Prior Authorization Decision Automation Pipeline

Overview

Provide deterministic code snippets and pipeline recipes for automating prior authorization (PA) workflows: parsing inbound requests, extracting clinical features, applying rules-based adjudication, training ML classifiers, and analyzing denial patterns.

Usage

Building or debugging X12 278 or FHIR PAS bundle parsers for PA intake
Training ML classifiers on historical PA decisions or generating SHAP explanations
Analyzing denial patterns to identify systemic documentation or policy gaps

Core Concepts

Automation Approach Selection

Condition	Approach	Rationale
Clear-cut policy rules (step therapy, age, lab threshold)	Rules engine	Auditable, deterministic, regulatory-safe
Ambiguous cases with soft criteria	ML classifier + SHAP	Handles nuance; SHAP provides explainability
< 1000 historical decisions available	Rules engine only	Insufficient data for reliable ML training
Multi-payer deployment	Separate model per payer	Policies differ; cross-payer models fail
Input is X12 278 (EDI)	`parse_278()` → segment iteration	Pipe-delimited, segment-based
Input is FHIR PAS Bundle	`parse_pas_bundle()` → resource extraction	JSON, resource-typed entries

Response Format

Lead with the command or code the user needs — explain after
Structure as: confirm inputs → working code → key parameters explained → gotchas
One complete working example per task; do not show every alternative
Keep code comments minimal and functional (what, not why-it-exists)
Target: 50-100 lines of code with brief surrounding explanation

1. PA Request Parsing

1.1 Parse X12 278 Transaction

The X12 278 Health Care Services Review transaction carries PA requests and responses.

python

"""Parse X12 278 prior authorization request into structured dict."""
import re
from dataclasses import dataclass, field
from typing import Optional


@dataclass
class PA278Request:
    member_id: str = ""
    provider_npi: str = ""
    diagnosis_codes: list[str] = field(default_factory=list)
    procedure_codes: list[str] = field(default_factory=list)
    service_date: str = ""
    quantity: int = 0
    place_of_service: str = ""


def parse_278(raw: str) -> PA278Request:
    """Parse X12 278 segments into a PA278Request."""
    req = PA278Request()
    segments = raw.replace("\n", "").split("~")
    for seg in segments:
        elements = seg.strip().split("*")
        seg_id = elements[0] if elements else ""
        if seg_id == "NM1" and len(elements) > 9:
            qualifier = elements[1]
            if qualifier == "IL":  # insured/member
                req.member_id = elements[9] if len(elements) > 9 else ""
            elif qualifier == "1P":  # provider
                req.provider_npi = elements[9] if len(elements) > 9 else ""
        elif seg_id == "HI":
            for el in elements[1:]:
                parts = el.split(":")
                if len(parts) >= 2:
                    code_qualifier, code = parts[0], parts[1]
                    if code_qualifier in ("ABK", "ABF"):  # ICD-10
                        req.diagnosis_codes.append(code)
        elif seg_id == "SV1" and len(elements) > 1:
            svc_parts = elements[1].split(":")
            if len(svc_parts) >= 2:
                req.procedure_codes.append(svc_parts[1])
        elif seg_id == "DTP" and len(elements) > 3:
            if elements[1] == "472":  # service date
                req.service_date = elements[3]
    return req

1.2 Parse FHIR Da Vinci PAS Bundle

python

"""Extract PA fields from a FHIR Da Vinci PAS Bundle."""
import json
from dataclasses import dataclass, field


@dataclass
class PASRequest:
    member_id: str = ""
    provider_npi: str = ""
    diagnosis_codes: list[str] = field(default_factory=list)
    service_codes: list[str] = field(default_factory=list)
    supporting_info_types: list[str] = field(default_factory=list)


def parse_pas_bundle(bundle: dict) -> PASRequest:
    """Extract PA data from a FHIR PAS transaction Bundle."""
    req = PASRequest()
    resources = {
        entry["resource"]["resourceType"]: entry["resource"]
        for entry in bundle.get("entry", [])
        if "resource" in entry
    }
    # Patient / member
    patient = resources.get("Patient", {})
    for ident in patient.get("identifier", []):
        if ident.get("type", {}).get("coding", [{}])[0].get("code") == "MB":
            req.member_id = ident.get("value", "")
            break
    # Practitioner / provider NPI
    practitioner = resources.get("Practitioner", {})
    for ident in practitioner.get("identifier", []):
        if ident.get("system", "").endswith("/npi"):
            req.provider_npi = ident.get("value", "")
            break
    # Claim resource — core of the PA request
    claim = resources.get("Claim", {})
    for dx in claim.get("diagnosis", []):
        coding = dx.get("diagnosisCodeableConcept", {}).get("coding", [])
        for c in coding:
            req.diagnosis_codes.append(c.get("code", ""))
    for item in claim.get("item", []):
        svc_coding = item.get("productOrService", {}).get("coding", [])
        for c in svc_coding:
            req.service_codes.append(c.get("code", ""))
    for info in claim.get("supportingInfo", []):
        cat_coding = info.get("category", {}).get("coding", [])
        for c in cat_coding:
            req.supporting_info_types.append(c.get("code", ""))
    return req

2. Clinical Feature Extraction

2.1 Feature Set for PA Adjudication

Feature	Source	Type	Description
`dx_specificity`	Diagnosis codes	int	ICD-10 code length (3=category, 4-7=specific)
`drug_class`	Service code (NDC/HCPCS)	categorical	Therapeutic class (e.g., biologic, opioid)
`prior_treatment_count`	Claims history	int	Number of prior drugs tried in same class
`step_therapy_complete`	Claims + formulary	bool	All required prior steps documented
`days_since_last_treatment`	Claims history	int	Gap since last related treatment
`lab_value_in_range`	Lab results	bool	Key lab (e.g., HbA1c) meets threshold
`provider_specialty`	Provider data	categorical	Specialty of ordering provider
`place_of_service`	Claim	categorical	Office, outpatient, inpatient, home
`prior_pa_denials`	PA history	int	Count of prior denials for same service
`documentation_score`	Supporting info	float	Completeness score (0-1) based on required docs

2.2 Feature Extraction Code

python

"""Extract clinical features from parsed PA request + claims history."""
import pandas as pd
from datetime import datetime


def extract_features(
    pa_request: dict,
    claims_history: pd.DataFrame,
    formulary: pd.DataFrame,
    lab_results: pd.DataFrame,
) -> dict:
    """Build feature vector for PA adjudication model.

    Args:
        pa_request: Parsed PA request (from parse_278 or parse_pas_bundle).
        claims_history: Member's prior claims with columns:
            [member_id, service_date, drug_class, ndc, diagnosis_code].
        formulary: Formulary table with columns:
            [ndc, tier, step_therapy_required, prior_drugs_required].
        lab_results: Lab results with columns:
            [member_id, test_code, result_value, result_date].
    """
    member_id = pa_request.get("member_id", "")
    dx_codes = pa_request.get("diagnosis_codes", [])
    svc_codes = pa_request.get("service_codes", [])

    # Diagnosis specificity: max ICD-10 code length
    dx_specificity = max((len(c.replace(".", "")) for c in dx_codes), default=3)

    # Prior treatment count in same drug class
    requested_drug = svc_codes[0] if svc_codes else ""
    drug_info = formulary[formulary["ndc"] == requested_drug]
    drug_class = drug_info["drug_class"].iloc[0] if len(drug_info) > 0 else "unknown"
    member_claims = claims_history[claims_history["member_id"] == member_id]
    prior_treatments = member_claims[member_claims["drug_class"] == drug_class]
    prior_treatment_count = prior_treatments["ndc"].nunique()

    # Step therapy completion
    required_steps = int(drug_info["prior_drugs_required"].iloc[0]) if len(drug_info) > 0 else 0
    step_therapy_complete = prior_treatment_count >= required_steps

    # Days since last treatment in class
    if len(prior_treatments) > 0:
        last_date = pd.to_datetime(prior_treatments["service_date"]).max()
        days_since = (datetime.now() - last_date).days
    else:
        days_since = -1  # no prior treatment

    # Lab value check (example: HbA1c for diabetes drugs)
    member_labs = lab_results[lab_results["member_id"] == member_id]
    recent_lab = member_labs.sort_values("result_date", ascending=False).head(1)
    lab_in_range = bool(recent_lab["result_value"].iloc[0] >= 7.0) if len(recent_lab) > 0 else False

    # Documentation completeness score
    supporting_info = pa_request.get("supporting_info_types", [])
    required_docs = {"clinical-note", "lab-result", "treatment-history", "diagnosis"}
    doc_score = len(set(supporting_info) & required_docs) / len(required_docs)

    return {
        "dx_specificity": dx_specificity,
        "drug_class": drug_class,
        "prior_treatment_count": prior_treatment_count,
        "step_therapy_complete": step_therapy_complete,
        "days_since_last_treatment": days_since,
        "lab_value_in_range": lab_in_range,
        "documentation_score": doc_score,
    }

3. Rules-Based Adjudication Engine

python

"""Rules-based PA adjudication engine."""
from dataclasses import dataclass
from enum import Enum


class Decision(Enum):
    APPROVE = "approve"
    DENY = "deny"
    PEND = "pend"


@dataclass
class AdjudicationResult:
    decision: Decision
    reason_code: str
    reason_text: str


def adjudicate(features: dict, policy: dict) -> AdjudicationResult:
    """Apply rules-based adjudication logic.

    Args:
        features: Feature dict from extract_features().
        policy: Policy config with keys:
            - require_step_therapy (bool)
            - min_dx_specificity (int)
            - min_documentation_score (float)
            - require_lab_in_range (bool)
    """
    # Rule 1: Documentation completeness
    if features["documentation_score"] < policy.get("min_documentation_score", 0.75):
        return AdjudicationResult(
            Decision.PEND, "PEND-001", "Insufficient documentation; request additional clinical records"
        )
    # Rule 2: Diagnosis specificity
    if features["dx_specificity"] < policy.get("min_dx_specificity", 4):
        return AdjudicationResult(
            Decision.DENY, "DENY-DX", "Diagnosis code lacks required specificity"
        )
    # Rule 3: Step therapy
    if policy.get("require_step_therapy", True) and not features["step_therapy_complete"]:
        return AdjudicationResult(
            Decision.DENY, "DENY-STEP", "Step therapy requirements not met"
        )
    # Rule 4: Lab value threshold
    if policy.get("require_lab_in_range", False) and not features["lab_value_in_range"]:
        return AdjudicationResult(
            Decision.DENY, "DENY-LAB", "Required lab value not within criteria range"
        )
    # All rules passed
    return AdjudicationResult(Decision.APPROVE, "APPROVE-001", "All clinical criteria met")

4. ML Classifier for PA Decisions

4.1 Training Pipeline

python

"""Train a gradient-boosted classifier on historical PA decisions."""
import pandas as pd
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import classification_report, roc_auc_score
import xgboost as xgb


def train_pa_classifier(
    data: pd.DataFrame, target_col: str = "decision",
    feature_cols: list[str] | None = None, n_folds: int = 5,
) -> tuple[xgb.XGBClassifier, pd.DataFrame]:
    """Train XGBoost on historical PA decisions (1=approved, 0=denied)."""
    if feature_cols is None:
        feature_cols = [c for c in data.columns if c != target_col]
    X, y = data[feature_cols].copy(), data[target_col].copy()
    cat_cols = X.select_dtypes(include=["object", "category"]).columns.tolist()
    X[cat_cols] = X[cat_cols].astype("category")
    model = xgb.XGBClassifier(
        n_estimators=300, max_depth=6, learning_rate=0.05,
        subsample=0.8, colsample_bytree=0.8, enable_categorical=True,
        eval_metric="logloss", random_state=42,
    )
    cv_results = []
    for fold, (ti, vi) in enumerate(StratifiedKFold(n_folds, shuffle=True, random_state=42).split(X, y)):
        model.fit(X.iloc[ti], y.iloc[ti], eval_set=[(X.iloc[vi], y.iloc[vi])], verbose=False)
        y_prob = model.predict_proba(X.iloc[vi])[:, 1]
        cv_results.append({"fold": fold, "auc": roc_auc_score(y.iloc[vi], y_prob)})
    model.fit(X, y, verbose=False)
    return model, pd.DataFrame(cv_results)

4.2 SHAP Explainability

python

"""Generate SHAP explanations for PA decisions."""
import shap


def explain_pa_decision(model, X, instance_idx: int = 0) -> dict:
    """Generate SHAP values for a single PA decision."""
    explainer = shap.TreeExplainer(model)
    shap_values = explainer.shap_values(X)
    explanation = dict(sorted(
        zip(X.columns, shap_values[instance_idx]),
        key=lambda x: abs(x[1]), reverse=True,
    ))
    return {
        "base_value": float(explainer.expected_value),
        "prediction": float(model.predict_proba(X.iloc[[instance_idx]])[:, 1][0]),
        "feature_contributions": explanation,
    }

5. Denial Reason Analysis

5.1 Denial Pattern Analysis

python

"""Analyze PA denial patterns to identify systemic issues."""
import pandas as pd


def analyze_denials(pa_decisions: pd.DataFrame, group_cols: list[str] | None = None) -> dict[str, pd.DataFrame]:
    """Analyze denial patterns across dimensions.

    Args:
        pa_decisions: DataFrame [pa_id, member_id, provider_npi, drug_class, denial_reason, decision, decision_date].
        group_cols: Columns to group by. Defaults to [denial_reason, drug_class, provider_npi].
    """
    denied = pa_decisions[pa_decisions["decision"] == "denied"].copy()
    if group_cols is None:
        group_cols = ["denial_reason", "drug_class", "provider_npi"]
    analyses = {}
    for col in group_cols:
        if col not in denied.columns:
            continue
        g = denied.groupby(col).agg(denial_count=("pa_id", "count"), unique_members=("member_id", "nunique")).sort_values("denial_count", ascending=False).reset_index()
        g["pct_of_denials"] = (g["denial_count"] / len(denied) * 100).round(1)
        analyses[col] = g
    if "denial_reason" in denied.columns:
        doc_gaps = denied[denied["denial_reason"].str.contains("documentation|insufficient", case=False, na=False)]
        analyses["documentation_gaps"] = doc_gaps.groupby("drug_class").agg(gap_count=("pa_id", "count")).sort_values("gap_count", ascending=False).reset_index()
    return analyses

5.2 Denial Reason Code Reference

Reason Code	Description	Remediation
DENY-STEP	Step therapy not completed	Document prior treatments with dates
DENY-DX	Non-specific diagnosis	Use highest-specificity ICD-10 code
DENY-LAB	Lab criteria not met	Resubmit with current lab results
DENY-MN	Medical necessity not established	Submit letter of medical necessity
DENY-EXP	Experimental/investigational	Cite peer-reviewed evidence
PEND-001	Incomplete documentation	Submit clinical notes, labs, history

6. Parameter Reference

6.1 XGBoost Hyperparameters for PA Classification

Parameter	Default	Range
`n_estimators`	300	100–1000
`max_depth`	6	3–10
`learning_rate`	0.05	0.01–0.3
`subsample`	0.8	0.5–1.0
`colsample_bytree`	0.8	0.5–1.0
`scale_pos_weight`	1.0	Set to neg/pos ratio for imbalanced data

6.2 Documentation Completeness Scoring

Document Type	Weight	Required For
Clinical notes	0.30	All PA requests
Lab results	0.25	Drug PAs with lab criteria
Treatment history	0.25	Step therapy drugs
Diagnosis confirmation	0.10	All PA requests
Specialist referral	0.10	Specialty drugs

7. Common Mistakes

Wrong: Training a PA classifier on imbalanced data without correction Right: Set
```
scale_pos_weight
```
to the neg/pos ratio, or apply SMOTE to balance the training set Why: PA datasets are often 70–80% approvals; without correction, the model learns to approve everything
Wrong: Including features derived from information not available at the time of the PA request Right: Ensure all features use only data available before or at the moment of submission (claims history, not future outcomes) Why: Leaking future data inflates model performance in training but fails completely in production
Wrong: Deploying a model trained on one payer's decisions to adjudicate another payer's requests Right: Train and validate separate models per payer, or include payer identity as a feature with sufficient per-payer training data Why: Each payer has independent clinical policies; a model trained on Payer A's criteria will make wrong decisions for Payer B
Wrong: Accepting SHAP explanations at face value without clinical validation Right: Verify that top SHAP features align with known clinical criteria from the payer's published policy Why: Spurious correlations in training data can produce plausible-looking but clinically meaningless explanations
Wrong: Hardcoding denial reason strings with free-text matching Right: Use standardized reason codes (CARC/RARC) and map them to structured enums Why: Free-text matching is brittle — minor wording changes break the logic and cause silent failures
Wrong: Replacing the rules engine entirely with an ML classifier Right: Use ML to augment deterministic policy rules; route clear-cut cases through rules and ambiguous cases through ML Why: Auditable, explainable decisions require deterministic rules for regulatory compliance; ML alone is not audit-defensible

pa-decision-automation

NPX Install

Tags

SKILL.md Content

Prior Authorization Decision Automation Pipeline

Overview

Usage

Core Concepts

Automation Approach Selection

Response Format

1. PA Request Parsing

1.1 Parse X12 278 Transaction

1.2 Parse FHIR Da Vinci PAS Bundle

2. Clinical Feature Extraction

2.1 Feature Set for PA Adjudication

2.2 Feature Extraction Code

3. Rules-Based Adjudication Engine

4. ML Classifier for PA Decisions

4.1 Training Pipeline

4.2 SHAP Explainability

5. Denial Reason Analysis

5.1 Denial Pattern Analysis

5.2 Denial Reason Code Reference

6. Parameter Reference

6.1 XGBoost Hyperparameters for PA Classification

6.2 Documentation Completeness Scoring

7. Common Mistakes