Data is akin to gold in the 21st century, and a vast amount of it is generated on a daily basis by almost every industry, be it B2B or B2C. The healthcare industry is no exception, contributing a significant portion of the 2.5 quintillion bytes of data generated every day. This comprises data (but not limited to) from electronic health records (EHRs), including appointments, lab test reports, and medical prescriptions, to data generated from medical devices, imaging systems, genetic sequencing, and mobile health applications. Healthcare is one of the most critical and dynamic industries in the world. It constantly evolves owing to emerging treatments, medical devices, and pharmaceuticals. Moreover, advances in information technology (IT) are reshaping how care is delivered in the healthcare industry.
Although the healthcare industry is rapidly evolving, it continues to grapple with significant challenges that primarily stem from the growing volume and complexity of data generated. As the industry becomes more digitized, the need to manage, secure, analyze, and integrate data across systems has become critical. As per reports, data generation is going to increase substantially over the coming years. The trends show that over the last two years alone, 90% of the data in the world was generated. There are certain data-related challenges grappling with the healthcare industry. Some of them are discussed in this article, along with their respective IT data solutions.
Unstructured Data
A significant portion of the healthcare data is unstructured, which makes data analysis and decision-making difficult for the industry's IT departments. Unstructured data refers to non-standardized data from various sources, such as health records, clouds, sensors, and medical devices. They include texts, images, audio, videos, blogs, and websites, among others. Coming in various formats, unstructured data makes it difficult to store, analyze, and retrieve efficiently.
Structured EHR systems enable healthcare providers to digitize patient records, allowing easy access and management of patient data. This helps in enhancing the quality of care by providing comprehensive patient histories to physicians and ensuring that data is up-to-date and accurate. Advanced data analysis using this data set after proper structuring can help pharmaceutical companies streamline the research process and discover new medications more efficiently.
Moving data to cloud-based platforms can reduce the complexity and cost of maintaining on-premises IT infrastructure. Cloud solutions provide scalable storage and advanced security features, ensuring that healthcare data is always available and protected. Cloud providers often provide integrated data analytics tools, which can be used to derive insights from patient data, optimize workflows, and improve patient outcomes.
There are some challenges and ethical considerations, such as data privacy and cybersecurity, associated with cloud-based healthcare solutions. A hybrid approach that can leverage on-premise and cloud-based data solutions can offer the best of both worlds. With this approach, sensitive data can be kept on-premise while leveraging the scalability and advanced analytics features of the cloud for less sensitive tasks. Platforms such as Snowflake, Databricks, and Oracle can be integrated to store and manage large datasets. Business intelligence (BI) tools such as Tableau, Power BI, and QlikView can be used to visualize and analyze healthcare data.
Read more: Data and Analytics in Healthcare: Benefits and its Importance
Data Security
The sensitivity of healthcare data makes it an attractive target for cybercriminals. In 2023, healthcare organizations reported a 239% increase in cyberattacks, and ransomware attacks were on the rise, too, in the same period. According to an annual report by IBM, the global average cost of a data breach in 2024 was found to be $4.88 million, a 10% increase over last year’s data and the highest total ever. Moreover, 40% of data breaches involved data stored across multiple environments. The data that was breached and stored in public clouds incurred the highest average breach cost at $5.17 million. Additionally, the report revealed that 67% of organizations deployed security AI and automation, which was nearly a 10% jump from the previous year, and 20% stated they integrated some form of generative AI security tools. Organizations employing security AI and automation extensively detected and contained data security incidents almost 98 days faster than organizations that did not use these technologies.
Implementing advanced encryption techniques ensures that patient data is secure in storage and during transmission. Encryptions, access controls, and intrusion detection systems can help protect sensitive patient data. Multi-factor authentication (MFA) along with role-based access control (RBAC) can assist in preventing unauthorized access to sensitive information. Blockchain technology can also be integrated to create immutable and tamper-proof health records, reducing the risk of data breaches and improving transparency in patient data handling. Organizations such as IBM and Microsoft offer comprehensive data security solutions.
Regulatory Compliance
Healthcare institutions have to manage vast amounts of sensitive data, making them critical targets for cyberattacks. Protecting patient data while maintaining compliance with strict regulations, including the HIPAA (Health Insurance Portability and Accountability Act) in the U.S., the GDPR (General Data Protection Regulation) in Europe, and the DPDP (Digital Personal Data Protection) Act, 2023 in India presents a major challenge. Healthcare IT systems must comply with these stringent regulations to ensure data protection and patient safety. Meeting these regulatory requirements is often time-consuming and expensive, especially when adopting new technologies.
Data solutions can automate compliance with healthcare regulations by integrating audit trails, automated reporting, and security monitoring. Intelligent robotic process automation (RPA) tools can be used to automate this process. This reduces the burden on healthcare institutions to manually ensure compliance while minimizing the risk of costly fines or penalties. AI-based solutions can continually monitor and report on compliance metrics, ensuring real-time adherence to evolving regulations.
Complex Data Formats and Lack of Interoperability and Data Integration
One of the most significant hurdles in healthcare IT is the lack of interoperability. Healthcare organizations often use various software systems and data formats, making it difficult for systems to exchange information seamlessly. This fragmentation leads to data silos, where patient information remains isolated within one system, causing inefficiencies in care coordination. For instance, a hospital’s EHR system might not communicate effectively with external laboratories or another healthcare provider’s system, leading to delays in patient care.
Master data management (MDM) solutions ensure that patient data from various systems and sources is unified and accessible across the healthcare continuum. Creating a dedicated data master for various entities and establishing stringent referential rules can ensure data consistency and integration.
Application programming interfaces (APIs) can be used to facilitate data exchange among disparate systems, helping to break down data silos as well as enable better communication among different healthcare organizations. FHIR (Fast Healthcare Interoperability Resources) is a data standard that can help different healthcare applications communicate and share data seamlessly, making it easier for healthcare institutions to have access to patient data from different systems. Tools such as Informatica and Talend can be used to integrate data from various sources.
Read more: The Ethics of Healthcare Technology: Balancing Innovation and Patient Privacy
Lack of Data Governance: Inconsistent Reporting, Inefficient Data Management, and Non-standard Process
The healthcare sector lacks uniform standards for capturing and storing data, leading to inconsistencies in how information is documented. The increasing volume and velocity of healthcare data generated from sources such as medical devices, wearables, and genomics presents significant challenges in terms of storage, processing, and analysis.
Data inconsistency in the healthcare industry’s supply chain management (SCM) occurs at various levels. In hospitals, SCM includes (but is not limited to) patient care units, medical supplies, non-medical supplies, hospital storage, inventory management, patients, vendors, equipment manufacturers, and distributors, among others. All these SCM components generate data that can often get out of synchronization and result in a SCM breakdown.
With the increasing digitization of healthcare records, organizations struggle to manage large data sets effectively. Various healthcare providers lack proper data governance frameworks, leading to poor data quality, duplication of records, and inconsistent data storage. This can directly impact patient care and decision-making.
Establishing IT data governance frameworks can help healthcare providers establish clear rules and procedures for data management, ensuring that patient information is accurate, consistent, and reliable. Implementing data validation and deduplication processes ensures that data is of high quality, with a reduction in errors and inefficiencies in care delivery. Examples of data governance tools include Collibra and Informatica.
Read more: Data & AI Solutions to Improve Patient Care and Healthcare Research
Predictive Analytics and AI-Driven Insights
While artificial intelligence (AI) and machine learning (ML) hold great importance in healthcare, their success depends on access to clean, accurate, and well-structured data. Several healthcare organizations face challenges in deploying these technologies because their data is either too fragmented or of poor quality. Data solution tools can be used to standardize the dataset.
Predictive analytics can be integrated to forecast patient outcomes, identify high-risk patients, and optimize treatment plans. AI algorithms analyze large datasets to uncover patterns and trends that may not be visible to healthcare providers. AI-driven solutions assist in medical imaging analysis, enabling faster and more accurate diagnoses. ML models can be used to predict disease outbreaks, monitor population health trends, and enhance public health initiatives. Advanced analytics algorithms and AI can significantly boost the efficiency of the healthcare industry.
Conclusion
The healthcare sector is known to generate vast amounts of data every day and is undergoing a period of rapid change. The use of information technology (IT) is becoming increasingly important in this industry. It has helped enhance the efficiency as well as the effectiveness of healthcare delivery, reduce costs, and improve patient outcomes. The incorporation of IT in the healthcare industry has brought about immense benefits, but it has also introduced new challenges, particularly related to data management, security, and interoperability. As exhibited in this article, data solutions play a crucial role in addressing the IT-related challenges faced by the industry by providing methods to integrate, secure, analyze, and manage data effectively.
By adopting these solutions and following standardized practices, healthcare providers can streamline their workflows, enhance patient care, and ensure the safety and privacy of sensitive patient data. The future of healthcare is undeniably data-driven, and with the right IT infrastructure and data solutions in place, the industry can overcome its current challenges and continue to innovate for better health outcomes.
About SG Analytics
SG Analytics (SGA) is an industry-leading global data solutions firm providing data-centric research and contextual analytics services to its clients, including Fortune 500 companies across BFSI, Technology, Media & Entertainment, and Healthcare sectors. Established in 2007, SG Analytics is a Great Place to Work® (GPTW) certified company with a team of over 1200 employees and a presence across the U.S.A., the UK, Switzerland, Poland, and India.
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