The Power of Digitized
Health Data: Time for
Healthcare to Evolve

Introduction

In an era where a single click can reveal a world of information, the healthcare industry stands at the cusp of a revolutionary transformation through digitizing health data. With the average hospital producing roughly 50 petabytes of medical data per year or 137 terabytes per day, it is unsurprising that data has been at the forefront of conversations in the healthcare industry over the last decade ⁽¹⁾.

Day-to-day challenges like fragmented patient records, time-consuming manual processes, and a high rate of medical errors continue to haunt healthcare practices and practitioners alike. Digitization emerges as a beacon of hope, ready to reshape and elevate healthcare systems. Imagine a patient’s entire medical history, from allergy details to the minutest lab result, accessible in real-time, paving the way for precise and swift decisions.

Upon first glance, it might appear this journey is merely about finding a place to store the mountain of information, but that is just a welcomed perk. At depth, it’s about unlocking the full potential of this data through analytics, leading to predictive healthcare models and personalized medicine. The conversion of medical health data isn’t just a technological upgrade; it’s a paradigm shift offering benefits in patient outcomes, efficiency, cost reduction, and the overall advancement of healthcare. This is not a distant dream but a rapidly unfolding reality.

Adopting digital health records as early as the 1960s laid the foundation for building a sustainable and forward-thinking healthcare system. Today, digitization paves the way for healthcare systems to be more responsive, patient-centric, and resilient to future challenges. By harnessing data’s power, healthcare providers can achieve a more holistic view of patient health, enhancing diagnostic accuracy and treatment efficacy.

In this article, we will review the current state of healthcare data, summarize the promising benefits of digitization, and highlight the challenges we must overcome to fully realize its potential. We will look at real-world examples, expert insights, and the latest research to understand how this evolution is reshaping the healthcare landscape for practitioners, patients, and the entire medical community.

Current Data Conditions in Healthcare

The Health Information Technology for Economic and Clinical Health (HITECH) Act and the 21st Century Cures Act formed the legislative framework that shaped healthcare data evolution.

The effectiveness of the Health Information Technology for Economic and Clinical Health (HITECH) Act and the 21st Century Cures Act can be assessed through their impacts on healthcare systems, particularly regarding electronic health records (EHRs) and health information technology (HIT).

The HITECH Act:

• Adoption of EHR Systems: A significant increase in the adoption of EHR systems was observed, which may have been influenced by the meaningful use (MU) program under the HITECH Act. The MU program aimed at improving healthcare outcomes by incentivizing the adoption of certified EHRs ⁽²⁾.
• Reduction in Medication Errors: Implementation of computerized provider order entry (CPOE), supported by the HITECH Act, was associated with a significant decline in preventable adverse drug events and medication errors in hospital settings ⁽³⁾.
• Challenges and Limitations: Despite the progress, there were challenges such as the poor usability of EHRs, limited ability to support multi-disciplinary care, and difficulties with health information exchange ⁽⁴⁾.

The 21st Century Cures Act:

• Interoperability and Data Exchange: The Act emphasized the importance of interoperability and secure electronic health information exchange. However, issues like information blocking and needing more robust data portability mechanisms were identified ⁽⁵⁾.
• Facilitation of Data Sharing: The Act supported the creation of an “Information Commons” to facilitate open and responsible sharing of genomic and other data for research and clinical purposes ⁽⁶⁾.

Although the digitization of medical health data began well before these two acts, they have been, along with the COVID-19 pandemic, the catalyst for an exponential increase in the movement toward a digital data-driven future. However, challenges such as EHR usability, information exchange, and implementation complexities remain areas for ongoing improvement.

Let’s continue our exploration by looking at how this movement plans to address some of the gaps left by existing systems.

Data Security in Digital Healthcare

Security Challenges

In 2023, data security and patient privacy continued to be issues in the digital healthcare landscape. Healthcare security systems are increasingly vulnerable to cyberattacks posing significant risks to patient privacy. Smart Internet of Medical Things (IoMT), such as wearables and health apps, transform patient care but also pose serious security and privacy concerns, highlighting the need for a secure digital chain of custody ⁽⁷⁾.

The rapid expansion of patient digital data and the integration of AI in healthcare necessitate precise control and safety measures to maintain patient trust and privacy. 

A study by Reddy, Elsayed, Elsayed, and Ozer highlights the escalating concern of data breaches in healthcare, emphasizing the urgent need for robust security solutions ⁽⁸⁾. This study purports that many of the vulnerabilities and challenges for hospitals arise from implementing the bare minimum of security measures to comply with standards like HIPAA and HITECH. While this is a good start, it is proving to not be substantial enough to thwart everything that medical entities handle constantly. The following is a list of the most prevalent types of cyber security breaches according to their study ⁽⁸⁾:

Highest Risks in Healthcare Organizations

Malicious Network Traffic (72%)
Description: This involves unauthorized or harmful data transmissions that compromise the security of the network.
Type of Attack: Network-based attacks aiming to disrupt, intercept, or steal data.

Phishing (56%)
Description: Fraudulent attempts, usually through email, to obtain sensitive information by masquerading as a trustworthy entity.
Type of Attack: Deception-based, exploiting human error to gain unauthorized access.

Vulnerable OS (High Risk) (48%)
Description: Operating systems with significant security weaknesses that are highly susceptible to attacks.
Type of Attack: Exploitation of software vulnerabilities.

Man-in-the-Middle Attack (16%)
Description: Unauthorized interception of communication between two parties to eavesdrop or alter the data.
Type of Attack: Infiltration of communication channels to capture or modify information.

Malware (8%)
Description: Malicious software designed to damage, disrupt, or gain unauthorized access to computer systems.
Type of Attack: Software-based threats including viruses, worms, and trojan horses.

Medium Risks in Healthcare Organizations

Configuration Vulnerabilities (60%)
Description: Weaknesses arising from improper system or network configurations.
Type of Attack: Exploiting misconfigurations to gain unauthorized access or cause disruption.

Risky Hot Spots (56%)
Description: Network areas particularly susceptible to security breaches due to high traffic or sensitive data.
Type of Attack: Targeting vulnerable points in a network.

Vulnerable OS (All) (56%)
Description: General operating system vulnerabilities that pose a security risk.
Type of Attack: Exploiting weaknesses in operating systems.

Sideloaded Applications (24%)
Description: Apps installed from sources outside the official app store, which may not have undergone thorough security checks.
Type of Attack: Introduction of potentially harmful software through unofficial channels.

Unwanted or Vulnerable Application (24%)
Description: Applications that are either not required by the user or have inherent vulnerabilities.
Type of Attack: Exploitation of software vulnerabilities.

Cryptojacking (16%)
Description: Unauthorized use of someone else’s computing resources to mine cryptocurrency.
Type of Attack: Hijacking system resources for cryptocurrency mining.

Third-party App Stores Installed (16%)
Description: The use of alternative app stores that might host less secure applications.
Type of Attack: Risk exposure through less-regulated software distribution channels.

Solutions for Security

As a solution to these security concerns, the group behind the study proposes preparing hospital staff with how to deal with cyber attacks as they happen.

While this is a necessary addition to a medical professional’s wealth of knowledge, it does not account for real world application and how thinly medical professionals are already spread. An alternative they offer is leaning into technology by utilizing AI and autonomic computing.

This is defined with the following parameters ⁽⁸⁾:

• Self-Awareness:
  Description: This attribute enables the computing system to monitor its current state and behavior.
• Self-Configuration:
  Description: The system possesses the capability to autonomously configure and reconfigure itself without external intervention.

• Self-Optimization:
  Description: This involves the continuous improvement of the system’s operational efficiency.

• Self-Healing:
  Description: In the event of system failures, this feature allows the computing system to automatically detect and rectify these issues.

• Self-Protection:
  Description: The system is equipped to proactively identify and defend against cyber threats.

• Context Awareness:
  Description: This ensures that the system remains aware of its operating environment and adjusts its actions accordingly.

• Adaptability to Open Environments:
  Description: This characteristic allows the system to operate effectively in its native environment and adapt to environmental changes.

• Estimated Resource Allocation:
  Description: The system is designed to assess and optimize the resources it requires for efficient operation.

Utilizing a system like this would allow for medical professionals to focus on their priority, saving lives.

These attacks are not new and they are not lessening in frequency, but the methods that sit on the horizon as a fix to them are new and the ability to effectuate a system like this begins with digitizing healthcare data. 

Let’s continue with a look at just exactly what technology healthcare professionals are seeing more frequently these days.

References

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3. Nuckols, T., Smith-Spangler, C., Morton, S., Asch, S., Patel, V., Anderson, L., Deichsel, E., & Shekelle, P. (2014). The effectiveness of computerized order entry at reducing preventable adverse drug events and medication errors in hospital settings: a systematic review and meta-analysis. Systematic Reviews, 3, 56 – 56.

4. Sheikh, A., Sood, H., & Bates, D. (2015). Leveraging health information technology to achieve the “triple aim” of healthcare reform. Journal of the American Medical Informatics Association : JAMIA, 22 4, 849-56

5. Black, J., Hulkower, R., & Ramanathan, T. (2018). Health Information Blocking: Responses Under the 21st Century Cures Act. Public Health Reports, 133, 610 – 613.

6. Majumder, M., Guerrini, C., Bollinger, J., Cook-Deegan, R., & McGuire, A. (2017). Sharing data under the 21st Century Cures Act. Genetics in Medicine, 19, 1289-1294.

7. Donga, L., Raj, R., & Mishra, S. (2022). Internet of Healthcare Things (IoHT): Towards a Digital Chain of Custody. 2022 IEEE 10th International Conference on Healthcare Informatics (ICHI), 524-526.

8. Reddy, J., Elsayed, N., Elsayed, Z., & Ozer, M. (2023). A Review on Data Breaches in Healthcare Security Systems. International Journal of Computer Applications.

9. Ahmed, S. F., Alam, M. S. B., Afrin, S., Rafa, S. J., Rafa, N., & Gandomi, A. H. (2024). Insights into Internet of Medical Things (IoMT): Data fusion, security issues and potential solutions. Information Fusion, 102, 102060. doi:10.1016/j.inffus.2023.102060

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