Decision support system development, when powered by artificial intelligence, big data, and predictive analytics in healthcare, provides extraordinary opportunities to the industry. Thanks to automation and the application of algorithms for decision making, healthcare analytics software allows for better decisions faster and with fewer errors. For instance, clinical decision support system implementation helps reduce medical and medication errors affecting nearly 1.3M Americans annually, mainly caused by humans.
As we look forward, the integration of an intelligent decision support system into modern clinical settings is becoming a standard. However, healthcare systems development does not eliminate human participation. Instead, the software serves as an assistant, leaving the final decision to clinicians. So how does a CDSS work, and what are the major benefits of its adoption?
Are you ready to optimize your clinical decision-making? Having a tailored decision support system can be the difference between a routine procedure and a life-saving intervention. Contact SPsoft to consult with our AI experts and build a CDSS that empowers your care teams and improves patient outcomes!
Table of Contents
The Basics of a Clinical Decision Support System
A clinical decision support system, or a CDSS, is a specific type of DSS designed to assist medical staff in decision-making. The system is most often used in point-of-care, and it may come in various forms and sizes, from standalone database entries to integrations into larger electronic health records (EHRs).
The goal of a DSS application in a medical setting is to enhance doctors’ prescriptions and directions for patients through data analysis. The system provides recommendations in diverse forms depending on the device connected and chosen algorithm. Interactive computer-based systems can work with tablets, smartphones, computers, or log recommendations to EHRs directly via a user-friendly dashboard.
Interestingly, the first computer-based systems for decision support appeared in the 1970s. The early expert system prototypes were slow and raised ethical concerns regarding responsibility in case the system provided a wrong suggestion for patient treatment. So, the fact that the system worked better than the doctors in some cases was also quite problematic.
However, modern intelligent DSS tools have evolved. Today, computerized decision support has improved clinician performance significantly and, thanks to machine learning algorithms, started providing more advanced, real-time opportunities for informed decisions.
The Market Overview of CDSS Solutions
While still evolving, DSS systems comprise an excellent piece of information system technology. In 2020, the relevant global market was valued at $2.1 billion and is expected to grow to $3.81 billion by 2030.
The market consists of various types of decision support systems like disease management, clinical informatics, and drug information. Hospitals lead as the most common decision makers utilizing this healthcare analytics software, including clinics and ambulatory surgical centers.
3 Core Layers of the CDSS Architecture
While the functionality and speed of operations has increased, the DSS tools architecture remains structured. An information system of this caliber consists of 3 primary elements:
- Data Management Layer. As the Base, it includes a clinical database, patient data, and a knowledge base or machine learning models. This layer acts like a data warehouse for raw data and historical data. Being the core, the mentioned three elements shape the entire system’s capabilities.
- Processing Layer. As the Interference Design, it uses the information and rules from the first layer to analyze data and apply it to specific patient cases.
- User Interface. This third layer provides the output via an application, an EHR, or a dashboard to the decision-maker.
While the architecture and the output type of a CDSS stay similar across various decision support software, the way they perform decision-making changes. Thus, there are two primary types of systems that support such workflows: knowledge-based and non-knowledge-based.
A Knowledge-Based CDSS
A knowledge-driven DSS is built on top of a base consisting of medical literature, practice evidence, and patient data. Using “if-then” rules, the system evaluates input data to make informed conclusions. Eventually, these knowledge-based systems provide output as an alert or treatment suggestion, where the doctor has the last word on the decision.
Non-Knowledge-Based CDSS
A data-driven DSS evaluates data using artificial intelligence without sticking to strict “if-then” rules while reaching for a source of data to perform analysis. Instead, data mining and neural networks analyze scenarios to find patterns and provide actionable insights to medical experts.
Genetic algorithms and artificial neural networks are most often used for CDSS. The first implies that the system adapts to new tasks by producing multiple solutions until the best fit occurs. The second, in turn, imitates the human brain by creating sets of neurods (like neurons) to transmit signals across the network. While more complex, such intelligent decision support systems require large amounts of data and learning time to ensure reliability and accurate outputs.
3 Major CDSS Applications for Modern Healthcare
While there are many things a CDSS can do for healthcare providers, various systems are more or less complex, standalone or integrated, knowledge-based or non-knowledge-based. Thus, in general, you should consider the three main areas where DSS software assists decision-making activities: administration, diagnostics, and clinical management.
Administration
A DSS takes the burden of administrative tasks off of medical staff. It acts as a management information system, automating the ordering of medical tests and finding proper coding for patient care. Based on robust pre-set algorithms, these support tools select relevant output actions while personnel handle higher-level organizational duties.
Diagnostics
CDSS implementation allows for more accurate diagnostic systems. Data-driven decision support allows algorithms to perform the following duties:
- Select relevant diagnoses
- Calculate drug dosage
- Check for allergies and drug interaction
- Prevent duplicate therapy
- Alert of abnormal conditions in real-time
As a result, the system outputs the best and most relevant conclusions for each patient, which provides a personal approach with minimum effort. This intelligent decision support minimizes human error, helping the physician make decisions with a higher degree of confidence.
Clinical management
The system for clinical decision support helps take better care of patients throughout their visits. It offers guidelines, protocols, and coding for treatment processes and medical services within a short reach. Different decision paths are managed by a model management system that alerts staff of deviations from standards.
Decision support handles patient care outside the doctor’s room and medical facility. Besides, a DSS system can alert clinicians if patients neglect preventive care or fail to follow a treatment plan they have been prescribed.
Benefits of Clinical Decision Support Software
Decision-support systems have enormous potential to transform healthcare by reducing medication errors and minimizing medical facilities’ expenses. Thus, from group decision support to individual patient scoring, the benefits are vast.
Decrease in Medication Errors
Using a DSS helps reduce medication errors. The losses caused by such a frequent issue are enormous — in the US alone, the total cost of drug errors makes up $40 billion annually. Also, 3 out of 4 errors could be prevented if not for the human factor. In particular, these mistakes occur so often because doctors overwork and face fatigue, which causes a lack of concentration and reduced attentiveness. And that is when CDSS implementation comes to the rescue.
Because it operates automatically, the computer system provides correct recommendations on drugs and their dosage to prescribe. Especially when it comes to common mild health concerns, it is reasonable to delegate this task to the system, taking the burden off of a fatigued clinician.
Treatment Tracking & Preventive Care
Sticking to the doctor’s recommendations is crucial to ensure the disease is treated and minimize the chances of it returning. Half of the treatment failures are caused by medication nonadherence, which is also the reason for 1 out of 4 hospital admissions. Thus, following the treatment plan helps prevent health conditions from progressing. But how can a medical specialist ensure that the patient takes medication and other preventive measures?
A clinical decision support system helps take care of patients even after the visit. Since treatment plans are recorded, the therapist gets a notification if there are any failures in the patient’s plan. So, the doctor can interfere and address the situation on time. This data-driven decision making helps mitigate the risks of unfinished treatments and reduce the number of cases when a patient’s condition worsens because of neglected prescriptions.
Misdiagnosing Reduction
Consider misdiagnosing that makes up 10-30% of errors in healthcare services. And the wrong diagnosis leads to various unfortunate outcomes, from financial losses to life-threatening states.Computer-based diagnostic systems offer a higher level of accuracy by being unbiased and data-driven. They improve the rates of correct diagnoses by processing unstructured data and company data from various data sources. Based on good sources of information and powered by image analysis, the system provides a quick and reliable conclusion for each patient.
Improvements in Employee Efficiency
Medical staff cover many mundane tasks. Justifying in an EHR or checking the patients’ past health concerns manually takes time and effort. This often increases the doctors’ proneness to making mistakes in more complex processes that require applying their skills and knowledge, such as diagnosing and prescribing medication.
Fortunately, DSS tools can compile useful information and create a comprehensive patient profile automatically. This allows employees to focus on high-level business decision making, increasing overall productivity and enhancing healthcare services.
Simple Access to Data
A DSS allows accessing all patient data and external data from a single place. Without CDSS implementation, a medical specialist would have to look for the needed records or other vital patient information on a device and, if unsuccessful, continue searching in other databases.Naturally, this takes a lot of time and effort, which can be dedicated to patient care. Besides, the relevant hassle requires additional costs to keep multiple data storage or database solutions. Healthcare systems development minimizes lengthy data digging and provides medical staff with valid, reliable, and regularly updated data that any specialist can access in just a few clicks.
Convenient Knowledgebase
A CDSS can provide a convenient knowledgebase of up-to-date literature or guidelines for a particular case. Regarding various medical processes and procedures, it is crucial to follow set protocols recognized in the industry as standards. The system helps nurses, clinicians, and other medical specialists complete processes correctly and on time by suggesting proper protocols and sending alerts.
In the long run, this will result in increased staff productivity and better patient turnaround. Also, the base of resources managed by healthcare analytics software is always up-to-date – the new literature is added while the outdated one is eliminated. This model management ensures that healthcare providers always rely on the most relevant resources.
Cost Reduction
Considering that medical errors round up to nearly $20 billion annually in the US, CDSS implementation reduces costs by preventing such issues. By eliminating medication and diagnosis errors in advance, the system saves resources on testing and stay length.
In turn, the cases of inaccurate healthcare services are minimized, and the decision-making time is shortened, also saving the medical facility’s resources. Thus, a communication-driven DSS can streamline group decision support systems, reducing the need for longer staff shifts.
Final Thoughts
As we navigate the complexities of modern healthcare, decision support system development has moved from a supplemental feature to a core necessity for clinical operations. By bridging the gap between vast historical data and real-time clinical needs, a well-implemented DSS tool empowers healthcare providers to deliver safer, more precise, and highly efficient patient care.
While the journey toward full digital transformation involves overcoming critical challenges, from HIPAA compliance to complex data management, the long-term clinical benefits and operational savings are undeniable. Lastly, using a DSS is about building a future where intelligent decision support and human expertise work in tandem to save lives and redefine global standards.
Are you considering building a data-driven clinical ecosystem? Contact SPsoft to discover how our custom healthcare software development allows you to integrate an intelligent DSS that perfectly aligns with your facility’s unique workflows and patient needs!
FAQ
What is the primary purpose of a decision support system in healthcare?
The primary purpose of a decision support system (DSS) in healthcare is to assist decision makers, such as doctors and nurses, in making informed decisions regarding patient care. By using computer-based algorithms to analyze data, they help identify potential health risks, suggest diagnoses, and recommend appropriate treatments. A DSS is an intelligent assistant that processes large amounts of data to provide actionable insights that might be missed by a human clinician. Ultimately, it aims to improve patient outcomes and clinical efficiency.
How does a data-driven DSS differ from a knowledge-driven DSS?
A data-driven DSS relies on data mining and artificial intelligence to find patterns within vast amounts of data without being restricted by pre-set rules. In contrast, a knowledge-driven DSS uses a specific knowledge base of medical rules and “if-then” logic to support decision-making. While a model-driven DSS might focus on statistical simulations, a data-driven decision support system learns from unstructured notes and company data to predict future outcomes. On the other hand, knowledge-driven systems apply established clinical guidelines to current data.
Can a DSS replace the human decision-making process in medicine?
No, a clinical decision support system is designed to support decisions, not replace the decision-maker. While the computer system is excellent at processing amounts of data and identifying correlations in real-time, the final responsibility for any management decision or clinical choice remains with the medical professional. DSS tools are meant to enhance the decision-making process by providing alerts and data to support the clinician’s expertise. That ensures the business decision regarding a patient’s health is as accurate as possible.
What are some common decision support system examples in a hospital?
Common decision support system examples cover drug-allergy alerts within an EHR, diagnostic systems that suggest potential diseases based on symptoms, and model-driven systems used for hospital resource planning. Other examples include group DSS platforms used by tumor boards to collaborate on complex cases and intelligent decision support tools that monitor sensor data from ICUs to predict sepsis. These types of decision support systems ensure that healthcare professionals have the right types of information at the right time to make decisions.
How do group decision support systems (GDSS) work?
Group decision support systems (GDSS) or group DSS systems are specialized information system tools designed to facilitate collaboration among a team of decision makers. Within a healthcare organization, a group decision support platform allows different specialists to share raw data, analyze different decision scenarios collectively, and reach a consensus on a patient’s treatment plan. These systems that support group interaction help eliminate biases and ensure that management information system data is used effectively across various departments.
What role does model management play in a DSS?
Model management is a critical component of a DSS that involves the systematic organization and maintenance of the mathematical and analytical models used to analyze data. A model management system ensures that DSS tools are utilizing the most accurate and up-to-date simulations to support decision making. Whether it is a model-driven DSS predicting patient flow or an intelligent decision support system analyzing genomic data, proper model management guarantees that the system’s outputs remain reliable and valid for the decision process.
Why is an intuitive user interface important for a clinical DSS?
An intuitive user interface and a clear dashboard are vital because healthcare professionals often need to make decisions under significant time pressure. If the DSS software is complex or difficult to navigate, it can hinder rather than support decision making. A well-designed user interface presents actionable insights and real-time alerts in a way that is easy to understand at a glance. This ensures that the computerized decision support tool is integrated seamlessly into the clinical workflow, reducing cognitive load and allowing doctors to focus on patient care.
How can a DSS help in management and organizational decision-making?
By using a management information system to analyze big data related to staffing, billing, and supply chains, hospital administrators can make informed choices about resource allocation. A data-driven DSS can identify areas of waste and suggest improvements in the decision-making process for the entire health system. This high-level business decision support helps healthcare organizations reduce costs while maintaining a high standard of healthcare service.
