Improving Healthcare Data Governance for Better Patient Care

What good is healthcare data – for patients and providers – if it cannot be trusted or accessed when needed?

This is where healthcare data governance comes into play. By establishing policies, processes, and controls, healthcare data governance ensures the proper handling of healthcare data throughout its lifecycle.

At its core, data governance aims to balance data accessibility for clinical and operational purposes while maintaining strict privacy and security standards. But achieving this balance is no easy feat.

It involves defining data ownership, establishing data stewardship roles, and implementing data quality controls and access protocols. This collaborative effort requires buy-in and participation from organizational stakeholders, including clinicians, IT professionals, and executives.

Without this collective commitment, even the most well-intentioned data governance initiatives will falter.

The Challenges of Data Governance in Healthcare

Consider a world-class hospital with incomplete patient records, scattered across disparate systems, and riddled with inaccuracies. The consequences could be dire, from misdiagnoses to life-threatening errors.

Effective healthcare data governance is essential for enabling data-driven decision-making, improving patient care, and fostering innovation in the industry. It addresses the unique challenges posed by the vast amounts of sensitive and diverse healthcare data generated from various sources, such as electronic health records (EHRs), medical imaging, genomic data, and patient-generated data from wearables.

Adding to this complexity is the intricate network of healthcare partners involved in providing care, including hospitals, primary care physicians, specialists, pharmacies, clinical laboratories, government agencies, insurance providers, and employers, each generating and requiring access to different aspects of patient data.

The core data governance challenges for healthcare providers include:

• Poor data quality leading to clinical errors: Ensuring data quality and consistency across multiple systems and data sources is a significant hurdle, as data may be incomplete, inaccurate, or inconsistently formatted, leading to potential errors in clinical decision-making and patient care.

• Patient data security breaches: Maintaining data integrity and preventing data breaches is paramount, as healthcare data often contains sensitive personal and medical information protected by regulations such as HIPAA. A real-life example is the $4.8 million fine and reputational damage faced by New York-Presbyterian Hospital in 2014 due to a failure to secure patient data properly. Can your organization afford such a costly misstep?

• Healthcare data integration: Integrating data from various healthcare providers, such as hospitals, clinics, and laboratories, can be challenging due to different data formats and standards, hindering seamless care coordination and comprehensive patient care.

• Data accessibility – for the right people: Balancing data accessibility for authorized personnel while maintaining strict access controls and data privacy is a delicate act, as demonstrated by the $875,000 fine imposed on Oklahoma State University in 2017 for delays in reporting a data breach and notifying affected individuals.

• Regulatory pressure: Keeping up with evolving regulations and industry standards, such as those related to data privacy and interoperability, requires continuous effort and adaptation to avoid costly compliance violations.

• Data literacy: Fostering a data-driven and data-literate culture while securing buy-in from stakeholders across the organization, including clinicians, IT professionals, and executives, can be challenging. This could potentially hinder the implementation of data governance. After all, culture change is often the most formidable obstacle to overcome.
  

8 Essential Practices for Data Governance in Healthcare

The challenges of healthcare data governance are significant. They can be overcome, but where do you begin?

Here are some key strategies to consider:

1. Establish a clear healthcare data governance framework with defined roles, responsibilities, and decision-making processes. This framework should align with the organization’s overall data management strategy and involve stakeholders from various departments, including clinical, IT, and executive leadership. Without a solid foundation, any data governance initiative is built on shifting sands.

2. Implement robust data quality management processes, including data validation, cleansing, and standardization, to ensure data accuracy and consistency across systems. This may involve leveraging data quality tools and establishing data quality metrics and monitoring mechanisms.

3. Prioritize data security and privacy by implementing strict access controls, encryption protocols, and regular security audits, in compliance with regulations like HIPAA. Conduct regular risk assessments and develop incident response plans to mitigate potential data breaches and protect sensitive patient information.

4. Leverage data cataloging and metadata management tools to enhance data discovery, understanding, and collaboration among healthcare professionals and data stewards. These tools can provide a centralized repository for data assets and metadata, facilitating data transparency and enabling more effective data utilization.

5. Adopt industry-specific data standards and terminologies like HL7 and LOINC to facilitate data interoperability and exchange across healthcare providers. This can improve care coordination, reduce redundancies, and enable a more comprehensive view of patient data. Imagine the power of seamless data sharing, where a patient’s complete medical history is available at the click of a button, regardless of where they seek care.

6. Foster a data-driven culture by providing training and resources to healthcare professionals, emphasizing the importance of data governance and quality. Encourage open communication and stakeholder collaboration to promote data-driven decision-making and continuous improvement. Culture change starts with education and empowerment.

7. Implement data lineage and traceability mechanisms to track healthcare data’s origin, transformation, and usage throughout its lifecycle. This can enable auditing, compliance monitoring, and the identification of potential data quality issues or risks. In a world where data is constantly in motion, these mechanisms act as breadcrumbs, allowing you to retrace your steps and ensure integrity.

8. Establish data governance committees or councils with cross-functional representation. These committees can oversee the implementation and ongoing maintenance of data governance practices, address emerging challenges, and drive continuous improvement. Collaboration is key, as data governance is a journey, not a destination.
   

Using Tools and Technologies for Healthcare Data Governance

To support effective healthcare data governance, organizations can leverage a range of tools and technologies:

• Master Data Management (MDM) tools help maintain a single, consistent view of critical data entities, such as patients, providers, and locations.

• Data quality management tools enable data profiling, cleansing, and standardization, ensuring data accuracy and consistency across systems.

• Data catalog and metadata management platforms facilitate data discovery, understanding, and collaboration by providing a centralized repository for data assets and metadata.

• Data access and security solutions, such as identity and access management (IAM) and data masking, ensure proper access controls and protection.

• Data governance platforms offer comprehensive capabilities for managing data governance policies, workflows, and collaboration across the organization.

• Cloud-based analytics solutions provide scalable and flexible data management and analysis capabilities, enabling data-driven decision-making.

• Interoperability solutions, such as HL7 FHIR-based platforms, facilitate data exchange and integration across healthcare systems and providers.

• Artificial Intelligence (AI) and Machine Learning (ML) technologies can help automate data quality management, pattern recognition, and predictive analytics for healthcare data.

• Data visualization and reporting tools enable the effective communication and presentation of data-driven insights to organizational stakeholders.
  

Boost Clinical Efficiency 

Effective healthcare data governance is essential for ensuring data quality, integrity, and compliance, enabling data-driven decision-making, and improving patient care.

While healthcare organizations face unique challenges due to the complexity, sensitivity, and diversity of data, implementing a comprehensive healthcare data governance framework, fostering a data-driven culture, and leveraging modern tools and technologies can help overcome these.

Need to drive improved data governance in your healthcare organization? Book a demo of the Semarchy Data Platform today to explore our exciting capabilities.