Evidence-Based Practice in CDS and Quality Improvement Essay
Evidence-Based Practice in CDS and Quality Improvement Essay
Clinical Decision Support
A clinical decision support system is a medical tool used by healthcare providers in making clinical and technical decisions for the well-being of a patient. CDSS is a tool that bridges health knowledge and observation to improve clinical choices and patient outcomes. CDS works by analyzing data to know the cause of the disease (Bezemer, et al, 2019). Therefore, it helps the physicians and other healthcare professionals to find solutions to the medical issues. CDS has both knowledge-based and non-knowledge-based decisions. Knowledge-based decisions are made from acquired information or research, while non-knowledge-based decisions are from past experiences or observation (Bezemer, et al, 2019). CDSS has reduced clinical errors by increased medical solutions. Clinical tools such as reminders, condition-specific order sets, clinical guidelines, diagnostic support, and documentation templates enhance the clinical decision support system. This paper describes the application of CDSS and its recommendation in ways that avoid alert fatigue, conditions that would allow on the override to an alert, methods that monitor compliance, factors that contribute to continuous overrides, and conditions under which an override is necessary. Evidence-Based Practice in CDS and Quality Improvement Essay
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How To Avoid Alarm Fatigue
Alarms systems are quick and effective communication and alert methods in a hospital, especially during emergencies. Often, alarms work together with other bedside equipment that monitors the patients. To ensure compliance, they are channeled to the relevant alarm management to ensure a timely response. However, a similar alarm from different monitoring systems results in noise pollution and eventually alarm fatigue. Alarm fatigue reduces the responses to urgent circumstances and compromises the patient’s health (Fernandes, et al, 2019). Alarm fatigue needs to be solved to improve patients safety and sentinel events related to poor alarm management. The clinical decision support system with the help of clinical equipment has reduced alarm fatigue through successful cleaning and monitoring the equipment to ensure they are functioning well. According to Fernandes, et al, (2019), functional equipment lessens the frequency of alerts related to malfunction. Decreasing clinically inconsequential alarms by reducing clinically insignificant events increases nurse responsiveness. These alarms are funneled to the right people directly through smartphones or WIFI tablets to reduce noise and promote an environment conducive to rest and healing. Installation of intelligent software helps to triage alerts by setting priority levels of alarm. This helps ensure critical alarms receive a timely response from the caregiver, enables tailoring of alerts to the patient’s characteristics. The hospital should also invest in advanced clinical alerting with valuable information for patient monitoring and nurse call systems. False alarms should be disconnected, especially when the monitoring equipment indicates a physiologic event. Effective alarm management will help reduce the incidence of alarm fatigue to improve patient safety.
Conditions That Would Allow An Override To Alerts
Overriding alerts is rejecting or cancellation of alerts to continue to the next action. Overriding is common when dispensing medication to the patient hence, increasing the risk of adverse drug reactions. During alerts override, physicians compare the alert with the clinical decision to achieve and the reported benefit to the quality of care and patient safety. Most of these alerts are from abnormal laboratory results or drug interaction alerts. However, the physicians tend to override the alerts in situations like the patient medical alert is well known to the medical community. Decreased misdirected communication with content-free comments due to the turnover of clinicians led to alert override. Some alerts did not need acknowledgment. Cognitive burdens upon clinicians when CPOE systems results to alert override. Evidence-Based Practice in CDS and Quality Improvement Essay
Explain How You Would Monitor Compliance
Functional clinical decision support enables improved patient care, good prognosis, reduces costs, and enhances the provider’s workflow. However, non-functional CDS results to alert fatigue, alert override and increases provider dissatisfaction. Monitoring and compliance are therefore necessary as part of clinical decision support. CDS compliance monitoring helps identify significant improvements or malfunction opportunities before the user complains of any problem. Therefore, monitoring compliance is part of implementing and maintaining CDS. Compliance monitoring often occurs before, during, or after the use of CDS (Edmisten, et al, 2017). Pre-activity monitor focuses on the approval of high-risk activities, during the activities intents to know its proficiency, and after activities determines the less risky events. Monitoring CDS also involves determining the comprehensiveness of an activity. For example, to ensure compliance with advanced clinical alert systems in the hospital, the management should ensure its compliance to maintain effective emergency response, ensure patient safety, and improve patient outcomes. Proper functioning of the advanced clinical alert requires continuous monitoring for the complexity of the system before, during, and after installation to maintain effectiveness. Installation of monitors within the hospitals ensures effective compliance to CDS. The electronic health record EHR is the most common monitor in the modern hospital system. It records the time of patient arrival to the time of attending the patient. This monitor is effective in ensuring healthcare professionals respond to alarms in good time, utilizes advanced clinical support, and also ensures alerts are channeled to the appropriate communication desk.
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Identify Factors That Might Contribute To Continuous Overrides
An override is the act of nullifying a solution that has already been set. According to Wong, et al, (2019), drug safety alerts are overridden by physicians and clinicians because they are mostly justified. However, continuous overrides are caused by provider ordering practices and computer availability, communication and hands-off, CDS medication database designs, CPOE data display, and local CDS designs. Ordering and computer availability cause overrides by placing continuous orders within minutes by different healthcare providers on rounds. Communication and hands-off cause duplication of orders during shift change. CDS and medication databases result in confusing alert content, high false-positive alerts, and CDS algorithms missing true duplicates. CPOE data display causes difficulties in reviewing existing orders and the local CDS causes medication in order set defaulted as ordered (Wong, et al, 2019). To reduce continuous overrides, the CDS should ensure the installation of electronic health record devices that ensure plan development and communication, order planning, order modification, and review before submission. Evidence-Based Practice in CDS and Quality Improvement Essay
Justify Conditions Under Which Overrides May be Necessary
CDS has an integrated alert program that improves patient safety. Drug safety overriding alerts are common, especially the high-level overdose. However, unjustified overriding is problematic regarding patient safety. The care provider should provide reasons for overriding decisions. Acceptable reasons are error-producing conditions in the alert system, poor signal-to-noise ratio that makes the alerts unnecessary, transfer of patients to another point of care without the physician’s knowledge, and forgetfulness or oversight (Gorin, et al, 2017). However, alert overriding is understood with the help of a reason’s model of accident causation and how it is designed to improve patient safety.
References
Bezemer, T., De Groot, M. C., Blasse, E., Ten Berg, M. J., Kappen, T. H., Bredenoord, A. L., … & Haitjema, S. (2019). A human (e) factor in clinical decision support systems. Journal of medical Internet research, 21(3), e11732.
Edmisten, C., Hall, C., Kernizan, L., Korwek, K., Preston, A., Rhoades, E., … & Zygadlo, S. (2017). Implementing an electronic hand hygiene monitoring system: lessons learned from community hospitals. American journal of infection control, 45(8), 860-865.
Fernandes, C. O., Miles, S., De Lucena, C. J. P., & Cowan, D. (2019). Artificial intelligence technologies for coping with alarm fatigue in hospital environments because of sensory overload: Algorithm development and validation. Journal of medical Internet research, 21(11), e15406.
Gorin, M., Joffe, S., Dickert, N., & Halpern, S. (2017). Justifying clinical nudges. Hastings Center Report, 47(2), 32-38.
Wong, A., Rehr, C., Seger, D. L., Amato, M. G., Beeler, P. E., Slight, S. P., … & Bates, D. W. (2019). Evaluation of harm associated with high dose-range clinical decision support overrides in the intensive care unit. Drug safety, 42(4), 573-579. Evidence-Based Practice in CDS and Quality Improvement Essay
