The adoption of cloud computing in the healthcare industry has brought a variety of options to the table - public, private and hybrid solutions, each with its pros and cons. Healthcare cloud computing new possibilities for organizations to refine their workflows and boost the efficiency of the operation.
The healthcare industry is a good example of the effective implementation of hybrid cloud solutions. The thing is - each medical workflow has its own requirements in terms of needs and goals. Because of this, the solution needs to be both diverse in its application and efficient in providing scalable infrastructure for it.
We have already covered the differences between public, private, and hybrid clouds. This time, we are going to tell you:
- Why a hybrid cloud model is the best option for healthcare?
- Explain the benefits of cloud computing in healthcare.
As was stated previously, healthcare is one of those industries that embraces all sorts of innovations in order to refine operations and make them reliable and effective. Hybrid Cloud is no different in that regard.
In the past, the healthcare industry used costly legacy infrastructures comprised of disparate elements. For a while, its use was justified by a lack of better options. However, with the emergence of cloud computing in healthcare - things have changed.
The biggest issue with legacy infrastructures was that they were unable to handle an exponentially growing volume of medical data.
- The thing is - healthcare operations produce vast amounts of data - patient admissions, diagnosis info, online interactions, discharges, the list goes on. The scope of data only goes on to expand.
In essence, hybrid cloud infrastructure is a natural solution for the healthcare industry. It can bring the efficiency of the healthcare workflow pipeline to a new level - faster, more scalable, and more productive.
One of the inherent features of healthcare workflow is its complexity. There are numerous elements involved - all tied together in complex systems. This medical cloud computing needs to handle lots of flows at the same time.
Let’s take patient treatment for example.
- The patient treatment plan is a customized workflow designed according to the patient’s condition and medical needs. The pipeline involves numerous examinations, medical tests, treatment sessions, data analysis, and further optimization of the treatment strategy according to test results.
- Every element of this pipeline has a workflow of its own. For example, blood testing facility:
- There is a general workflow. Its operational requirements figure in:
- the needs of the facility itself for proper functioning (regarding the expertise and use of resources);
- needs of patients it serves (get accurate results and effective treatment as a result);
- the needs of healthcare institutions in general (overarching “save people’s lives”).
- Then there is a workflow for a particular sample.
- There is a queue of samples. Each set of samples has its own set of requirements (i.e., what kind of tests to make, the urgency of results for a particular case).
- Overall, the samples are organized according to:
- the priority in the general pipeline (for the most part, it is either routine or emergency tests);
- The complexity of the testing;
- available resources.
That’s just one of the examples. Pretty much every other component of healthcare institutions is operating this way.
Such process overlap and dependency creates a necessity of having a cost-effective, easily manageable system with clearly defined and transparent processes. Which is exactly what cloud infrastructure is aimed at achieving.
Cost-effectiveness is one of the key benefits of adopting cloud computing in the healthcare industry. Due to the intricacy of the workflow - It is much less taxing on the budget to use cloud infrastructure than to maintain your own hardware infrastructure.
The reason why a hybrid cloud is the preferable type of infrastructure for healthcare is simple - it provides more flexibility in terms of arranging and managing operations. The other important aspect is control over data.
- On one hand, you can use the public cloud for resource-heavy operations and avoid overpaying for cloud services.
- The volume of resource use for each element differs. This aspect makes it reasonable to keep components on a “pay as you use model”.
- On the other hand, you can keep sensitive data on the private cloud safe with regulated access management.
The higher level of flexibility allows much better use of resources and, as a result, much more efficient budget spending. With hybrid cloud infrastructure, each element is presented as a self-contained application that interacts with the rest of the system via API.
The other crucial benefit of using hybrid cloud infrastructure is the better manageability of the workflow and its infrastructure. Given the fact of how many moving parts healthcare operation involves - this is one of the key requirements for the medical cloud computing pipeline.
- In the hybrid cloud configuration, the system is broken down into self-contained components.
- Each of them is doing its own thing using as many resources as it requires to do it properly. Because of the use of the public cloud, the workload of the particular element is not affecting the other components of the system.
- At the same time, the interaction between the system components is strictly regulated through a constellation of APIs.
- there is a request for several different tests for the patient - blood test, liver function test, and MRI. Each of them is handled by its own component.
- There is a central element in the form of patients’ electronic health records. This one is on a private cloud.
- There are also contributing elements that handle the test. They operate on a public cloud and rely on its autoscaling features. The resulting data is sent back to patient's EHR on a private cloud. The cycle repeats over the course of treatment.
With the general pipeline and workflow of the particular elements set apart and clearly defined - it is far easier to oversee the operation in its full scope, analyze its effectiveness and plan its further optimization and transformation.
Reliability is one of the key requirements for the operational infrastructure of cloud computing in healthcare. For the most part, the reliability requirements manifest themselves in the following needs:
- Work results of each component need to be accurate and contribute to the accomplishment of the overarching goal of the workflow (to treat patients and ultimately cure them of their ailments).
- The workflow needs to be uninterrupted and capable of handling as much workload as it needs, whether it is a regular or emergency level. At the same time, the system needs to be optimized and refined according to the ever-changing operational needs (i.e. more or fewer resources, etc)
Because the workflow elements are intertwined and codependent on each other, it is important to keep the consequences of one element failing from spreading to the entire system.
- For example, in a monolithic structure, this means that if one of the elements fails for some reason - this throws a wrench into the entire workflow. The database goes down and you’re busted. The aftermath of such downtime in healthcare might be dire.
- On the other hand, if something happens to one of the self-contained cloud components - it is contained in the component and not spreading elsewhere (aside from API call error messages).
Here’s how a hybrid cloud for healthcare makes it work like clockwork.
- The accuracy of results is secured by the use of public cloud resources. Whatever the operation requires to do, the job will be handled with public cloud autoscaling features.
- The consistency of the workflow and optimization of its elements is maintained through the blue-green deployment approach. Here’s how. In essence, there are two versions of an application. One of them is server A and it is operating at the forefront. Then there is server B with another version that undergoes all sorts of refinement, expansions, optimizations, etc. When it comes to upgrading the component, the servers are seamlessly switched with little to no downtime. While this approach wasn’t originally designed for healthcare, in this industry it can be used to test out experimental features of the application and apply them to real data, without affecting the workflow.
Maintaining patient privacy is one of the most problematic aspects of modern healthcare operations. With the increasing scope of digital transformation and cloud adoption, growing cybersecurity threats, and implementation of government regulations regarding patient data usage - this is a considerable challenge.
We live in the age of big data breaches. Every once in a while some company gets into hot water, either because of some security compromise or because it was downright hacked.
In the case of healthcare, data breaches and other types of security compromises can be extremely damaging, both for the reputation of the institution, and the safety of its patients.
Here’s how a hybrid cloud solution can handle cybersecurity requirements.
- The structure of the hybrid cloud combines public and private cloud servers. The majority of resource-heavy operation is done on public servers, while sensitive information like patient data is kept on the private cloud with limited access.
- In this configuration, there is more control over data and access to it. This approach provides more transparency regarding who is using sensitive data and where it is used.
- In addition to that, keeping sensitive data on a private cloud allows taking more security and data loss prevention solutions (you can read more about it in our article on DLP).
Then there is regulatory compliance. Such regional data protection regulations as GDPR (EU), PIPEDA (Canada) and HIPAA (USA) clearly define how patient data should be handled, and describe the consequences of misusing or compromising sensitive data.
Here’s how a hybrid cloud makes it easier to be compliant with such regulations.
- In the hybrid cloud configuration, sensitive data of any kind is kept on private cloud servers with limited access for the applications operating on public cloud servers.
- The cloud computing applications in the healthcare process only that data they require for proper functioning (for example, MRI for medical images requires input images and so on).
- The processed data is then transmitted back to the private cloud and added to the patient’s file.
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Most healthcare institutions have a mix of old and new equipment that uses different software. This aspect complicates the process of digital transformation towards the cloud for healthcare.
For instance, there are elements that you can implement into one system and then there are older elements that are incompatible due to age or software specifications. This is the case with some of the older, larger equipment.
With a hybrid cloud, it becomes less of a problem as you can balance out the system according to its state. For example, you can tie together the compatible elements into a set of microservice applications. The elements that you can’t transform on the spot can use conversion points in order to feed data into the system and maintain workflow efficiency at the required scope.
Healthcare is probably one of the biggest beneficiaries of cloud adoption as it relies on technical innovation by design. The adoption of cloud computing in healthcare has made each aspect of it bigger, better, and much more efficient in terms of performance and reliability.
With a hybrid cloud, healthcare operations can handle immense workloads without compromising the integrity and safety of data. At the same time, hybrid cloud infrastructure makes the workflow of each component more balanced and transparent, which makes it easier to manage and refine.
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