What is Healthcare Data Management and Why is it Important?

EHRs, labs, pharmacies, and more, or non-clinical sources such as public databases, wearable devices, and more. It involves aggregating structured or unstructured data from sources, standardizing org-wide use of data, and maintaining various data types including patient information, clinical records, administrative data, medical images, and research data.

By leveraging a unified data model, health systems can clean, standardize, and unify all healthcare data sources—including clinical, claims, labs, pharmacy, SDoH, devices, and more—to drive improved collaboration, and a future-proofed foundation designed for native interoperability and accelerated innovation.

1. Data Collection: Aggregation of relevant data from various sources, such as electronic health records (EHRs), medical devices, laboratory tests, and patient-reported information.


2. Data Storage: Storing healthcare data in a secure and accessible manner typically utilizing databases, data warehouses, or cloud storage systems to ensure data integrity, confidentiality, and availability.


3. Data Integration: Bringing data from multiple sources to create a comprehensive and unified view. A classic example can be combining data from different departments within a healthcare organization or data from external sources, such as health information exchanges or public health databases.


4. Data Quality and Governance: Data collected and stored must be of high quality and must undergo required quality checks, cleansing, and validation processes to identify and correct errors or inconsistencies. Approved quality and processes clear ground for central governance accuracy, consistency, and completeness of healthcare data.


5. Data Security and Privacy: It involves the implementation of security measures, adhering to privacy regulations (such as HIPAA in the United States and GDPR in the UK), employing encryption, access controls, and audit trails to protect healthcare data from unauthorized access, breaches, or misuse.


6. Data Analysis and Reporting: This refers to extracting insights and knowledge from healthcare data through various analytical techniques including data mining, ML-based algorithms, or statistical methods. Predictive analytics in healthcare can help identify patterns, trends, and correlations for clinical decision-making, population health management, research, and healthcare planning.


7. Data Sharing and Interoperability: The exchange of healthcare data between different systems and stakeholders, such as healthcare providers, hospitals, clinics, laboratories, and public health agencies to ensure data sharing, data integrity, and patient privacy with required access controls.

Effective healthcare data management is crucial for improving patient care, enhancing operational efficiency, supporting clinical research, enabling population health management, and facilitating evidence-based decision-making in healthcare organizations.

1. Improved Patient Care: Holistic data management can empower providers to access comprehensive and accurate patient information, including medical history, medications, allergies, and test results. This can significantly improve clinical decision-making, drive accurate diagnoses, and enable personalized treatment plans by leveraging unified data as a single source of truth to coordinate among multiple healthcare providers involved in a patient's treatment.


2. Enhanced Patient Safety: Data management systems can reduce the risk of medical errors and adverse events. If managed and updated on a regular basis, it can facilitate access to complete and up-to-date patient information, including medication records and allergies that can help in patient identification, prevent medication errors, and any risk due to allergic reactions.


3. Operational Efficiency: Streamlining data management processes can improve efficiency in healthcare organizations by multiple folds. Electronic health records (EHRs) and digital documentation eliminate the need for paper-based records, reducing administrative burden, storage costs, and the risk of lost or misplaced records. Automating tasks such as data entry, appointment scheduling, and billing can improve workflow efficiency allowing providers to focus more on patient care.


4. Population Health Management: Healthcare data management lays solid groundwork for deep analysis of aggregated and de-identified data to identify health trends, disease patterns, and population health risks. The insights can help providers develop preventive strategies, implement public health interventions, and allocate resources for specific communities or regions.


5. Clinical Research and Evidence-Based Medicine: Healthcare organizations can support their clinical research by providing access to comprehensive and diverse datasets for analysis and studies. Researchers can use high-quality data to identify potential research participants, conduct epidemiological studies, evaluate treatment outcomes, and develop evidence-based guidelines and protocols.


6. Healthcare Policy and Planning: Private and public healthcare organizations can leverage aggregated data to support the policy development process, and strategically allocate resources for scalable healthcare planning at the regional, national, and global levels. Healthcare databases on disease prevalence, healthcare utilization, and outcomes can inform policy decisions, public health strategies, and resource allocation for healthcare infrastructure, workforce, and interventions for chronic conditions or during pandemic outbursts such as COVID-19.


7. Compliance and Privacy: Cloud-native data management systems ensure compliance with privacy and security regulations, such as HIPAA in the United States. Healthcare organizations managing data operations in private data centers can protect patient information, maintain confidentiality, and prevent unauthorized access or breaches by implementing appropriate security measures, data encryption, access controls, and audit trails.

Healthcare data management is critical for improving patient care, patient safety, operational efficiency, research, population health management, and evidence-based decision-making for healthcare organizations. It plays a pivotal role in leveraging data to drive improved health outcomes and ensure the delivery of quality healthcare services.

Innovaccer is at the forefront of helping health systems move beyond point solutions and basic interoperability to true data readiness, using a scalable platform that accelerates innovation and digital transformation in healthcare.

The Innovaccer Platform rapidly establishes the highest possible level of interoperability between legacy, mission-critical, and other healthcare IT systems; aggregates and unifies (cleans) the data from multiple sources; “hydrates” the data to enable any application to use it; “activates” the data to make it useable in integrated workflows; and lastly, “harmonizes” the data to ensure it’s synchronized across all relevant systems and care settings.

Innovaccer’s cloud-native data platform makes it easy for healthcare organizations to collect, standardize, monitor, and perform analytics. It unifies patient data across systems and care settings and empowers healthcare organizations with scalable, modern applications that improve clinical, financial, operational, and experiential outcomes. It establishes a unified data model (UDM) inclusive of the clinical, financial, and operational data that drive healthcare for all stakeholders, including providers, payers, employers, life sciences companies, and patients and their families.

With approximately 70 entities and 2,800 data elements, the data model is robust enough to support thousands of use cases. Innovaccer continuously expands the UDM as new value levers drive additional entities and attributes. Customers can also extend the data model within their deployment to incorporate additional entities, objects, and attributes.

To accommodate this continuous data model expansion and extension, the platform includes a sophisticated schema evolution framework and architectural controls to provide appropriate versioning management and backward compatibility. UDM updates are regularly synchronized across deployments without impacting any customer extensions or existing production functionality in interfaces, applications, and analytics.

To learn more about how our data platform can help your organization create a unified patient record that brings all of your patients’ data together—including social determinants and other data that exists beyond the health system’s four walls—to enable advanced analytics, machine learning, and integrated workflows that help care teams work better together, and get the patient-centered insights they need at the point-of-care to achieve their better business and clinical outcomes, read this whitepaper or get a demo today.