by Sex

The number of unduplicated patients served is reported, split down by age group (single and five years old) and gender. Along with Tables 3B and 4, it builds the patient profile.

For each age group, report the count of Male and female patients who had at least one visit. Line 39 is the total number of patients. Age is determined as of December 31 of the reporting year.

EHR/registration (date of birth and sex on record). Use data from all qualifying visits in the year.

Each patient is counted once in the age-sex table, in the row corresponding to their age at Dec 31 and sex assigned at birth. “Male” and “Female” must sum to the total patients. The UDS does not capture nonbinary gender; see FAQs.

• Ensure that this is an unduplicated count; a patient with several visits will only display once.
• Check that the sum on Table 3A equals the totals on Tables 3B and 4.
• Double-check your age calculation: it must be as of December 31, not the last visit date.
• If reporting grant-specific patients, the grant-subset count for each age group must be ≤ the total on the universal report.

If a FQHC had 180 male and 220 female patients aged 30-34 at the end of the year, Table 3A would show the following: Male 180, Female 220, Total 400.

Table 3B: Demographic Characteristics

Reports patients’ Hispanic/Latino ethnicity, race, and native language. Together with Tables 3A and 4, it completes the basic demographic profile.

Patients are counted in ethnicity and race categories. 

• For example, Lines 1–3 ask “Hispanic or Latino/a?” with columns for Yes, No, Unreported/Unknown, and Total. 
• Lines 9–12 report race in similar columns. 
• Line 12 captures patients best served in a language other than English. 
• The exact layout may vary by year.
  

Patient self-reported demographics in the EHR/registration system. Ethnicity and race should follow the OMB/HHS standards. Language is the self-reported preferred language. All data must be current.

Each patient is counted once in each segment.

• For example, a patient can be classified as Hispanic/Latino or Non-Hispanic based on ethnicity.
• Similarly, patients are assigned to only one race category.
• Totals on each section must match the total patients in Table 3A.
  

Check for missing or “Unknown” race/ethnicity entries; UDS encourages collecting this at registration. Make sure totals match across tables. Note that a patient not providing race/ethnicity is allowed, but will appear under “Unreported/Unknown”.

If 30% of a FQHC’s patients identify as Hispanic/Latino, 60% as Non-Hispanic, and 10% decline to report, Table 3B may display Hispanic/Latino 900, Non-Hispanic 1800, and Unreported 300.

Table 4: Selected Patient Characteristics

Purpose: Details key patient attributes like income, insurance status, managed-care enrollment, and membership in special populations. Together with Tables 3A/B, it profiles patients’ socioeconomic and eligibility characteristics.

The table is multi-part. Lines 1–6 cover income categories. Lines 7–12 cover primary medical insurance. Lines 13a–13c count total managed care member-months by payer type. Lines 14–26 cover special populations: migratory/seasonal workers, the homeless, patients at school-based sites, veterans, and patients in public housing.

Multiple sources within the center: patient intake forms and EHR, insurance enrollment records, and program records. Income is usually self-reported and must be kept up to date. Insurance is taken from the last visit’s primary coverage.

• For each line, count unduplicated patients fitting the criteria. 
• E.g., Line 7 counts patients whose primary insurance is none; Lines 8–11 count those with Medicaid, CHIP, Medicare, Other Public, or Private. 
• If a patient has multiple coverages, use the primary medical coverage. 
• Managed care member-months are the number of months each patient was enrolled in managed care throughout the year.
• Special populations: for example, count every patient who has been homeless or a migrant worker at some point in the year. 
• Homelessness lines detail shelter type; Line 23 is the total ever-homeless. 
• Each patient is counted once per applicable line.
  

Use the poverty guideline of the health center’s location, not the patient’s home state, for consistency. If income is unknown, report it as “Unknown,” not as ≤100% FPL. 

Always report patients by their primary medical insurance from the last visit, even if the service wasn’t billed. Dental-only visits do not change a patient’s medical insurance on this table. For Medicaid expansion plans, count as Medicaid if identifiable, otherwise as Private.

Managed care member-months may exceed 12× patient count if enrolled without visits. – Special pops: All FQHCs are required to disclose totals for migrants, homeless, veterans, and so on, even if they do not have a grant for them; in this instance, details are grayed out. – Cross-checks: Totals should match the relevant totals on Table 9D to allow for PMPM calculations, etc.

Mock example row: If a center serviced 500 patients at ≤100% FPL, 1000 at 101-200% FPL, and 1500 over 200% FPL, it would list Line 1: ≤100% FPL 500; Line 2: 101-200% 1000; Line 3: 201-300% 500; Line 4: >300% 1000; Line 5: Unknown 0; Total: 3000.

Table 5: Staffing and Utilization (plus Selected Service Addendum)

Staffing, visitation, and patient count data are summarized by service category. It displays how many full-time equivalent employees work in medical, dental, mental health, substance use disorder, enabling, and administrative roles, as well as how many visits and patients they produce. A related addendum categorizes mental health and SUD visits by provider type. This table is crucial for determining productivity and staffing ratios.

Key fields: Column A: Personnel FTEs by category. Columns B/B2: Clinic visits and Virtual visits by provider line. Column C: Patients by category. The Selected Service Addendum has Column A1 listing providers of mental health or SUD, with separate visits/patients columns.

Payroll and human resources records, as well as scheduling and visitor logs. FTEs are employees employed or contracted and scaled to full-time equivalents for the year. Visits are counted only if they match UDS criteria and are recorded in the EHR or visit log. Patients are individuals who had one or more visits during the year, tallied once per service type.

• An FTE is the equivalent of one person working full-time for a year.
• Part-time employees, contract providers, volunteers, and trainees should all be counted proportionally.
• FTEs should not be adjusted downward for vacation or leave; instead, report full contracted capacity.
• Visits: count one visit per patient per service category per day. 
• For example, if a patient sees an MD and a PA on the same day for primary care, count as two separate medical visits. 
• Virtual visits count if they meet the criteria of being synchronous and with a licensed provider. 
• One count per patient in each category in which they received care.
  

Assign each FTE line to the correct service licensure. Include all eligible staff in the FTE count, including full- and part-time workers, contract clinicians, volunteers, residents, debt repayment providers, and so on.

• Do not double-count: if a provider is contracted on a fee-for-service basis, they will appear in the addendum but not in the main FTE columns.
• Visits must align with providers: e.g., only medical providers’ visits go in the medical lines, only dentists’ in dental, etc. 
• Verify patient counts on this table against Table 3A totals by category to catch omissions.
  

If a center has 5.5 FTE MDs and they recorded 4,000 clinic medical visits and 200 virtual visits, with 2,500 unduplicated medical patients, a sample line might read “Physicians: FTE 5.50; Clinic Visits 4000; Virtual 200; Patients 2500.”

Table 6A: Selected Diagnoses and Services Rendered

Captures counts of clinical events: two parts: 

• Selected diagnoses, such as common chronic conditions and behavioral health categories 
• Selected services/tests like screenings, immunizations, and preventive services that occurred in the year. It helps identify disease burden and service volume beyond basic visit counts.
  

Lines 1–20 report diagnoses. For each, Column A is “Visits with diagnosis,” and Column B is “Patients with diagnosis.” Lines 21–26 list tests/screenings, also with visits and patients. Lines 27–34 list dental services similarly. The instructions include specific ICD-10 or CPT code sets for each line.

EHR/problem list and billing records, and service logs. For example, count any visit where ICD-10 codes for diabetes appear on the encounter as a “diabetes visit.” Services can be recorded from lab order records or procedure claims.

For each line, count visits in which the condition or service occurred and patients who had at least one such visit. A single visit with multiple services counts only once for each applicable line. Visits in Column A include clinic and virtual. Count a patient only once in Column B per line, regardless of multiple visits. Children’s preventive services should count age-eligible patients per measure specs. Dental service lines count any in-person or teledentistry visit meeting the service code.

Ensure all diagnoses are drawn from actual visit records; do not use population registers. Be careful with overlapping categories. For screenings/immunizations, include services done as part of a visit or standing order. All dental services in Column A must reflect actual patient appointments with dental staff. Validate that Column B does not exceed the total patients in Table 5. Also, confirm that the number of prenatal care patients on 6A corresponds to the prenatal denominator in Table 6B.

Table 6A would read “Diabetes: Visits 600; Patients 450” if there were 600 visits with a diabetes diagnosis and 450 distinct individuals.

Table 6B: Quality of Care Measures

Reports selected clinical quality measures, e.g., preventive and chronic care indicators, that HRSA uses to gauge performance. Prenatal care, pediatric vaccines and screenings, cancer screening, tobacco screening, HIV care linkage, and drug use treatment participation are a few examples.

Organized by patient population/measure. 

• For each, UDS collects: Column A = number of patients in the denominator, 
• Column B = number of records reviewed, 
• Column C = number meeting numerator. 
• For example, Prenatal Care, Early Entry: Denominator = all live birth mothers; Numerator = those who started care in the 1st or 2nd trimester. The table spans many lines.

Chart review or EHR data per the official measure definitions. Many measures align with CMS eCQM specifications. Data is typically pulled from clinical registries or the EHR’s quality reporting module. For chart-based measures, a manual or EHR query is required.

Follow each measure’s precise definition. Example: “Controlling High BP” measures the denominator as adult patients with hypertension; the numerator is those whose last BP < 140/90. Each patient counts only once. Some measures have subgroups by age or sex. Be sure to use the last menstrual period or the best pregnancy data for prenatal timing. New in recent UDS versions are measures for SUD treatment initiation/engagement among adolescents/adults diagnosed with new substance use episodes. Carefully apply exclusions and exceptions per the measure spec. For instance, in Cervical Cancer Screening, a patient self-swab or an unsatisfactory Pap smear should not count as a numerator. 

Only eligible patients belong in each denominator. Also note differences: Table 6B may not exactly match counts on Table 7 due to data collection methods; small discrepancies are expected. Use the UDS Manual’s FAQs for each measure to clarify tricky cases. For “Childhood Immunization Status,” if 80 patients turned 2 years old and 64 of them were fully immunized by age 2, Table 6B would show Denominator 80; Numerator 64.

Table 7: Health Outcomes and Disparities Measures

Reports certain clinical outcome indicators, primarily control of hypertension and diabetes, broken down by race/ethnicity, plus birth outcomes. This table highlights health disparities and summarizes key long-term health results for the population.

The main sections include Deliveries and Birth Weight: number of births and percentages with low birth weight; and Hypertension Control and Diabetes Poor Control, each by race/ethnicity groups. Data are reported as the number of patients in the measure and those meeting the desired outcome. 

The table effectively tallies “controlled hypertension” and “uncontrolled diabetes” by race/ethnicity, along with total rates. EHR clinical data. For birth outcomes, use obstetric records. For hypertension/diabetes, use active problem list or diagnosis codes plus recent vital signs or lab results to determine control. Race/ethnicity from patient demographics.

Follow definitions in the UDS manual: e.g., “controlling high blood pressure” includes adults 18–85 with diagnosed hypertension; numerator = last BP reading <140/90. Race/ethnicity categories on this table must match those used on Table 3B. For diabetes, “poor control” is typically HbA1c >9%. The table usually counts those as NOT meeting control. If a patient appears in a race/ethnicity group on Table 3B, they must be placed in the same group here. Birthweight is reported for each birth, and overall rates are computed.

Ensure patients are assigned to the correct race/ethnicity row to avoid mis-mapping disparities. Only include diagnosed patients in these measures. For prenatal care, use consistent timing rules. The UDS Manual points out that Table 7 now requires individual patient submission via UDS+ for these measures; however, FQHCs still submit aggregate values. Validate that the number of hypertensive patients on Table 7 does not exceed the total adult patients on Table 3A.

If 500 patients had been diagnosed with hypertension and 350 had their BP controlled, then across all races, you’d report Denominator 500; Numerator 350. If 200 of those patients are Black and 140 of those were controlled, the Black patient row shows 500:350 overall, with 200:140.

Table 8A: Financial Costs

Summarizes accrued costs by service category for the year. It shows how much money was spent on medical, dental, mental health, SUD, pharmacy, enabling, administration, etc., including a share of facility and support costs. This helps analyze cost per service and overall budgeting.

Column A: direct costs for each category. Column B: allocated portion of facility and non-clinical support costs to each category. Column C: total costs after allocation. Lines cover categories like Medical, Dental, Lab, Mental Health, Pharmacy, Vision, enabling services, and Administration. Facility and Support costs are on Lines 14–15 and get allocated in Column B.

The general ledger or cost accounting system. Centers pull total expenses for each program/service. Allocations require supporting schedules.

In Column A, report full direct costs for each cost center. Include depreciation expense, but exclude any non-operational charges like bad debt or loan principal repayment. Then allocate facility costs and non-clinical support to services using a logical method. After allocation, Column C equals Column A + Column B. Totals should match internal financial reports for the year.

Ensure consistency between Table 5 staff FTEs and Table 8A costs. Allocate facility costs systematically: e.g., if Medical uses 40% of square footage, it should get 40% of facility costs. Maintain documentation for allocations. Check that total costs equal the center’s audited total expenses for the scope of the project. Note that donated services or drugs are not included in Table 8A.

If the center spent $1,200,000 on Medical services and $300,000 on Dental, and allocated $100,000 facility costs to each, the medical line would read Accrued $1,200,000; Allocated $100,000; Total $1,300,000.

Table 9D: Patient Service Revenue

Reports patient-service revenue by payer and category. It captures gross charges, collections, adjustments, and self-pay discounts/write-offs for services provided. The table gives insight into payer mix, average charge per visit, and the charge-to-cost ratio.

• A) Full Charges – gross billed charges by payer; 
• B) Collections – actual cash collected from each payer in the year; 
• C1–C4) Retroactive Settlements/Other Payments/Paybacks – adjustments like wrap payments or quality bonuses; 
• D) Adjustments – contractual write-offs and other reductions; 
• E) Net Revenue – Collections plus retroactive minus adjustments. Rows break out by payer source, and then patient sliding discounts and bad debt.

The billing/accounting system. Centers typically run a UDS-specific query on their billing data. “Full Charges” use the center’s fee schedule. “Collections” are cash receipts recorded in that year, regardless of service date. Retroactive payments come from state Medicaid reconciliations or pay-for-performance bonuses. Adjustments include contractual differences.

Enter full gross charges for each payer in Column A. Do not report zero charges for uncompensated services, only services expected to have a fee. Collections are the sum of money received in the year from all sources. 

Be careful: actual Medicare FQHC encounter rates or negotiated payer rates are not used as “charges” in Col A; instead, use your standard fee. Columns C1–C4 capture special items, all of which also flow into the total Collections. Column D adjustments remove reductions. Finally, check that “Net Revenue” in Col E equals Col B+B(C1–C4)–D.

Never enter the amount Medicare or Medicaid actually paid as the charge; this double-counts discounts. 

• For example, if Medicare’s FQHC rate was entered as a charge, that’s incorrect; the charge should be what you would bill a private patient. 
• Include all cash received, even if it was for prior-year services. 
• For self-pay, include sliding fee discounts and bad debt write-offs appropriately. 
• The total Medicaid collections on 9D divided by total Medicaid patients from Table 4 should yield a plausible PMPM or per-patient revenue. 
• Always do not duplicate revenue on 9D and 9E: e.g., a grant payment reported on 9E should not be on 9D.

If total billed charges to Medicaid were $2,000,000 and Medicaid collections were $1,800,000, the Medicaid line might read Charges $2,000,000; Collections $1,900,000 (which includes the $100,000 retroactive); Adjustments $100,000; Net $1,800,000.

Table 9E: Other (Non–Patient Service) Revenue

Summarizes non–patient-service revenue, grants, contracts, and miscellaneous receipts that fund the health center’s scope of project but are not tied to specific clinical encounters. This includes federal/state grants, local funds, loans forgiven, donations, and COVID relief, etc. It shows the center’s funding diversification.

BPHC grants, HRSA capital grants, other HRSA COVID grants, other federal grants, state/local government grants, and funds like Title X. Line 6a captures Indigent Care program receipts, line 7 foundation/private grants, and line 9 other revenue. Column A is “Cash Receipts”. Column B might be collected loans; column C is Total. Accounting records of cash receipts. Drawdowns from grants should be recorded in the year drawn. For in-kind revenue, see the FAQ.

Follow the “last-party rule”: attribute revenue to the funding entity that delivered it. Report only cash basis receipts in the year. Do not include any amounts already on 9D. For example, a drawn federal COVID relief grant goes on the appropriate line in 9E. Indigent Care funds are cash from state/local charity care programs.

Do not double-count: receipts on 9E should not appear on 9D. Total receipts should equal the sum of these lines. Use the last-party rule carefully: e.g., if HRSA’s BHW gave funding via a BPHC grant, report it as BPHC. If a state grant came through a local agency, report it as state. Be mindful that some historic lines were removed. Check that the total “Other Revenue” matches the audited financial statements. Also note: Look-alikes do not report BPHC 330 grants on 9E.

Related: HRSA UDS Reporting Requirements & Compliance Guide

Summary of UDS Tables

National Trends (UDS Data Highlights)

• Health centers collectively served ~28.6 million patients in 2020, 30.2 million in 2021, and a record 30.5 million in 2022, reaching ~32.4 million by 2024.
• Roughly 29–30% of patients are children, ~59% are ages 18–64, and the 65+ share is rising. About 64% of patients identify as racial/ethnic minorities. Notably, 90% of health center patients are at or below 200% of the federal poverty line, reflecting the centers’ mission.
• In 2022, UDS reported ~9.6 million patients in rural areas, 1.4 million experiencing homelessness, 1.0 million agricultural workers, 925,000 served at school-based centers, and 395,000 veterans. These figures underscore the role of UDS in tracking reach to vulnerable groups.
• UDS data also show quality trends; for example, over 80% of children up to age 2 received all recommended immunizations in recent years, and blood pressure control rates are typically ~65%.
• Nationally, health centers reported patient service revenues of tens of billions annually. For 2024, aggregated UDS showed ~$47 billion in total patient revenue and $7+ billion in other funding. On average, health centers cover ~90% of costs via grants and patient revenue.

These national figures come from HRSA’s UDS data portal and reports and provide context for each center’s own data. FQHC look-alikes report separately; in 2024, 153 look-alikes served ~1.5 million patients, a much smaller scale than awardees.

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