pa-decision-automation

name	pa-decision-automation
description	Pipeline skill for automating prior authorization decision workflows. Use when the user asks to parse PA request data (X12 278 or FHIR PAS bundles), extract clinical features for adjudication, build rules-based PA decision engines, train ML classifiers on historical PA decisions, analyze denial patterns, or generate SHAP explanations for PA outcomes. Triggers include "parse 278", "FHIR PAS bundle", "PA automation", "adjudication logic", "PA classifier", "denial analysis", "prior auth ML", "SHAP explainability", "PA feature extraction", "rules engine PA", "PA decision pipeline", "authorization workflow", "clinical criteria extraction", "denial pattern mining", "PA turnaround time".
usage	Use when building or running prior authorization automation pipelines including parsing, classification, and denial analysis.
version	1.0.0
validated_against	{"date":"2025-01-15T00:00:00.000Z","packages":{"fhir-resources":"R4","scikit-learn":"1.4"}}
tags	["skill","category:pipeline","prior-authorization","automation","hcls"]

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.

"""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":  
                req.member_id = elements[]  (elements) >   
             qualifier == :  
                req.provider_npi = elements[]  (elements) >   
         seg_id == :
             el  elements[:]:
                parts = el.split()
                 (parts) >= :
                    code_qualifier, code = parts[], parts[]
                     code_qualifier  (, ):  
                        req.diagnosis_codes.append(code)
         seg_id ==   (elements) > :
            svc_parts = elements[].split()
             (svc_parts) >= :
                req.procedure_codes.append(svc_parts[])
         seg_id ==   (elements) > :
             elements[] == :  
                req.service_date = elements[]
     req

1.2 Parse FHIR Da Vinci PAS Bundle

"""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", "")
            
    
    practitioner = resources.get(, {})
     ident  practitioner.get(, []):
         ident.get(, ).endswith():
            req.provider_npi = ident.get(, )
            
    
    claim = resources.get(, {})
     dx  claim.get(, []):
        coding = dx.get(, {}).get(, [])
         c  coding:
            req.diagnosis_codes.append(c.get(, ))
     item  claim.get(, []):
        svc_coding = item.get(, {}).get(, [])
         c  svc_coding:
            req.service_codes.append(c.get(, ))
     info  claim.get(, []):
        cat_coding = info.get(, {}).get(, [])
         c  cat_coding:
            req.supporting_info_types.append(c.get(, ))
     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

"""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]
    prior_treatment_count = prior_treatments[].nunique()

    
    required_steps = (drug_info[].iloc[])  (drug_info) >   
    step_therapy_complete = prior_treatment_count >= required_steps

    
     (prior_treatments) > :
        last_date = pd.to_datetime(prior_treatments[]).()
        days_since = (datetime.now() - last_date).days
    :
        days_since = -  

    
    member_labs = lab_results[lab_results[] == member_id]
    recent_lab = member_labs.sort_values(, ascending=).head()
    lab_in_range = (recent_lab[].iloc[] >= )  (recent_lab) >   

    
    supporting_info = pa_request.get(, [])
    required_docs = {, , , }
    doc_score = ((supporting_info) & required_docs) / (required_docs)

     {
        : dx_specificity,
        : drug_class,
        : prior_treatment_count,
        : step_therapy_complete,
        : days_since,
        : lab_in_range,
        : doc_score,
    }

3. Rules-Based Adjudication Engine

"""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(, )   features[]:
         AdjudicationResult(
            Decision.DENY, , 
        )
    
     policy.get(, )   features[]:
         AdjudicationResult(
            Decision.DENY, , 
        )
    
     AdjudicationResult(Decision.APPROVE, , )

4. ML Classifier for PA Decisions

4.1 Training Pipeline

"""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=).split(X, y)):
        model.fit(X.iloc[ti], y.iloc[ti], eval_set=[(X.iloc[vi], y.iloc[vi])], verbose=)
        y_prob = model.predict_proba(X.iloc[vi])[:, ]
        cv_results.append({: fold, : roc_auc_score(y.iloc[vi], y_prob)})
    model.fit(X, y, verbose=)
     model, pd.DataFrame(cv_results)

4.2 SHAP Explainability

"""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

"""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"]..contains(, =, na=)]
        analyses[] = doc_gaps.groupby().agg(gap_count=(, )).sort_values(, ascending=).reset_index()
     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