Healthcare data is exploding thanks to electronic health records (EHRs), imaging, wearables and other digital technologies. This data promises to transform care and improve operational efficiency. With 36 percent annual data growth, however, healthcare organizations face enormous challenges with data management and security.
In this dynamic, healthcare data management (HDM) has become a critical focus for healthcare organizations. HDM is a systematic process of collecting, storing, protecting and analyzing health-related information to improve patient outcomes. It also promises to reduce costs through greater operational efficiency.
AI adoption is also driving growth in HDM. Healthcare organizations are prioritizing data quality and standardized access to ensure that AI models produce reliable and unbiased insights.
Despite its value, HDM faces major hurdles due to siloed data, quality and standardization problems, and immense security, privacy and regulatory risks. Organizations need robust data architectures and governance strategies to manage the increasing volume, velocity and variety of health data.
Healthcare organizations recognize that simply storing data is no longer enough. High-quality, trusted data is now seen as a strategic asset. As a result, organizations are investing in systems that consolidate fragmented data from EHRs, financial systems and other sources.
Interoperable platforms enable organizations to unify data into a single source of truth. Secure, scalable storage such as cloud-native data lakehouses and vendor-neutral archives facilitates the management of petabytes of structured and unstructured data.
AI is both driving and facilitating HDM. AI-powered tools automate data entry and extraction, ensure accuracy and completeness, and integrate disparate sources. Generative AI and natural language processing (NLP) automate note-taking and transcribe patient-doctor conversations. An Enterprise Master Patient Index assigns each patient a unique identifier and links the patient’s records from different systems.
Data quality and governance are key components of HDM. Organizations must establish policies to ensure data is accurate, complete and consistent across systems to prevent life-threatening medical errors. Robust security controls, including encryption, multifactor authentication and zero-trust architectures, are essential for adhering to HIPAA, GDPR and other data privacy regulations.
HDM can deliver real, bottom-line benefits. Data analytics helps reduce costs and optimize staffing levels and resource allocation. It also helps organizations comply with evolving regulations. For example, automated and interoperable systems are essential for meeting strict new requirements for prior authorizations, data sharing and transparency.
Data governance is the price of admission for the AI and analytics tools currently scaling across the healthcare industry. Seamless data access helps organizations move from experimentation to gaining a measurable ROI from AI-powered tools. High-quality data ensures that generative AI and predictive models produce consistent, trustworthy results.
From a clinical perspective, effective data management enables timely interventions, better diagnoses and tailored care plans, directly impacting patient outcomes. Accurate and complete data is crucial for demonstrating quality and managing costs in new payment models.
Organizations are increasingly treating data visibility and consent as central to the patient experience. Patients expect transparency regarding who accesses their records and how their information is used to power new AI models.
Breaking down data silos and ensuring data quality can be a monumental task, particularly when the technology environment has evolved over time. In many healthcare organizations, outdated, incompatible software creates integration gaps. Systems can’t talk to each other and data is stored in disparate formats.
Healthcare organizations also struggle with a talent gap. They often lack skilled data scientists and analysts to interpret complex health data. Protecting sensitive information and navigating complex and evolving regulations adds a significant burden.
Technologent can help organizations overcome these challenges. Our data practice spans analytics, governance, security and other disciplines, providing our clients with a one-stop source for advisory and engineering services. We help our clients manage huge volumes of structured and unstructured data and establish clear policies for management, access and use. We also help organizations develop AI strategies to drive innovation.
Healthcare data now accounts for 30 percent of global data volume, and it’s doubling every year. Let Technologent help you leverage healthcare data management to optimize your operations and enhance patient care.