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Challenges and methods for securing Picture Archiving and Communication Systems (PACS)

Challenges and methods for securing Picture Archiving and Communication Systems (PACS) | Healthcare and Technology news | Scoop.it

Medical data is a valuable commodity for identity theft. Despite HIPAA privacy rules being in effect for more than two decades, millions of health records, including images, have been stored on unsecured servers by healthcare provider officers across the United States. 

 

A ProPublica investigation revealed that 187 servers in the U.S. with medical records such as X-rays, MRIs, CT scans, for instance, are findable with a simple online search. One imaging system had open internet access to patients’ echocardiograms, which were minimally secured. 

 

While securing Picture Archiving and Communication Systems (PACS) can be challenging, in part, because of the need for multiple providers to access the same data, the images stored in PACS are Protected Health Information (PHI) and must be kept private in accordance to HIPAA rules. 

 

To address this issue, in September 2019 the National Institute of Standards and Technology (NIST) released new draft guidelines to secure PACS, Special Publication 1800-24C - Securing Picture Archiving and Communication Systems (PACS). 

The Challenges of Securing PACS

Over the past decade, healthcare images have shifted from hard copy to mostly digital. These digital images are easier to share, speeding up the diagnosis time.

 

Of course, the fact that healthcare images can now be uploaded, shared on personal mobile devices, such as smartphones and tablets, and stored digitally, also makes them a target for cybercriminals. 

 

PACS also interact with multiple other systems: electronic health records, regulatory registries hospital information systems, and even government, academic, and commercial archives. This creates plenty of potential security gaps for cybercriminals to lurk and steal this data. 

 

Here are the most common challenges in securing PACS:

  • Monitoring and controlling internal user accounts and identifying outliers in behavior (e.g., large number of downloads in a small period of time)
  • Controlling and monitoring access by external users
  • Enforcing least privilege and separation-of-duties policies for internal and external users
  • Ensuring data integrity of the images
  • Securing and monitoring connections to the system
  • Securing and monitoring connections to and from systems outside of the in-house system
  • Providing security, data protection, and access management without affecting productivity and system performance

 

As you can see, these are common cybersecurity challenges. The draft PACS security guidelines are adapted from the NIST Cybersecurity Framework. While the challenge of securing medical images is real, this is a framework that any HIPAA-covered entity can use to help secure their PACS.

A Security Architecture for PACS

Using commercially available products, NIST created a reference network architecture. It provides an example for healthcare providers to separate their networks into zones to decrease cross-network access and, thus, risk. 

 

The NIST SP 1800-24C guidelines are just that: guidelines. Information technology professionals need to adapt the architecture and framework guidance to their particular organization’s IT stack and security goals. 

 

To mitigate risks, the NIST practice guide’s reference architecture includes technical and process controls to implement. They are:

  • A defense-in-depth solution, including network zoning that allows for more granular control of network traffic flows and limits communications capabilities to the minimum necessary to support business function
  • Access control mechanisms that include multi-factor authentication for care providers, certificate-based authentication for imaging devices and clinical systems, and mechanisms that limit vendor remote support to medical imaging components  
  • A holistic risk management approach that includes medical device asset management, augmenting enterprise security controls and leveraging behavioral analytic tools for near real-time threat and vulnerability management in conjunction with managed security solution providers

 

NIST Cybersecurity Guidance also recommends a thorough cybersecurity risk assessment to identify areas of weakness and to help determine how to optimize your network for cybersecurity.

 

Recommended capabilities for a secure PACS environment include:

  • Role-based access control
  • Authentication
  • Network access control
  • Endpoint protection
  • Network and communication protection
  • Micro-segmentation
  • Behavioral analytics
  • Tools that use cyber threat intelligence
  • Anti-malware
  • Data security
  • Segregation of duties
  • Restoration and recoverability
  • Cloud storage

The Importance of User Training

While not included in this particular NIST publication, it is always good to remember that user training is critical to the success of any cybersecurity initiative. Many Digital Imaging and Communications in Medicine (DICOM) images are shared via mobile devices. 

 

Password protections are also important, as is understanding HIPAA compliance involving social media and basic HIPAA security procedures.

 

PACS do enable better patient outcomes, but they are a potential target for cybercriminals. Following the guidance from NIST, healthcare organizations can help ensure the continued privacy of their patients’ protected health information. 

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The radical potential of open source programming in healthcare

The radical potential of open source programming in healthcare | Healthcare and Technology news | Scoop.it

Everyone wants personalized healthcare. From the moment they enter their primary care clinic they have certain expectations that they want met in regards to their personalized medical care.


Most physicians are adopting a form of electronic healthcare, and patient records are being converted to a digital format. But electronic health records pose interesting problems related to sorting through vast amounts of patient data.


This is where open source programming languages come in, and they have the ability to radically change the medical landscape.

So why aren’t EHRs receiving the same care that patients expect from their doctor? There are a variety of answers, but primarily it comes down to how the software interprets certain types of data within each record. There are a variety of software languages designed to calculate and sort through large amounts of data that have been out for years, and one of the most prominent language is referred to as “R”.

What is R?

According to r-project.org “R is an integrated suite of software facilities for data manipulation, calculation, and graphical display.” Essentially this programming language has been built from the ground up to handle large statistical types of data.


Not only can R handle these large data sets, but it has the ability to be tailored to an individual patient or physician if needed. There are a variety of other languages focused on interpreting this type of data, but other languages don’t have the ability to handle it as well as R does.

How can a language like R change the way in which EHRs function?

Take, for instance, the recent debate regarding immunization registry. EHRs contain valuable patient data, including information associated with certain types of vaccine.


If you were able to cross reference every patient that had received a vaccine, and the side effects associated with said vaccine, then you could potentially sort out what caused the side effect and create prevention strategies to deter that certain scenario from happening again.


According to Victoria Wangia of the University of Cincinnati, “understanding factors that influence the use of an implemented public health information system such as an immunization registry is of great importance to those implementing the system and those interested in the positive impact of using the technology for positive public health outcomes.”


This type of system could radically change the way we categorize certain patient health information.


Programming languages like R have the ability to map areas that have been vaccinated versus those that haven’t. This would be ideal for parents who wish to send their children to a school where they know that “x” number of students have received a shot versus those that haven’t. Of course, these statistics would be anonymous, but this information might be critical for new parents who are looking for a school that fits their needs.


This technology could have much bigger implications pertaining to personalized data, specifically healthcare records. Ideally, an individual could tailor this programming language to focus on inconsistencies within patient records and find future illnesses that people are unaware of.


This has the potential to stop diseases from spreading, even before the patient is aware that they might have a life threatening illness. Although such an intervention wouldn’t necessarily stop a disease, it could be a great prevention tool that would categorize certain types of illness.

Benefits of open source

One of the more essential functions that R offers is the ability to be tailored to patient or doctor’s needs. Most information regarding patient health depends on how a physician documents the patient encounter, but R has the ability to sort through a wide variety of documentation pertaining to important statistical information that is relevant to physician needs. This is what makes open source programming languages ideal for the medical field.


One of the great components associated with open source programming languages in the medical field is the cost. R is a completely free language to start working in, and there is a large amount of great documentation available to start learning the language. The only associated cost would be paying a developer to set up, or create a program that quickly sorted through personalized information.


Essentially, if you were well rounded in this language, the only cost associated with adopting it would be the paper you would need to print information on.


Lastly, because of HIPAA, the importance of information security has been an issue, and should be a primary concern when looking at any sensitive electronic document. Cyber security is always going to be an uphill battle, and in the end if someone wants to get their hands on certain material, they probably will.


Data breaches have the ability to cost companies large amounts of money, and not even statistical data languages are safe from malicious intent. A recent issue has been the massive amount of resources that are being built in R that have been shared online. Although this is a step in the right direction for the language, people are uploading malicious code. But if you are on an encrypted machine, ideally the information stored on that machine is also encrypted. Cloud based systems like MySQL, a very secure open source server designed to evaluate data, offer great solutions to these types of problems.


These are some of the reasons why more physicians should adopt these types of languages, especially when dealing with EHRs. The benefits of implementing these types of systems will radically alter the way traditional medicine operates within the digital realm.


More statistical information about vaccinations and disease registries would greatly benefit those that are in need. The faster these types of systems are implemented, the more people we are able to help before their diseases becomes life threatening.


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