The Significance of Clinical Decision Support for Healthcare Evolution

Each person makes many decisions every day, and most of them are negligible. But in the healthcare sector, everything is different – right decisions save lives, and the faulty lead to irreversible consequences. Before deciding on a particular case or patient, healthcare professionals must carefully analyze many details and factors. They must do it quickly and often under pressure when every minute counts.

As an integral part of the medical sphere, decision support systems magnify the likelihood of making the right choices. Although doctors are still accountable for outcomes, they increasingly utilize these systems to filter valuable knowledge from the vast amount of medical data and perform routine tasks. Consequently, the main challenge for IT organizations helping healthcare professionals is to provide end-users with more feature-rich and lower-cost products.

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. These systems increase the level of control over operations and planning by assessing the significance of uncertainties and trade-offs in choosing alternatives.

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).

Up-to-date Examples of Clinical Decision Support Systems

Have you heard of Electronic Health Records? These are complex program systems containing digitized confidential information that medics use to help patients. Structuring and analyzing this clinical information allows determining which treatments are more effective, improving the quality of services.

As the industry accumulates an enormous amount of data, the decision support systems (DSS) take on the tasks associated with finding, transmitting, and analyzing information used by healthcare providers and assist people in difficult decision-making processes.

The list of applications includes all kinds of analytics, hybrid and manual systems, and sophisticated software. Some of them perform simple functions, such as sending reminders. In turn, others include multiple modules and cover a comprehensive spectrum of processes. First, let’s consider the most famous decision support systems examples.

Reminders and Warnings

Alerts and Reminders keep track of doctor’s appointments and recommended actions, which notify users with a pop-up window.

Guided Clinical Workflows

This pack of toolsets assists in terms of long-term and multi-stage care. It provides evidence-based advice and guidance based on previous outcomes and treatment responses.

Diagnostic Decision Support

This toolkit is used in diagnostic decision-making and aids – clinicians ask patients more specific questions, request data, and analyze the outcome. As part of an electronic medical record, it keeps a list of symptoms and signs of a suspected diagnosis and compares it with the patient’s medical story.

Documentation Forms and Templates

As an attribute of the clinical decision support toolkits, forms and templates afford the collection and registration of the necessary data and supplementary information to compose a complete outlook of the patient’s condition, including complaints, symptoms, etc.

Kits for Special Conditions

Orders act as pre-defined templates for a medical procedure or specific condition and control adherence rules by decreasing the risk of error.

Cedar Sinai saved an estimated $3.7 million annually while maintaining a high level of service quality by using strategic order kits and preference lists.

In recent decades, the focus has been on patient-specific recommendation tools called extended CDSSs. Extended CDSSs may include, for example, an individualized dosing guide, drug-disease interaction testing, or laboratory test steps.

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.

Crucially, CDSSs were not designed to replace physician 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.

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.

The Main Categories of Decision Support Systems

DSSs are divided into five categories based on their basic sources of information.

1. Knowledge-Driven is referred to as advisory systems and provides specialized experience in problem-solving by suggesting or recommending actions to specialists. DSSs are used for diagnostics, classification, interpretation, planning, and forecasting and often applied data mining to sift databases and establish relationships among data content.
2. Document-Driven. These systems combine storage and processing technologies to search and analyze documents (for example, search engines).
3. Data-Driven. These DSSs include management reporting, executive information, and file systems. They emphasize accessing and managing massive bases of structured data, time series of internal and occasionally external data.
4. Group and Communication-Driven. The group system focuses on supporting decision-making teams to analyze and solve decision-making ussies. Whereas DSS, driven by communication, enables collaboration, coordination, and communication among people working on a shared task.
5. Model-Based. These are systems that use financial and representative models. They do not require a lot of data. Although they use simple analytical tools, they are characterized as hybrid DSS systems.

Decision Support System Components

The Management Study HQ identified three essential components of DSS:

• Database. It brings together a mixture of sources, including data generated by applications, internal and external data obtained from the Internet or purchased from third parties. The size of the DSS database ranges from a small stand-alone system to a large data store.
• Software package. The system is usually built on a model – including user criteria and context.
• User interface. Typically, these are message boards, templates, and other tools for viewing and interacting with results.

Advantages of Clinical Decision Support System 

For maximum impact, vendors merge clinical decision support instruments with the EHR workflow. Built-in toolsets such as alerts, reminders, and templates also raise the overall quality of service. Reminders can double the proportion of patients receiving timely preventive care, alerts prevent medication errors and remind people to retest, and forms keep the documentation to reimburse costs or meet reporting requirements.

CDSS has several distinct benefits to the healthcare industry, including:

• Improving the quality and efficiency of medical care;
• Accessing information in one interface;
• Reducing the risk of misdiagnosis and medication errors;
• Improving health outcomes;
• Minimizing errors and conflicts;
• Optimizing service profitability;
• Improving provider and patient satisfaction.

The University of Utah concluded that when an organization uses CDSS in workflow, it has a 112 times greater chance of gaining the quality of medical care.

Decision Support Systems and Analytics

Along with data warehouses and data mining, the DSSs are considered one element of analytical systems. But clinical analytics is a broader category of technologies, services, and applications for collecting, accessing, storing, interpreting data for decision making. In comparison, DSS applications are purposefully designed to support specific solutions.

Hospital clinical and IT leaders can expect to see the adoption of AI-powered analytical tools, primarily in academic healthcare centers. These tools will transform the skill level of clinicians in making diagnostic decisions and improve the reliability of underlying data.

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 Data is Included in Decision-Making and Help

Since the link between clinical decisions is medical information, EMRs will be the logical platform for betterment. Automatic collection and display of newly available data (not yet entered the EMR) are required to complete the clinical picture. This can be data from wearable sensors, information entered by the patient, or pre-medical staff in real-time.

Diagnostic, therapeutic, prognostic, and general documentation suggestions are based on a combination of analyzed data provided by these multiple sources. These may include supplementing the required missing data with additional testing, refining free text records for standard coding purposes, identifying suggestive but otherwise difficult to identify patterns and data sets, and so on.

According to the Black Book study, 86 percent of respondents using new systems reported that the technology is highly scalable. It allows hospitals to independently determine if existing datasets support new algorithms designed to retrieve actionable information from clinical, financial, operational, and customer-oriented sources.

Immediate Prospects for The Future

Artificial intelligence will play a fundamental value in clinical decision support evolution. Healthcare providers will use AI as the most reliable tool for analyzing patient data in the EHR to strengthen clinical decision support across all stages of care. In general, AI technologies have been proven to provide deeper insights into data than humans can.

Machine learning will improve the quality of the information presented to the user. The system will learn and remember how to use the information. A growing body of medical data comprises images, extraction, interpretation, and imaging technologies. For this, advanced algorithms for image classification are used, primarily deep learning (DL). Through DL and big data, information discovery can generate new combinations of information, broader knowledge, and unique patterns.

As the world will be full of autonomous devices, the use of IoT and the Internet of Medical Things (IoMT) is likely to become commonplace. As a result, the industry can expect increased input from IoMT devices (such as wireless sensors or wearables technologies), including genetic, microbiome, or proteomic information, and precision medicine. This will allow a better understanding of what helps patients and what does not.

Medical data will be stored securely through a blockchain running in an encrypted cloud.

The near future of CDSS will be closely related to the use of robotics – in surgery, medical diagnostics, and rehabilitation. Robotic systems are likely to shrink and be integrated into wearable devices.

In addition, the development of the newest drugs and treatment strategies will be carried out using efficient algorithms – methods and their launch in cutting-edge decision support systems.

What a Quality System Looks Like

Conducting a clinical decision support system involves several key steps, such as determining users’ needs and what the system will do, deciding to buy a ready-made system, or creating it from scratch for the clinic’s specific needs. Not only the implementation process should be planned, but also an assessment of how well the system meets the stated requirements should be done. Moreover, the system must be:

• Modifiable to include new and innovative methods of clinical prediction and decision support.
• Flexible so that important new information can be communicated to management in emergencies, such as epidemics or natural disasters.
• Thoroughly tested in parallel with the available system before implementing it in daily practice. Testing will determine usability and detect types of system errors that are only detected when used in a real clinical context.

The application’s design should be “tuned around the edges but standardized in the kernel” so that users can customize their version of the application. This is possible if data standardization is not compromised and allows users to have reasonable control over their interactions with the system.

Customization should not be allowed if it becomes difficult or nearly impossible for software developers to investigate system errors and unexpected events.

System data should be stored in databases for ongoing analysis to provide real-time help and plan clinical support methods, including practice guidelines and research.

User decision reports should be generated in terms of consistency with best practices suggested by the system.

Simply put, a proper clinical decision support system should assure a complete, integrated workflow – from data analysis to reporting, helping the user interpret a wealth of information.

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.

Make sure the app has critical notification prioritization settings, as redundant notifications cause fatigue. It should be relatively easy to integrate into your workflow. 

Without a doubt, the most significant benefit comes from choosing a decision management system based on prescriptive analytics. Such a solution requires an enormous investment but is more reasonable to exceed expectations and increase your return on investment. This eliminates guesswork when deciding, and since the model reproduces a specific area, this option of the decision support system is more suitable to propose workable and rational judgments.

Conclusion

There is no denying the contradiction between current knowledge-based practice and the development of a modern approach to treatment not yet entered the canons. This contradiction comes from the gap between research and practice: up to now, the doctors have merged this process without adequate data support. They had to make decisions experimentally without the help of thoroughly analyzed datasets.

Clinical decision support system vendors aim to combine useful elements of these advances into an established information system that integrates both previously available and ongoing medical information.

Empower technology to draw conclusions instead of people is a risky step, but only in this way will healthcare truly benefit from big data. This will require algorithmic adjustments to the information presented to the user so that it transforms important discoveries into a revised and dynamically controlled system of clinical practice.

Jelvix specializes in the development and customization of clinical decision support applications. Check out our digital healthcare solutions that support collaborative decision building for clinicians and patients.