Alert Fatigue Clinical Decision Support

Trends In Cds Interventions

Approaches to Manage Alert Fatigue and Suggestions for Appropriate Metrics

Features present in 4 or more included studies are plotted cumulatively, over time, in . Three of the most common interactive featurestiering alerts, providing shortcuts for common corrective actions, and requiring a reason to overrideare described and illustrated in .

Feature prevalence over time. Pharmacists Received CDS is a subcategory of No Modals Interrupted Prescribers. All others are subcategories of Modals Interrupted Prescribers. CDS: clinical decision support.

The most commonly reported type of CDSwhich comprised 83% of resultsinterrupted prescribers with modal dialogs. The most common variants were tiered to convey levels of risk, provided shortcuts for common corrections, or required a reason to override.

We also found advisories that were not automatically issued using computerized systems. These included fax or mail alerts, and interactive designs in which a user manually retrieved a list of alerts or manually triggered a battery of modal dialogs. Only 1 article documented a design that allowed the user to dismiss a modal, and then retrieve it later for reference, rather than memorizing the contents of the alert. A list of all designs for presenting CDS is available in the Supplementary Appendix.

Data Collection And Analysis

In this section, we describe the planned methods for selecting studies and extracting and managing data. We also describe the means by which we will assess the quality of each study included in the review and how we will analyse and present the review findings.

Study selection

Search results will be entered into Mendeley Reference Management Software where duplicate entries will be removed automatically by an in built algorithm supported by manual checking.

Study selection will be undertaken by two reviewers independently.

Titles and abstracts will be screened with reference to the eligibility criteria to remove all ineligible articles. Translation of abstracts will be sought if required, prior to eligibility assessment. The full text of articles remaining will be sought and considered for inclusion in the review using the full eligibility criteria. Part translations of articles not in English will be undertaken to facilitate this process. Disagreements between reviewers will be resolved through discussion and with referral to a third reviewer if required. Articles excluded from the review at the full-text stage will be noted on the data extraction form as will the eligibility criteria these did not satisfy.

Critical appraisal, quality of reporting and data extraction

Clinical Decision Support Essay

An Analysis of Clinical Decision Support for an Effective Population Health ManagementEffectively managing the health of the population requires tremendous efforts and strategies of a healthcare organization. Leveraging the use of health information technology such as enabling the use of clinical decision support at the point care is one of the strategies for an effective population health management . Rush-Copley Medical Center has successfully demonstrated the effectiveness of CDS

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Data Analysis And Synthesis Of Results

We coded features and measurement methods as short descriptions . We sorted these descriptions into categories as commonalities emerged.

We also paid attention to the methods used to construct acceptance rates. In this article, we refer to the 2 main methods as in-dialog action analysis and event analysis.

In-dialog action analysis is only applicable when the CDS intervention takes the form of a dialog that features a button that the prescriber can click to modify or discard their order . Researchers count the number of times the acceptance button was clicked, and divide that count by the total number of dialogs that appeared.

Event analysis may be applied to any form of CDS, including dialogs. When conducting an event analysis, researchers search the patient chart for evidence that the prescriber accepted advice, in addition to any changes that prescribers may have made by clicking buttons inside CDS dialogs. For example, a prescriber might dismiss a modal dialog warning against a warfarin order, and then reduce the dose later. Or, a pharmacist might receive an alert from a CDS system, and counsel the prescriber by phonein which case the researchers must check to see if the prescriber made a change to the chart.

Next, we used a t test to compare acceptance rates between CDS systems by interactive design and clinical role-tailoring. In addition, we constructed a plot to holistically examine prescribers acceptance rates by feature.

Not All Alerts Are Created The Same

At Sentara Healthcare, Combating Alert Fatigue with ...

To fight this, you must categorize your alerts according to number of factors and carefully assign them to a different tool with a different look, feel and interaction. For example before you put an alert in the system, ask yourself:

Does the clinician need to take an action NOW?

Should the alert stop the clinicians current workflow and require them to perform something else thats critical?

Are there any valid reasons to override the alert? Whats the acceptable override rate?

Would notifying the clinicians unobtrusively achieve the same goal?

Can the alert be placed in the context of a current screen in the EMR instead of a pop-up type of screen?

Going through such process for every type of alert you have will help you achieve your objective with as little disruption as possible to the clinicians, which is a main factor in compliance with your CDS recommendations. Alerts should vary in their methodology according to answers to questions like the above. Some examples would be:

A peculiar icon in a screen like the Medication Administration Record, or the medications list that notifies the physicians of a moderate interaction

A message in their inbox that reminds the clinician of an action that should be taken, but not urgent

Finally, the infamous modal pop-up message with the red title on top, that interrupts current workflow, and forces the clinician to take immediate action. These should be your last resort

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Cds Acceptance By Feature

For the analysis of feature acceptance, we included the 22 studies that used event analysis. Of those studies, 15 were based on CDS systems that interrupted prescribers with modal dialogs. Among the 7 alternatives, 4 presented alerts pertaining to areas such as antimicrobial stewardship or renal dosing to pharmacists, 2 delivered fax or mail alerts to prescribers,, and 1 depended on the prescriber to manually trigger a review process.

We compared those interventions that interrupted prescribers with modal dialogs with all other interventions. The group of alternative interventions included any alerts that were sent to the pharmacist instead of the prescriber, as well as any alerts that were sent to the prescriber but were not modal dialogs. Using a t test, we found that prescriber-interrupting modals were accepted significantly less often, as predicted . The acceptance rate distributions are shown in .

Prescribers acceptance rates for clinical decision support advice, by feature, measured using event analysis. CDSs with multiple features appear on multiple lines.

Visual inspection suggested that prescribers accepted advice from CDS-guided pharmacists more frequently and with less variability than they accepted advice when interrupted by modal dialogs.

Data Extraction And Analysis

Results will be taken from the included papers, including appendices where appropriate, and will be imported into analysis software . Inductive thematic analysis will then be performed to form overarching third order themes. To enable this, we will use the three stages described by Thomas and Harden in the thematic synthesis of qualitative research. The first is the coding of the findings of the primary studies. The second is the categorisation of these codes into descriptive themes. The third is development of analytical themes to describe the themes that have emerged in the second phase. The approach we adopt will be inductive, i.e. themes emerge from the data through repeated examination and comparison. The emerging descriptive themes will be analysed in consideration of the contextual factors, i.e. healthcare context, setting and reminder usage within clinical decision systems to assess if these contextual factors have any impact upon the ensuing analytical themes. The findings will be verified by utilising independent coding by two reviewers, the triangulation of these codes and iterative discussions amongst all reviewers of the coding framework at each of the three phases of the analysis.

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Predicting Inappropriate Alerts And Responses

To better evaluate and improve CDS alert appropriateness, we first propose the use of the alert evaluation framework developed and evaluated in prior research that utilizes retrospective chart review to determine which alerts are clinically relevant and which overrides are justifiable. The framework classifies alert and clinician response appropriateness, identifying successes, justifiable overrides, provider nonadherence, and unintended consequences . This approach aims first to identify predictors of alert and response inappropriateness to eliminate the need for manual reviews, and second to validate our findings in both ambulatory and community hospital settings.

Box 1 Methods And Sources Used For This Overview

Philips Alarm Advisor alerts clinicians to silencing behavior

MEDLINE search 1980-January 2018. Key words: CDSS, diagnostic decision support system/DDSS, personal health record/PHR decision support, EHR decision support
Hand searches of the references of retrieved literature

University libraries searching for texts on clinical decision support systems and other keywords mentioned above
Personal and local experience working with healthcare technology and decision support systems

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The Importance Of Actionable Alerts: Reducing Alert Fatigue To Improve Clinical Decision Support

Raymond BlackElizabeth StutlerVigiLanz

The healthcare industry has long struggled with how to handle vast amounts of data. Over a decade ago, everything existed on paper and hospitals had to rely on those printed pages and the memory of employees to prevent errors. Todays clinicians benefit from technology that collects and collates large amounts of data to identify patterns or potential issues, with the ultimate goal of improving patient care. This data can exist in many different forms, and be stored in disparate sources. Therefore, its vital to understand what kind of data exists.Lets suppose we can sort alerts on patient data into three buckets:

Additionally, if the workday is consumed by responding to the alerts in Bucket A or B, there often isnt time to seek out additional quality issues that could be impacting an organization.

No matter the bucket from which they are generated, alerts remind clinicians about everything from hospital-acquired infections, to patients drug allergies, possible drug interactions, dosing guidelines, and lab testing guidance. Clinicians can either follow the alerts recommendations, override them, or ignore them.

Recommendations For Future Work

Given the preceding discussion, we propose the following 3 recommendations for future CDS research:

First, we recommend that researchers consider alternatives to prescriber-interruptive modal dialogs, since there is evidence that the latter suffers from relatively lower acceptance. Role-based tailoring appeared to improve acceptance rates, and further work is needed in this area. Ideally, those who will receive the alerts should be involved in role-tailoring decisions. Alternatives to modal dialogs should also be explored.

Second, recommend measuring acceptance rates using event analysis, rather than in-dialog action analysis. Because event analysis is more widely applicable, using it will enable meta-analyses that accommodate varied CDS interventions.

Last, we recommend reporting both acceptance rates and patient outcomes. Much of the literature that we saw in our review reported one or the other few reported both. This has made it difficult to analyze patient outcomes as a function of CDS design and role-tailoring, mediated by acceptance.

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Presentation Of Findings And Reporting Methods

The review will describe participant and setting characteristics, data collection and analysis methods. The findings of the primary papers will be summarised in tabular form describing the key characteristics of each. In addition, we will describe each paper narratively. The review findings will be classified into key themes as informed by the analysis. The reviews findings will also be summarised visually in a proposed conceptual framework explaining the relationship between the key factors influencing the efficacy of electronic alerts.

The protocol was developed and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis . The review methods and results will be reported according to the ENTREQ for reporting synthesis of qualitative studies . The final literature searches will be reported using the STARLITE .

Guidelines For Policymaking Regulations And Strategies Clinical Decision Support

For policymaking, regulations and strategies, clinical decision support provides clinicians, staff, patients or other individuals with knowledge and person-specific information, intelligently filtered or presented at appropriate times, to enhance health and health care. CDS 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

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S For Improving Alerts

Several projects have attempted to improve CDS alerts and reduce override rates by turning off frequently overridden alerts.- Duke and Bolchini developed a model for creating context-aware drug-drug interaction alerts that allowed tailoring alert displays based on relevant patient-specific information, resulting in improved acceptance of the alerts. However, alerts deemed inappropriate in some clinical scenarios should also be suppressed. No consistent method exists to avoid false positive alerts and false negative alerts that is generalizable across systems and clinical domains.

Alert Fatigue And Clinical Decision Support

Clinical Decision Support has been called out as an important part of an EMR system. Youll get no argument from me on this. What I have been thinking a lot about is what people call Alert Fatigue. For those unfamiliar with the term, it basically means that a doctor gets so many alerts that they grow numb to the alerts and stop looking at them. For those that are married, its like your wifes nagging. It happens so much that you stop listening .

I think this concept of alert fatigue is really important and I think it will be impossible to create an EMR that strikes the perfect balance. Some EMR offer too many alerts and some probably offer too few. So, my question for you is which side should we adopt? Is it better to have too many alerts which doctors then might ignore or is it better to have too few alerts and not be alerted to something important?

Theres some real challenging issues associated with both. Liability unfortunately being a major part of each. Where do you stand on this issue?

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Healthcare Organizations Across The Country Are Finding Creative Ways To Reduce Ehr Clinical Decision Alert Fatigue Through Optimization And Teamwork

May 13, 2021 – EHR alerts serve a significant purpose, but they can also result in EHR clinical decision alert fatigue, clinician burnout, or even frustration.

EHR alerts permit clinicians to access real-time patient data, ideally resulting in enhanced patient safety and medication accuracy. Alerts can also notify clinicians about potential adverse drug interactions.

According to Stanford University health IT professionals, EHR alerts are a vital part of EHRs that are not merely the use of technology it is using technology to find meaningful information to make clinical decisions and provide the best possible patient care.

Although a clinicians first instinct might be to close the alert to limit frustration, healthcare organizations attempt to limit alert quantity and improve alert quality to boost clinician satisfaction.

Clinical Decision Support Systems

AI’s Impact on Clinical Decision Making

Clinical Decision Support Systems can be described as information systems to improve the decision making ability of people interacting with it . Hospitals with integrated Health Information Systems are encouraged to utilize CDSS . Multiple fragments such as Electronic Health Record , laboratory information system, Computerized Physician Order Entry , radiology information system and pharmacy information system combine together

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Alert Fatigue And Inappropriate Alerts

Studies have found up to 95% of CDSS alerts are inconsequential, and often times physicians disagree with or distrust alerts. Other times they just do not read them. If physicians are presented with excessive/unimportant alerts, they can suffer from alert fatigue.

Disruptive alerts should be limited to more life-threatening or consequential contraindications, such as serious allergies. However even allergy alerts can be incorrect, and clinicians will often verify themselves, especially if the source is another site/hospital/practitioner., Medication alerts can also be specialty specific, but irrelevant when taken out of context. For example, an alert against using broad-spectrum antibiotics such as vancomycin may be inappropriate in ICU. An alert against duplicate medications may be inappropriate in inflammatory bowel disease clinics, where the same class of drug can be applied through different administration routes for increased effect.

Lack Of Transportability And Interoperability

Despite ongoing development for the better part of three decades, CDSS suffer from interoperability issues. Many CDSS exist as cumbersome stand-alone systems, or exist in a system that cannot communicate effectively with other systems.

What makes transportability so difficult to achieve? Beyond programming complexities that can make integration difficult, the diversity of clinical data sources is a challenge. There is a reluctance or perceived risk associated with transporting sensitive patient information. Positively, interoperability standards are continuously being developed and improved, such as Health Level 7 and Fast Healthcare Interoperability Resources . These are already being utilized in commercial EHR vendors. Several government agencies, medical organizations and informatics bodies are actively supporting and some even mandating the use of these interoperability standards in health systems.,,

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Nurses ‘ Usage Of Clinical Decision Support Systems

Little is know regarding nurses usage of Clinical Decision Support Systems . Few studies in this subject focus on nurses adherence and engagement on using this healthcare information technology to ensure patient safety and delivery of the best care possible . Clinical decision support systems are defined as electronic systems designed to aid directly in clinical decision making, in which patients characteristics are used to generate

Optimizing Or Eradicating Low

Clinician burnout and EHR fatigue caused by alerts have been an issue for clinicians struggling with EHR usability overload.

Although EHR alerts can offer providers practical suggestions and updates, EHR alert fatigue has been an issue for clinicians already struggling with EHR usability problems. Low-value EHR alerts can disrupt patient care and contribute to clinician burnout.

At Brigham and Womens Hospital, clinicians were getting roughly one alert for every two medication orders, and clinicians were overriding an astounding 98 percent of the alerts.

One of the big issues is that many of the clinical systems that are in routine use today, alert too frequently, David Bates, MD, chief of the Division of General Internal Medicine at Brigham and Womens Hospital, said in an interview with EHRIntelligence. When clinicians are overriding that high a proportion of alerts, clinicians get very used to closing the alert, and sometimes they arent fully processing what the alerts are saying and they tend to stop paying attention to the important alerts.

Unsatisfied with how their EHR vendor fired off alerts, Bates and his health IT team tapped Seegnal eHealth to leverage its EHR alert solution and conduct an EHR alert study at the hospital.

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