How to Choose Decision Support System for Your Healthcare Business

Clinical Decision Support System were not designed to replace physicians judgment but to enable timely, informed, and best decision making.

Experienced clinicians make decisions based on minimal or “sufficient” data — they understand it is costly to get unnecessary data. These costs include the apparent human, financial and clinical risks of further testing, as well as the inevitable distractions from data overload.

For more complicated tasks, the latest CDSSs are used to process data and observations that are not otherwise available or interpreted by humans.

What is Decision Support System in Healthcare?

Sets of intelligent applications act as interactive “knowledge systems” that use a lot of patient data to make recommendations on a case-by-case basis. They make routine tasks, alert about problems, and afford suggestions for consideration by doctors and patients.

Clinical decision support comprises any IT system, workflow, or activity designed to deliver:

• correct information (clinical need or evidence-based guidance);
• the right people (care teams, doctors, or patients);
• through multiple channels (e.g., mobile device, EHR, or online portal);
• in the correct formats (e.g., order sets, routings, patient lists, dashboards);
• at the right time in the workflow (for decisions or actions).

What is a Clinical Decision Support System?

The idea is not new: Stanford University built the first CDSS prototype back in the 70s on an artificial intelligence model. Although the prototype outperformed the staff in its accuracy, they did not embed it into practice because of poor performance and ethical and legal concerns.

Since then, systems have developed significantly. Modern solutions can analyze massive datasets, affording recommendations that eliminate guesswork when deciding.

There are as many options for CDSSs – from personal digital assistant apps configured by a single physician to mainframe-based multihospital surveillance systems designed to provide care for thousands of patients. Current CDSSs include many tools for qualified clinical decision-making and are used in the field to enable medics to merge their expertise with information or suggestions provided by the system.

Types of Clinical Decision Support Systems

These systems are subdivided into non-knowledge- and knowledge-based CDS.

The knowledge-based created rules that were planned using data based on practice, literature, or patient-supplied data. The system extracts data as per rules and performs an action or result.

The non-knowledge-based solutions proceed with data through statistical pattern recognizing, AI, and machine learning. Those systems can ease the pressure on medical experts and even reduce the healthcare budget. But complex training, the need for large datasets, and a lack of interpretability hampered their widespread adoption.

Therefore modern CDSSs are mostly knowledge-based.

Also, CDSS can be active or passive.

Active systems provide clinicians with information got by comparing current patient information with pre-programmed rules, guidelines, and protocols for using the knowledge base and output engine. Warnings and advice regarding drug dosage, allergies, laboratory parameters are available immediately.

Passive CDSS requires starting a process by sending a request to the system. For example, it provides additional available resources that a physician can get from a link if more information is required.

Clinical Decision Support Systems Area Challenges

Despite the explicit benefits, CDSS is not infallible and can make wrong or irrational decisions. This happens when:

• The information provided by the CDSS is inappropriate;
• The system is not working optimally;
• Users are not trained to use the system properly;
• CDSS is poorly integrated into the existing workflow.
• The user interface is overloaded or challenging to navigate.
• The CDSS problems mentioned below can significantly slow down the workflow, reducing the quality of patient care. In addition, you can also mention such pitfalls as:
• The initial costs of installing and integrating new systems can be pretty significant.
• Users can trust CDSS to solve specific problems, resulting in cognitive impairment.
• There are specific difficulties with updating systems, as knowledge inevitably changes.
• The quality of the data can affect the character of decision support.
• Many CDSSs are bulky stand-alone systems or are on a system that is not compatible with other systems.
• Fragmented workflows increase the time needed to complete tasks and decrease face-to-face communications with patients.

How to Choose DSS System for Your Needs

Research has revealed that clinical decision support toolsets refine the quality of medical care and reduce errors. Weak systems of recent decades are gradually being replaced by solutions with service-oriented interfaces and architectures, improved analytics with open standards to ensure interoperability with as many systems as possible.

CDSS can be a generic program that can be used as designed by a vendor, a system with custom components, or a system designed specifically for an organization. The choice depends on the organizational maturity, complexity, and, to some extent, the size and capabilities of the organization.

If the budget is limited, the best choice would be ready-made solutions with a module structure and flexible settings to adapt to your requirements. But remember that customizing and integrating with your existing infrastructure will increase the total cost of implementation.

If you want more details, check the Jelvix blog .