The Impact of EHR-Based Alerts on Clinician Workflow and Patient Care

Understanding the Landscape of Clinical Decision Support Systems

The widespread adoption of electronic health records (EHRs) over the past decade has paved the way for the integration of sophisticated clinical decision support (CDS) systems. These systems leverage the wealth of clinical data captured in EHRs to provide targeted, patient-specific recommendations and alerts aimed at improving the quality and safety of care delivery.

CDS systems represent a paradigm shift in modern healthcare, serving as powerful tools to augment clinicians in their complex decision-making processes. Since their introduction in the 1980s, CDS technologies have undergone rapid evolution, with increasing integration into routine clinical workflows through EHRs and other computerized systems.

Despite the promise of CDS, there remain significant unknowns regarding their true impact on clinician experiences, patient outcomes, and healthcare costs. While there have been numerous success stories, notable setbacks have also revealed the potential risks and unintended consequences associated with these systems.

In this in-depth article, we will provide a comprehensive overview of the use of CDS systems in medicine, including:

• The different types of CDS systems and their core components
• Current use cases with proven efficacy
• Common pitfalls and potential harms
• Evidence-based recommendations for minimizing risk in CDS design, implementation, evaluation, and maintenance

By the end, you’ll have a thorough understanding of the state-of-the-art in CDS and practical strategies for leveraging these powerful technologies to drive meaningful improvements in patient care.

Defining Clinical Decision Support Systems

A clinical decision support system (CDSS) is a software application designed to assist healthcare providers in clinical decision-making. These systems aim to enhance medical decisions by integrating targeted clinical knowledge, patient information, and other health data.

A traditional CDSS is comprised of software that matches the characteristics of an individual patient to a computerized clinical knowledge base, and then presents patient-specific assessments or recommendations to the clinician for consideration.

As EHRs have become ubiquitous, CDS systems are increasingly being integrated directly into clinical workflows, providing real-time guidance and alerts to providers at the point-of-care.

CDS systems can be broadly classified into two main categories:

1. Knowledge-based CDS: These systems rely on programmed rules (IF-THEN statements) based on clinical evidence, expert knowledge, or patient-specific data. The system retrieves relevant data, evaluates the rules, and generates an output or recommended action.

2. Non-knowledge-based CDS: These systems do not rely on predefined rules, but instead leverage artificial intelligence (AI), machine learning (ML), or statistical pattern recognition to analyze data and produce recommendations. While a rapidly growing area, non-knowledge-based CDS systems face challenges related to transparency and data availability.

Regardless of the underlying approach, all CDS systems share common core components:

1. The Base: This includes the knowledge base (rules or algorithms), as well as the available data sources.

2. The Inference Engine: This component takes the rules/algorithms and patient data, and applies them to generate recommendations or actions.

3. The Communication Mechanism: This is the user interface (e.g. EHR, website, mobile app) through which the end-user interacts with the system.

Over the past two decades, CDS systems have seen growing adoption, with government incentives and regulatory requirements driving widespread integration into EHRs. In the U.S., an estimated 41% of hospitals with an EHR also had a CDSS in 2013, and this number has continued to rise.

Leveraging CDS to Improve Patient Safety

One of the most well-established use cases for CDS systems is in the realm of medication safety. Strategies to reduce medication errors commonly leverage CDS technologies, particularly in the areas of:

Drug-Drug Interaction (DDI) Alerts: CDS-enabled computerized provider order entry (CPOE) systems can provide real-time alerts to warn clinicians about potentially harmful drug combinations. Studies show that up to 65% of inpatients may be exposed to one or more DDIs, highlighting the value of these safety checks.

However, significant variability exists in how DDI alerts are implemented, including differences in alert prioritization, display formats, and the underlying algorithms used to identify interactions. Efforts are underway to establish standardized “high-priority” DDI lists to promote more consistent deployment.

Dosing Checks and Contraindication Alerts: CDS can also assist in identifying inappropriate doses or contraindicated medications based on patient factors like age, weight, kidney function, and allergies. These automated checks can help prevent serious adverse events.

Closed-Loop Medication Administration: CDS is a key component of “closed-loop” medication management systems that integrate CPOE, automated dispensing, and barcode-assisted administration. These systems have demonstrated effectiveness in reducing medication errors at the bedside.

Beyond medication safety, CDS can also enhance patient safety in other areas, such as:

Glucose Monitoring Protocols: A CDS system in the ICU was able to decrease the number of hypoglycemic events by automatically prompting nurses to measure glucose levels according to a predetermined protocol.

Preventive Care Reminders: CDS can help ensure patients receive recommended screening tests, vaccinations, and other preventive services by providing alerts and reminders to clinicians.

While CDS has proven valuable for improving patient safety, it is important to recognize that these systems are not infallible. Errors can still occur if clinicians choose to override or bypass the technology. Careful design and implementation, along with robust monitoring, are essential to maximize the benefits and minimize the risks.

Improving Guideline Adherence with CDS

A key benefit of CDS is its ability to enhance clinician adherence to evidence-based clinical guidelines and care pathways. Traditional guidelines have often fallen short in real-world practice, as the assumption that clinicians will read, internalize, and consistently implement new recommendations has not held true.

CDS systems can help bridge this gap by literally encoding guideline-based rules and algorithms into the software. This can take various forms, such as:

• Standardized order sets for specific clinical scenarios
• Alerts that notify clinicians when a patient matches the criteria for a particular guideline
• Reminders to complete recommended tests, referrals, or follow-up activities

CDS can also assist in managing patients enrolled in clinical trials or research protocols, ensuring adherence to study-specific criteria and processes.

Furthermore, CDS can help identify eligible patients for clinical trials and facilitate the enrollment process, improving recruitment and expanding access to innovative treatments.

Optimizing Resource Utilization and Reducing Costs with CDS

In addition to improving safety and guideline adherence, CDS systems have demonstrated the ability to drive cost savings and resource optimization for healthcare systems. Some examples include:

Reducing Test Duplication: A CPOE-integrated CDS rule limited the frequency of certain lab tests in a pediatric ICU, resulting in a projected annual cost savings of over $700,000 without negatively impacting length of stay or mortality.

Automating Drug Substitutions: A German hospital developed a CDS-powered algorithm to automatically substitute medications on the hospital formulary, reducing workload, errors, and costs associated with manual substitutions.

Improving Coding Accuracy: CDS tools can suggest the most appropriate diagnostic codes to help clinicians select the most suitable codes, improving the accuracy of clinical documentation and coding.

Enhancing Vaccination Rates: A CDS system was able to improve documentation of splenectomy on patient problem lists, which then triggered appropriate vaccination reminders and improved vaccination rates for this high-risk population.

While the potential for cost savings is promising, it is important to note that the business case for CDS remains a complex and evolving area. Rigorous cost-benefit analyses are needed to fully understand the financial implications of these technologies.

Leveraging CDS for Diagnosis and Clinical Workflows

Beyond medication safety and resource optimization, CDS systems are being applied to support clinical diagnosis and enhance workflow efficiency. These applications include:

Diagnostic Decision Support Systems (DDSS): DDSS provide clinicians with a computerized “consultation” by generating a list of possible diagnoses based on patient data and symptoms. While DDSS have not yet seen widespread adoption, they hold promise for improving diagnostic accuracy, particularly in primary care settings where diagnostic errors are common.

Imaging Order Guidance: CDS can assist radiologists in selecting the most appropriate imaging tests, providing reminders of best practices and alerting to potential contraindications. Studies have shown these interventions can reduce unnecessary imaging utilization.

Automated Image Interpretation: The rise of advanced imaging analytics powered by artificial intelligence and machine learning holds potential to augment radiologists in extracting insights from medical images beyond human capability. While still an emerging field, early results suggest AI-based image analysis may soon rival human experts in certain domains.

Lab Test Interpretation: CDS can enhance the utility of laboratory testing by providing automated interpretation of results, particularly for tests with highly personalized reference ranges (e.g. based on age, sex, or disease subtype).

Pathology Report Generation: CDS has been applied to automate certain aspects of pathology reporting, such as tumor grading, to improve consistency and accuracy.

While these CDS applications hold promise, their true impact on clinical outcomes remains an area of active research. Careful design, integration into existing workflows, and a focus on user-centered design will be critical to driving meaningful improvements in care delivery.

Empowering Patients through Personal Health Records and CDS

The emergence of personal health records (PHRs) integrated with CDS functionality represents an important step towards patient-centered care. PHRs allow patients to access their health information, communicate with providers, and actively engage in managing their own care.

When linked to EHRs, PHRs can facilitate a two-way exchange of information, with patient-entered data available to providers and EHR data accessible to patients. This enables shared decision-making and can help overcome barriers related to lack of information.

PHRs integrated with CDS offer a variety of capabilities, such as:

• Symptom tracking and triage tools to guide patients on appropriate care settings
• Medication management features, including reminders and interactions checks
• Condition-specific educational content and self-management resources

Furthermore, PHRs can integrate data from wearable devices and remote monitoring tools, allowing providers to track patient health trends between visits and intervene proactively.

Examples of successful PHR-CDS implementations include systems for managing chronic conditions like diabetes, heart failure, and hypertension. These technologies have demonstrated improvements in provider workflow, patient-provider communication, and ultimately, quality of care.

As PHRs and patient-facing CDS continue to evolve, a key focus will be on designing systems that truly empower patients as active partners in their health, rather than simply serving as passive repositories of information.

Understanding the Challenges of CDS Integration

While CDS systems hold great promise, their successful integration into clinical workflows is far from guaranteed. Numerous challenges can arise, including:

Disruption to Workflow: CDS can disrupt established clinical workflows, particularly if the systems are not well-integrated into existing processes and user interfaces. This can increase cognitive load, reduce time spent with patients, and lead to clinician dissatisfaction.

Alert Fatigue: Clinicians are often overwhelmed by an excessive number of CDS alerts, many of which are perceived as irrelevant or unnecessary. This “alert fatigue” can lead to important alerts being ignored or overridden.

Lack of Trust and Acceptance: Clinicians may be hesitant to rely on CDS recommendations, especially if they conflict with the provider’s own experience and clinical judgment. Overcoming this resistance requires careful attention to usability, transparency, and integration into existing workflows.

Maintaining Knowledge Bases: Keeping CDS knowledge bases up-to-date with the latest clinical evidence and guidelines is an ongoing challenge, as medical knowledge is constantly evolving.

Interoperability Barriers: Seamless integration of CDS systems with EHRs and other health IT infrastructure remains a significant hurdle, hindering widespread adoption and effectiveness.

Financial Sustainability: The upfront and ongoing costs associated with CDS implementation and maintenance can be substantial, posing barriers to widespread adoption.

Addressing these challenges will require a multifaceted approach, encompassing robust user-centered design, rigorous evaluation, and continuous optimization. Principles from human factors engineering and implementation science can provide valuable frameworks for successful CDS integration.

Recommendations for Effective CDS Implementation

Based on the evidence and insights from the literature, we offer the following recommendations to healthcare organizations and informaticists seeking to implement effective CDS systems:

1. Focus on User-Centered Design: Engage clinicians and end-users throughout the CDS design and development process. Employ human factors engineering methods, such as task analysis and usability testing, to ensure the system aligns with clinical workflows and cognitive processes.

2. Minimize Alert Fatigue: Carefully curate CDS alerts to only include the most critical and actionable information. Prioritize alerts based on their potential impact and ensure they are delivered at the appropriate time in the clinical workflow.

3. Foster Trust and Acceptance: Maintain transparency around the evidence and logic behind CDS recommendations. Provide opportunities for clinician feedback and customization to build trust and ownership in the system.

4. Ensure Knowledge Base Maintenance: Establish robust processes for regularly updating CDS knowledge bases to keep pace with evolving clinical guidelines and evidence. Consider leveraging natural language processing and machine learning to automate knowledge curation.

5. Prioritize Interoperability: Adopt industry standards and frameworks, such as HL7 FHIR, to enable seamless integration of CDS systems with EHRs and other health IT infrastructure. Explore cloud-based architectures to enhance flexibility and scalability.

6. Measure and Optimize Continuously: Incorporate CDS evaluation into ongoing quality improvement efforts. Monitor both process and clinical outcomes, and be prepared to iterate on the design and implementation based on user feedback and observed impacts.

7. Address Financial Sustainability: Develop a comprehensive business case that accounts for both the costs and potential benefits of CDS implementation. Explore innovative funding models and shared savings arrangements to ensure long-term viability.

By following these recommendations, healthcare organizations can maximize the potential of CDS to drive meaningful improvements in patient safety, clinical outcomes, and operational efficiency, while minimizing the risks and unintended consequences that can arise from these powerful technologies.

Conclusion

Clinical decision support systems represent a transformative force in modern healthcare, empowering clinicians with targeted, patient-specific guidance to enhance the quality and safety of care delivery. As EHR adoption continues to rise, the integration of CDS technologies offers unprecedented opportunities to harness the wealth of clinical data for improved decision-making.

The existing literature provides numerous examples of the positive impact CDS can have, from reducing medication errors to improving guideline adherence and resource utilization. However, the integration of these systems is not without its challenges, including the potential for disruption to clinical workflows, alert fatigue, and interoperability barriers.

To realize the full potential of CDS, healthcare organizations must adopt a user-centered, evidence-based approach to design, implementation, and continuous optimization. By addressing the key challenges outlined in this article, informaticists and clinical leaders can ensure CDS systems consistently deliver on their promise to improve patient outcomes and support the transition to a more digitally-enabled, data-driven healthcare system.

As the field of clinical decision support continues to evolve, driven by advancements in artificial intelligence, natural language processing, and human-computer interaction, the opportunity to transform care delivery has never been greater. By staying attuned to the latest research, best practices, and implementation strategies, healthcare organizations can position themselves to harness the power of CDS and drive meaningful, lasting improvements in the lives of the patients they serve.