Chronic Disease Registry Query

Overview

Construct virtual disease registries by querying FHIR Condition, Observation, MedicationRequest, and Encounter resources. Support six registries: diabetes, hypertension, COPD, asthma, CHF, and CKD. For each registry, determine patient enrollment, severity/stage distribution, current treatment patterns, outcome metric trends, and intervention triggers. See references/registry-criteria.md for inclusion codes and stratification logic.

FHIR Resources Used

Instructions

Step 1: Select Registry

Determine which disease registry to query. Supported registries and their primary codes (see references/registry-criteria.md for full code sets):

Step 2: Build Registry Population

Query patients with the target condition:

Tool: fhir_search
resourceType: "Condition"
queryParams: "code=[snomed-code]&clinical-status=active&_count=200&_include=Condition:subject"

If zero results, retry with ICD-10:

Tool: fhir_search
resourceType: "Condition"
queryParams: "code=http://hl7.org/fhir/sid/icd-10-cm|[icd10-prefix]&clinical-status=active&_count=200"

Collect all unique patient IDs. Fetch Patient demographics:

Tool: fhir_search
resourceType: "Patient"
queryParams: "_id=[id1],[id2],...&_count=50"

Exclude deceased patients (deceasedBoolean = true or deceasedDateTime present).

Step 3: Determine Severity/Stage Distribution

Query condition-specific severity metrics.

Diabetes -- A1c-based stratification:

Tool: fhir_search
resourceType: "Observation"
queryParams: "code=http://loinc.org|4548-4&date=ge[12-months-ago]&_sort=-date&_count=200"

Stratify: Well controlled (A1c < 7%), Moderate (7-9%), Poor (> 9%), Unknown (no recent A1c).

Hypertension -- BP-based staging:

Tool: fhir_search
resourceType: "Observation"
queryParams: "code=http://loinc.org|85354-9&date=ge[6-months-ago]&_sort=-date&_count=200"

Stratify per AHA: Normal (< 120/80), Elevated (120-129/<80), Stage 1 (130-139/80-89), Stage 2 (>= 140/90), Crisis (> 180/120).

CHF -- BNP/NYHA-based:

Tool: fhir_search
resourceType: "Observation"
queryParams: "code=http://loinc.org|42637-9&date=ge[12-months-ago]&_sort=-date&_count=200"

LOINC 42637-9 = BNP. Also check for NYHA classification in Condition.severity or Observation with LOINC 88020-3.

CKD -- eGFR staging:

Tool: fhir_search
resourceType: "Observation"
queryParams: "code=http://loinc.org|33914-3&date=ge[12-months-ago]&_sort=-date&_count=200"

Stratify: Stage 1 (eGFR >= 90 with kidney damage), Stage 2 (60-89), Stage 3a (45-59), Stage 3b (30-44), Stage 4 (15-29), Stage 5 (< 15).

COPD/Asthma -- Spirometry:

Tool: fhir_search
resourceType: "Observation"
queryParams: "code=http://loinc.org|19926-5&date=ge[12-months-ago]&_sort=-date&_count=200"

LOINC 19926-5 = FEV1/FVC. GOLD staging for COPD: Mild (>= 80%), Moderate (50-79%), Severe (30-49%), Very Severe (< 30%).

Step 4: Analyze Treatment Patterns

Query active medications for registry patients by therapeutic class.

Diabetes medications:

Tool: fhir_search
resourceType: "MedicationRequest"
queryParams: "patient=[patient-id]&status=active&_count=50"

Classify medications into: Metformin, Sulfonylureas, DPP-4 inhibitors, GLP-1 agonists, SGLT2 inhibitors, Insulin (basal, bolus, premixed), Thiazolidinediones, Other. See references/registry-interventions.md for drug class mappings.

Calculate: % on monotherapy, % on dual therapy, % on triple therapy, % on insulin, % on cardioprotective agents (GLP-1 or SGLT2).

Hypertension medications: Classify: ACE inhibitors, ARBs, Calcium channel blockers, Thiazide diuretics, Beta blockers, Mineralocorticoid receptor antagonists, Other.

CHF medications: Track guideline-directed medical therapy (GDMT): % on ACEi/ARB/ARNI, % on beta blocker, % on MRA, % on SGLT2i, % on all four pillars.

Step 5: Track Outcome Trends

Query historical observations for trend analysis (last 24 months, quarterly intervals):

Tool: fhir_search
resourceType: "Observation"
queryParams: "code=[loinc-code]&date=ge[24-months-ago]&_sort=date&_count=500"

For each quarter, compute: mean value, median, % at target, % worsening. Present trend as a table.

Step 6: Identify Comorbidity Burden

For each registry patient, query additional active conditions:

Tool: fhir_search
resourceType: "Condition"
queryParams: "patient=[patient-id]&clinical-status=active"

Calculate: mean number of comorbidities, most common comorbidity pairs (e.g., diabetes + hypertension, CHF + CKD), % with >= 3 chronic conditions.

Step 7: Check for Complications

Query condition-specific complications. See references/registry-criteria.md for complication codes.

Diabetes complications:

Tool: fhir_search
resourceType: "Condition"
queryParams: "code=http://snomed.info/sct|422034002,http://snomed.info/sct|127013003,http://snomed.info/sct|421986006&clinical-status=active&_count=100"

SNOMED: 422034002 = Diabetic retinopathy, 127013003 = Diabetic renal disease, 421986006 = Diabetic peripheral neuropathy.

Step 8: Flag Intervention Triggers

Based on references/registry-interventions.md, flag patients meeting intervention criteria:

• Therapy escalation: At-goal metric worsening over 2+ consecutive measurements
• Specialist referral: Severity exceeding primary care management threshold
• Hospitalization risk: High-risk combination of metrics (e.g., CHF with BNP > 500 + recent ED visit)
• Care management enrollment: Multiple intervention triggers active simultaneously

Step 9: Present Registry Report

CHRONIC DISEASE REGISTRY: [Disease Name]
==========================================
Registry Size: [N] patients
Report Date: [today]

DEMOGRAPHICS
------------
Mean Age: [age] | Male: [%] | Female: [%]
Age Distribution: 18-40: [N] | 41-65: [N] | 66+: [N]

SEVERITY DISTRIBUTION
---------------------
[Severity Level 1]: [N] ([%])
[Severity Level 2]: [N] ([%])
[Severity Level 3]: [N] ([%])
Unknown/No Data:    [N] ([%])

TREATMENT PATTERNS
------------------
[Drug Class 1]:     [N] ([%])
[Drug Class 2]:     [N] ([%])
Monotherapy:        [N] ([%])
Dual therapy:       [N] ([%])
Triple+ therapy:    [N] ([%])

OUTCOME TRENDS (Quarterly, Last 12 Months)
-------------------------------------------
Quarter  | Mean [Metric] | % at Target | Trend
Q1 2024  | [value]       | [%]         | --
Q2 2024  | [value]       | [%]         | [up/down/stable]
Q3 2024  | [value]       | [%]         | [up/down/stable]
Q4 2024  | [value]       | [%]         | [up/down/stable]

COMPLICATIONS
-------------
[Complication 1]: [N] ([%])
[Complication 2]: [N] ([%])
[Complication 3]: [N] ([%])

INTERVENTION TRIGGERS
---------------------
Patients needing therapy escalation:  [N]
Patients needing specialist referral: [N]
High hospitalization risk:            [N]
Care management candidates:           [N]

Examples

Example 1: Diabetes Registry Query

User says: "Pull up the diabetes registry. Show severity breakdown and treatment patterns."

Actions:

1. Search Condition for SNOMED 44054006 and 46635009 (T1DM + T2DM). Returns 94 patients.
2. Fetch demographics. Exclude 2 deceased. Registry = 92 patients.
3. Query A1c (LOINC 4548-4) in past 12 months. Stratify by control level.
4. Query MedicationRequest for each patient. Classify by drug class.
5. Query diabetes complications (retinopathy, nephropathy, neuropathy).

Result:

CHRONIC DISEASE REGISTRY: Diabetes Mellitus
=============================================
Registry Size: 92 patients (89 T2DM, 3 T1DM)
Report Date: 2024-11-15

DEMOGRAPHICS
------------
Mean Age: 58.4 | Male: 48 (52.2%) | Female: 44 (47.8%)

SEVERITY (A1c-based)
---------------------
Well Controlled (< 7%):    52 (56.5%)
Moderate (7.0-8.9%):       24 (26.1%)
Poor Control (>= 9%):       9 (9.8%)
No Recent A1c:               7 (7.6%)

TREATMENT PATTERNS
------------------
Metformin:           78 (84.8%)
SGLT2 inhibitor:     31 (33.7%)
GLP-1 agonist:       22 (23.9%)
DPP-4 inhibitor:     12 (13.0%)
Sulfonylurea:        18 (19.6%)
Insulin (any):       24 (26.1%)
Monotherapy:         34 (37.0%)
Dual therapy:        38 (41.3%)
Triple+ therapy:     20 (21.7%)
Cardioprotective (GLP-1/SGLT2): 41 (44.6%)

COMPLICATIONS
-------------
Retinopathy:    11 (12.0%)
Nephropathy:    14 (15.2%)
Neuropathy:     19 (20.7%)
Any complication: 28 (30.4%)

INTERVENTION TRIGGERS
---------------------
Need therapy escalation (A1c rising): 6 patients
Need ophthalmology referral (no eye exam): 23 patients
Need nephrology referral (eGFR < 30): 3 patients

Example 2: CHF Registry with GDMT Analysis

User says: "How many heart failure patients are on full GDMT? Show me the gaps."

Actions:

1. Search Condition SNOMED 42343007 (CHF). Returns 34 patients.
2. Query MedicationRequest for each patient. Check for 4 GDMT pillars: ACEi/ARB/ARNI, beta blocker, MRA, SGLT2i.
3. Query BNP (LOINC 42637-9) for severity.

Result:

CHF REGISTRY - GDMT ANALYSIS
==============================
Registry Size: 34 patients

GDMT PILLAR COVERAGE
---------------------
ACEi/ARB/ARNI:    28 (82.4%)
Beta Blocker:      30 (88.2%)
MRA:               18 (52.9%)
SGLT2 Inhibitor:   14 (41.2%)

COMBINATION ANALYSIS
--------------------
All 4 pillars:     11 (32.4%)
3 of 4 pillars:    12 (35.3%)
2 of 4 pillars:     8 (23.5%)
1 or fewer:          3 (8.8%)

LARGEST GAP: SGLT2 inhibitors (20 patients not on one)
- 8 of these have no contraindication documented
- ACTION: Review for SGLT2i initiation

PATIENTS MISSING MULTIPLE PILLARS:
1. Robert Ellis (MRN: 44102) - On beta blocker only - BNP: 890 - HIGH PRIORITY
2. Carol Fisher (MRN: 55210) - On ACEi + BB - BNP: 620 - Add MRA + SGLT2i
3. James Ward (MRN: 33098) - On BB only - BNP: 445 - Needs ACEi/ARB + MRA + SGLT2i

Troubleshooting

Registry population is much smaller than expected

• Check for alternate coding. Many FHIR servers use ICD-10 exclusively. Add ICD-10 codes to the search: code=http://hl7.org/fhir/sid/icd-10-cm|E11,http://snomed.info/sct|44054006.
• Conditions may be stored with verificationStatus = provisional or unconfirmed. Include these if the registry should capture suspected cases: verification-status=confirmed,provisional.
• Some systems record historical conditions with clinical-status = inactive but never update to active after confirmation. Consider including inactive with recent onsetDateTime.

Medication classification fails due to non-standard codes

• Fall back to medicationCodeableConcept.text for string matching against drug class keywords (e.g., "metformin", "lisinopril", "carvedilol").
• If the server uses Medication references instead of CodeableConcept, resolve the reference with fhir_read on the Medication resource to get the code.
• Request medications with _include=MedicationRequest:medication to pull contained Medication resources inline.

Observation queries return data for patients outside the registry

• Always filter returned Observations by matching subject.reference against the registry patient ID list.
• If the server supports it, use patient-specific queries: patient=[id]&code=[loinc]&_sort=-date&_count=1 to get the most recent value per patient. This is slower but more precise.

Related Skills

• patient-panel-overview -- lighter-weight panel aggregate without registry depth
• quality-measure-dashboard -- formal HEDIS/CMS measure calculation
• care-gap-identifier -- individual patient care gap identification
• diabetes-panel-review -- individual patient diabetes review
• renal-function-dashboard -- individual CKD patient analysis