The Important Attributes of a Healthcare DOS

 Before we go into the topic of healthcare data operating systems Healthcare DOS.  It is helpful to first define what an operating system is so we can better understand the broader concept.  An operating system is system software that manages computer hardware and software resources and provides common services for computer programs.

 

There are two main types of operating systems. They are the single-tasking operating system and the multitasking operating system. A single-tasking system can only run one program at a time, while a multitasking operating system allows more than one program to be running in concurrency. This is achieved by time-sharing, dividing the available processor time between multiple processes that are each interrupted repeatedly in time slices by a task-scheduling subsystem of the operating system. Multi-tasking may be characterized in preemptive and cooperative types. In preemptive multitasking, the operating system slices the CPU time and dedicates a slot to each of the programs. Unix-like operating systems, e.g., Solaris, Linux, as well as AmigaOS support preemptive multitasking. Cooperative multitasking is achieved by relying on each process to provide time to the other processes in a defined manner. 16-bit versions of Microsoft Windows used cooperative multitasking. 32-bit versions of both Windows NT and Win9x, used preemptive multitasking.

 

Ok, now that we have that out of the way, let's talk about data operating systems and healthcare data operating systems.

 

For a Data Operating system to be classified as one, there are certain attributes it has to have. These attributes are important and embody what they whole purpose of a DOS is and should be. A lack in one can adversely affect the rest. There are seven main attributes to look out for

 

  • Reusable clinical and business logic – Registries, value sets, and other data logic lays on top of the raw data to be accessed, reused, and updated through open APIs in the healthcare IT environment, specifically enabling third-party application development against it.
  • Streaming data – Near- or real-time data streaming from the source all the way through to the expression of that data through DOS, that can support transaction-level exchange of data or analytics processing.
  • Integrated structured and unstructured (text) data in the same environment – This will eventually incorporate images.
  • Closed loop capability – Methods for expressing knowledge in DOS, including the ability to deliver that knowledge at the point of decision making (e.g., back into the workflow of source systems, such as an EHR.)
  • Microservices architecture – The ability to update constantly, with continuous development and release. This eliminates the painful upgrades to which healthcare has become accustomed and desensitized. In addition to abstracted data logic, open microservices APIs exist for DOS operations, such as authorization, identity management, data pipeline management, and DevOps telemetry. These microservices also enable third parties to develop applications on DOS without having to recreate them.
  • Machine learning – DOS natively runs machine learning models and enables rapid development and utilization of those models, embedded in all applications. This is a primary strength of the big data Hadoop ecosystem that came out of Silicon Valley. It is natively designed to support machine learning and computational analytics that traditional relational databases cannot.
  • Agnostic data lake – Some or all of DOS can be deployed over the top of any healthcare data lake. The reusable forms of logic must support different computation engines (e.g., SQL, Spark SQL, SQL on Hadoop).

 

If a Healthcare DOS Healthcare has these seven attributes, then it is well on its way to meeting the future needs of the healthcare organization. It is wise for healthcare practitioners and healthcare organizations in general to invest in a quality DOS as this will solve and prevent many future potential problems.