What are the best practices for health data management?

Data is at the heart of every decision in healthcare. From digital health records to clinical trial insights and patient monitoring systems, modern health organizations must combine vast and disparate data sets to deliver the care and support patients need.

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Managing this data is increasingly complex and requires more than just storage. Data must be accurate, secure, and accessible in a fast-paced environment shaped by strict regulations and emerging cybersecurity threats.

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Health data management (HDM) is a discipline that helps organizations make sense of their data and keep it safe and compliant while supporting the incredible work physicians, researchers, scientists, and care coordinators do every day.

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This guide will outline the benefits and challenges of managing that data at scale and provide a list of best practices for improving your data handling processes and consistently achieving positive outcomes. Now, let's explore exactly what HDM entails. 

What is health data management? 

Health data management (HDM) is the process of cataloging, analyzing, and leveraging health information to support clinical decision-making and improve patient outcomes. Because public health data is highly sensitive, security and privacy are of the utmost importance; systems must always ensure accuracy, integrity, and confidentiality.

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HDM systems are comprised of several essential components. These include:

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• Data collection: Gathering comprehensive and relevant data sets from various sources, including patients, clinical trials, and white papers.
• Data storage: Securely storing that data in a centralized repository. Health organizations use Electronic Health Records (EHRs) and Electronic Data Capture (EDC).
• Data sharing: Ensuring data can be shared with authorized users to coordinate care.
• Data security: Implementing security measures such as access controls and encryption to protect sensitive data.

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Health data management systems take many forms. The term covers many systems to improve patient care while supporting business-critical objectives such as compliance management and enhanced operational efficiencies. Examples of HDMs in action include:

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• A telemedicine provider delivering care to individuals with chronic conditions by leveraging a cloud-based data solution with remote patient monitoring to track vital signs and detect abnormalities.
• A large biomedical care and research center aggregates data from EHRs and other sources using a cloud HDM to conduct research and clinical trials.

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The Health Insurance Portability and Accountability Act (HIPAA) governs health data management in the United States. Its main goals are to protect patient information while modernizing data flows to reduce care costs.

What are the requirements for managing health data? 

HIPAA sets standards for how sensitive patient health information (PHI) is used, disclosed, and protected and dictates many requirements for handling health data.

Data quality   

When managing health data, you need to meet certain data quality standards. Systems must contain accurate, complete, and available data to empower healthcare professionals to make timely and informed decisions. 

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A breakdown in data quality can result in medical errors and poor patient outcomes. To ensure the highest standards, we recommend regularly verifying data for accuracy, conducting audits, and updating records using a cloud-based data solution.

Data security

The healthcare industry is a popular target for cybercriminals due to employees' regular exchange and remote access to data. 

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Robust security measures need to be implemented to protect it from authorized access. Organizations must comply with the HIPAA Security Rule, which governs health organizations that use electronic protected health information (ePHI). 

Interoperability

Ensuring interoperability and the seamless sharing and exchange of data across systems is vital for continuity of care. The quality of data cannot be compromised at either transmission or rest. 

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To achieve interoperability, healthcare organizations adhere to two main standards: HL7 and FHIR (Fast Healthcare Interoperability Resources).

Compliance

Compliance underpins all aspects of data handling. Healthcare organizations must follow strict laws and regulations to protect data and avoid violations, which result in legal action and significant fines. 

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The protection of patient data is enshrined in HIPAA in the United States and regulated by the General Data Protection Regulation (GDPR) in Europe.

Scalability

Data isn’t static; volume grows over time, which can cause speed and security issues if systems are not scalable. Larger healthcare institutions can manage millions of records. These records must be cataloged and made available, even as providers expand their services and adapt to working and regulatory changes. 

Accessibility

Health data needs to be readily accessible, but only to those authorized to use it. Balancing ease of use with security is fundamental to effective health data management and supporting physicians in their quest to deliver speedy and accurate diagnoses and treatment plans.

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What are the benefits of health data management? 

Effective health data management removes barriers and reduces silos, which can lead to significant benefits for healthcare organizations. These include:

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1. Improved patient outcomes and engagement: A centralized and cataloged database allows physicians to quickly access patient histories and lab results and track trends to deliver better treatment plans. Cloud solutions also improve patient engagement by using predictive analytics to send reminders, alerts, and care suggestions.

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2. Increased efficiency and cost-effectiveness: Effective health data management streamlines workflows and reduces redundancies, helping organizations lower costs and allocate resources efficiently. Leveraging evidence-based practices (EBP) from comprehensive data also improves patient outcomes and fosters innovation in care delivery.

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3. Enhanced business decision-making: The benefits snowball beyond patients and care departments. The broader business can also thrive; high-quality data allows marketers to target the right patients, HR to recruit the best medical professionals, and supply chain and procurement to invest in premium equipment. Data-driven decisions become a part of business culture.

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4. Improved communication and collaboration: Legacy electronic health records (EHRs) were often confined to a single entity. Cloud-driven health data management allows healthcare providers to collaborate to improve care delivery and coordination. A data exchange platform also enables researchers from different institutions to work together to develop new treatments.  

What are the challenges of health data management?

Data management offers plentiful rewards, but health providers must mitigate risks and overcome challenges to reap them. 

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• Data security and privacy: HIPAA compliance is mandatory for healthcare providers. Violations can lead to serious fines and penalties. Ensuring the security and privacy of data is an ongoing challenge amid a rise in sophisticated and targeted cyberattacks. 
• Data quality and integration: Fragmentation is a common problem. Medical data comes from various sources, such as digital documents, scanned paper, images, and spreadsheets, which makes it difficult to integrate and manage. Quality can also be compromised when data is duplicated and stored in different systems.
• Legacy systems: Many healthcare organizations, particularly publicly funded ones, rely on outdated technology that complicates data integration and hinders effective management.
• Interoperability and standardization: Systems need to communicate to uphold the integrity and quality of data. Achieving interoperability can be difficult due to the complexity of the task and organizational resistance to using comprehensive standards.
• Ethical considerations: Obtaining consent to store and use data is an ethical consideration that needs to be factored in when managing data. Healthcare providers have a moral obligation to protect the privacy of their patients. 

What are the different types of health data management solutions? 

Healthcare organizations use a variety of health data management solutions to address these challenges and deliver the highest quality of care. These solutions work together to create an interoperable healthcare ecosystem.

Electronic health records

Electronic health records are systems for storing medical records digitally. Previously, these were built as ‘standalone’ or ‘enterprise systems’ designed to support independent or large-scale organizations. Cloud storage is more common; its flexibility and scalability are better suited to growing record volumes.

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EHRs store many patient data sets, including medical histories and medications. They also feature integrated tools to support clinical decision-making. This helps to efficiently improve levels of care while reducing the errors in written notes. 

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Challenges still persist, though. EHRs can be costly to implement and support for smaller practices. There can also be issues with interoperability and the ongoing threat of breaches and unauthorized access.

Data warehouses and data marts

Health organizations have to manage multiple systems, each with its own data store. Data warehouses connect the dots between these sources and aggregate the mass of data. This allows healthcare professionals to analyze data at scale and leverage insights to enhance resource allocation and improve care. 

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There are also smaller, more targeted ‘data marts’, commonly deployed by departments within a hospital for specific use cases. Both data warehouses and marts are vital for collecting and storing data. However, integrating different systems can be costly and difficult. 

Clinical decision support systems (CDSS)

Physicians rely on evidence-driven clinical decision support systems (CDSS) to diagnose diseases and outline treatment plans. The clinical data recommendations offered by CDSS streamline workflows, reduce costs, and increase efficiency. There are three common types of CDSS: 

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1. Knowledge-based CDSS: Uses medical rules to offer clinical advice.
2. Case-based CDSS: Compares new cases to past ones for recommendations.
3. Hybrid CDSS: Combines rule-based and case-based systems for support.

Population health management systems

Improving long-term outcomes means looking beyond patients and taking a broader view of demographics. Population health management systems identify the root causes of disease and glean insights from data that can support better preventative care in communities. A common challenge is ensuring the data that’s acted on is accurate and complete.

Cloud-based HDM solutions

Cloud and hybrid cloud solutions offer unprecedented scalability for healthcare organizations. Common types include:

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• Infrastructure as a Service (IaaS): This provides virtualized computing resources over the Internet, offering flexibility for managing hardware needs.
• Platform as a Service (PaaS): Offers a platform for developing, running, and managing applications without the complexity of building and maintaining infrastructure.
• Software as a Service (SaaS): Delivers software applications over the internet on a subscription basis, eliminating the need for installation and maintenance.

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These solutions are cost-effective and flexible, allowing for on-demand data storage customization. However, organizations may face challenges with limited control over infrastructure and potential vendor lock-in. 

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When choosing a solution, healthcare organizations should consider their specific needs: SaaS is often ideal for straightforward software needs, while PaaS is better for those looking to develop custom applications.

Emerging technologies 

Digital transformation has revolutionized data health management. Healthcare data management now requires new technologies to be integrated into existing systems to ensure continuity and support growth at scale while delivering more efficient and cost-effective patient outcomes. 

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• AI and machine learning: Artificial intelligence promises vast improvements to core data handling processes. Business intelligence tools with AI algorithms can now automate data entry and identify and correct inconsistencies to improve reliability and authenticity.
• Cloud computing: Cloud management systems support on-demand provisioning of resources to scale up and down based on workloads and data demands.
• Internet of Things (IoT): IoT, or “smart” devices, reduce the burden of manual data entry by automatically gathering data. This data better reflects a patient’s real-time condition and eliminates the risk of human error. 

The best practices for health data management 

Data needs to be managed securely, ethically, and compliantly. Let’s explore some of the best practices for achieving this objective.

Data governance and management policies

Data management requires oversight and accountability. You should document clear policies explicitly stating how data will be accessed and shared and assign data ownership to relevant individuals or departments. It is common for a Chief Information Officer (CIO) or Data Management Office (DMO) to oversee data strategies.

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We also recommend the adoption of key data governance frameworks, such as COBIT or NIST, to structure governance efforts and minimize the risk of non-compliance. 

Data security and privacy measures

Health organizations need to comply with the HIPAA Security Rule to protect patient data and prevent breaches. To reduce the chances of violations, you should always:

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• Implement robust access controls to ensure only authorized individuals can access and use data.
• Use the highest levels of encryption. AES-256 is the strongest algorithm and the widely recognized standard for electronic medical records.
• Implement multi-factor authentication (MFA) across all IoT devices.
• Regularly update software and tools and download security patches.
• Educate employees about cybersecurity and stress the importance of using strong passwords and identifying a phishing or pretexting attack.
• Document an incident response plan to react promptly to security events.

Data quality assurance

Upholding the integrity and quality of data is key to informed decision-making. To mitigate the risks of system degradation, you need to define clear standards for data quality: What needs to be done to keep it accurate, complete, consistent, and timely? We recommend the following:

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• Accuracy: Establishing rules that enable you to check whether data is accurate and cross-validating using multiple sources to identify efforts. Conducting regular audits is key.
• Completeness: Implementing checks to ensure mandatory data fields are completed and using technological solutions to find and correct missing data
• Consistency: Establishing and maintaining a central data repository and setting data format standards for consistency across systems
• Timeliness: Using IoT to capture data in real-time and monitoring and optimizing data latency to reduce delays.

Interoperability standards

Systems need to communicate and share data seamlessly. The best way to achieve this is by adopting industry-recognized interoperability standards. Collaboration is also fundamental to patient care. 

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Cloud infrastructure can be transformative here, acting as a bridge between two parties and empowering providers to exchange critical data on a flexible and scalable platform. 

How RecordPoint can help support health data management?

The best way to improve patient outcomes and drive growth for your business is by managing data across its lifecycle. You need to know where it is and who has access to it. And ensure it’s available exactly when the people who can change lives need it the most. 

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RecordPoint has built a cloud data service from the ground up for exactly this purpose. Our healthcare solution can automatically analyze and classify data and store it in a perpetual inventory for deeper insights and more relevant decisions. 

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When you have control, you can start implementing additional security measures to secure your data sources and use advanced reporting and analytics functions to support healthcare choices. 

Every organization has to manage data, but not all systems are equal. RecordPoint can transform your data processing capabilities and power a future of fast, secure, and smart decision-making. Contact us today to schedule a demo and start your data transformation journey.