The healthcare sector in the United States is changing quickly because of cloud computing and telehealth services. These new technologies help more people get medical care, make healthcare work faster, and improve patient results. But with these benefits come serious cybersecurity problems that affect medical administrators, healthcare owners, and IT managers. Keeping patient data safe and private is very important.
This article talks about the main security risks of cloud computing and telehealth systems in healthcare. It also shares ways to protect data like using encryption and multi-factor authentication (MFA). It highlights why checking vendors for risks is important and looks at how AI and automation help healthcare while keeping data secure.
Cloud computing lets healthcare groups store and manage data on remote servers instead of local computers. Telehealth uses the internet and cloud technology to offer remote doctor visits, diagnosis, and monitoring. These tools have helped people, especially those in rural parts of the U.S., get better access to care. They also helped keep care going during emergencies like the COVID-19 pandemic.
Even with these benefits, cloud computing and telehealth bring cybersecurity problems. Patient health information (PHI) and personal information (PII) stored or sent through these platforms can be hacked or seen by people who should not have access. Also, many network-connected medical devices linked to these systems increase the chance of cyber attacks.
Healthcare organizations face these main challenges with cloud and telehealth:
The Health Industry Cybersecurity Practices (HICP) Technical Volume 2 lists these risks and stresses that healthcare groups must follow solid cybersecurity steps to lower risks and protect patient data.
Encryption means changing data into a secret code so only allowed people can read it. This is a key security step in healthcare cloud and telehealth systems. It protects data when it moves and when it is stored, so even if someone gets the data, they cannot understand it.
Data in Transit Encryption: Telehealth appointments and data sent between devices and cloud servers should use safe protocols like Transport Layer Security (TLS). End-to-end encryption keeps communication between a patient’s device and the healthcare system private during the session.
Data at Rest Encryption: Patient data stored on cloud servers or local devices should be encrypted with strong methods like AES-256. This protects stored data if someone gets unauthorized access or steals hardware.
Using encryption also follows Health Insurance Portability and Accountability Act (HIPAA) rules. It helps lower the risk of expensive data breaches, which could hurt a healthcare organization’s reputation and cause fines.
Access controls decide who can see or change sensitive health data and systems. Multi-factor authentication (MFA) adds extra security by making users prove who they are in more than one way.
Common MFA methods include:
MFA lowers the chance that stolen or guessed passwords alone let attackers in. The HICP guidelines suggest MFA as a good way to protect telehealth portals and cloud services from unauthorized access.
Healthcare providers often hire outside vendors for cloud hosting, telehealth software help, and device management. These partnerships can help with costs and special skills but create risks if vendors don’t protect data well.
Checking vendors for risks is important to find security weaknesses and see how well they protect data. A full vendor risk check includes:
The 405(d) Program, a federal effort to improve healthcare cybersecurity, highlights vendor risk management as a key way to lower cyber problems in healthcare.
Healthcare organizations must constantly watch cloud and telehealth systems to find suspicious actions and stop threats quickly. This means tracking network data, access records, and system behavior to spot unusual or unauthorized activity.
Incident response plans should be ready and tested often so that breaches can be controlled, investigated, and fixed fast. Working closely with vendors and internal teams makes response faster and better.
Artificial intelligence (AI) and automation are used more and more in healthcare to improve admin tasks, clinical work, and patient communication. Some companies, like Simbo AI, offer AI phone systems that help healthcare offices handle patient calls without extra work.
AI tools in healthcare provide both benefits and risks for data security:
Healthcare groups should have AI rules made by legal, clinical, IT, and vendor teams. These rules ensure AI is used ethically and approved properly. Also, AI systems and data should be watched regularly to prevent unauthorized use and keep up with new cyber threats.
The U.S. Department of Health and Human Services (HHS) 405(d) Program gives tools to help healthcare groups improve cybersecurity. This program works with healthcare teams and government agencies. It shares good practices like the Health Industry Cybersecurity Practices (HICP). These guides help medical administrators and IT managers create consistent security steps for cloud and telehealth systems.
Donna Grindle, leader of the 405(d) Task Group, says it is important to teach healthcare staff about AI risks and benefits and to keep a strong security culture. She suggests regular reviews of legal risks, updating contracts for AI data use, and having teams from different fields watch over AI and automated systems.
Healthcare facilities in the U.S. using cloud, telehealth, and AI should consider these actions:
By following these steps, healthcare groups can better protect patient data, meet rules, and keep patient trust while using cloud computing, telehealth, and AI technologies.
AI enhances healthcare by enabling personalized treatments, predictive analytics, and improving operational efficiency. It streamlines clinical workflows and business operations, leading to better patient care and resource management.
Many AI applications in healthcare lack thorough security evaluations, creating risks of unauthorized access to PHI, potential ethical issues, and legal consequences. Protecting data is essential to maintain patient trust and safety while complying with evolving regulations.
Organizations must educate teams on AI types, risks, and advantages. Training should cover staff and vendors about identifying AI usage, policy adherence, potential pitfalls, and document such training to ensure awareness and compliance.
They should assess legal and regulatory risks, update Business Associate Agreements for AI data handling, implement AI-specific data privacy protocols, conduct custom security risk analyses, ensure ongoing compliance, and document all assessments and reviews rigorously.
Organizations should continuously monitor new and existing AI tool usage, detect unauthorized implementations, review AI features and compliance regularly, adapt protocols based on evolving threats, and document all findings for accountability.
AI governance should comprise multi-disciplinary input (legal, clinical, IT, HR, etc.), transparency of AI decisions, clear AI usage definitions, approval processes for AI deployment, boundaries for generative AI use, and thorough documentation of decisions.
Risks include patient harm from device manipulation, breaches exposing PHI/PII, and exploitation of devices as network entry points facilitating wider cyberattacks.
Key protections include detailed device inventory, baseline behavior monitoring, network segmentation, routine software updates, strong access control (e.g., MFA), encryption of data, incident response plans, and collaboration with manufacturers for security updates.
These technologies increase risks like data breaches due to centralized storage, unsecured communication channels in telehealth, device vulnerabilities, and threats to data integrity and privacy requiring specialized cybersecurity controls.
Best practices include enforcing MFA and RBAC, encrypting data at rest and in transit, using secure telehealth platforms with end-to-end encryption, educating users on secure device/network use, performing security audits, continuous monitoring, and strict vendor management with security assessments.