The digitization of healthcare and the subsequent surge in mHealth data generation are fundamentally changing care delivery and data management systems. Healthcare organizations must now be able to share health information between different departments and external partners quickly and efficiently. This capability is not merely about efficiency; it’s a matter of patient outcomes and responsiveness in emergencies. The solution to successful data management lies in developing a sustainable, comprehensive data strategy.
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Table of Contents
The Unstoppable Data Deluge in the Healthcare Industry
After the push of the global pandemic in 2019, the active digitization of healthcare data started to grow exponentially. With nearly two zettabytes in 2019, healthcare data circulating in the healthcare industry reached four zettabytes in 2022. This exponential growth proves that data is growing at an unprecedented rate. Besides, the amount of data will only grow further: it is expected to cross ten zettabytes in 2025.
Large data volumes like these need to be appropriately managed. Healthcare leaders and healthcare executives ask: How can organizations approach the management of data loads today and from now on? The answer lies in designing and executing the right data strategies that allow them to leverage this information. A proactive data strategy for healthcare is essential to modernize the health system and ensure health equity is considered in all data use cases.
4 Foundational Pillars of a Modern Healthcare Data Strategy
To transform healthcare and provide more efficient patient care, healthcare organizations must move beyond simple storage and focus on four foundational pillars. These practices are crucial for building a scalable data platform and thriving in a data-driven era.
Data Augmentation & Automation: Leveraging AI and Advanced Data Techniques
One powerful approach to data management is by using Machine Learning (ML) algorithms to augment human intelligence and switch to automated processes. ML allows computers to learn from past data-driven experiences without being explicitly programmed for each task. That translates into machines being able to adapt and assist staff, which is especially critical when something goes wrong.
Advanced Analytics and Predictive Use Cases
The true value of this pillar lies in predictive analytics. Artificial intelligence (AI) is the intelligence layer that runs atop the data platform. By analyzing massive data sets of patient data, AI can identify complex health trends and predict events, such as patient no-shows or the likelihood of a readmission. That is where advanced data techniques really make data actionable.
- Clinical Decision Support (CDS). AI can process real-time clinical and financial data to empower healthcare providers with instantaneous recommendations, improving patient outcomes and the quality of care. That is a prime use case for data-driven systems.
- Operational Optimization. ML models can optimize resource allocation, predicting spikes in demand for specific health services or equipment maintenance needs. That helps automate logistics and streamline the organizational workflow.
- Data Collection and Labeling. ML helps automate data collection and labeling, significantly reducing costs and human data entry errors. That improves data quality and speed, ensuring the analytic systems are fed accurate health information.
The Impact on Care Delivery and Patient Experience
Using automated technology like chatbots, patients can interact directly with their doctors via text or voice messaging. The data is then processed automatically while AI defines how to proceed with the patient. That allows more patients to receive attention and decrease wait times — a massive win for both parties. Eventually, with less time spent on administrative tasks, staff members can focus on providing patient care services in the first place, improving the overall patient experience. This approach directly supports care delivery goals.
Moving to the Cloud: Building a Scalable Data Architecture for Data Modernization
A cloud is a powerful tool. Moving to it can help your healthcare organization in many ways. Cloud-based solutions are more flexible, reliable, secure, scalable, and cost-effective than traditional on-site systems. Amazon Web Services (AWS) and similar services provide the necessary data infrastructure and security frameworks for health data.
Architectural Choices: Data Lakes vs. Warehouses
Healthcare organizations must decide on the appropriate data architecture to support their data modernization.
- Data Warehouse. Designed for structured clinical and financial data, perfect for traditional reporting and business intelligence analytics.
- Data Lakes. Store massive volumes of raw, unstructured data sets (like medical images, doctor’s notes, device data) at scale. This data platform is vital for running sophisticated ML/AI models that leverage data that wouldn’t fit into traditional structures. The ideal health system today employs a Lakehouse architecture that combines the flexibility of data lakes for advanced data exploration with the structure of a warehouse for reliability.
Real-Time Sync and Access to Data
Moving to the cloud enables healthcare organizations to automate administrative tasks. For example, automated patient electronic health records upload and real-time synchronization of data across all departments eliminates manual tasks. Also, that makes data accessible anywhere by anyone with the correct data access. The cloud provides the necessary infrastructure to ensure data available instantly, enabling real-time care delivery decisions.
Strong Data Governance Structure: Ensuring Data Quality and Security
Data governance establishes policies, procedures, and protocols to ensure that an enterprise can control its data assets. It is the crucial framework that guides all data use and data sharing activities, defining how resources are allocated across different organizational functions.
The Foundation for Trust: Handling Sensitive Information
A clear data governance structure ensures data quality and provides a framework for handling compliance requirements. This structure is paramount for protecting sensitive information like patient data. Organizational confusion over data use can lead to privacy breaches. Data governance helps fix problems related to billing, data entry in electronic health records, and security threats before they become too costly. Such clarity is a key step to modernize practices.
Implementing Clear Data Policies and Audit Processes
Data governance requires a continuous audit cycle to ensure compliance with regulatory requirements. That involves:
- Data Stewardship. Assigning individuals responsible for the accuracy and ethical data use of specific data sets.
- Data Lifecycle Management. Defining how long health information can be retained (retention policies) and when it must be archived or deleted.
- Access Control. Defining strict role-based access to ensure only authorized healthcare providers have data access to the data they need. That helps optimize security and minimize the exposure of patient data.
Staff’s Data Literacy Training: Empowering the Workforce and Improving Patient Care
Data literacy helps users understand what they can and cannot do with healthcare data. That is about learning how and why data is collected and using it effectively within the health system.
Tailored Training for Different Roles
Data literacy training should be role-specific.
- Clinicians. Need to understand the limitations of data sets, how to interpret analytic dashboards (actionable insights), and how data quality affects diagnosis and treatment. This mindset directly helps improve patient outcomes.
- Administrators. Must understand data governance policies, compliance requirements, and how to automate reporting workflows.
- IT/Engineering Teams. Need training on Fast Healthcare Interoperability Resources (FHIR) standards and cloud data architecture best practices to build a truly scalable data platform.
The Impact on Patient Experience and Health Outcomes
Data literacy allows staff to identify errors in EHRs or bottlenecks in scheduling, improving the flow of critical information. That improves the patient experience and facilitates better care delivery. Educating medical staff on how to work with data is key because the improvement of clinical data storage leads to better clinical patient support systems and health outcomes.
The Critical Engine: Interoperability & Seamless Data Exchange
A strategic focus on interoperability is the critical engine that connects the four foundational pillars. Without it, the data remains siloed, and the analytics systems starve.
FHIR as the Technical Solution for Interoperable Data
The biggest technical hurdle in healthcare is the fragmentation of data formats. The adoption of FHIR is the industry’s answer. FHIR defines a set of interoperable data standards using modern web technology to enable seamless data exchange. That facilitates the integration of data from various data sources into a data platform.
- Bridging Different Data Sources. FHIR ensures that data across various systems (EHRs, labs, pharmacy) can be aggregated, helping to achieve a single source of truth. This interoperability is essential for any modern data strategy for healthcare.
- API-First Approach. FHIR’s API-first approach grants specific, secure data access to third-party applications and developers, which is essential for innovation and running advanced data analytics.
Establishing a Health Information Exchange (HIE)
Data sharing is not just internal. Instead, it must extend to the broader health system. Establishing or participating in a Health Information Exchange (HIE) is a strategic decision that allows healthcare organizations to safely and securely share data with external partners.
- Improving Care Coordination. HIEs ensure that when a patient moves between healthcare providers (e.g., from primary care to a specialist), the receiving care provider has immediate access to data such as medication lists and allergies. That improves care coordination and patient outcomes.
- Public Health Reporting. HIEs are vital for automated public health reporting and tracking health trends in real-time, which is a key application of aggregated health data.
The Non-Negotiable Foundation: Security & Compliance
The sheer volume of sensitive information handled by the health system dictates: security and compliance must be integrated into each step of the data architecture. You should not treat them as an afterthought.
Integrating Security and Privacy Controls
A robust healthcare data strategy must enforce the strictest security and privacy standards across the entire data lifecycle.
- Encryption. All patient data and health information must be protected using state-of-the-art encryption both at rest (when stored in data lakes or warehouses) and in transit (during seamless data exchange).
- Role-Based Access Control. Data governance policies must be enforced through technical controls, limiting access to data based on a user’s role (e.g., a billing clerk does not need clinical data).
- Auditability. Every transaction involving critical information must be logged and audited. That involves collecting logs via services like Amazon Web Services (AWS) CloudTrail, enabling the organization to react instantly to any security event and make data useful for forensic review.
Compliance Requirements and the BAA
The data strategy must explicitly address how the healthcare organization will comply with all regulatory requirements.
- Business Associate Agreement (BAA). Any third-party vendor that handles patient data must sign a BAA, committing to the same level of security required of the healthcare organization. AWS requires a BAA for all its healthcare clients.
- Continuous Monitoring. Compliance is not a one-time event. The strategy must include automated continuous monitoring systems and regular vulnerability testing to optimize the security posture and ensure ongoing compliance with laws and regulations.
Execution: Stakeholder Engagement and Phased Implementation
The success of any data strategy hinges on organizational adoption. Healthcare leaders must drive the implementation, but success is owned by every employee.
Establish a Clear Data Vision for Stakeholders
Healthcare executives must articulate a clear data vision, ensuring that the defined data strategies meet the data needs of all organizational stakeholders. The organizational complexity of the health system means that IT, clinical, and financial departments all have different data needs, but the data platform must serve them all.
- Stakeholder Participation. Stakeholders should participate in the data strategy process to have a say on what patient data is collected and how it is used. They know their business better than anyone else, ensuring the strategy focuses on actionable insights rather than theoretical ones.
- Focusing on Critical Information. The strategy must highlight the immediate value — how the integration of data will improve care coordination, reduce costs, and enhance the health status of patients.
Assess Your Existing Data Infrastructure
The next step in designing your data strategy is to assess your existing data infrastructure and analytics systems. That involves understanding the current state of your data, including how it is being collected, used and stored. You should also be able to identify any gaps or issues that may exist within your current system. So to get a complete image of how data flows within your organization, try to think of the following:
- how the data was collected (e.g., via manual entry or automatically)
- the quality of the data (e.g., accuracy and completeness)
- why the data was collected (e.g., because of a regulatory compliance requirement)
- how long can you retain the data you collect
Finding answers to the above and diving deeper into your existing data infrastructure will help you understand what you lack in your current system and what you need for healthcare and clinical data storage improvement.
Optimize and Prioritize: Solving One Problem at a Time
Now that you have determined your starting point and desired destination, you can start looking for solutions. It is crucial that you should not target all areas at the same time. That will be too demanding and inefficient.
- Phased Implementation. Focus on the issues that are the most urgent or offer the highest return on investment (data projects). The strategy should target solving specific, measurable problems (use case) first, which builds momentum.
- Iterative Solutions. The solutions should aim to optimize workflows and make data actionable. The goal is not just to collect data, but to ensure data available and delivered in a format that healthcare providers can immediately make decisions from. That solidifies the single source of truth for all data sources.
Final Thoughts
Data is an integral, strategic asset for a healthcare organization, and it is crucial to treat it as such. Without a quality data strategy, the potential to improve patient health outcomes and achieve more efficient organizational operations is limited. Effective data strategies help healthcare providers leverage the power of data to drive insights into health trends and improve health and health care generally. This comprehensive data strategy is the future of care delivery.
Are you considering building or modernizing your healthcare data strategy? We will design a project plan and architect the necessary advanced data solutions — leveraging FHIR, AI, and AWS — to achieve your strategic data goals!
FAQ
What is the difference between data governance and data management?
Data governance and data management are related but distinct concepts. Data governance is the organizational framework defining the policies and procedures, roles, and responsibilities for handling data, setting the rules for data use and data quality. Data management is the execution of those rules — the operational activities like collection, storage, cleansing, and security of the clinical and financial data. Governance answers who and why; management — how and where.
Why is interoperability a mandatory component of a data strategy for healthcare?
Interoperability is mandatory because healthcare data exists across different data sources (electronic health records, labs, wearables). A data strategy focused on interoperability ensures seamless data exchange and the integration of data from these sources. That is essential for achieving a single source of truth about a patient’s health status and generating real-time analytics and making effective data-driven decisions for care delivery.
How does artificial intelligence fit into the four practices of a data strategy?
Artificial intelligence (AI) is the intelligence layer that sits atop a robust data platform. It fits into the strategy by requiring high-quality data and running on a scalable data environment. AI is the ultimate tool for data augmentation & automation, as it automates mundane tasks and performs advanced data analytics on massive data sets to identify complex health trends.
What is the importance of a single source of truth for healthcare data?
The single source of truth is a core data architecture goal. It means all clinical and organizational staff access to data from one definitive, verified location. That prevents conflicting reports and eliminates redundant manual data entry and reconciliation. Besides, it ensures that everyone (from billing to clinicians) is using the same critical information to make data decisions. This consistency is vital for improving data quality and ensuring health equity.
What are mHealth analytics systems, and how do they benefit the patient experience?
mHealth analytics systems process health information generated by mobile apps and wearable devices. These systems leverage data to give healthcare providers real-time insights into a patient’s behavior and health status outside the clinic. Such constant monitoring allows for proactive interventions, personalized reminders, and immediate communication with the care team. That drastically improves the patient experience and overall patient outcomes.
Why do healthcare organizations need a specific focus on data literacy training?
Healthcare organizations need a specific focus on data literacy training because front-line medical providers are the primary collectors and consumers of patient data. They need to understand not only how to use the new data tools but also how to interpret the analytic insights and recognize when the data they need might be flawed. Investing in literacy training ensures that staff can correctly analyze data, building trust in the data platform and maximizing the ROI.
What is the significance of including clinical and financial data in one strategy?
The significance of including clinical and financial data in one strategy is to gain true visibility into the value-based care delivery model. Clinical data (electronic health records) shows the quality and outcome, while financial data (billing, claims) shows the cost. Combining these allows the health system to perform analytics that identify where care is both high-quality and cost-efficient. That enables organizations to optimize processes and improve patient outcomes.
How do data lakes and the cloud factor into achieving a scalable data strategy?
Data lakes are repositories for storing large volumes of structured and unstructured data sets (medical images, clinical notes, etc.) in the cloud. That factors into a scalable data strategy by allowing the healthcare organization to cheaply store raw different data at virtually unlimited scale. The cloud provides the elastic infrastructure and computational power to run data analytics and AI models across vast data lakes without disrupting current clinical workflows.
