Driving Digital Transformation: Technology Design, Delivery, and Development in Healthcare

Cloud enablement, compliance, and artificial intelligence (AI) are at the forefront of healthcare transformation. As innovators address the intricacies of designing, delivering, and developing technology solutions tailored specifically to the healthcare sector, modernizing healthcare infrastructure while still maintaining compliance with regulatory authority guidelines becomes paramount. 

Trends and technologies

Many trends and technologies are driving digital transformation in the healthcare industry. For example, the adoption of cloud technologies for the secure storage and management of vast amounts of patient data is on the rise. In fact, one report determined the global healthcare data storage market is growing from $4.88 billion in 2022 to $5.7 billion in 2023 at a compound annual growth rate of 16.7 percent. 

Cloud solutions offer several benefits, such as providing scalable storage, which allows healthcare organizations to handle growing data volumes efficiently. Cloud platforms also enable cost reduction, provide flexibility in scaling solutions, and make remote access and telehealth possible. Another 2023 report elaborates on the advantages of cloud-based services, noting how cloud platforms can help healthcare organizations realize cost savings, expedite critical drug development, enhance data security and compliance, and improve collaboration. This report also pointed out how cloud computing “allows healthcare organizations to access powerful analytical tools and artificial intelligence (AI) algorithms, providing insights into patient care, population health management, and research, ultimately leading to improved outcomes and cost savings.” 

AI plays a significant role in the design, delivery, and development of technology in healthcare. For instance, scientists recently used AI to discover a new class of antibiotics that can treat Methicillin-resistant Staphylococcus aureus (MRSA) bacteria. AI-powered chatbots and virtual assistants also have tremendous potential to provide around-the-clock support, enhancing the user experience and content generation with new large language models (LLM). Healthcare companies also use data analytics to measure and improve the trials run by contract organizations and other vendors. Real-time monitoring of key performance indicators helps identify improvement areas and ensure compliance with regulatory standards. Technologies such as deep learning have the potential to transform the expensive drug development process into a relatively cheaper and quicker process and help get novel drugs into patients’ hands quickly.

Factors to consider

There are a number of factors that healthcare organizations need to consider when modernizing their healthcare infrastructure and technology. For example, it is essential for healthcare entities to maintain compliance with stringent regulations to protect patient data, ensure data privacy, and account for rigorous migration of critical regulatory content. This process requires addressing several aspects, such as data security, privacy regulations, consent management, computer software assurance, risk assessment, and incident response. 

With regard to data security, it is vital for healthcare organizations to uphold patient safety and security by implementing robust cybersecurity measures, safeguarding against data breaches, and maintaining the integrity of medical devices and systems. Some steps to prevent data breaches include establishing clear policies and procedures and ensuring secure hiring and termination procedures. In addition, the Federal Trade Commission provides a guide to data breach response, noting that “the only thing worse than a data breach is multiple data breaches.” 

Several measures are known to reduce the risk of data breaches for computer systems, such as ensuring that all computer systems and databases using sensitive data reside under a VPN with strict access controls in place, for instance. It’s also advisable to guide access to information and computer systems through organization-managed two-factor authentication, as well as utilizing “passwordless” systems such as TruU authentication for access. Ensuring all entries and exits from computer systems are audited and that audit logging of all sensitive actions in applications are key preventative steps as well.

Improving communication and outcomes

AI’s utility throughout healthcare continues to grow. One 2023 survey of healthcare executives found respondents will prioritize the use of AI in several areas in the near future, including clinical decision support tools (30 percent), predictive analytics and risk stratification (25 percent), and clinical workflow optimization and automation (23 percent). 

Healthcare providers can leverage AI to improve operations, care, and patient experience. LLMs are new and relatively untested, but for content generation, they have the potential to use existing datasets with healthcare companies to quickly generate content to be submitted to health authorities. LLMs are deep learning algorithms that can be trained to process and generate text. In a 2023 study, a team, including researchers from Dresden University of Technology in Dresden, Germany, explained how LLMs’ capabilities can improve patient care. “Throughout medical disciplines, human communication is an integral part of patient care. Accurate interpretation of spoken language is one of the most critical factors that influence the success of communication,” the authors wrote, noting that accurate interpretation is “vital for the patient-caregiver rapport, patient satisfaction and to enable optimal clinical outcomes.”

The researchers also mentioned that medical professionals frequently use written text to communicate regarding patients, such as reports on diagnostic and therapeutic procedures, results, and related implications. The link between unclear patient reports and diminished quality of care is natural, and ineffective communication between providers results in “a substantial economic burden for clinical institutions and healthcare systems,” the researchers wrote. They described three examples of how LLMs can be used to improve patient care: conveying medical knowledge, assisting communication with patients through translations and summaries, and simplifying documentation tasks by converting between unstructured and structured information.

Testing computer systems is another significant component of any software implementation in health technology. AI can automate this testing and make releases faster. 

Laws governing computer system software assurance are issued by health authorities such as the Food and Drug Administration (FDA), the Medicines and Healthcare products Regulatory Agency (MHRA) and others. These laws encompass a range of regulations, including data protection acts, cybersecurity standards, and industry-specific compliance requirements like the Health Insurance Portability and Accountability Act (HIPAA). They ensure the security, privacy, and reliability of software systems, imposing legal obligations on organizations to meet certain standards.

AI can expedite the software assurance process by automating tasks such as code analysis, vulnerability scanning, and compliance checks. Machine learning algorithms can identify potential issues faster than manual methods, helping organizations detect and rectify vulnerabilities promptly. AI-driven tools can also streamline documentation and reporting, enhancing compliance efforts while saving valuable time and resources. This accelerates the development and deployment of secure and compliant software systems.

Ultimately, there is a delicate balance between innovation and compliance in healthcare technology. With strict regulatory standards governing patient data and care delivery, it is essential to know how technology design can navigate these complexities, ensuring that solutions meet and exceed compliance requirements.