In the recent decade, health information technology systems have moved in massive strides. That is why healthcare companies and organizations are looking for trusted software development partners that can improve service provision. In such a case, decision support software development is one of the top priorities in the healthcare industry.
According to GlobeNewswire, the global clinical decision support system (CDSS) market is currently valued at $2.1 billion and is expected to reach $3.8 billion by 2030. These numbers show that healthcare providers envision decision support software development as a way to improve their services and profit. Respectively, let us explore the concept of CDSS in greater detail, its link to artificial intelligence (AI), and determine whether building CDSS from scratch is more beneficial than leveraging an existing one.
What is the Clinical Decision Support System (CDSS)?
In short, CDSS is a different health information technology system designed to enable healthcare providers with better support in clinical-related decision-making. Decision support software development is closely related to Big Data services and focuses on:
- Data storage
- Analysis
- Management
Yet, what is unique about CDSS is that data management in healthcare is all clinically related. The technology generally focuses on preventive care, diagnostics, and follow-up management (see Fig. 1).
While being around for many years, healthcare professionals have only recently started paying close attention to CDSS. As a unique healthcare application, the key objective of the technology is to engrain data analytics and management into existing healthcare systems like EHR. In the context of global data automation in the healthcare market rise, healthcare providers started using CDSS to screen certain groups of patients for different conditions. But the instrument will be further intertwined with predictive analytics and data streaming.
How Artificial Intelligence (AI) Transforms CDSS and Medical Intelligence?
Advances in AI and Machine Learning (ML) presented massive opportunities for their application in tens of industries. Healthcare is not an exception (see Fig. 2).
Because of the rising volume of clinical data available to healthcare professionals and researchers, CDSS is improving data analysis’s precision and efficiency. In such a case, AI plays a major role in that. CDSS, coupled with AI, allows professionals to:
- Analyze vast volumes of data
- Convert the insights into better patient care and clinical decision-making
There are apparent benefits of decision support system development, and the availability of AI is one of the foundational reasons for these advantages. Without AI-based algorithms, it is virtually impossible to manage and optimize the amount of available clinical data. Yet, it is crucial to remember AI in CDSS is all about analysis. At this point, to have everything intact, you also need to know healthcare data storage options.
So, artificial intelligence in CDSS is an excellent tool for improving the prognosis, diagnosis, and treatment of certain conditions. However, AI gives this tool analytic capabilities, the ones that can be turned into clinical-based decision-making translated to better patient outcomes.
Engaging in Clinical Decision Support System Software Development From Scratch
When it comes to decision support software development, there are two options available.
- First, you can hire a company with proficiency in healthcare to have them build your CDSS from scratch, which is a laborious process.
- Second, you can leverage an existing CDSS, which requires less input.
However, to understand which approach is better, let us explore the pros and cons, starting with the building-from-scratch option.
Pros of Building CDSS from Scratch
It is crucial to explore the advantages of diagnostic accuracy, informed decision-making, and physician assistance.
Top-to-Bottom Diagnostic Accuracy
One of the key aspects of clinical decision-making is medical treatment prediction. Clinicians use data to understand the risk factors and anticipate treatment plans required to provide the best patient outcomes. This study indicates that higher diagnostic accuracy leads to lower mortality. At this point, having a CDSS built from scratch ensures the best diagnostic accuracy this tool can offer.
When knowing the project top-to-bottom, it can be adapted to a particular provider’s needs. Respectively, decision support software development from scratch is a great opportunity to understand how diagnostic accuracy can be aligned with a specific healthcare provider. In such a case, using the existing CDSS does not provide the same degree of accuracy as the building-from-scratch option.
Double-Checking Informed Decisions Aspect
The risk of misdiagnosing patients is the core issue linked to the work of healthcare providers and medical professionals. Here, the phenomenon is directly linked to the quality of decision-making involved. According to Medium, AI has a chance to improve clinical decision-making, thus driving down the chance of medical errors.
However, to have AI properly coupled with CDSS, you must undergo a laborious software development process. When building the tool from scratch, your development partner can test and double-check the product at every stage of the development cycle. Besides, this health information technology system can be adapted to your particular framework. Finally, when going through all the stages of CDSS development, you can integrate more advanced solutions like cloud computing.
As a result, with the technology tuned to your existing system, there is a high chance you get the best degree of informed decisions the instrument can offer. You can face challenges with CDSS not adapting to your required decision-making in different scenarios.
Establishing Links with Physicians Boosting Assistance
Finally, one of the key benefits of the building-from-scratch option is the opportunities to help and assist physicians you know and work with. Decision support software development is there to equip medical professionals with insights that improve patient outcomes. However, particular physicians often deal with specific problems and concerns.
With the build-from-scratch option, you can tune the CDSS to help certain groups of physicians achieve maximum effectiveness. They will get an opportunity to identify, resolve, and prevent specific patient-related health concerns. According to PWC, physicians equipped with AI-based CDSS can identify patient groups at risk of developing certain conditions. Yet, to tap into the technology, they need to know where to look first. As a result, building CDSS from scratch is a great way to align the instrument with specific assistance physicians need.
Cons of Building CDSS from Scratch
Building your own CDSS grants better diagnostic accuracy, ensures the most informed decisions and provides fine-tuned assistance to physicians. But this approach is not an all-in-one solution. At this point, let us focus on aspects like users, trust, and “explainable AI.”
Getting Users on Board from the Get-Go
As with any new healthcare software, you need to know how to get new users on board. In addition, implementing tools like AI is as challenging as adopting any change management aspect. People using CDSS will need to use new protocols and processes. Thus, before using the tool, you need to have staff trained and ready to support such an innovative solution.
Moreover, getting users on board might require involving them in designing and delivering such an AI-based solution. In such a case, you have at least one more factor to consider in the decision support software development process. As a result, getting users on board increases the complexity of building CDSS from scratch.
Setting Trust and Reaching High Performance
Next, while existing CDSS have their reviews and established trust, it is hard to achieve with the building-from-scratch option. Generally, when using AI and ML technology, unintended consequences might be high. You need to undergo a trial-and-error approach to achieve high performance and only then apply the tool on a broad scale to get the required degree of trust. As a result, with newly built CDSS, you need to spend a great deal of time and resources to reach high performance and gain trust.
The Problem with “Explainable AI”
There are challenges with almost any healthcare data management aspect. However, the explainability of AI is at the top of the list. In short, practitioners face the issue of using the tool while needing to understand how the instrument processes data. While the application grows, the problem remains (see Fig. 3).
At this point, dealing with the issue means you need to spend extra time and money to teach users about the methods of AI. With the building-from-scratch decision support software development option, you need to do that on the go because all the documentation and manuals are written during the development process. As a result, having your CDSS built means you both need to get users on board and explain in detail how AI is a critical part of the technology.
Leveraging an Existing Decision Support Software Development Product
In contrast to the building-from-scratch option, you can always tap into the existing CDSS. You will instantly get the toolkit and not need to undergo the laborious process of decision support software development.
Pros of Using an Existing CDSS
Let us discuss compliance cost factors, data management aspects, and potential integrations regarding this option. These are the variables turning people to choosing existing CDSSs.
Containing Compliance Costs
Suppose there is an option to use CDSS. In that case, it means the providers have undergone the necessary steps to comply with the HITECH Act. In turn, companies using products that are HITECH non-compliant can face fines as high as $50,000 per single violation. It means that by using the existing CDSS, you automatically deal with compliance and can be sure you will not be fined. In turn, the building-from-scratch option requires going through all the stages of compliance, which means additional time and expenditures.
Data Management
As we mentioned before, CDSS works with vast amounts of data from EHRs while providing medical treatment predictions. Most existing decision support systems have an established connection to various databases and learning models, allowing them to structure data properly before analyzing it. While cloud computing might help some of the data management challenges, it cannot deal with all of them. To tap into what CDSS can offer, you need to have a pre-established connection to healthcare databases.
Existing platforms grant such an option. In turn, with the building-from-the-scratch option, you need to undergo the process of establishing the connection and ensuring that correct learning models are used to achieve the best results. At this point, ready-to-go decision support software development products take away at least one pain linked to data management.
Integrations
You need to understand that CDSS can be integrated with more than just EHR (see Fig. 4). To get the most out of the technology, you need to choose the proper foundation for a given integration.
With a ready-to-go option, you have a list of integrations to know how the technology works with a particular platform.
- You can see whether the CDSS you are looking at will or will not work with your data management system.
- You can choose the instrument that will operate with different integrations, thus preparing for potential updates in your system.
Having such a degree of flexibility is hard to achieve with the building-from-scratch option.
Cons of Leveraging an Existing CDSS
Yet, both approaches have their disadvantages. This option has potential design flaws, computer literacy, and financial issues.
System Design Flaws
Data automation in healthcare can be a marvel. However, if it comes with a flawed design, it would be a disaster. The same is valid for leveraging an existing decision support software development product. While building the instrument from scratch, you know all the ins and outs. When using a ready-to-go option, there is an additional trial-and-error method required.
Lack of Computer Literacy and Technology Knowledge
This study suggests that despite the rise in digital healthcare, many healthcare professionals face the issue of computer illiteracy. Using the ready-to-go option is a bit of a gamble. You must wait to test it first on your users, and preparing your staff for such a change is hard. With a build-from-scratch system, you can get users on board gradually and train them. That is something hard to achieve by using an existing CDSS.
Financial Challenges
The evidence dictates about 74% of CDSS users indicate the system’s financial viability being a significant struggle. Besides, with all potential integrations, the final cost will add up. Moreover, you should expect a company offering a ready-to-go product to set a particular profit margin. It all means this option takes a major toll in terms of finances. While decision support software development from scratch is a gradual process with expenditures coming in a certain period, the second approach requires immediate input.
Final Thoughts
All in all, it is hard to support the initial hypothesis and claim that building CDSS from scratch is better than using an existing one. It is all a matter of perspective. What is clear is that knowing which approach to choose depends on your needs and understanding of the instrument.
Decision support software development from scratch will be an excellent fit for providers intending to engage in a gradual process spending time getting users on board and improving their computer literacy. Besides, the building-from-scratch approach will be best for boosting diagnostic accuracy by tuning the instrument to your existing system.
In turn, appealing to the existing CDSS will be great for providers looking for multiple integrations. In addition, while the option can cost a lot in the short term, keep in mind that you save money on compliance and time on development. However, it would be best if you still found a way to prepare users for looming change.
Regardless of the option, always get in-depth insights on all the aspects at play, and make sure you have an objective perspective to make an informed decision.
