Clinical decision support system
A clinical decision support system (CDSS) is a health information technology that provides clinicians, staff, patients, and other individuals with knowledge and person-specific information to help health and health care. CDSS encompasses a variety of tools to enhance decision-making in the clinical workflow. These tools include computerized alerts and reminders to care providers and patients, clinical guidelines, condition-specific order sets, focused patient data reports and summaries, documentation templates, diagnostic support, and contextually relevant reference information, among other tools. CDSSs constitute a major topic in artificial intelligence in medicine.
Effectiveness[edit]
The evidence of the effectiveness of CDSS is mixed. There are certain diseases which benefit more from CDSS than other disease entities. A 2018 systematic review identified six medical conditions in which CDSS improved patient outcomes in hospital settings, including blood glucose management, blood transfusion management, physiologic deterioration prevention, pressure ulcer prevention, acute kidney injury prevention, and venous thromboembolism prophylaxis.[13]
A 2014 systematic review did not find a benefit in terms of risk of death when the CDSS was combined with the electronic health record.[14] There may be some benefits, however, in terms of other outcomes.[14]
A 2005 systematic review had concluded that CDSSs improved practitioner performance in 64% of the studies and patient outcomes in 13% of the studies. CDSSs features associated with improved practitioner performance included automatic electronic prompts rather than requiring user activation of the system.[15]
A 2005 systematic review found "Decision support systems significantly improved clinical practice in 68% of trials."' The CDSS features associated with success included integration into the clinical workflow rather than as a separate log-in or screen, electronic rather than paper-based templates, providing decision support at the time and location of care rather than prior, and providing care recommendations.[16]
However, later systematic reviews were less optimistic about the effects of CDS, with one from 2011 stating "There is a large gap between the postulated and empirically demonstrated benefits of [CDSS and other] eHealth technologies ... their cost-effectiveness has yet to be demonstrated".[17]
A five-year evaluation of the effectiveness of a CDSS in implementing rational treatment of bacterial infections was published in 2014; according to the authors, it was the first long-term study of a CDSS.[18]
Challenges to adoption[edit]
Clinical challenges[edit]
Much effort has been put forth by many medical institutions and software companies to produce viable CDSSs to support all aspects of clinical tasks. However, with the complexity of clinical workflows and the demands on staff time high, care must be taken by the institution deploying the support system to ensure that the system becomes an integral part of the clinical workflow. Some CDSSs have met with varying amounts of success, while others have suffered from common problems preventing or reducing successful adoption and acceptance.
Two sectors of the healthcare domain in which CDSSs have had a large impact are the pharmacy and billing sectors. Commonly used pharmacy and prescription-ordering systems now perform batch-based checking orders for negative drug interactions and report warnings to the ordering professional. Another sector of success for CDSS is in billing and claims filing. Since many hospitals rely on Medicare reimbursements to stay in operation, systems have been created to help examine both a proposed treatment plan and the current rules of Medicare to suggest a plan that attempts to address both the care of the patient and the financial needs of the institution.
Other CDSSs that are aimed at diagnostic tasks have found success, but are often very limited in deployment and scope. The Leeds Abdominal Pain System went operational in 1971 for the University of Leeds hospital. It was reported to have produced a correct diagnosis in 91.8% of cases, compared to the clinicians' success rate of 79.6%.
Despite the wide range of efforts by institutions to produce and use these systems, widespread adoption and acceptance have still not yet been achieved for most offerings. One large roadblock to acceptance has historically been workflow integration. A tendency to focus only on the functional decision-making core of the CDSS existed, causing a deficiency in planning how the clinician will use the product in situ. CDSSs were stand-alone applications, requiring the clinician to cease working on their current system, switch to the CDSS, input the necessary data (even if it had already been inputted into another system), and examine the results produced. The additional steps break the flow from the clinician's perspective and cost precious time.[19]
Technical challenges and barriers to implementation[edit]
Clinical decision support systems face steep technical challenges in a number of areas. Biological systems are profoundly complicated, and a clinical decision may utilise an enormous range of potentially relevant data. For example, an electronic evidence-based medicine system may potentially consider a patient's symptoms, medical history, family history and genetics, as well as historical and geographical trends of disease occurrence, and published clinical data on therapeutic effectiveness when recommending a patient's course of treatment.
Clinically, a large deterrent to CDSS acceptance is workflow integration.
While it has been shown that clinicians require explanations of Machine Learning-Based CDSS, in order to able to understand and trust their suggestions,[20] there is an overall distinct lack of application of explainable Artificial Intelligence in the context of CDSS,[21] thus adding another barrier to the adoption of these systems.
Another source of contention with many medical support systems is that they produce a massive number of alerts. When systems produce a high volume of warnings (especially those that do not require escalation), besides the annoyance, clinicians may pay less attention to warnings, causing potentially critical alerts to be missed. This phenomenon is called alert fatigue. [22]
Maintenance[edit]
One of the core challenges facing CDSS is difficulty in incorporating the extensive quantity of clinical research being published on an ongoing basis. In a given year, tens of thousands of clinical trials are published.[23] Currently, each one of these studies must be manually read, evaluated for scientific legitimacy, and incorporated into the CDSS in an accurate way. In 2004, it was stated that the process of gathering clinical data and medical knowledge and putting them into a form that computers can manipulate to assist in clinical decision-support is "still in its infancy".[24]
Nevertheless, it is more feasible for a business to do this centrally, even if incompletely, than for each doctor to try to keep up with all the research being published.
In addition to being laborious, integration of new data can sometimes be difficult to quantify or incorporate into the existing decision support schema, particularly in instances where different clinical papers may appear conflicting. Properly resolving these sorts of discrepancies is often the subject of clinical papers itself (see meta-analysis), which often take months to complete.
Evaluation[edit]
In order for a CDSS to offer value, it must demonstrably improve clinical workflow or outcome. Evaluation of CDSS quantifies its value to improve a system's quality and measure its effectiveness. Because different CDSSs serve different purposes, no generic metric applies to all such systems; however, attributes such as consistency (with and with experts) often apply across a wide spectrum of systems.[25]
The evaluation benchmark for a CDSS depends on the system's goal: for example, a diagnostic decision support system may be rated based upon the consistency and accuracy of its classification of disease (as compared to physicians or other decision support systems). An evidence-based medicine system might be rated based upon a high incidence of patient improvement or higher financial reimbursement for care providers.
Research[edit]
Prescription errors[edit]
A study in the UK tested the Salford Medication Safety Dashboard (SMASH), a web-based CDSS application to help GPs and pharmacists find people in their electronic health records who might face safety hazards due to prescription errors. The dashboard was successfully used in identifying and helping patients with already registered unsafe prescriptions and later it helped monitoring new cases as they appeared.[40][41]
In today's rapidly advancing healthcare landscape, clinical decision support systems (CDSS) play a pivotal role in improving patient care, enhancing clinical outcomes, and supporting healthcare professionals in making informed decisions. This article explores the concept, benefits, challenges, and future prospects of CDSS.
A Clinical Decision Support System (CDSS) is a computerized tool designed to assist healthcare providers in making clinical decisions by integrating medical knowledge with patient data. These systems utilize algorithms, databases, and patient information to provide tailored recommendations, alerts, and reminders to healthcare professionals at the point of care.
1. **Knowledge Base**: Contains medical guidelines, protocols, best practices, and clinical rules.
2. **Patient Data Interface**: Integrates with electronic health records (EHR) systems to access patient demographics, medical history, test results, and current medications.
3. **Inference Engine**: Analyzes patient data and applies clinical rules to generate suggestions or alerts based on predefined algorithms.
4. **User Interface**: Presents recommendations, alerts, and relevant information to healthcare providers in a user-friendly format.
1. **Improved Clinical Decision Making**: CDSS provides evidence-based recommendations, reducing errors and variability in clinical practice.
2. **Enhanced Patient Safety**: Alerts for drug interactions, allergies, and potential adverse events help prevent medical errors and improve patient outcomes.
3. **Efficiency**: Streamlines workflow by providing quick access to relevant information, reducing the time spent on manual data retrieval and analysis.
4. **Cost-Effectiveness**: Helps in optimizing resource utilization, reducing unnecessary tests, treatments, and hospitalizations.
5. **Continuing Education**: Acts as a learning tool by keeping healthcare providers updated with the latest medical research and guidelines.
1. **Integration Complexity**: Integrating CDSS with existing EHR systems and workflows can be challenging and time-consuming.
2. **Data Quality and Interoperability**: Dependence on accurate and complete data is crucial for the effectiveness of CDSS.
3. **User Acceptance**: Resistance to change and unfamiliarity with new technology among healthcare providers.
4. **Alert Fatigue**: Overwhelming healthcare providers with excessive alerts and reminders, leading to desensitization.
5. **Legal and Ethical Issues**: Concerns regarding liability, privacy, and confidentiality of patient data.
1. **Artificial Intelligence and Machine Learning**: Advanced algorithms for predictive analytics, personalized medicine, and real-time decision-making.
2. **Mobile and Cloud-based Solutions**: Remote access and seamless integration across different healthcare settings.
3. **Natural Language Processing**: Enhancing CDSS capabilities to interpret unstructured data such as clinical notes and imaging reports.
4. **Patient-Centered CDSS**: Involving patients in decision-making processes and personalized health management.
Clinical Decision Support Systems represent a transformative technology in healthcare, offering substantial benefits in clinical practice, patient safety, and healthcare efficiency. While challenges remain in implementation and adoption, ongoing advancements in technology and healthcare delivery are poised to further enhance the capabilities and impact of CDSS in improving overall healthcare outcomes.
In conclusion, CDSS are pivotal tools in the evolving landscape of healthcare technology, enabling healthcare professionals to leverage data-driven insights and medical knowledge effectively at the point of care, ultimately leading to better patient outcomes and enhanced healthcare delivery.