Decision support system development, when powered by AI, 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, CDSS implementation helps reduce medical and medication errors affecting nearly 1.3M Americans annually, mainly caused by humans.
But 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 clinical decision support system work, and what are the particular benefits of its implementation?
What Is a Clinical Decision Support System?
A clinical decision support system, or a CDSS, is software 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 electronic health records (EHRs) to integrations into larger systems.
The goal of a CDSS implementation in a medical setting is to enhance doctors’ prescriptions and directions for patients through data analysis. The system can provide recommendations in diverse forms depending on the device connected and chosen algorithm. It can work with tables, smartphones, computers, or, for example, log recommendations to EHRs directly.
Interestingly, the first clinical decision support systems appeared in the 1970s and were quite successful in output — their recommendations were accurate and valid. But they were relatively slow and raised many ethical concerns, like who was the one to take responsibility in case the system provided a wrong suggestion for patient treatment. The fact that the system worked better than the doctors in some cases was also problematic.
That led CDSSs to face a pause in their development until humanity was ready to accept the machine’s excellence. Now, healthcare systems development has not only improved in terms of their performance but is also enabled by AI to provide more advanced opportunities.
CDSSs: Market Overview
While still in the early stages of development and implementation, CDSSs comprise an excellent piece of healthcare technology. In 2020, the global CDSS market was valued at $2.1 billion and was expected to grow to $3.81 billion by 2030.
The market consists of various directions of CDSS application, including disease management, clinical informatics, drug information, and surveillance. Hospitals lead in the list among the most common end users of healthcare analytics software, including clinics, ambulatory surgical centers, and other medical facilities.
While the functionality and speed of operations of CDSSs have grown significantly over time, the underlying architecture has not changed much. The system consists of 3 elements:
- data management layer, or the base
a) a clinical database
b) patient data
c) a knowledge base/machine learning models
- processing layer, or the interference engine
- user interface
As you can see, the data management layer can be divided into three more elements. Since it is the system’s core, these elements shape the system’s capabilities.
Then, the second layer, the interference engine, uses the information and rules from the first layer to apply to patient data. The output from these operations is provided with a user interface, an application, an EHR, or any other form.
While the architecture and the type of output of a CDSS stay somewhat similar across various decision support software, the way they perform the decision-making changes. With this, there are two types of CDSSs: knowledge-based and non-knowledge-based clinical decision support systems.
The name of knowledge-based CDSS stands for itself, meaning that the system is built on top of a knowledge base. This base can consist of literature or practice evidence and patient data. Then, using if-then rules, the system evaluates the input data according to the knowledge base and, using the rule, provides a conclusion.
Eventually, the knowledge base allows for analyzing new data and providing total output. The result may come as an alert or treatment suggestion, where the doctor has the last word on the decision.
Non-knowledge-based CDSSs evaluate data using artificial intelligence while still reaching for a source of data to perform analysis. Machine learning algorithms, pattern recognition, neural networks, and other related technologies work with information from a data source to draw conclusions and recommend decisions to medical specialists.
So instead of sticking to a set of if-then rules, a non-knowledge-based system analyses scenarios that have taken place previously and learn from the found patterns to make conclusions for new cases.
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 other one, in turn, imitates the human brain by creating sets of neurods (like neurons) to transmit signals across the network.
While more complex, promising, and beneficial, non-knowledge-based healthcare systems development is still relatively young, provoking several issues. They require massive datasets and lots of learning time to provide accurate outputs. Besides, these systems’ decisions have no reasoning, compromising their reliability.
While there are many things a CDSS can do for healthcare providers, different systems are more or less complex, standalone or integrated, knowledge-based or non-knowledge-based. So, in general, it is possible to identify three main areas of decision support software applications: administration, diagnostics, and clinical management.
A CDSS takes the burden of administrative tasks off of medical staff. Things like ordering medical tests and procedures and finding the proper coding and protocols for patient care are automated. Based on pre-set algorithms, the system selects the most relevant output actions, like suggesting particular protocols or ordering a test at the lab, while the personnel handles other duties.
CDSS implementation provides the opportunity for more accurate medical diagnostics. In particular, healthcare analytics software allows utilizing algorithms to perform the following:
- select the most relevant diagnoses
- drug selection
- drug dosage calculation
- drug allergy checking
- drug interaction checking
- duplicate therapy elimination
- alerting of abnormal conditions
With this, the system outputs the best and most relevant conclusions for each patient, which provides a personal approach with minimum effort. That also minimizes the chance of human error in the diagnostics process, instead helping the physician conclude and leaving the final decision to them.
3. Clinical management
The system for clinical decision support helps take better care of patients throughout their visits. It offers guidelines, protocols, and coding for various treatment processes and healthcare services within a short reach. Medical staff is alerted in case of deviations from the standards. Thus, they can manage such deviations as soon as possible.
Besides, decision support handles patient care outside the doctor’s room and medical facility. Among all, the system alerts clinicians if patients neglect preventive care or fail to follow a treatment plan they have been prescribed. Additionally, a CDSS efficiently manages patient follow-ups for research and referrals.
Benefits of a Clinical Decisions Support Software
Clinical decision support software has enormous potential in terms of the improvement of healthcare services. The system can drastically boost patient care’s success by reducing medication errors to minimizing medical facilities’ expenses. Let us discuss what benefits it brings to the industry in detail.
Decreased Medication Errors
One of the advantages of using CDSSs is that it helps reduce medication errors. The losses caused by medication errors that occur annually are enormous — in the US alone, the total cost of drug errors makes up $40B. And what is more, 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 this is when CDSS implementation comes to the rescue.
Because it operates automatically, it is not a challenge for the healthcare systems development to provide correct recommendations on what drug or dosage should be prescribed. Especially when it comes to common mild health concerns, it is reasonable to delegate this task to the system and take the burden off of the doctor.
Tracked Treatment & 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.
So 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 CDSS helps take care of the patients even after the visit. Since treatment plans are recorded in the system, the doctor can track how the patient’s treatment is going. In turn, they will get a notification if there are any failures in the patient’s plan, so the doctor can interfere and address the situation on time.
In the long run, decision support helps mitigate the risks of unfinished treatments and reduce the number of cases when a patient’s condition worsens because of neglected prescriptions or preventive measures.
Again, medical errors occur often, usually caused by the human factor. Considering misdiagnosing 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.
Decision support systems for clinicians help reduce the number of misdiagnoses by offering a higher level of accuracy than humans. Based on good sources of information and powered by image analysis, the system provides a quick and reliable conclusion for each patient.
In addition, healthcare analytics software stays unbiased, which may occur during human decision-making. With this, decision support systems improve the rates of correct diagnoses, helping patients receive proper and effective care.
Improved Employee Efficiency
The medical staff has to cover a lot of mundane tasks that take away their attention and productivity. Justifying in an EHR or checking the patients’ past health concerns manually takes time and effort. Eventually, this increases the doctors’ proneness to making mistakes in more complex processes that require applying their skills and knowledge, such as diagnosing and prescribing medication.
Among all, searching for the needed information and creating a comprehensive patient profile with further recommendations are some of the tasks a CDSS can handle. So with decision support systems, employees can automate these manual processes, focusing on high-level tasks with a total capacity.
Eventually, medical staff can dedicate their shifts to providing good quality patient care and, in the long run, increasing its efficiency. And when this level of their work is boosted by the decision support system, too, the output implies drastically enhanced healthcare services.
Simple Access to Data
A clinical decision support system allows accessing all patient data and valuable resources from a single place. Without CDSS implementation, a medical specialist would have to look for the needed records or information on a device and, if unsuccessful, continue searching in other databases.
Naturally, this takes not only time but also lots of effort, which could have been dedicated to patient care. Besides, the hassle would require additional costs to keep multiple data storage or database solutions.
Luckily, healthcare systems development minimizes the need for lengthy data digging in several places and provides medical staff with valid, reliable, and regularly updated data that any specialist can access in just a few clicks.
A CDSS can provide the capability to suggest accurate 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.
A CDSS can help nurses, clinicians, and other medical specialists complete processes correctly and on time by suggesting the correct protocols and sending alerts. In the long run, this provides increased staff productivity and better patient turnaround.
Besides, 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. That also helps reduce the errors caused by medical staff relying on resources that are no longer relevant.
As a result of all the listed benefits, CDSS implementation reduces the costs of the medical facility and the patients receiving care.
Considering that medical errors round up to nearly $20B annually in the US, the ability of CDSSs to prevent some of these errors leads to a significant cost reduction. So by eliminating medication and diagnosis errors and finding the proper treatment and drug recommendations quickly, the system saves patients’ resources on medical testing and length of stay.
In turn, the cases of inaccurate healthcare services are minimized, and the decision-making time is shortened. That saves the medical facility’s resources, too. Less workload and reduced need for longer staff shifts help minimize expenses while creating an opportunity to provide care to more patients.
Decision support software greatly assists clinicians and medical facilities in general, providing them opportunities to enhance their internal processes and patient care in various ways.
But the technology is still relatively new, so it is crucial to have your software or integration developed by engineers with the needed expertise. Contact us to find your dedicated team for CDSS software development, and we will provide a solution that meets your unique needs.