Abstract
Developments in medical data analysis bring important social benefits but also challenge privacy and other ethical values. At the same time, excessive data protection restrictions can be a serious threat to observational studies. The debate on whether data privacy or data correlation should be the core value of research policies has remained unresolved. Today, in the health care sector, which is developing rapidly, it is highly important to maintain integrity, privacy, and access to data (1). Data governance plays an important role in navigating this path, ensuring compliance with regulations, and paving the way for improved patient outcomes. Data governance in the field of health care is an organized and standardized method for managing, analyzing, and sharing medical data in an ethical and transparent manner, all of which align with legal and ethical standards (1, 2).
Data Governance in Healthcare
It covers the entire life cycle of patient information, from initial admission to long after discharge. In essence, healthcare data is a valuable asset. Effective data governance is very useful in making informed decisions and providing high-level patient care. A concrete example can be given from an article entitled “Challenges Caused by the COVID-19 Pandemic: The Long-Standing Need for Strong Data Governance”, which revealed addressing aspects such as intellectual property rights, interoperability, data storage, sharing, and ensuring equitable access to healthcare resources (1). According to case reports, there are also obstacles and challenges for the management of healthcare and health data, the most important of which are mentioned below:
· Privacy and data security
· Compliance with regulations
· Management of diverse data sources
· Ensuring interoperability in different systems and data formats (2).
Addressing these barriers and challenges with a comprehensive data management strategy can simplify processes and create data standards, in addition to improving data quality and access to qualitative data and strengthening collaboration between stakeholders. Therefore, in a wide treatment network with numerous electronic files, better coordination and coherence have been created, and while speeding up treatment processes, conditions will be provided for an advanced and more effective health control operation.
Based on Figure 1, effective data governance in health care will have several advantages. In fact, data governance is based on a specific pattern of input and output. Data input includes data and metadata management, especially data definitions and standards, quantitative classification and data quality control. If these important factors and indicators of data and information input are done accurately and without mistakes, the result of the work, i.e., the output of data governance, will lead to the adoption of appropriate strategies to meet the demand of the industry, more effective clinical care and reliable operations in the health system (2, 4).
Effective Data Governance in Healthcare Reveals Several Advantages
1- Improving patient care through interoperability: By setting standards and promoting data integration, data governance strengthens interoperability and ultimately leads to improved care coordination and comprehensive patient care.
2- Optimized services and resource efficiency: Simplifying data processes and eliminating redundant items can lead to providing optimal services and improving patient experiences.
3- Fostering innovation and research: By establishing clear data access and sharing policies, data governance supports innovation and research and enables large-scale data analysis, trend identification, and epidemiological studies.
4- Informed decision-making: Improving the quality and accuracy of data is the basis of informed decision-making and increases patient safety and care results. It also enables data-driven insights into various aspects of healthcare, including resource allocation and clinical outcomes.
5- Compliance with regulations and ethical data management: Ensuring alignment with legal requirements and the best industrial practices reduces risks and sets a standard for ethical data practices (3, 4).
Creating a Comprehensive Data Governance Framework
Creating a data governance framework for health care includes drawing policies, standards, roles, responsibilities, and accountability related to data. The key components of this framework include data standards and definitions, data quality management, metadata management, master data management, and data architecture (2).
Implementing Data Governance: A Step-by-Step Guide
1- Define the scope: Specify the organizational structure, authorities, councils, and roles.
2- Form a governance team: Create a team that includes a chief data officer and a chief medical information officer to drive accountability.
3- Identify key metrics: Focus on data accuracy, process improvement, and efficiency.
4- Understand compliance standards: Familiarize yourself with existing regulations and policies.
5- Secure the system: Implement encryption, masking, and hashing policies for protected health information.
6- Setting user permissions: Create authentication protocols and monitor data access.
7- Educate your team: Educate your teams on adopting and following a data governance plan(2, 5).
Evaluation of Data Governance Solutions
When evaluating solutions, prioritize capabilities such as data classification and tagging, data search and discovery, metadata management, data provenance and traceability, data security and privacy, compliance and reporting, and integration and interoperability (4, 5).
In summary, data governance is of practical importance at all levels of healthcare and health and ensures that doctors, therapists, and health care providers have access to accurate, safe, and private data of patients. This requires an effort at all organizational levels (Ministry of Health), including interdisciplinary teams of experts. This commentary provides a foundation for understanding the importance, benefits, framework components, and steps for implementing data governance in healthcare. As the healthcare landscape continues to evolve, data governance initiatives must also evolve to improve the efficiency of health system management.