Clinical decision support (CDS) can significantly impact improvements in quality, safety, efficiency and effectiveness of health care. Complete records allow CDS systems to help with diagnoses and track for negative drug interactions by having a better view of a patient’s whole health.

Clinical decision support systems (CDSS) are computer-based programs that analyze data within electronic health records (EHRs) to provide prompts and reminders to assist health care providers in implementing evidence-based clinical guidelines at the point of care. CDSS help clinicians improve complex decision-making processes

Before implementing a CDSS, stakeholders should be aware of the potential benefits, challenges and uses. Keep in mind that it’s important to follow evidence-based recommendations for minimizing risk in CDSS design, implementation, evaluation and maintenance of CDSS.

Benefits of CDS

The best CDSS provide measurable value, leverage multiple data types, produce actionable insights, deliver information to the user, demonstrate good usability principles, are testable in small settings and support participation in quality and value improvement.

All of this leads to increased quality of care and enhanced health outcomes, avoidance of errors and adverse events, and improved efficiency, cost-benefit and provider and patient satisfaction.

Challenges to CDS

Before implementation, it’s important to understand some of the challenges:

  • Workflow disruption — CDSS can disrupt clinician workflow, especially in the case of stand-alone systems, leading to increased cognitive effort, more time required to complete tasks and less time face-to-face with patients. 
  • Alert fatigue and inappropriate alerts — Up to 95% of CDSS alerts are inconsequential, and often physicians disagree with or distrust alerts, or simply don’t read them. This leads to alert fatigue. They should be limited to more life-threatening or consequential contraindications, such as serious allergies. 
  • Impact on user skill — CDSS can create the impression that verifying the accuracy of an order is unnecessary or automatic. Also, over time CDSS can exert a training effect, so that the CDSS itself may no longer be required. Conversely, providers may develop too much reliance or trust on a CDSS for a specific task.
  • Operational impact of poor data quality and incorrect content — EHRs and CDSSs rely on data from external, dynamic systems, which can create issues. For instance, some CDSS modules might encourage ordering even when the hospital lacks adequate supplies. Also, medication and problem lists can be problematic, if not updated or used appropriately. 
  • Quality of data can affect quality of decision support. If data collection or input into the system is not standardized, the data is effectively corrupted. 
  • Lack of transportability and interoperability — Despite ongoing development, both CDSS and EHRs can experience interoperability issues. Many CDSS exist as cumbersome stand-alone systems or exist in a system that cannot communicate effectively with other systems.

Use Cases 

The basic principles of CDS can be applied to questions of patient care in endless ways, from the early detection of infection to delivering insights into highly personalized cancer therapies.

Some promising use cases from the provider community include:

  • A hospital in Alabama decreased its sepsis mortality rates by 53% after implementing a computerized surveillance algorithm.  Real-time analytics alerted providers to new diagnoses of sepsis or worsening vital signs and provided reminders about best practices for treating patients with the deadly condition.
  • Mayo Clinic employs a CDS tool that helps nurses deliver complete and accurate phone screenings of patients seeking advice or appointments.  The computerized decision software guides triage nurses through a series of standardized questions based on current care guidelines so that they do not miss important information about the patient’s health.
  • At a Department of Veterans Affairs site in Indiana, clinical decision support tools geared towards reducing unnecessary lab utilization helped decrease total test volume by 11.18% per year, generating cost savings of more than $150,000 without impacting care quality.

CDSS Evidence of Impact

Here is brief look at CDSS impact across key areas:

  • Health

    • CDSS leads to significant improvements in 1) recommendations for screening, such as for blood pressure or cholesterol, and other preventive care, 2) evidence-based clinical tests related to a particular condition, and 3) condition-related treatments prescribed.
  • Patient Safety

    • CDSS reduces medication error by improving process of care and patient outcomes. Larger samples and longer-term studies are required to ensure more reliable evidence base on the effects of CDSS on patient outcomes.
  • Health Disparity

    • Some evidence exists that CDSS leads to successful health outcomes when used in underserved communities. This means that CDSS has the potential to eliminate barriers and reduce disparities in care.
  • Economic

    • The ability to determine an overall estimate of the cost or economic benefit of CDSS is limited. But available studies show that health care costs are more likely to decrease than increase after CDSS implementation.

CDSS Implementation Considerations

  • Settings

    • Different types of CDS may be ideal for different processes of care in different settings, and effective CDS must be relevant to those who can act on the information in a way that supports completion of the right action.
  • Policy and Law-Related

    • Throughout the process of prioritizing evidence for dissemination via CDS it is important to recognize and manage external factors, such as the marketplace, policy, legal and governance factors that affect developing, dissemination and implementation processes for patient-centered CDS.
  • Implementation Guidance

    • Most knowledge-based CDS systems consist of a data repository, an inference engine and a mechanism to communicate, and they commonly operate under if-then rules. For instance, if the knowledge based CDSS is trying to assess potential drug interactions, then a rule might be that if drug A is taken and drug B is prescribed, then an alert should be issued. A CDSS without a knowledge base relies on machine learning to analyze clinical data.

Strategies and Guidelines

In 2005, the American Medical Informatics Association (AMIA) developed a strategy to guide federal and private sector activities to advance the development and adoption of CDS. The resulting roadmap includes three pillars and six strategic objectives for CDS to ensure that optimal, usable and effective clinical decision support is widely available to providers, patients and individuals where and when they need it.

The roadmap’s three pillars and their objectives:

  • Best knowledge available when needed

    • Clinical knowledge and CDS interventions should be in standardized formats so that a variety of knowledge developers can produce this information in a way that knowledge users can readily understand, assess and apply.
    • Collect, organize and distribute clinical knowledge and CDS interventions in one or more services from which users can easily find the specific material they need and incorporate it into their own information systems and processes.
  • High adoption and effective use

    • Address policy/legal/financial barriers and create additional support and enablers for widespread CDS adoption and deployment.
    • Improve clinical adoption and usage of CDS interventions by helping clinical knowledge and information system producers and implementers design CDS systems that are easy to deploy and use, and by identifying and disseminating best practices for CDS deployment.
  • Continuous improvement of knowledge and CDS methods

    • Assess and refine the national experience with CDS by systematically capturing, organizing and examining existing deployments. Share lessons learned and use them to continually enhance implementation best practices.
    • Advance care-guiding knowledge by fully leveraging the data available in interoperable EHRs to enhance clinical knowledge and improve health management.

The Future of CDSS

In order to implement and utilize a CDSS in a medical service, clinical information should be generated and managed in a standardized form. For this purpose, standardization of terminology, coding of prescriptions and unification, such as the preparation method and the weights and measures, should be integrated.

In the near future, more complex, useful systems will be developed, forging CDSS into an essential part of care. However, it will be necessary to better understand the algorithms embedded in CDSS and to assess them correctly to ensure their true potential for improving quality, safety, efficiency and effectiveness of health care. 

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