This module provides practice illustrating the complete healthcare patient journey, mapping data touchpoints across intake, referral, discharge, and follow-up using Microsoft Cloud for Healthcare workflow context. Learners will be able to create a workflow illustration showing where healthcare data is generated, accessed, and transferred at each stage of the patient journey. Healthcare administrators must understand the full patient flow to support any individual task; every process connects to something before and after it.

This module introduces healthcare informatics, including how data from clinical, operational, and administrative sources connect to support coordinated healthcare delivery in Microsoft Cloud for Healthcare. You will be able to explain how informatics organizes data relationships across systems and why this matters for downstream reporting, care coordination, and operational decisions. Informatics is the discipline that turns disconnected healthcare data into connected, actionable information.

This module builds analytical skills for reviewing healthcare datasets and identifying data quality problems, missing fields, inconsistent values, outdated entries, and classification issues that must be resolved before data can be mapped to system entities. You will be able to examine a sample patient dataset and systematically identify quality and classification issues using predefined criteria in Microsoft Cloud for Healthcare. Data quality is the gatekeeper: if data isn't clean and properly classified, every downstream process produces unreliable results.

This module provides practice mapping cleaned and classified healthcare data elements to Microsoft Cloud for Healthcare (Dataverse) entities for basic reporting scenarios. You will be able to align source data fields from a healthcare dataset to the correct Microsoft Cloud for Healthcare entities and recommend controls before cloud upload using the Microsoft Dataverse healthcare data model. Data mapping is how information from source systems becomes usable in Microsoft Cloud for Healthcare; every integration, report, and dashboard depends on correct mapping.

This module introduces how electronic medical records are structured when integrated into Microsoft Cloud for Healthcare, covering demographics, encounters, orders, documentation, and discharge information. You will be able to explain how common EMR components map to healthcare administration workflows using Microsoft Dataverse entities and integrated EMR views. Understanding EMR structure is foundational for every data retrieval, quality check, and coordination task in healthcare administration, and as Microsoft tools become increasingly common alongside existing EMR systems across healthcare organizations, this knowledge positions entry-level staff to work effectively in both environments.

This module provides practice in retrieving the correct EMR records for a defined healthcare workflow, focusing on a claims submission/rework scenario where you must identify missing clinical documentation to support claim resubmission. You will be able to select the correct encounter and patient records from integrated EMR data in Microsoft Cloud for Healthcare to satisfy a specific workflow requirement. Claims rework is one of the highest-volume administrative tasks in healthcare; knowing which records to pull and why is the difference between a clean resubmission and another rejection.

This module provides practice applying health information lifecycle management—how records are captured, updated, retained, and shared under controlled conditions using Microsoft Cloud for Healthcare and Microsoft Purview data lifecycle policies. You will be able to classify healthcare records and apply the correct retention label or lifecycle policy based on data type, Protected Health Information (PHI) status, and regulatory requirements. Data lifecycle management ensures healthcare records are available when needed, protected while active, and properly archived or destroyed when required—a core compliance responsibility.

This module builds analytical skills for reviewing patient record datasets and identifying quality issues, such as missing fields, conflicting values, and outdated entries, using completeness, consistency, and timeliness criteria in Microsoft Cloud for Healthcare standards. You will be able to systematically review a sample healthcare dataset, pinpoint quality issues, and document findings with recommended corrections. Record quality reviews are performed before audits, integrations, migrations, and reporting—every downstream process depends on the data being clean.

This project integrates skills from across courses on healthcare data domain knowledge, informatics mapping, EMR record retrieval, and record quality analysis into a comprehensive health record quality validation exercise. Learners will be able to use Microsoft Cloud for Healthcare, Microsoft Azure Health Data Services, and EMR system documentation as reference context to evaluate a healthcare dataset for operational readiness using data quality criteria and informatics mapping frameworks, documenting all findings with severity classifications and remediation recommendations. Quality validation is performed before every major integration, migration, or reporting initiative. This deliverable mirrors the pre-production review and serves as a portfolio-ready artifact.