The healthcare industry generates massive amounts of sensitive data daily, including EHRs, genomic sequences, and wearable device metrics. This data could transform patient care and medical research, but it also poses unprecedented risks. A strong framework to govern, protect, and ethically manage healthcare data is needed after high-profile ransomware attacks, accidental data leaks, and patient consent issues. Governance, privacy, and security—three pillars essential to modern healthcare system trust and innovation—are integrated in this conceptual model.
Healthcare is a promising field for data analytics in an era when data drives industry decision-making. Data is increasingly used to improve patient outcomes, operational efficiency, and healthcare delivery as healthcare systems evolve worldwide. Data analytics can revolutionise patient care by changing how healthcare professionals gather, interpret, and apply data. This blog will discuss data analytics in healthcare, its types, benefits, and future impact on patient care.
The Importance of Data Governance in Healthcare
Each year shows that data is central to most businesses, making high-quality standards essential. This is crucial for industries like healthcare that need accurate data to keep people safe.
In this Jama Network survey, 1 in 5 patients found a mistake on their medical notes, and 40% considered it serious. These errors included diagnosis, medical history, medications, physical exam, test results, and wrong patient notes. A recent John Hopkins University study found that medical errors kill 250,000 Americans annually.
Even though Statista's 2022 survey found that 73% of patients are willing to share their data for medical research, nearly 62% are concerned about its safety. Due to this willingness to share data and valid concerns, healthcare businesses must take data governance seriously more than ever.
What is Data Governance in Healthcare?
Healthcare produces massive amounts of data daily, including patient records, medical research, and financial data. Patient care and cost management depend on this data. The growing amount of data makes it difficult to manage and ensure its accuracy, completeness, and security.
A data governance framework addresses these challenges by managing data throughout its lifecycle. This includes data collection, storage, processing, analysis, and sharing. Organisations can comply with regulations and protect their data by setting data management standards and policies.
A well-designed data governance framework includes:
- Policies and Standards: Aligning with regulations such as HIPAA, GDPR, and FIPPs (Fair Information Practice Principles) to ensure ethical data handling.
- Roles and Responsibilities: Defining the duties of data stewards, custodians, and users. For example, modern mental health practice management software must have strict data governance to protect sensitive records.
- Quality Assurance: Regular audits and validation processes to maintain data accuracy, completeness, and consistency.
Types of Information Present in Electronic Health Records and Their Sensitivity Level
Common Features
Description
Sensitivity Level
Entity Identifiers
Name, address, email, phone number, etc.
Identifiable
Demographic Information
Person classification by age, education, gender, location, etc.
Quasi-Identifiable
Clinical Records
Patient medical history includes diagnoses, treatments, and medications.
Quasi-Identifiable and sensitive
Medical Biometrics
Patient health data like blood pressure, heart rate, X-ray, test results, etc.
sensitive
Mental Health Information
Sleep, diet, psychosocial, and other psychological information about the patient.
sensitive
Activities and lifestyle
Lifestyle information like diet, exercise, and physical activity.
sensitive
Financial Information
Financial information like health insurance, medical billing experts, reimbursements, financial class, etc.
Quasi-Identifiable and sensitive
IoT and Wearable Data
Wearable and monitoring data includes healthcare wearables, IoT devices, sensors, etc.
Quasi-Identifiable and sensitive
Insufficient data management may result in legal obligations. To reduce risks, healthcare organisations must protect patient data with strong security and privacy protocols. Electronic health records are sensitive, usable, and accessible, so healthcare institutions should adopt preemptive data governance frameworks. These frameworks ensure operational efficiency without compromising data confidentiality, even from malicious internal users with authorised system access. The Institute of Medicine (IOM) introduced EHR Governance in their 2003 report "Key Capabilities of an Electronic Health Record System". This report stressed the need for a systematic approach to managing EHR systems, including governance structures to oversee development, implementation, and maintenance. The 2008 HHS report "Nationwide Privacy and Security Framework for Electronic Exchange of Individually Identifiable Health Information" was similar.
This report emphasises the need for governance frameworks to secure and efficiently use EHRs. Guidelines and frameworks from the ONC aid EHR governance. These resources address data privacy, security, quality, and interoperability. Organisational data governance includes comprehensive data management. It involves assigning data management roles and responsibilities, establishing regulations and protocols for data accuracy, confidentiality, protection, and compliance, and setting up mechanisms to enforce these regulations. Data governance aims to establish clear guidelines for collecting, storing, accessing, and using data to maximise its usefulness while minimising risks like data breaches and legal violations.
Contributions:
By examining healthcare data governance, we were able to review data privacy and security issues. We categorised data governance activities in section after extensive literature review. Key objectives of this critical evaluation:
- Examine modern healthcare data governance models/systems.
- Classify data governance activities/elements to analyse the healthcare data chain from a privacy and security perspective.
- Provide a conceptual healthcare data governance framework for privacy and security.
Existing Healthcare Data Governance Models
In Helen Nissenbaum's (2010) approach to privacy, privacy is about the appropriate flow of private information within specific social contexts. The study [24] presents a fascinating work for accessing data governance in the context of private health data. This study examines the Royal Free Trust and Alphabet's AI Venture DeepMind Health use case. It illuminates partner disagreements on governance systems, goals, and initiative gains.
Researchers emphasise the complexities of governing PHI data to advance healthcare, protect privacy, and benefit the public. The authors found six interconnected but distinct models for governing Personal Health Information (PHI) by focussing on its value beyond data privacy and security. Data governance for Personal Health Information (PHI) should consider the field of data, those involved, the significance or use of the PHI, the governance objective, and the governance platform.
The authors examined the modern data governance model and focused on data postulates, metadata, accuracy, access management, and information management cycle. In contrast, a composite synthesis of research papers examines 145 academic and practitioner data governance papers from 2001 to 2019. The second proposes pyramidal governance to balance data, realm, and organisational capacity. These mechanisms are shaped by organisational ethical and technological “antecedents” before data ingestion and risk control and performance-driven “consequences” afterward. Data governance activities in academic and practitioner articles are compared.
The analysis examined 120 governance, action, and decision domain information management elements. The authors developed a case study-based organisation governance model with three levels and their relationships. A data governance council approves guidelines, coordinates business and data projects, and evaluates data-related budget requests at the strategic level. Tactical data custodians and stewards also matter. Major data stakeholders from various user groups operate at the lowest level. This model helps understand organisational layer data governance duties but does not set up data governance.
The study also examines Kenyan health professionals regulating entities' data governance conditions, factors, and potential challenges. This paper focuses on building a framework for healthcare governing bodies to develop an official data governance initiative. This work assessed data maintenance quality, customer satisfaction, data security and control, and operational effectiveness as the driving force behind data governance in governing authorities.
Insufficient data governance knowledge, management ownership and backing, and limited funds and resources hinder data administration in these bodies. The scholarly article suggested a comprehensive big data governance layout based on ten Chinese medical information exchange organisation case studies. The framework was reduced to drive, capability, and support domains. It also includes 12 elements, including massive data strategy formation, legal and regulatory aspects, business actions practices, assistance, big medical data maintenance authority, data collection, preservation, process and analysis, usage, resource utilisation, quality management, and domain-specific data protection safeguards.
Healthcare Data Privacy & Security Framework
These studies provide comprehensive data governance frameworks/elements/activities, which we categorised into three pillars: Data Governance Organisation, Data Communication, and Data Privacy and Security by Design (Figure 1). This categorisation highlights healthcare data security and privacy issues from record collection to sharing analysed results. However, modern healthcare data governance frameworks included privacy in the design process rather than as an afterthought.
Data Governance
A successful data governance program requires data governance. It organises data management with policies, rules, regulations, and procedures. The framework defines data owners, stewards, custodians, and users' roles. Employee training and accountability are also important and difficult parts of healthcare data governance organisation.
Policies, Standards, Regulations:
Modern data protection laws are based on Fair Information Practice Principles (FIPPs). The US established these principles in the 1970s, and many countries have adopted them. The FIPPs allow fair and transparent collection, use, and disclosure of personal data. The EU's General Data Protection Regulation (GDPR), the US's Health Insurance Portability and Accountability Act (HIPAA), Canada's PIPEDA, and others reflect FIPPs in healthcare. Healthcare organisations must also establish internal policies and regulations for data privacy, security, consent management, and other compliance issues.
Roles & Responsibilities:
Data governance requires stakeholder collaboration and communication to manage data efficiently and securely. Data governance roles vary by organisation size, structure, and industry. The Data Governance organisation describes typical roles: 1) the composition, charter, and leadership of the Data Governance Committee; 2) other relevant committees' tasks 4) Top-level funding and positions in large organisations with connections. The structure also shows how the Data Governance process supervises data owners, administrators, guardians, IT staff, compliance officers, and data users, who manage data warehouses.
Employees Training:
Healthcare workers lack tech skills and data security knowledge, and healthcare technologies lag behind those of the financial sector. Education and training are needed to understand data protection issues. Staff training handles low-tech data breaches well. Educating end-users about medical provider policies and basic security measures helps protect data. Teaching staff about security risks like tapping email links, pressing computer desk login credentials, and visiting unapproved websites can prevent accidental interference.
Accountability:
Healthcare businesses must implement evaluation and surveillance systems to ensure staff compliance with company policies. They set rules to prevent security breaches and handle infractions. Healthcare data is ideal for identity thieves. Being proactive and following through is crucial. Playing catch-up after a security breach is ineffective.
Data Communication
Organizing data elements with their descriptions and other metadata provides many insights into core data or business concepts and terminologies. Communication is easy because everyone in the data management cycle uses the same terms. Correct communication reduces operational friction and data misuse due to misunderstanding. We divide this pillar into five parts to examine healthcare security and privacy.
Data Warehouse Governance (DWG):
Data Warehouse Governance ensures gain, utility, significance, and risk management. Data warehouse governance should prioritize strategic choice-making and monitoring, with secondary goals of resource distribution, investment value, and risk reduction. Given the sensitive nature of protected health information, DWG may be more concerned with security, confidentiality, compliance, and risk prevention in healthcare settings.
Data Analytic:
Due to algorithm and computing infrastructure failures, interest in artificial intelligence (AI) has fluctuated since the 1950s. Big data, machine learning, deep learning algorithms, and suitable computing infrastructure have revived interest in artificial intelligence (AI) technology and accelerated its adoption across several industries. Modern AI techniques like machine learning have only recently been used in healthcare, but the prospects for better outcomes are promising. Privacy-preserving data analytics involves mining data without revealing personally identifiable or sensitive personal information.
AI and machine learning will keep improving care delivery by analyzing patient data. computer vision development services are changing diagnostic procedures by providing more accurate and timely medical image interpretations.
Data Access:
Data breaches and fraud affect institutions and individuals. Most healthcare organizations have advanced security protocols to protect patient data. Cyber criminals can steal private information through software flaws, phishing, identity theft, and fraudulent attempts, resulting in identity theft, financial loss, anxiety, depression, prejudice, humiliation, assault, and other issues. Insider threats are harder to spot and stop than external ones. Insider threats are attacks by authorized users on an organization's network, apps, or data. Therefore, strong security management structures are needed to prevent outside parties and malicious insiders from accessing healthcare data.
Implementing strict access controls to protect sensitive data from external threats and internal misuse. Telemedicine can convert audio to video tech to share data while keeping patient records private.
Data Quality:
Safe, efficient, and effective patient care, medical research and innovation, healthcare policies, quality improvement initiatives, interoperability, and legal and regulatory compliance depend on healthcare data quality. Because privacy and security compliance require accurate and consistent data, governments should create a data quality management framework with six dimensions: accuracy, completeness, consistency, uniqueness, timelessness, and validity.
Data Privacy & Security by Design
The proposed data governance framework's third pillar is data fortification by design. Data Privacy and Security by Design (PSbD) emphasizes privacy and security in system, product, and service development. Designing, developing, and implementing systems with privacy and security in mind. After reading law articles, the privacy-by-design framework was established. Thus, medical data privacy risks can be reduced and individuals can gain more control over their sensitive data.
Privacy Enhancing Technologies:
PETS are tools, techniques, and systems that protect and enhance digital privacy. They protect sensitive data, limit data collection and sharing, and empower individuals. PETs can help healthcare providers meet their legal and ethical data protection obligations across the data lifecycle.
PETs are vital for limiting data collection, sharing, and protecting sensitive data. These technologies are especially pertinent to new solutions like Murf voice cloning software, which can create realistic voice models but must be carefully controlled to avoid misuse.
Policy-Based Automated Compliance Checking:
ccPETS increase digital privacy with tools, methods, and systems. They empower people, limit data collection and sharing, and protect sensitive data. PETs can help healthcare providers protect data ethically and legally throughout the lifecycle.
Conclusion
Data proliferation has transformed many fields. Cutting-edge Electronic Medical Records technology improves illness treatment and benefits insurance, law enforcement, pharmaceutical, and other product-selling companies. The healthcare industry still faces data protection issues. Healthcare data mishandling can result in significant liability for patients and organizations. Healthcare providers must follow many regulations to protect patient data. After reviewing the literature, we found that cutting-edge healthcare data governance frameworks often treated privacy as a secondary consideration rather than an essential design component. After a thorough review of existing studies, we proposed a conceptual healthcare data governance framework that prioritizes data privacy and security.
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