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Clinical decision support systems can transform healthcare workflows beyond traditional methods. Based on our experience implementing AI in the healthcare industry, we’ve compiled a list of examples of how healthcare companies can use one system for multiple purposes.
In this post, we have 10 examples of clinical decision support systems that you can apply to your business.
A clinical decision support system (CDSS) analyzes relevant data to help healthcare professionals make better decisions and improve patient care.
You can apply clinical decision support systems in multiple ways to maximize support and make better decisions in many aspects of your business.
The most common use of a clinical decision support system is as a diagnostic tool. For example, CDSS can:
Integrating a patient’s history with CDSS allows for a thorough diagnostic process. This helps find recurring symptoms, keep track of progressive conditions, and enhance the diagnostic process as a whole.
A clinical decision support system can also optimize treatment planning and drug management to enhance patient care efficiency and safety. For example, CDSS can analyze patients' data, such as previous diagnoses, medical history, laboratory results, age, weight, and negative drug interactions.
With such a specific and thorough analysis, CDSS can tailor and suggest medication and determine the right dosage for each patient.
That’s precisely what Jayodita Sanghvi, Senior Editor of Data Science at Included Health, emphasized on our podcast.
“AI has the power to enable us to deeply understand each individual, what their clinical needs are, what their demographic needs are, if they have any care gaps…” - Jayodita Sanghvi
When a CDSS recognizes and flags potential drug interactions, it also suggests alternative medication.
Using the up-to-date information from the company’s workflow, CDSS facilitates adherence to the guidelines and best practices. With such extensive information, medical organizations can rest assured that CDSS follows regulatory standards and includes the latest research. This helps CDSS improve patient outcomes faster and much more accurately than any traditional method.
For example, CDSS is very useful because of its dynamic approach to medication adjustments, even when the patient experiences side effects.
With continuous data analysis, clinical decision support systems can help manage chronic diseases with proactive healthcare interventions.
Integration in data sources like electronic health records, patient monitoring devices, or even wearable technology helps provide real-time tracking of vital signs. In such cases, CDSS can track vitals like blood pressure, glucose levels, and heart rate and alert healthcare professionals in case of abnormalities.
This helps healthcare professionals save time on analysis, notice things that might go unnoticed using traditional methods, and provide needed care much faster.
We witness this firsthand with our AI solutions, but Mark Michalski, CEO of Ascertain, also confirmed it on our podcast:
“AI algorithms can process and analyze data more comprehensively and accurately than traditional methods, leading to precise risk assessments.” - Mark Michalski
CDSS can provide personalized care plans for each patient by adjusting treatments based on patient reports. The system can help suggest alternative medicine or therapy.
A tailored approach using CDSS helps reduce complications and the number of emergency hospital visits and improves patients’ overall outcomes.
By communicating with other healthcare professionals, CDSS provides suggestions and solutions using up-to-date and comprehensive information for each patient. With such a coordinated approach, every team member in the company is aware of the patient’s condition and treatment plans, which improves the care quality.
A clinical decision support system can keep patient information, including medical history, current conditions, diagnostic tests, treatments, and outcomes.
Patients’ electronic information is widely accessible to healthcare professionals and CDSS uses this information to provide guidelines, diagnostic support, and treatment recommendations.
CDSS works in real-time, so it can flag potential drug interactions when healthcare personnel add data to a patient’s record. The system also provides recommended diagnostic tests and suggests alternative treatments, all based on the patient’s information. In most situations, CDSS’s recommendations tend to be much more effective, faster, and more accurate than traditional methods.
CDSS also helps standardize clinical practices and ensure they align with current guidelines. Electronic health records kept with the help of CDSS are accessible in different healthcare settings. With successful integration, the accuracy of diagnosis, personalized treatment plans, and overall patient safety is increased.
Medical record keeping with CDSS also helps identify trends, support ongoing research, and improve healthcare practices. It’s the help that ensures all healthcare providers make more informed decisions, reduce medication errors, and improve efficiency.
Clinical decision support systems can analyze and use evidence-based data (practices and guidelines) to ensure the information it relies on is accurate and up to date.
Such practice ensures that the company’s system includes readily accessible treatment protocols to provide actionable decision rules. These rules apply in real time whenever clinicians enter patients’ data into the system.
The combination of the guidelines, information, and patient data reports helps CDSSs provide automated guidance. This guidance is tailored to a specific clinical context to ensure patients’ needs are met.
For example, when treating a patient with chronic heart disease, the CDSS automatically presents possible interventions. These interventions are based on the latest guideline recommendations and they can include lifestyle changes, appropriate medication, and more.
CDSS can also prevent potential adverse reactions by relying on patient-specific information in the system. When this happens, CDSS sends out real-time reminders and alerts.
Image recognition and analysis rely on artificial intelligence, machine learning, and health information technology to analyze medical images (X-rays, MRIs, and CT scans). The combination of such technology helps CDSSs enhance diagnostic accuracy.
After entering a medical image into the CDSS, AI helps analyze the image against a database of annotated images for subtle patterns and anomalies.
The CDSS can detect abnormalities like tumors or fractures that aren’t always apparent to the human eye. After flagging the medical image, CDSS can also help differentiate types of tumors on MRI and other scans, to help simplify the decision-making process.
Such use helps speed up the diagnostic process, improve accuracy, reduce human error, and provide very accurate image interpretation. Radiologists can also rely on this information as a second opinion.
CDSS learns over time with exposure to new data, which further enhances the accuracy of diagnostic capabilities. The results include earlier detection of diseases and better outcomes for patients.
Clinical decision support systems utilize advanced algorithms and data repositories to connect patients with appropriate clinical trials. This approach enhances the personalization and effectiveness of clinical trials.
After analyzing patients’ complete medical history, CDSS matches the patient with cutting-edge therapies or interventions. Identifying opportunities for patients who underwent standard treatment options is manual and labor-intensive work that requires resources and time.
CDSS’s matching systems can consider specific genetic markers and patients’ history to suggest new therapy trials. CDSS makes this happen much faster than traditional methods. This helps lift the burden of making these matches off medical professionals and gives them the resources they need to make more appropriate decisions.
The CDSS can aid patients in accessing potentially life-saving treatments, but it also supports medical research advancement. Quicker and more accurate enrollment in trials leads to quicker completion of medical studies.
With more data from medical studies, CDSS can continuously learn to tailor the approach and provide better outcomes, regardless of the rapid advancements in the healthcare industry.
Emergency department triage has always been a resource-exhausting part of healthcare that requires speed, efficiency, and accuracy. CDSS can help assess incoming patient data quickly, determine the level of medical need, and influence the speed and order of medical responses.
Upon patient arrival, CDSS analyzes clinical data (patient symptoms, vital signs, and medical history). This information is manually entered into the system but it can also be automatically connected to the system through medical devices.
CDSS takes over right away and immediately flags the priority, required medical responses, and the order itself.
This type of integration is most efficient when connected with other hospital systems. This allows CDSS to track real-time resource availability, like the number of available beds, current staff levels, and operating room status.
Such an approach helps emergency department staff make informed decisions faster which optimizes patient flow and resource allocation. The results reduce waiting and improve patient outcomes.
Automating a triage system with CDSS helps eliminate biased decision-making issues. With the highest levels of adherence to standardized criteria, patients’ care decisions are made objectively. Allocation of critical resources where they’re needed most urgently enhances the effectiveness of the emergency department.
Radiation dose monitoring safeguards patients undergoing diagnostic or therapeutic procedures (radiation, X-rays, CT scans, and radiation therapy). A clinical decision support system can track and manage radiation doses to eliminate overexposure.
CDSS analyzes the amount of administered radiation data to ensure optimal diagnostic and therapeutic outcomes.
Collected learning data includes:
Patient-specific information includes age, body mass, and previous radiation exposure to ensure the patient doesn’t exceed the recommended safety threshold.
The monitoring process helps eliminate the risk of long-term health issues like cancer due to overexposure. CDSS prevents and minimizes risk with real-time feedback and recommendations to clinicians. Such a system can also suggest alternative imaging modalities to reduce the amount of radiation (like ultrasound or MRI).
CDSS also helps support compliance with national and international radiation safety standards while maintaining rigorous quality control.
CDSS analyzes real-time data from various existing hospital systems to guide decision-making about resource use.
Such management helps improve patient flow, reduce operational bottlenecks, and keep hospital beds, medical equipment, and staff resources efficient.
The system monitors changes like patient admissions, discharges, and transfers, as well as the availability of ICU beds, ventilators, and operating rooms.
CDSS can even predict resource needs and recommend adjustments to accommodate incoming patient loads. When there’s a sudden flux of patients, the CSDS identifies areas of the hospital that can handle additional cases.
An analysis of patterns in patient admissions and peak hospital activity times can recommend optimal staffing levels. With such use, there can always be enough healthcare providers to handle busy periods without overscheduling during quieter times.
CDSS can also help identify trends in resource utilization that could lead to improvement. Overuse of specific diagnostic tests or underutilization of hospital facilities are just some of the examples.
Did these examples give you an idea of how you can improve your existing workflow? Please schedule a 30-minute call with our experts. We can discuss your needs and the best ways to implement a clinical decision support system in your workflow.
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