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Clinical Decision Support Systems (CDSS) are essential tools for healthcare professionals in their daily decision-making processes. CDSS provides clinicians with real-time, evidence-based recommendations that help improve the quality of care, increase patient safety, reduce healthcare-related costs, and enhance healthcare efficiency. With the rise of electronic health records (EHRs) and healthcare information systems, CDSS are becoming increasingly integrated into clinical workflows, enabling healthcare professionals to access patient data and clinical guidelines seamlessly.
In this post, we explore the various applications of CDSS and their impact on healthcare delivery. For this analysis, the applications of CDSS have been categorized into six segments based on clinical point of care along the healthcare spectrum: Preventative Care, Diagnosis, Planning or Implementing Treatment, Follow Up Management, Hospital and Provider Efficiency, and Cost Reductions and Improved Patient Convenience.
Preventative Care
CDSS can play a vital role in preventative care by enabling healthcare professionals to identify patients who are at possible risk of developing certain conditions and by providing them with appropriate interventions early. Through leveraging personal health records, CDSS can screen patient data such as medical history, medications, and demographic information and conduct genomic interpretations and predictive analytics that are specifically geared towards the patient, thus conducting a thorough risk assessment of the patient prior to any potential onset of disease. The advancement of electronic health records and its ability to easily integrate third party software has enabled CDSS to gain access to such data.
Diagnosis
CDSS have significant potential to support healthcare professionals in the diagnostic process. Similar to CDSS, Diagnostic Decision Support Systems (DDSS) aims to guide healthcare professionals through the diagnostic procedure ensuring that all potential diagnosis is considered. Throughout the process, elements such as patient health records and test results are all factored in for the DDSS to process and analyze in hopes of outputting probable diagnoses, or at a minimum guide healthcare professional with potential steps moving forward.
Analyzing laboratory test results and radiology images are perhaps two of the most common applications of DDSS. Through leveraging non-knowledge based DDSS for radiology test results, AI and ML algorithms can be used to accurately detect diseases ranging from Alzheimer’s disease to dermatological conditions. Similarly, the rise in enhanced imaging and precision radiology (radiomics) has the potential to revolutionize cancer care through providing clinicians with more detailed information about the characteristics of tumors and how they are likely to respond to different treatments. The process of extracting and analyzing quantitative features of a radiographic image allows the characterization of tumor biology, treatment response, and clinical outcomes. Through leveraging ML algorithms to analyze large amounts of data from medical images, radiomics is capable of identifying patterns and associations that may not be visible to the human eye.
Likewise, through EHR integration, DDSS can access patient-specific laboratory and pathology test results and apply predictive algorithms to help diagnose conditions or suggest further testing. Analyzing these test results in a deeper and more complex manner may avoid the need for patients to pursue more invasive diagnostic procedures. On a surface level, DDSS can identify and interpret abnormal test results and suggest further testing or diagnoses based on the patient’s medical history and other clinical factors.
Planning & Implementing Treatment
CDSS technology can play an integral role in treatment planning and implementation. Such systems can provide clinicians with real-time, evidence-based recommendations for treatment plans, which can help improve overall health outcomes and reduce the risk of errors. There is currently a wide range of different CDSS platforms on the market that are specialized towards provider-specific targeted tools, care-setting specific tools, and disease specific tools. However, there are two underlying applications where CDSS are most commonly used: clinical management adherence and drug selection.
CDSS can be used to monitor and aid a healthcare provider’s treatment and clinical practice guidelines and ensure a level of care quality that patients deserve. One of the most beneficial features of CDSS is that it adds an element of personalization. Such systems will take into consideration medical history and patient preferences into the equation thus generating a treatment plan and establishing a care pathway that is unique to the patient, thus increasing the likelihood of improved outcomes. Furthermore, CDSS can be adopted by hospitals to ensure that providers remain adherent to hospital policies, protocols, and operating procedures. Simultaneously, providers would be able to have continued access to the latest medical knowledge and discoveries to ensure that their patients are receiving the most up-to-date treatment.
Another common application of CDSS is its use within the Drug Selection process. Leveraging its access to EHRs and personal medical history, these systems can consider many factors that can be missed by patients and doctors such as potential drug allergies, ideal drug dosage and frequency, potential of duplicate therapy in the presence of a common active ingredient, and harmful drug to drug or drug to gene interactions. By allowing CDSS to run an additional layer of analysis will prevent any further downstream complications and help plan and implement a patient’s treatment.
Follow-Up Management
Use of CDSS in follow up management allows healthcare providers with the necessary information and reminders to ensure timely and appropriate follow-up care for patients. For instance, CDSS can be used to alert clinicians when a patient is due for a follow-up appointment or a diagnostic test. As a result, an element of automation is established in the administrative and back-end work of a healthcare setting. However, the recent rise of remote patient monitoring solutions has potential ties to CDSS. Through leveraging remote patient monitoring technology, healthcare providers would have continued access to patient health data in real-time, enabling them to detect potential health issues and intervene appropriately. All of this data would be collected and stored in a patient’s EHR, and because CDSS are integrated into EHRs, CDSS can help monitor patients for potential adverse events related to their treatment and provide recommendations for any necessary follow-up actions.
Hospital & Provider Efficiency
Navigating the healthcare landscape is a complex and timely process, and while not an absolute solution to the problem, CDSS can play an important role in improving hospital operations and provider efficiency. By improving the accuracy and speed of diagnosis, CDSS can help reduce the time required to diagnose and treat patients, leading to improved patient outcomes and provider efficiency, whilst simultaneously allowing for a smoother healthcare experience for all stakeholders involved. CDSS can also help streamline clinical workflows by providing real-time information and decision support. This can help providers and hospitals prioritize tasks, optimize resource utilization, and reduce administrative and operation burden. Care coordination is also an aspect of healthcare services that can be vastly improved through the implementation of CDSS. Through careful treatment planning, patients and primary care providers can anticipate potential referrals of other providers. Combined with the ability to provide access to patient information across different care settings and necessary healthcare stakeholders, this can help reduce duplication of services, improve communication between providers, and ultimately improve health outcomes and patient experience, all whilst minimizing cost.
Cost Reduction & Patient Convenience
As previously stated, CDSS can help healthcare providers make more accurate diagnoses and treatment decisions, which can lead to fewer medical errors, fewer complications, and shorter hospital stays. Additionally, the cost of medications and other treatments may fall as CDSS may have the capability of identifying generic alternatives and cheaper options that are equally as effective. As a result, CDSS can help healthcare providers deliver high-quality care at a lower cost, which can be beneficial to both patients and healthcare organizations.
CDSS can also improve patient convenience by providing more accessible, convenient, and personalized care. Patients can use CDSS tools such as telemedicine platforms, chatbots, and mobile apps to communicate with their healthcare providers, schedule appointments, renew prescriptions, and access their health records. This can help reduce the need for in-person visits, which can be time-consuming and expensive, and provide patients with more flexibility in how they receive care. Moreover, CDSS can help patients better manage chronic conditions by providing personalized recommendations, reminders, and alerts. This can help patients stay on top of their health and avoid unnecessary hospitalizations or emergency room visits.
Conclusion
CDSS have emerged as invaluable tools in modern healthcare. Their applications are far-reaching, encompassing the entire clinical point of care on the healthcare service spectrum. As technology continues to advance, the potential for CDSS to revolutionize healthcare delivery and improve patient experiences is boundless.
Our next post of this CDSS series will explore the benefits and drawbacks of CDSS. Stay tuned!