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Real-Time Surveillance for Rapid Correction of Clinical Decision Support Failures

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Real-Time Surveillance for Rapid Correction of Clinical Decision Support Failures

The utilization of clinical decision support (CDS) is increasing among healthcare facilities which have implemented computerized physician order entry or electronic medical records. Formal prospective evaluation of CDS implementations occurs rarely, and misuse or flaws in system design are often unrecognized. Retrospective review can identify failures but is too late to make critical corrections or initiate redesign efforts. Real-time surveillance of user responses and patient outcomes comprises one approach to give immediate feedback to CDS designers and help operate a safety net which intercepts CDS failures. We outline four types of CDS (passive alerts, interruptive alerts, order sets, and complex ordering advisors) and describe common failures with surveillance applications for high-alert medications such as aminoglycosides, anticoagulants, and insulin. We then present a computerized tool for high-alert medication prescriptions which serves developers, clinical pharmacists, and institutional physician leaders. The tool has two views: the surveillance view allows users to scan all CDS failures and prioritize high-risk scenarios, and the patient detail view provides context for understanding CDS failures. Entries on the surveillance tool populate automatically when CDS is used or ignored, allowing verification of prescription safety and accuracy.

The utilization of clinical decision support (CDS) is increasing among healthcare facilities which have implemented computerized physician order entry or electronic medical records. Formal prospective evaluation of CDS implementations occurs rarely, and misuse or flaws in system design are often unrecognized. Retrospective review can identify failures but is too late to make critical corrections or initiate redesign efforts. Real-time surveillance of user responses and patient outcomes comprises one approach to give immediate feedback to CDS designers and help operate a safety net which intercepts CDS failures. We outline four types of CDS (passive alerts, interruptive alerts, order sets, and complex ordering advisors) and describe common failures with surveillance applications for high-alert medications such as aminoglycosides, anticoagulants, and insulin. We then present a computerized tool for high-alert medication prescriptions which serves developers, clinical pharmacists, and institutional physician leaders. The tool has two views: the surveillance view allows users to scan all CDS failures and prioritize high-risk scenarios, and the patient detail view provides context for understanding CDS failures. Entries on the surveillance tool populate automatically when CDS is used or ignored, allowing verification of prescription safety and accuracy.

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Real-Time Surveillance for Rapid Correction of Clinical Decision Support Failures

  1. 1. Real-Time Surveillance for Rapid Correction of Clinical Decision Support Failures Allison Beck McCoy, MS
  2. 2. Introduction <ul><li>Increasing implementation of CPOE and EMRs </li></ul><ul><li>Addition of decision support reduces errors, but also introduces potential for failures </li></ul><ul><ul><li>Unjustified provider overrides </li></ul></ul><ul><ul><li>Error-producing conditions within the technology </li></ul></ul><ul><li>Understanding of failures is critical </li></ul>
  3. 3. Prior Surveillance Work <ul><li>Surveillance systems are often utilized to reduce errors </li></ul><ul><ul><li>E-mail notifications about patient conditions </li></ul></ul><ul><ul><li>Daily printouts or computerized monitoring by pharmacy of high-alert medications </li></ul></ul><ul><li>Real-time surveillance of decision support has not been implemented at the patient level </li></ul><ul><ul><li>Failures may not be apparent outside of the patient context </li></ul></ul><ul><ul><li>Technical failures and usage failures may be difficult to differentiate </li></ul></ul>
  4. 4. Goal/Main Idea/Thesis (?) <ul><li>Common implementations of decision support for high-alert medications each have potential for failure and application for surveillance </li></ul><ul><li>Surveillance tool prototype </li></ul><ul><ul><li>Acts as a safety net for failures </li></ul></ul><ul><ul><li>Gives feedback to developers </li></ul></ul>
  5. 5. Passive Alerts <ul><li>Interventions that display text without interrupting workflow </li></ul><ul><ul><li>Drug interactions </li></ul></ul><ul><ul><li>Changing lab values </li></ul></ul><ul><li>May be unnoticed or intentionally ignored </li></ul>
  6. 6. Interruptive Alerts <ul><li>Interventions that interrupt workflow to display information and require an acknowledgment or response </li></ul><ul><li>Frequently overridden </li></ul>
  7. 7. Order Sets <ul><li>Promote adherence to clinical guidelines </li></ul><ul><ul><li>Appropriate medications for a given condition </li></ul></ul><ul><ul><li>Appropriate drug monitoring </li></ul></ul><ul><li>May not be used or may be misused </li></ul>
  8. 8. Complex Ordering Advisors <ul><li>Computerized tools that generate orders adhering to drug selection or dosing guidelines and protocols </li></ul><ul><ul><li>Dose calculations </li></ul></ul><ul><ul><li>Administration intervals </li></ul></ul><ul><ul><li>Appropriate monitoring </li></ul></ul><ul><li>May be confusing, or inputs may be incorrect or incomplete </li></ul>
  9. 9. Surveillance Applications <ul><li>Safety net to prevent patient harm </li></ul><ul><li>Identification of frequent failures and generation of solutions </li></ul><ul><ul><li>User education </li></ul></ul><ul><ul><li>Redesign of decision support rules or method </li></ul></ul>
  10. 10. Prototype <ul><li>Web-based dashboard </li></ul><ul><li>Available to developers, clinical pharmacists, and institutional leaders </li></ul><ul><li>Displays high-risk medication decision support usage across all adult units and services </li></ul><ul><li>Automatically populated at any point during a patient’s admission that targeted decision support is triggered </li></ul>
  11. 11. Surveillance View <ul><li>Displays all currently admitted patients that have received decision support </li></ul><ul><li>Allows identification of patients at high risk for harm following multiple decision support failures </li></ul><ul><li>Allows identification of decision support with high failures rates </li></ul>
  12. 12. Surveillance View
  13. 13. Patient Detail View <ul><li>Displays a detailed timeline for an individual patient </li></ul><ul><ul><li>Orders, order administrations, lab values </li></ul></ul><ul><ul><li>Decision support interactions </li></ul></ul><ul><li>Allows understanding of provider actions and patient conditions in conjunction with decision support failures </li></ul>
  14. 14. Patient Detail View
  15. 15. Patient Detail View
  16. 16. Requirements <ul><li>Electronic data </li></ul><ul><ul><li>Patient lab results </li></ul></ul><ul><ul><li>Ordering history </li></ul></ul><ul><ul><li>Decision support logs </li></ul></ul><ul><li>Monitoring staff </li></ul>
  17. 17. Conclusions <ul><li>Surveillance team members can rapidly identify decision support failures, take actions to prevent further patient harm, and initiate improvement efforts </li></ul>
  18. 18. Acknowledgments <ul><li>National Library of Medicine </li></ul><ul><ul><li>T15 LM 007450-07 </li></ul></ul><ul><li>PhD Committee </li></ul><ul><ul><li>Russ Waitman, Josh Peterson, Cindy Gadd, Bob Lobo, Jack Starmer </li></ul></ul>

Editor's Notes

  • Increasing numbers of healthcare facilities are implementing computerized provider order entry (CPOE) and electronic medical records (EMRs). Within these systems exists clinical decision support (CDS) functionality to assist providers in a variety of roles, such as promoting correct orders and providing patient-specific recommendations. Although CDS improves patient care in many settings, CDS failures are common, particularly at the point of first implementation. Failures include unjustified provider overrides and error-producing conditions within the technology. Understanding these failures is critical to avoiding user dissatisfaction and preventing other unintended adverse consequences.
  • Many facilities are implementing surveillance methods to reduce errors. One method of surveillance utilizes e-mail messages to notify care team members about changes in patient conditions. Pharmacy surveillance of ordered doses is often conducted with daily paper printouts or computerized displays of patients receiving a drug. Other surveillance tools have been implemented to evaluate CDS in real-time, but these have evaluated usage in the aggregate and not at the patient level. Failures are not often readily apparent outside the context of an individual patient care episode, and it can be difficult to differentiate a technical failure from a usage failure without sufficient clinical detail presented in tandem with the CDS triggers and user response.
  • We present common CDS implementations for high-alert medications, describing potential failures and applications for surveillance. Based on a working prototype within our development environment, we discuss how use of a surveillance tool for rapid evaluation of CDS may improve the quality and efficacy of hospital information systems.
  • Systems commonly employ alerts that display text without interrupting the workflow to notify providers of drug interactions, changing laboratory values, or other information relevant to the patient’s medical condition. Passive alerts may fail when they are intentionally ignored or unnoticed by busy providers due to suboptimal placement or other design flaws. Surveillance allows a third party, such as a clinical pharmacist or quality manager, to identify passive alerts that persist for extended time periods and to take action, when necessary, to prevent patient harm. Surveillance can also identify the need to educate providers regarding alert objectives or function. Alerts with particularly high failure rates that are deemed important by institutional leaders or content experts may be promoted by developers to interruptive alerts in order to increase appropriate usage.
  • CDS interventions which interrupt workflow to display information are frequently implemented to communicate safety information regarding high-alert medications. Providers must acknowledge the alert or record a response in order to resume working. Interruptive alerts are frequently overridden in multiple content domains, including drug-drug interactions, drug-allergy warnings, and drug-laboratory interactions (e.g. a nephrotoxin in setting of rising creatinine). Overrides or deviations from CDS recommendations occur frequently for interruptive alerts. Surveillance applications for interruptive alerts are similar to those presented for passive alerts. Reviewers can identify patients at particularly high risk for adverse drug events following overrides. Developers can also identify overrides that may be appropriate, for example if an alert appeared to a clinician outside of the providing team or if an alert does not apply to the patient based on an excluding condition.
  • Order sets comprise an approach frequently implemented to promote adherence to clinical guidelines. Order sets may ensure that patients with a given condition receive appropriate medications and promote drug level monitoring for high-risk medications. Failures occur when providers do not use applicable order sets for a given patient condition or deviate from recommendations. For example, providers may fail to order an appropriate drug or monitoring laboratory tests from the order set. The use, nonuse, or misuse of order sets warrants surveillance in a number of clinical scenarios. Surveillance augments retrospective evaluation by providing patient context and user information which could be used to improve understanding of the strengths and weaknesses of the order set. Monitoring by clinicians permits real-time interventions for optimal patient care. Surveillance can also identify order sets with minimal or no usage, in which case providers could receive additional education.
  • In many CPOE systems, providers interact with intricate computerized tools to generate orders that adhere to drug selection or dosing guidelines and protocols. These advisors may assist providers with calculations, administration intervals, and appropriate laboratory test monitoring. Common failures include incorrect inputs for calculation, unclear or confusing workflow, and incomplete use of the advisor. Surveillance can highlight failures related to misuse, allowing a content expert to intervene and prevent inappropriate dosing. Recurring misuse identified by surveillance can expose the need for provider training on correct usage of the advisors.
  • Potential failures and surveillance applications vary for distinct CDS types. However, many of these surveillance applications generalize to themes common to all CDS types. The primary motivation for surveillance is the ability to act as a safety net to prevent patient harm. Reviewers can identify overrides or deviations from recommendations and take actions to correct orders. Surveillance also allows developers and institutional leaders to easily identify CDS that is not working as intended and quickly generate solutions to improve functionality. These failures may be technical in nature and not related to provider error. Some technical failures may be corrected through user education, while others may require adjustments to the CDS rules or approach.
  • Utilizing an existing web-based pharmacy surveillance dashboard, we developed a prototype tool for surveillance of CDS failures. The tool allows various users, including developers, clinical pharmacists, and institutional leaders to review high-risk medication CDS usage across all units and services. At any point during a patient’s admission that CDS is triggered, whether it is used or ignored, entries automatically populate on the surveillance tool.
  • The first type, the surveillance view, displays all currently admitted patients that have experienced a CDS interaction in a table format. This view allows pharmacists or other safety net providers to identify patients at high risk for harm following multiple CDS failures. Developers can also use this tool to identify CDS modules with high failure rates in order to initiate further evaluation or redesign efforts.
  • Screenshots here
  • The second view type, the patient detail view, displays a detailed timeline for an individual patient in reverse chronological order to give context for the CDS interaction events populated on the surveillance view. The timeline includes all orders, order administrations, laboratory values, and CDS interactions. Surveillance team members can use the detail view to understand provider actions and patient condition changes occurring in conjunction with CDS failures without having to redirect to and search a patient’s EMR. At this point, the cause of the CDS failure, whether it resulted from provider or design error, may be apparent.
  • Screenshots here
  • Though we present many complex CDS applications for surveillance, a valuable surveillance tool may be developed at other institutions using a small number of basic CDS implementations. To build the tool, developers must be able to query data of interest, including patient laboratory results, ordering history, and CDS logs, at a minimum. Additional data, such as medication administrations or detailed CDS usage information, adds to the reviewer understanding of patient context, but it does not prevent the tool from adding some benefit to patient care and CDS evaluation. In addition to technology, a requirement for a successful surveillance tool includes monitoring staff. Clinical pharmacists or care team leaders must be able and willing to regularly devote time to monitoring the tool. In some cases, this may involve hiring of additional staff or rearranging existing workflow processes.
  • The tool allows surveillance team members to rapidly identify CDS failures and take actions to prevent further patient harm, in addition to initiating CDS improvement. The two views available for the tool allow flexibility in use. The surveillance view permits quick identification of high-risk patients and prioritization of those in danger following a CDS failure. In contrast, the patient summary view provides context to CDS failures and allows in-depth understanding of provider actions. Errors in both use and design contribute to commonly occurring failures for all CDS types. Surveillance by clinicians and developers allows identification of these failures with time for patient care interventions and early design corrections.
  • R01 LM 009965-01 and R03 LM 009238-02

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