Real-world AI Solutions: Lessons from the Field

Overview

Generative AI offers significant potential to streamline processes in domains with complex regulatory or clinical documentation. For example, in the context of prior authorization for surgical procedures, automated response generation can help parse detailed guidelines—such as eligibility criteria based on patient age, BMI thresholds, comorbid conditions, and documented behavioral interventions—to produce accurate and consistent outputs. The following document outlines best practices along with recommended architecture and process breakdown approaches to ensure that GenAI-powered responses are accurate, compliant, and reliable.

1. Understanding the Use Case

\n
• Recognize the complexity of policy and clinical documents. Use cases like prior authorization for obesity surgery require the system to capture salient details (e.g., BMI ranges, diagnosis codes, behavioral intervention documentation) and interpret them correctly.
\n
• Identify core elements (eligibility criteria, screening protocols, and required documentation) that the automated system must consider when generating responses.
\n

2. Data Preparation and Augmentation

Data quality is crucial for an AI model's performance. Consider the following best practices:

\n
• Input Quality: Use only clean, recent, and well structured source documents. In cases like obesity surgery policies, ensure that the policy guidelines and associated criteria are pre processed to remove noise.
\n
• Augmentation: Enhance base document inputs with contextual data (e.g., policy numbers, applicable codes, and screening methods) to provide the GenAI model with a complete picture.
\n
• Structuring: Divide large input texts into logical segments (eligibility criteria, testing thresholds, and treatment protocols) so that the model can accurately reference each section when prompted.
\n

3. Reference Architecture for Automated Response Generation

Below is a reference architecture diagram illustrating a high-level view of how an automated response generation system may be structured:

\n
1. Data Sources\n
   \n
   • Gather a variety of data sources to inform the system
   \n
   \n
\n
2. Data Ingestion Layer\n
   \n
   • Perform data preprocessing tasks to clean and enhance data quality.
   \n
   \n
\n
3. Data Segmentation Module\n
   \n
   • Divide documents into meaningful sections for targeted analysis (e.g., eligibility criteria, required documentation, medical necessity guidelines)
   \n
   \n
\n
4. Knowledge Base & Metadata Store\n
   \n
   • Populate one or more knowledge bases with appropriate metadata fields and tags
   \n
   \n
\n
5. Prompt Engineering Module\n
   \n
   • Follow prompt engineering best practices, leveraging prompt variants and evaluation
   \n
   \n
\n
6. GenAI Engine\n
   \n
   • Test multiple Azure OpenAI models for performance and latency
   \n
   \n
\n
7. Output Post-Processing\n
   \n
   • Validate, refine, and format AI-generated responses
   \n
   • Map AI output to structured formats (e.g., standardized prior authorization forms)
   \n
   \n
\n
8. User Interface & Feedback Loop\n
   \n
   • Display results in provider portals, payer systems, or API endpoints
   \n
   • Capture feedback to refine model accuracy
   \n
   \n
\n

This architecture ensures modularity, enabling organizations to scale and update individual components as needed while maintaining compliance with industry standards.

4. Process Breakdown: Tackling Complexity in Stages

In some cases, it is beneficial to deconstruct the end-to-end process into smaller, focused stages. It might even be best to do a multi-step approach to get to final results. For example, rather than attempting to generate a complete response in one step, the process can be divided as follows:

1. Data Identification and Extraction

\n
• Identify where the core data resides—be it in a clinical policy bulleting, structured documents, or external references.
\n
• Execute data extraction routines to capture raw inputs and segmentation markers. This stage includes cleaning, indexing, and formatting the data appropriately.
\n

2. Data Analysis and Context Building

\n
• Once the data is extracted, analyze its structure to determine key segments and thematic areas. For instance, pulling BMI criteria, screening requirements, and documentation standards separately.
\n
• Build a contextual knowledge base and metadata store that allows the prompt engineering module to target the precise content required for response generation.
\n

3. Automated Response Construction:

\n
• Use the refined and segmented data to craft targeted prompts that guide the GenAI engine.
\n
• Generate a draft response, ensuring that the output maintains both logical flow and compliance with the identified guidelines.
\n
• Execute post processing routines to validate and format the automated response before it is delivered to the end user.
\n

Breaking the process into these stages helps simplify troubleshooting, allows for iterative testing at each step, and ensures that each module meets quality and compliance standards before integration into the final response.

5. Prompt Engineering and Model Configuration

Well-designed prompts are crucial to generating relevant responses:

\n
• Clear, Specific Prompts: Design prompts to include necessary details (e.g., patient age, BMI, and co morbidities) as extracted in earlier modules.
\n
• Contextual Coherence: Ensure that the prompt structure reflects any hierarchical or enumerated guidelines, preserving the logic of the original policy.
\n
• Handling Nuances: Equip the system to identify subtle variations and ensure that all criteria, regardless of expression, are captured correctly.
\n

A simple example of a prompt for the prior authorization use case is shown below: 

\n

You are an insurance decision maker. You are presented a claim and a guidebook. Decide if the claim should be approved, rejected, or if the information is not enough to support your decision. If you decide to reject a claim, explain why. Prior auth process involves 2 steps

\n

Step 1: Determine if a service needs Prior Authorization.

\n

This determination is based on an information below.

\n

Service Category UM Policy CPT Plan A. DME Microprocessor Controlled Knee Prostheses, with or without Polycentric, Three-Dimensional Hip Joint System Wheelchairs, A7025, ...

\n

Bariatric Surgery UM Policy 43644, ...

\n

Step 2: Determine if Prior Auth is approved or denied. Here is a partial medical necessity criterion for Bariatric surgery.

\n

Medically Necessary check from Bariatric Surgery UM Policy. Gastric bypass and gastric restrictive procedures are considered medically necessary when all of the following criteria are met:

\n

\n
1.  Individual is age 18 years or older; and
\n
2.  The recommended surgery is one of the following procedures:\n
   \n
   1.  Biliopancreatic bypass with duodenal switch
   \n
   2.  Laparoscopic adjustable gastric banding
   \n
   3.  Roux-en-Y procedure up to 150 cm
   \n
   4.  Sleeve gastrectomy
   \n
   \n
\n
3.  A body mass index (BMI) of 40 or greater, or BMI of 35 or greater with an obesity-related co-morbid condition including, but not limited to ... 
\n

\n

Let us have a sample prior auth request to process. These will be in the form of a json file with the following rows.

\n

[{  Last Name: Jack,

\n

  First Name: Smith,

\n

  CPT code: 43775,

\n

  BMI: 35,

\n

  Conditions i.e. CPT codes: [ 100, 200]}]

\n

6. Quality Assurance and Compliance Validation

Ensuring accuracy and compliance is essential in regulated industries:

\n
• Expert Verification: Subject all outputs to domain expert review (e.g., clinicians, compliance specialists) to ensure that generated responses accurately mirror policy requirements.
\n
• Iterative Testing: Utilize real world test cases, including edge cases, to ensure robustness and consistency.
\n
• Error Handling: Implement guidelines for incremental review and correction in case any section is ambiguous or incomplete during the generation process.
\n

7. Data Security, Privacy, and Compliance

Maintaining confidentiality and regulatory adherence is critical when deploying AI in sensitive domains. 

\n
• Confidentiality: All processing of clinical or sensitive information should adhere to data protection regulations and anonymization standards.
\n
• Compliance: Ensure that the system meets regulatory standards (e.g., HIPAA) and that policy guidelines are appropriately cited and adhered to in the final responses.
\n

8. Deployment, Monitoring, and Optimization

A successful rollout requires ongoing monitoring and iterative improvements. There are tools and products which can be used to support all of these in Azure. 

\n
• Controlled Rollout: Deploy the system in phases, comparing GenAI outputs with manually crafted responses to validate accuracy.
\n
• Feedback Loop: Establish channels for user reports and expert feedback, integrating these insights into iterative adjustments of prompts and data processing flows.
\n
• Performance Metrics: Monitor key indicators, such as accuracy, relevancy, and error rates, to ensure continuous improvement of the automated response generation process. 
\n

Conclusion

Using GenAI for automated response generation—especially in complex domains like clinical prior authorization—offers significant efficiency and quality benefits. By architecting the system into clear, manageable modules, carefully preparing and segmenting the input data, and breaking the process into stages (from data extraction and contextual analysis to response generation), organizations can ensure the system produces responses that are both reliable and compliant. These best practices, complemented by the reference architecture and process breakdown strategy, provide a robust framework for successfully deploying automated responses in regulated and high-stakes environments.

Additional AI Best Practices blog posts:

Best Practices for Requesting Quota Increase for Azure OpenAI Models

Best Practices for Leveraging Azure OpenAI in Constrained Optimization Scenarios

Best Practices for Structured Extraction from Documents Using Azure OpenAI

Real-world AI Solutions: Lessons from the Field

Overview

Generative AI offers significant potential to streamline processes in domains with complex regulatory or clinical documentation. For example, in the context of prior authorization for surgical procedures, automated response generation can help parse detailed guidelines—such as eligibility criteria based on patient age, BMI thresholds, comorbid conditions, and documented behavioral interventions—to produce accurate and consistent outputs. The following document outlines best practices along with recommended architecture and process breakdown approaches to ensure that GenAI-powered responses are accurate, compliant, and reliable.

1. Understanding the Use Case

\n
• Recognize the complexity of policy and clinical documents. Use cases like prior authorization for obesity surgery require the system to capture salient details (e.g., BMI ranges, diagnosis codes, behavioral intervention documentation) and interpret them correctly.
\n
• Identify core elements (eligibility criteria, screening protocols, and required documentation) that the automated system must consider when generating responses.
\n

2. Data Preparation and Augmentation

Data quality is crucial for an AI model's performance. Consider the following best practices:

\n
• Input Quality: Use only clean, recent, and well structured source documents. In cases like obesity surgery policies, ensure that the policy guidelines and associated criteria are pre processed to remove noise.
\n
• Augmentation: Enhance base document inputs with contextual data (e.g., policy numbers, applicable codes, and screening methods) to provide the GenAI model with a complete picture.
\n
• Structuring: Divide large input texts into logical segments (eligibility criteria, testing thresholds, and treatment protocols) so that the model can accurately reference each section when prompted.
\n

3. Reference Architecture for Automated Response Generation

Below is a reference architecture diagram illustrating a high-level view of how an automated response generation system may be structured:

\n
1. Data Sources\n
   \n
   • Gather a variety of data sources to inform the system
   \n
   \n
\n
2. Data Ingestion Layer\n
   \n
   • Perform data preprocessing tasks to clean and enhance data quality.
   \n
   \n
\n
3. Data Segmentation Module\n
   \n
   • Divide documents into meaningful sections for targeted analysis (e.g., eligibility criteria, required documentation, medical necessity guidelines)
   \n
   \n
\n
4. Knowledge Base & Metadata Store\n
   \n
   • Populate one or more knowledge bases with appropriate metadata fields and tags
   \n
   \n
\n
5. Prompt Engineering Module\n
   \n
   • Follow prompt engineering best practices, leveraging prompt variants and evaluation
   \n
   \n
\n
6. GenAI Engine\n
   \n
   • Test multiple Azure OpenAI models for performance and latency
   \n
   \n
\n
7. Output Post-Processing\n
   \n
   • Validate, refine, and format AI-generated responses
   \n
   • Map AI output to structured formats (e.g., standardized prior authorization forms)
   \n
   \n
\n
8. User Interface & Feedback Loop\n
   \n
   • Display results in provider portals, payer systems, or API endpoints
   \n
   • Capture feedback to refine model accuracy
   \n
   \n
\n

This architecture ensures modularity, enabling organizations to scale and update individual components as needed while maintaining compliance with industry standards.

4. Process Breakdown: Tackling Complexity in Stages

In some cases, it is beneficial to deconstruct the end-to-end process into smaller, focused stages. It might even be best to do a multi-step approach to get to final results. For example, rather than attempting to generate a complete response in one step, the process can be divided as follows:

1. Data Identification and Extraction

\n
• Identify where the core data resides—be it in a clinical policy bulleting, structured documents, or external references.
\n
• Execute data extraction routines to capture raw inputs and segmentation markers. This stage includes cleaning, indexing, and formatting the data appropriately.
\n

2. Data Analysis and Context Building

\n
• Once the data is extracted, analyze its structure to determine key segments and thematic areas. For instance, pulling BMI criteria, screening requirements, and documentation standards separately.
\n
• Build a contextual knowledge base and metadata store that allows the prompt engineering module to target the precise content required for response generation.
\n

3. Automated Response Construction:

\n
• Use the refined and segmented data to craft targeted prompts that guide the GenAI engine.
\n
• Generate a draft response, ensuring that the output maintains both logical flow and compliance with the identified guidelines.
\n
• Execute post processing routines to validate and format the automated response before it is delivered to the end user.
\n

Breaking the process into these stages helps simplify troubleshooting, allows for iterative testing at each step, and ensures that each module meets quality and compliance standards before integration into the final response.

5. Prompt Engineering and Model Configuration

Well-designed prompts are crucial to generating relevant responses:

\n
• Clear, Specific Prompts: Design prompts to include necessary details (e.g., patient age, BMI, and co morbidities) as extracted in earlier modules.
\n
• Contextual Coherence: Ensure that the prompt structure reflects any hierarchical or enumerated guidelines, preserving the logic of the original policy.
\n
• Handling Nuances: Equip the system to identify subtle variations and ensure that all criteria, regardless of expression, are captured correctly.
\n

A simple example of a prompt for the prior authorization use case is shown below: 

\n

You are an insurance decision maker. You are presented a claim and a guidebook. Decide if the claim should be approved, rejected, or if the information is not enough to support your decision. If you decide to reject a claim, explain why. Prior auth process involves 2 steps

\n

Step 1: Determine if a service needs Prior Authorization.

\n

This determination is based on an information below.

\n

Service Category UM Policy CPT Plan A. DME Microprocessor Controlled Knee Prostheses, with or without Polycentric, Three-Dimensional Hip Joint System Wheelchairs, A7025, ...

\n

Bariatric Surgery UM Policy 43644, ...

\n

Step 2: Determine if Prior Auth is approved or denied. Here is a partial medical necessity criterion for Bariatric surgery.

\n

Medically Necessary check from Bariatric Surgery UM Policy. Gastric bypass and gastric restrictive procedures are considered medically necessary when all of the following criteria are met:

\n

\n
1.  Individual is age 18 years or older; and
\n
2.  The recommended surgery is one of the following procedures:\n
   \n
   1.  Biliopancreatic bypass with duodenal switch
   \n
   2.  Laparoscopic adjustable gastric banding
   \n
   3.  Roux-en-Y procedure up to 150 cm
   \n
   4.  Sleeve gastrectomy
   \n
   \n
\n
3.  A body mass index (BMI) of 40 or greater, or BMI of 35 or greater with an obesity-related co-morbid condition including, but not limited to ... 
\n

\n

Let us have a sample prior auth request to process. These will be in the form of a json file with the following rows.

\n

[{  Last Name: Jack,

\n

  First Name: Smith,

\n

  CPT code: 43775,

\n

  BMI: 35,

\n

  Conditions i.e. CPT codes: [ 100, 200]}]

\n

6. Quality Assurance and Compliance Validation

Ensuring accuracy and compliance is essential in regulated industries:

\n
• Expert Verification: Subject all outputs to domain expert review (e.g., clinicians, compliance specialists) to ensure that generated responses accurately mirror policy requirements.
\n
• Iterative Testing: Utilize real world test cases, including edge cases, to ensure robustness and consistency.
\n
• Error Handling: Implement guidelines for incremental review and correction in case any section is ambiguous or incomplete during the generation process.
\n

7. Data Security, Privacy, and Compliance

Maintaining confidentiality and regulatory adherence is critical when deploying AI in sensitive domains. 

\n
• Confidentiality: All processing of clinical or sensitive information should adhere to data protection regulations and anonymization standards.
\n
• Compliance: Ensure that the system meets regulatory standards (e.g., HIPAA) and that policy guidelines are appropriately cited and adhered to in the final responses.
\n

8. Deployment, Monitoring, and Optimization

A successful rollout requires ongoing monitoring and iterative improvements. There are tools and products which can be used to support all of these in Azure. 

\n
• Controlled Rollout: Deploy the system in phases, comparing GenAI outputs with manually crafted responses to validate accuracy.
\n
• Feedback Loop: Establish channels for user reports and expert feedback, integrating these insights into iterative adjustments of prompts and data processing flows.
\n
• Performance Metrics: Monitor key indicators, such as accuracy, relevancy, and error rates, to ensure continuous improvement of the automated response generation process. 
\n

Conclusion

Using GenAI for automated response generation—especially in complex domains like clinical prior authorization—offers significant efficiency and quality benefits. By architecting the system into clear, manageable modules, carefully preparing and segmenting the input data, and breaking the process into stages (from data extraction and contextual analysis to response generation), organizations can ensure the system produces responses that are both reliable and compliant. These best practices, complemented by the reference architecture and process breakdown strategy, provide a robust framework for successfully deploying automated responses in regulated and high-stakes environments.

Additional AI Best Practices blog posts:

Best Practices for Requesting Quota Increase for Azure OpenAI Models

Best Practices for Leveraging Azure OpenAI in Constrained Optimization Scenarios

Best Practices for Structured Extraction from Documents Using Azure OpenAI