Healthcare IT Decoded - Applications of Prompt Engineering

Explore how healthcare IT revenue cycle management handles appointment intake, patient registration, and pre-registration for surgeries, and assess insurance eligibility within hospital systems.

Explore how insurance eligibility works, from individual checks via websites and apps to hospital-level validation using EDI 270/271, clearinghouses, and ABN workflows.

Explore how admission and registration data, including allergies and medications, shape the patient journey, while front-desk counselling clarifies costs and connects patients with aid within revenue cycle management.

Explore the outpatient journey in the OPD, from front-office registration and vitals to lab and radiology reports, pharmacy prescriptions, and the patient’s path to diagnosis.

Compare inpatient department workflow with the OPD, focusing on ICU monitoring, hourly vitals, SpO2, ABG tests, and discharge summary in pneumonia care.

Explain how medical transcription converts physician notes to digital records and how medical coding uses ICD diagnosis codes to standardize billing and revenue cycle management.

Explore healthcare codes and terminology standards, including ICD, SNOMED, LOINC, RxNorm, NDC, and NDF, and data transmission standards HL7, FHIR, CCD/CCR, and DICOM across front, middle, and back office.

Explain back office activities in revenue cycle management, from charging and billing edits to EDI 837 claim submission and 835 remittance, AR management, and integration engines like Cloverleaf and Rhapsody.

Identify and map source systems to understand where hospital data comes from, then analyze data across emr, labs, radiology, and billing to visualize the patient journey.

Identify source systems and extract data using healthcare standards such as HL7, CCD, and Fire, with formats XML, CSV, and JSON, to structure patient information for transfer.

Clean and standardize healthcare data after extraction by deduplicating across modules, standardizing units and terminology, and mapping laboratory terms with Relma to enable accurate cross-system reporting.

Identify key performance indicators (kpis) and build a logical data model that separates master data from transactional data, guiding what to analyze before loading databases.

Identify source system, extract and transform data in parallel with modeling, then load it using tools like Informatica, DM Express, Mirth, Cloverleaf, and Rhapsody for HL7, EDI, and CCD standards.

Analyze healthcare data with charts to derive insights and foresights, using Tableau or Power BI for reporting, and use R or Python for forecasting, prediction, and text mining.

Trace the patient journey, identify source systems, extract data, transform and load it, build logical and physical models, and generate insights with Tableau, Power BI, R, or Python.

Learn how data is extracted, cleaned, and standardized from source systems, loaded into data model, and yields insights with tableau, r, and python while illustrating analytics: descriptive, diagnostic, predictive, prescriptive.

Discover how the fox uses real-time data—weather, images, videos, and audio—to predict prey location in the next hour through cognitive analytics. Map this extension to Gartner’s ascendancy model.

Explore artificial intelligence, machine learning, and deep learning, and how AI analyzes data like patient appointments. Forecasting with ARIMA and using neural networks for chatbots show practical healthcare applications.

Explore how supervised learning uses labeled data to train a model, test its accuracy, and evaluate performance, illustrated by apples and grapes and common features like color, shape, and texture.

Explore unsupervised learning by clustering data based on features when no labels exist, using image categories and initial customer segments defined by basic information like age and gender.

Explore reinforcement learning by examining how positive and negative reinforcement use rewards and penalties, from classroom point systems to robot navigation and the Deep Blue vs Kasparov chess match.

Explore the basics of a neural network through a cake-baking analogy. See how inputs and weights produce an output, and how errors trigger weight updates and activation functions shape learning.

Discover how artificial, convolutional, graphical, and recurrent neural networks apply to healthcare, including x-ray classification with Dex-net and ResNet, and drug discovery.

Explore how ChatGPT trains its language model through four steps: pre-training with next token prediction, supervised fine tuning, reward model training, and reinforcement learning to improve output quality.

Explore the fundamentals of prompt engineering and techniques like zero shot, one shot, few shot prompts, chain of thought, rule based prompting, prompt templating, and multi modal prompt.

Explore zero shot prompts by providing instructions, optional context, and a task with an expected output format, through a healthcare example diagnosing conditions from symptoms.

Learn how one-shot prompts guide model outputs using a single input-output example, illustrated with ICD-10 coding for type 2 diabetes and acute appendicitis.

Explore how few-shot prompts shape model responses by providing multiple input-output examples, comparing zero-shot and one-shot prompts, and structuring outputs for medical diagnoses and triage.

Learn chain of thought prompting and step-by-step reasoning to diagnose, assess stroke risk in atrial fibrillation, and guide anticoagulant treatment options, with physician validation.

Role based prompting instructs the model to respond as a specific expert or persona, such as a clinical or healthcare IT specialist, performing tasks and delivering outputs in that role.

Explore prompt chaining by feeding outputs into successive prompts to summarize global diabetes trends, identify key studies, assess the US impact, and propose three mitigation strategies.

Explore self consistency prompts by running the same prompt multiple times, using temperature to balance determinism and creativity, and select the most common differential diagnosis.

Explore prompting templates and variables, using input placeholders to create dynamic, consistent, and reusable prompts for medical coding tasks, including ICD-10-CM and CPT code outputs, modifiers, and accuracy checks.

Learn to build multimodal prompts in healthcare it that fuse patient text with chest x-ray images, then generate top two abnormalities and top three diagnoses with reasoning.

Guardrails act as safety mechanisms and design constraints to guide and restrict AI behavior, ensuring safe, reliable, and ethical use in healthcare while protecting privacy, compliance (HIPAA), and trust.

Learn how content filtering guardrails shape prompts for a health insurance virtual assistant, blocking unsafe content and medical advice while testing prompts to provide general insurance information with plan-based disclaimers.

Learn how memory and session guardrails protect privacy in a virtual health insurance app by preventing data recall across sessions and requiring re-entry of policy or claim details.

Apply guardrails to filter PII and protect privacy under data regulations. See prompts switch to generic health insurance questions and validate prompts without exposing personal claim details.

Detect and prevent bias in prompt engineering by examining age, gender, culture, and other biases in end-user questions to ensure inclusive, medically accurate outputs.

Explore injection detection and guardrails that stop end users from overriding prompts via crafted prompt inputs and manipulating model behavior in health IT chatbots.

Explore guards for prompt injection detection, examining hidden text in papers, css-encoded prompts, and techniques to scan for invisible instructions that override safety measures.

Detect and neutralize toxic prompts from end users by identifying offensive language, profanity, sarcasm, and aggression, and implement guardrails guiding empathetic responses.

Apply guardrails for image moderation to flag explicit, violent, or unrelated content and protect end users in a healthcare app, with a medical relevance disclaimer.

Learn guardrails to prevent hallucinations by restricting answers to a defined data set and avoiding fabricated, unverifiable responses. Request sources, and redirect users to official support when you cannot answer.

Explore how prompt engineering enables named entity extraction from clinical notes, identifying diagnoses, symptoms, dates, medications, and tests using NLP tools like Google Health API and AWS Comprehend Medical.

Learn to craft role-based prompts that extract diagnosis, symptoms and signs, and medications from unstructured clinical notes, with guardrails to validate the output structure.

Explore the structure of ICD-10 codes, including digits, decimal placement, laterality, and encounter details. See how tools like Google Health API and AWS Comprehend Medical extract codes via prompt engineering.

Demonstrates creating a role-based prompt to extract ICD-10-CM diagnosis codes from clinical notes, identify entities like diagnoses and symptoms, assign all relevant codes, and format output with guardrails.

Learn how Loinc codes standardize lab terms and HL7 observations, map systolic blood pressure and other tests, and validate codes via Loinc websites and the Google Health API.

Learn to design role-based prompts for lab data normalization to extract lab entities and LOINC codes from clinical notes and output data with test name, description, specimen, units, and date/time.

Explore RxNorm based medication coding by mapping clinical notes to RxNorm codes using Google Health API and AWS Health API, including NDCs and drug components.

Learn how to craft role-based prompts for RxNorm medication coding, extract medication entities, and produce RxNorm codes with dosage, route, frequency in a structured output.

Explore SNOMED CT as a comprehensive clinical terminology, contrast it with ICD ten, and learn to craft structured prompts to extract SNOMED concepts from clinical notes using AWS Comprehend.

Design a SNOMED-CT prompt to extract clinical entities from notes, map them to domain codes, and present results in a structured table; validate with SNOMED browsers.

Explore ICD-10 procedure codes and how they differ from diagnosis codes. Seven-digit codes encode section, body system, root operation, body part, approach, device, and qualifier, with example 0SRD0JZ.

Learn to transform patient–physician conversations into SOAP notes by mapping subjective, objective, assessment, and plan elements, and explore prompt engineering with transcription tools like AWS Transcribe Health Scribe and nabla.

Create an AWS transcribe job to generate a soap note from a medical recording, using S3 bucket setup in region us-east-1 with an IAM role and Bedrock LLM.

Discover how the output is created for clinical docs, download the JSON file, view it in a JSON viewer, and troubleshoot issues with support.

Install and use Nabla Copilot, a chrome extension, to run a patient encounter and generate a soap note from the transcript, including symptoms, tests, and treatment steps.

Learn to craft a structured soap note prompt for clinical documentation, defining the role of a clinical documentation specialist and the subjective, objective, assessment, and plan components with guardrails.

Explore HIPAA data masking to de-identify protected health information in clinical notes. Craft prompts that identify PHI elements like name, dates, and contact details, replacing with square-bracket masking.

Apply prompt engineering to healthcare sentiment analysis using Bengaluru hospital reviews from Kaggle, mapping feedback to sentiment labels and ratings, with a prompt-based approach achieving 92% sentiment accuracy.

Explore HL7 data generation in healthcare, using trigger events and message structures like ADT, PID, PV1, and lab orders, with version 2.8 pipe-delimited segments.