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Sundermann M, Clendon O, McNeill R, Doogue M, Chin PKL. Optimising interruptive clinical decision support alerts for antithrombotic duplicate prescribing in hospital. Int J Med Inform 2024; 186:105418. [PMID: 38518676 DOI: 10.1016/j.ijmedinf.2024.105418] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 03/05/2024] [Accepted: 03/17/2024] [Indexed: 03/24/2024]
Abstract
INTRODUCTION Duplicate prescribing clinical decision support alerts can prevent important prescribing errors but are frequently the cause of much alert fatigue. Stat dose prescriptions are a known reason for overriding these alerts. This study aimed to evaluate the effect of excluding stat dose prescriptions from duplicate prescribing alerts for antithrombotic medicines on alert burden, prescriber adherence, and prescribing. MATERIALS AND METHODS A before (January 1st, 2017 to August 31st, 2022) and after (October 5th, 2022 to September 30th, 2023) study was undertaken of antithrombotic duplicate prescribing alerts and prescribing following a change in alert settings. Alert and prescribing data for antithrombotic medicines were joined, processed, and analysed to compare alert rates, adherence, and prescribing. Alert burden was assessed as alerts per 100 prescriptions. Adherence was measured at the point of the alert as whether the prescriber accepted the alert and following the alert as whether a relevant prescription was ceased within an hour. Co-prescribing of antithrombotic stat dose prescriptions was assessed pre- and post-alert reconfiguration. RESULTS Reconfiguration of the alerts reduced the alert rate by 29 % (p < 0.001). The proportion of alerts associated with cessation of antithrombotic duplication significantly increased (32.8 % to 44.5 %, p < 0.001). Adherence at the point of the alert increased 1.2 % (4.8 % to 6.0 %, p = 0.012) and 11.5 % (29.4 % to 40.9 %, p < 0.001) within one hour of the alert. When ceased after the alert over 80 % of duplicate prescriptions were ceased within 2 min of overriding. Antithrombotic stat dose co-prescribing was unchanged for 4 out of 5 antithrombotic duplication alert rules. CONCLUSION By reconfiguring our antithrombotic duplicate prescribing alerts, we reduced alert burden and increased alert adherence. Many prescribers ceased duplicate prescribing within 2 min of alert override highlighting the importance of incorporating post-alert measures in accurately determining prescriber alert adherence.
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Affiliation(s)
- Milan Sundermann
- Department of Medicine, University of Otago, Christchurch, New Zealand
| | - Olivia Clendon
- Department of Clinical Pharmacology, Te Whatu Ora Health New Zealand - Waitaha Canterbury, New Zealand
| | - Richard McNeill
- Department of Medicine, University of Otago, Christchurch, New Zealand
| | - Matthew Doogue
- Department of Medicine, University of Otago, Christchurch, New Zealand; Department of Clinical Pharmacology, Te Whatu Ora Health New Zealand - Waitaha Canterbury, New Zealand
| | - Paul K L Chin
- Department of Medicine, University of Otago, Christchurch, New Zealand; Department of Clinical Pharmacology, Te Whatu Ora Health New Zealand - Waitaha Canterbury, New Zealand.
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Molloy MJ, Muthu N, Orenstein EW, Shelov E, Luo BT. Clinical Decision Support Principles for Quality Improvement and Research. Hosp Pediatr 2024; 14:e219-e224. [PMID: 38545665 PMCID: PMC10965756 DOI: 10.1542/hpeds.2023-007540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/02/2024]
Abstract
Pediatric hospitalists frequently interact with clinical decision support (CDS) tools in patient care and use these tools for quality improvement or research. In this method/ology paper, we provide an introduction and practical approach to developing and evaluating CDS tools within the electronic health record. First, we define CDS and describe the types of CDS interventions that exist. We then outline a stepwise approach to CDS development, which begins with defining the problem and understanding the system. We present a framework for metric development and then describe tools that can be used for CDS design (eg, 5 Rights of CDS, "10 commandments," usability heuristics, human-centered design) and testing (eg, validation, simulation, usability testing). We review approaches to evaluating CDS tools, which range from randomized studies to traditional quality improvement methods. Lastly, we discuss practical considerations for implementing CDS, including the assessment of a project team's skills and an organization's information technology resources.
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Affiliation(s)
- Matthew J. Molloy
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio
- Divisions of Hospital Medicine
- Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio
| | - Naveen Muthu
- Department of Pediatrics, Emory University School of Medicine, Atlanta, Georgia
- Division of Hospital Medicine, Children’s Healthcare of Atlanta, Atlanta, Georgia
| | - Evan W. Orenstein
- Department of Pediatrics, Emory University School of Medicine, Atlanta, Georgia
- Division of Hospital Medicine, Children’s Healthcare of Atlanta, Atlanta, Georgia
| | - Eric Shelov
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Section of Pediatric Hospital Medicine
- Department of Biomedical and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Brooke T. Luo
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Section of Pediatric Hospital Medicine
- Department of Biomedical and Health Informatics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania
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Rule A, Kannampallil T, Hribar MR, Dziorny AC, Thombley R, Apathy NC, Adler-Milstein J. Guidance for reporting analyses of metadata on electronic health record use. J Am Med Inform Assoc 2024; 31:784-789. [PMID: 38123497 PMCID: PMC10873840 DOI: 10.1093/jamia/ocad254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 12/14/2023] [Accepted: 12/18/2023] [Indexed: 12/23/2023] Open
Abstract
INTRODUCTION Research on how people interact with electronic health records (EHRs) increasingly involves the analysis of metadata on EHR use. These metadata can be recorded unobtrusively and capture EHR use at a scale unattainable through direct observation or self-reports. However, there is substantial variation in how metadata on EHR use are recorded, analyzed and described, limiting understanding, replication, and synthesis across studies. RECOMMENDATIONS In this perspective, we provide guidance to those working with EHR use metadata by describing 4 common types, how they are recorded, and how they can be aggregated into higher-level measures of EHR use. We also describe guidelines for reporting analyses of EHR use metadata-or measures of EHR use derived from them-to foster clarity, standardization, and reproducibility in this emerging and critical area of research.
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Affiliation(s)
- Adam Rule
- Information School, University of Wisconsin-Madison, Madison, WI 53706, United States
| | - Thomas Kannampallil
- Department of Anesthesiology, Washington University School of Medicine, St Louis, MO 63110, United States
- Institute for Informatics, Data Science and Biostatistics, Washington University School of Medicine, St Louis, MO 63110, United States
| | - Michelle R Hribar
- Office of Data Science and Health Informatics, National Eye Institute, National Institute of Health, Bethesda, MD 20892, United States
- Department of Ophthalmology, Casey Eye Institute, Portland, OR 97239, United States
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR 97239, United States
| | - Adam C Dziorny
- Department of Pediatrics, University of Rochester School of Medicine, Rochester, NY 14642, United States
| | - Robert Thombley
- Department of Medicine, Center for Clinical Informatics and Improvement Research, University of California, San Francisco, San Francisco, CA 94118, United States
| | - Nate C Apathy
- National Center for Human Factors in Healthcare, MedStar Health Research Institute, Washington, DC 20782, United States
- Center for Biomedical Informatics, Regenstrief Institute Inc, Indianapolis, IN 46202, United States
| | - Julia Adler-Milstein
- Department of Medicine, Center for Clinical Informatics and Improvement Research, University of California, San Francisco, San Francisco, CA 94118, United States
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Fallon A, Haralambides K, Mazzillo J, Gleber C. Addressing Alert Fatigue by Replacing a Burdensome Interruptive Alert with Passive Clinical Decision Support. Appl Clin Inform 2024; 15:101-110. [PMID: 38086417 PMCID: PMC10830237 DOI: 10.1055/a-2226-8144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Accepted: 12/11/2023] [Indexed: 02/02/2024] Open
Abstract
BACKGROUND Recognizing that alert fatigue poses risks to patient safety and clinician wellness, there is a growing emphasis on evaluation and governance of electronic health record clinical decision support (CDS). This is particularly critical for interruptive alerts to ensure that they achieve desired clinical outcomes while minimizing the burden on clinicians. This study describes an improvement effort to address a problematic interruptive alert intended to notify clinicians about patients needing coronavirus disease 2019 (COVID) precautions and how we collaborated with operational leaders to develop an alternative passive CDS system in acute care areas. OBJECTIVES Our dual aim was to reduce the alert burden by redesigning the CDS to adhere to best practices for decision support while also improving the percent of admitted patients with symptoms of possible COVID who had appropriate and timely infection precautions orders. METHODS Iterative changes to CDS design included adjustment to alert triggers and acknowledgment reasons and development of a noninterruptive rule-based order panel for acute care areas. Data on alert burden and appropriate precautions orders on symptomatic admitted patients were followed over time on run and attribute (p) and individuals-moving range control charts. RESULTS At baseline, the COVID alert fired on average 8,206 times per week with an alert per encounter rate of 0.36. After our interventions, the alerts per week decreased to 1,449 and alerts per encounter to 0.07 equating to an 80% reduction for both metrics. Concurrently, the percentage of symptomatic admitted patients with COVID precautions ordered increased from 23 to 61% with a reduction in the mean time between COVID test and precautions orders from 19.7 to -1.3 minutes. CONCLUSION CDS governance, partnering with operational stakeholders, and iterative design led to successful replacement of a frequently firing interruptive alert with less burdensome passive CDS that improved timely ordering of COVID precautions.
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Affiliation(s)
- Anne Fallon
- Division of Pediatric Hospital Medicine, Department of Pediatrics, University of Rochester Medical Center, Rochester, New York, United States
| | - Kristina Haralambides
- Department of Otolaryngology, University of Rochester Medical Center, Rochester, New York, United States
| | - Justin Mazzillo
- Department of Emergency Medicine, University of Rochester Medical Center, Rochester, New York, United States
| | - Conrad Gleber
- Division of Hospital Medicine, Department of Medicine, University of Rochester Medical Center, Rochester, New York, United States
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Shakowski C, Page II RL, Wright G, Lunowa C, Marquez C, Suresh K, Allen LA, Glasgow RE, Lin CT, Wick A, Trinkley KE. Comparative effectiveness of generic commercial versus locally customized clinical decision support tools to reduce prescription of nonsteroidal anti-inflammatory drugs for patients with heart failure. J Am Med Inform Assoc 2023; 30:1516-1525. [PMID: 37352404 PMCID: PMC10436140 DOI: 10.1093/jamia/ocad109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 05/09/2023] [Accepted: 06/08/2023] [Indexed: 06/25/2023] Open
Abstract
OBJECTIVE To compare the effectiveness of 2 clinical decision support (CDS) tools to avoid prescription of nonsteroidal anti-inflammatory drugs (NSAIDs) in patients with heart failure (HF): a "commercial" and a locally "customized" alert. METHODS We conducted a retrospective cohort study of 2 CDS tools implemented within a large integrated health system. The commercial CDS tool was designed according to third-party drug content and EHR vendor specifications. The customized CDS tool underwent a user-centered design process informed by implementation science principles, with input from a cross disciplinary team. The customized CDS tool replaced the commercial CDS tool. Data were collected from the electronic health record via analytic reports and manual chart review. The primary outcome was effectiveness, defined as whether the clinician changed their behavior and did not prescribe an NSAID. RESULTS A random sample of 366 alerts (183 per CDS tool) was evaluated that represented 355 unique patients. The commercial CDS tool was effective for 7 of 172 (4%) patients, while the customized CDS tool was effective for 81 of 183 (44%) patients. After adjusting for age, chronic kidney disease, ejection fraction, NYHA class, concurrent prescription of an opioid or acetaminophen, visit type (inpatient or outpatient), and clinician specialty, the customized alerts were at 24.3 times greater odds of effectiveness compared to the commercial alerts (OR: 24.3 CI: 10.20-58.06). CONCLUSION Investing additional resources to customize a CDS tool resulted in a CDS tool that was more effective at reducing the total number of NSAID orders placed for patients with HF compared to a commercially available CDS tool.
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Affiliation(s)
| | - Robert L Page II
- Department of Clinical Pharmacy, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Garth Wright
- Department of Clinical Pharmacy, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Cali Lunowa
- Department of Clinical Pharmacy, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Clyde Marquez
- Department of Clinical Pharmacy, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Krithika Suresh
- Adult and Child Center for Outcomes Research and Delivery Science Center, Aurora, Colorado, USA
- Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, Colorado, USA
| | - Larry A Allen
- Adult and Child Center for Outcomes Research and Delivery Science Center, Aurora, Colorado, USA
- Division of Cardiology, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Russel E Glasgow
- Adult and Child Center for Outcomes Research and Delivery Science Center, Aurora, Colorado, USA
- Department of Family Medicine, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Chen-Tan Lin
- UCHealth, Aurora, Colorado, USA
- Division of Internal Medicine, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | | | - Katy E Trinkley
- UCHealth, Aurora, Colorado, USA
- Department of Clinical Pharmacy, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
- Adult and Child Center for Outcomes Research and Delivery Science Center, Aurora, Colorado, USA
- Department of Family Medicine, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
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Gosselin L, Leguillon R, Rollin L, Lejeune E, Darmoni SJ, Grosjean J. Trends in computerized provider order entry: 20-year bibliometric overview. Front Digit Health 2023; 5:1217694. [PMID: 37497185 PMCID: PMC10367087 DOI: 10.3389/fdgth.2023.1217694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 06/28/2023] [Indexed: 07/28/2023] Open
Abstract
Background Drug-related problems (DRPs) can lead to serious health issues and have significant economic impacts on healthcare systems. One solution to address this issue is the use of computerized physician order entry systems (CPOE), which can help prevent DRPs by reducing the risk of medication errors. Objective The purpose of this study is to provide an analysis on scientific production of the past 20 years in order to describe trends in academic publishing on CPOE and to identify the major topics as well as the predominant actors (journals, countries) involved in this field. Methods A PubMed search was carried out to extract articles related to computerized provider order entry during the period January 1st 2003- December 31st 2022 using a specific query. Data were downloaded from PubMed in Extensible Markup Language (XML) and were processed through a dedicated parser. Results A total of 2,946 articles were retrieved among 623 journals. One third of these articles were published in eight journals. Publications grew strongly from 2002 to 2006, with a dip in 2008 followed by an increase again in 2009. After 2009, there follows a decreasing until 2022.The most producing countries are the USA with 51.39% of the publication over the period by France (3.80%), and Canada (3.77%). About disciplines, the top 3 is: "medical informatics" (21.62% of articles), "pharmacy" (19.04%), and "pediatrics" (6.56%). Discussion This study provides an overview of publication trends related to CPOE, which exhibited a significant increase in the first decade of the 21st century followed by a decline after 2009. Possible reasons for this decline include the emergence of digital health tools beyond CPOE, as well as healthcare professionals experiencing alert fatigue of the current system. Conclusion Future research should focus on analyzing publication trends in the field of medical informatics and decision-making tools to identify other areas of interest that may have surpassed the development of CPOE.
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Affiliation(s)
- Laura Gosselin
- Department of Digital Health, Rouen University Hospital, Rouen, France
- Department of Pharmacy, Rouen University Hospital, Rouen, France
| | - Romain Leguillon
- Department of Digital Health, Rouen University Hospital, Rouen, France
- Department of Pharmacy, Rouen University Hospital, Rouen, France
- Laboratoire D'Informatique Médicale et D'Ingénierie des Connaissances en e-Santé (LIMICS), U1142, INSERM, Sorbonne Université, Paris, France
| | - Laetitia Rollin
- Laboratoire D'Informatique Médicale et D'Ingénierie des Connaissances en e-Santé (LIMICS), U1142, INSERM, Sorbonne Université, Paris, France
- Institute of Occupational Medicine, Rouen University Hospital, Rouen, France
| | - Emeline Lejeune
- Department of Digital Health, Rouen University Hospital, Rouen, France
| | - Stéfan J. Darmoni
- Department of Digital Health, Rouen University Hospital, Rouen, France
- Laboratoire D'Informatique Médicale et D'Ingénierie des Connaissances en e-Santé (LIMICS), U1142, INSERM, Sorbonne Université, Paris, France
| | - Julien Grosjean
- Department of Digital Health, Rouen University Hospital, Rouen, France
- Laboratoire D'Informatique Médicale et D'Ingénierie des Connaissances en e-Santé (LIMICS), U1142, INSERM, Sorbonne Université, Paris, France
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MacDonald I, de Goumoëns V, Marston M, Alvarado S, Favre E, Trombert A, Perez MH, Ramelet AS. Effectiveness, quality and implementation of pain, sedation, delirium, and iatrogenic withdrawal syndrome algorithms in pediatric intensive care: a systematic review and meta-analysis. Front Pediatr 2023; 11:1204622. [PMID: 37397149 PMCID: PMC10313131 DOI: 10.3389/fped.2023.1204622] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 05/15/2023] [Indexed: 07/04/2023] Open
Abstract
Background Pain, sedation, delirium, and iatrogenic withdrawal syndrome are conditions that often coexist, algorithms can be used to assist healthcare professionals in decision making. However, a comprehensive review is lacking. This systematic review aimed to assess the effectiveness, quality, and implementation of algorithms for the management of pain, sedation, delirium, and iatrogenic withdrawal syndrome in all pediatric intensive care settings. Methods A literature search was conducted on November 29, 2022, in PubMed, Embase, CINAHL and Cochrane Library, ProQuest Dissertations & Theses, and Google Scholar to identify algorithms implemented in pediatric intensive care and published since 2005. Three reviewers independently screened the records for inclusion, verified and extracted data. Included studies were assessed for risk of bias using the JBI checklists, and algorithm quality was assessed using the PROFILE tool (higher % = higher quality). Meta-analyses were performed to compare algorithms to usual care on various outcomes (length of stay, duration and cumulative dose of analgesics and sedatives, length of mechanical ventilation, and incidence of withdrawal). Results From 6,779 records, 32 studies, including 28 algorithms, were included. The majority of algorithms (68%) focused on sedation in combination with other conditions. Risk of bias was low in 28 studies. The average overall quality score of the algorithm was 54%, with 11 (39%) scoring as high quality. Four algorithms used clinical practice guidelines during development. The use of algorithms was found to be effective in reducing length of stay (intensive care and hospital), length of mechanical ventilation, duration of analgesic and sedative medications, cumulative dose of analgesics and sedatives, and incidence of withdrawal. Implementation strategies included education and distribution of materials (95%). Supportive determinants of algorithm implementation included leadership support and buy-in, staff training, and integration into electronic health records. The fidelity to algorithm varied from 8.2% to 100%. Conclusions The review suggests that algorithm-based management of pain, sedation and withdrawal is more effective than usual care in pediatric intensive care settings. There is a need for more rigorous use of evidence in the development of algorithms and the provision of details on the implementation process. Systematic Review Registration https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021276053, PROSPERO [CRD42021276053].
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Affiliation(s)
- Ibo MacDonald
- Institute of Higher Education and Research in Healthcare, University of Lausanne, Lausanne, Switzerland
| | - Véronique de Goumoëns
- La Source School of Nursing, HES-SO University of Applied Sciences and Arts Western Switzerland, Lausanne, Switzerland
- Bureau d’Echange des Savoirs pour des praTiques exemplaires de soins (BEST) a JBI Center of Excellence, Lausanne, Switzerland
| | - Mark Marston
- Institute of Higher Education and Research in Healthcare, University of Lausanne, Lausanne, Switzerland
- Department Woman-Mother-Child, Lausanne University Hospital, Lausanne, Switzerland
| | - Silvia Alvarado
- Institute of Higher Education and Research in Healthcare, University of Lausanne, Lausanne, Switzerland
- Department Woman-Mother-Child, Lausanne University Hospital, Lausanne, Switzerland
| | - Eva Favre
- Institute of Higher Education and Research in Healthcare, University of Lausanne, Lausanne, Switzerland
- Department of Adult Intensive Care, Lausanne University Hospital, Lausanne, Switzerland
| | - Alexia Trombert
- Medical Library, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Maria-Helena Perez
- Department Woman-Mother-Child, Lausanne University Hospital, Lausanne, Switzerland
| | - Anne-Sylvie Ramelet
- Institute of Higher Education and Research in Healthcare, University of Lausanne, Lausanne, Switzerland
- Bureau d’Echange des Savoirs pour des praTiques exemplaires de soins (BEST) a JBI Center of Excellence, Lausanne, Switzerland
- Department Woman-Mother-Child, Lausanne University Hospital, Lausanne, Switzerland
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Cánovas-Segura B, Morales A, Juarez JM, Campos M. Meaningful time-related aspects of alerts in Clinical Decision Support Systems. A unified framework. J Biomed Inform 2023:104397. [PMID: 37245656 DOI: 10.1016/j.jbi.2023.104397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 03/11/2023] [Accepted: 05/15/2023] [Indexed: 05/30/2023]
Abstract
Alerts are a common functionality of clinical decision support systems (CDSSs). Although they have proven to be useful in clinical practice, the alert burden can lead to alert fatigue and significantly reduce their usability and acceptance. Based on a literature review, we propose a unified framework consisting of a set of meaningful timestamps that allows the use of state-of-the-art measures for alert burden, such as alert dwell time, alert think time, and response time. In addition, it can be used to investigate other measures that could be relevant as regards dealing with this problem. Furthermore, we provide a case study concerning three different types of alerts to which the framework was successfully applied. We consider that our framework can easily be adapted to other CDSSs and that it could be useful for dealing with alert burden measurement thus contributing to its appropriate management.
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Affiliation(s)
| | - Antonio Morales
- AIKE Research Group (INTICO), University of Murcia, Murcia, Spain.
| | - Jose M Juarez
- AIKE Research Group (INTICO), University of Murcia, Murcia, Spain.
| | - Manuel Campos
- AIKE Research Group (INTICO), University of Murcia, Murcia, Spain; Murcian Bio-Health Institute (IMIB-Arrixaca), Murcia, Spain.
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Rabbani N, Ho M, Dash D, Calway T, Morse K, Chadwick W. Pseudorandomized Testing of a Discharge Medication Alert to Reduce Free-Text Prescribing. Appl Clin Inform 2023; 14:470-477. [PMID: 37015344 PMCID: PMC10266904 DOI: 10.1055/a-2068-6940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Accepted: 04/03/2023] [Indexed: 04/06/2023] Open
Abstract
BACKGROUND Pseudorandomized testing can be applied to perform rigorous yet practical evaluations of clinical decision support tools. We apply this methodology to an interruptive alert aimed at reducing free-text prescriptions. Using free-text instead of structured computerized provider order entry elements can cause medication errors and inequity in care by bypassing medication-based clinical decision support tools and hindering automated translation of prescription instructions. OBJECTIVE The objective of this study is to evaluate the effectiveness of an interruptive alert at reducing free-text prescriptions via pseudorandomized testing using native electronic health records (EHR) functionality. METHODS Two versions of an EHR alert triggered when a provider attempted to sign a discharge free-text prescription. The visible version displayed an interruptive alert to the user, and a silent version triggered in the background, serving as a control. Providers were assigned to the visible and silent arms based on even/odd EHR provider IDs. The proportion of encounters with a free-text prescription was calculated across the groups. Alert trigger rates were compared in process control charts. Free-text prescriptions were analyzed to identify prescribing patterns. RESULTS Over the 28-week study period, 143 providers triggered 695 alerts (345 visible and 350 silent). The proportions of encounters with free-text prescriptions were 83% (266/320) and 90% (273/303) in the intervention and control groups, respectively (p = 0.01). For the active alert, median time to action was 31 seconds. Alert trigger rates between groups were similar over time. Ibuprofen, oxycodone, steroid tapers, and oncology-related prescriptions accounted for most free-text prescriptions. A majority of these prescriptions originated from user preference lists. CONCLUSION An interruptive alert was associated with a modest reduction in free-text prescriptions. Furthermore, the majority of these prescriptions could have been reproduced using structured order entry fields. Targeting user preference lists shows promise for future intervention.
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Affiliation(s)
- Naveed Rabbani
- Department of Pediatrics, Stanford University School of Medicine, Stanford, California, United States
| | - Milan Ho
- Department of Pediatrics, University of Texas Southwestern Medical School, Dallas, Texas, United States
| | - Debadutta Dash
- Department of Emergency Medicine, Stanford University School of Medicine, Stanford, California, United States
| | - Tyler Calway
- Department of Pediatrics, Stanford University School of Medicine, Stanford, California, United States
| | - Keith Morse
- Department of Pediatrics, Stanford University School of Medicine, Stanford, California, United States
- Division of Hospital Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, United States
| | - Whitney Chadwick
- Department of Pediatrics, Stanford University School of Medicine, Stanford, California, United States
- Division of Hospital Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, United States
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10
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Abstract
OBJECTIVES To assess the current landscape of clinical decision support (CDS) tools in PICUs in order to identify priority areas of focus in this field. DESIGN International, quantitative, cross-sectional survey. SETTING Role-specific, web-based survey administered in November and December 2020. SUBJECTS Medical directors, bedside nurses, attending physicians, and residents/advanced practice providers at Pediatric Acute Lung Injury and Sepsis Network-affiliated PICUs. INTERVENTIONS None. MEASUREMENTS AND MAIN RESULTS The survey was completed by 109 respondents from 45 institutions, primarily attending physicians from university-affiliated PICUs in the United States. The most commonly used CDS tools were people-based resources (93% used always or most of the time) and laboratory result highlighting (86%), with order sets, order-based alerts, and other electronic CDS tools also used frequently. The most important goal providers endorsed for CDS tools were a proven impact on patient safety and an evidence base for their use. Negative perceptions of CDS included concerns about diminished critical thinking and the burden of intrusive processes on providers. Routine assessment of existing CDS was rare, with infrequent reported use of observation to assess CDS impact on workflows or measures of individual alert burden. CONCLUSIONS Although providers share some consensus over CDS utility, we identified specific priority areas of research focus. Consensus across practitioners exists around the importance of evidence-based CDS tools having a proven impact on patient safety. Despite broad presence of CDS tools in PICUs, practitioners continue to view them as intrusive and with concern for diminished critical thinking. Deimplementing ineffective CDS may mitigate this burden, though postimplementation evaluation of CDS is rare.
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Park H, Chae MK, Jeong W, Yu J, Jung W, Chang H, Cha WC. Appropriateness of Alerts and Physicians’ Responses with a Medication-related Clinical Decision Support System (Preprint). JMIR Med Inform 2022; 10:e40511. [PMID: 36194461 PMCID: PMC9579928 DOI: 10.2196/40511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Revised: 09/13/2022] [Accepted: 09/18/2022] [Indexed: 12/02/2022] Open
Abstract
Background Alert fatigue is unavoidable when many irrelevant alerts are generated in response to a small number of useful alerts. It is necessary to increase the effectiveness of the clinical decision support system (CDSS) by understanding physicians’ responses. Objective This study aimed to understand the CDSS and physicians’ behavior by evaluating the clinical appropriateness of alerts and the corresponding physicians’ responses in a medication-related passive alert system. Methods Data on medication-related orders, alerts, and patients’ electronic medical records were analyzed. The analyzed data were generated between August 2019 and June 2020 while the patient was in the emergency department. We evaluated the appropriateness of alerts and physicians’ responses for a subset of 382 alert cases and classified them. Results Of the 382 alert cases, only 7.3% (n=28) of the alerts were clinically appropriate. Regarding the appropriateness of the physicians’ responses about the alerts, 92.4% (n=353) were deemed appropriate. In the classification of alerts, only 3.4% (n=13) of alerts were successfully triggered, and 2.1% (n=8) were inappropriate in both alert clinical relevance and physician’s response. In this study, the override rate was 92.9% (n=355). Conclusions We evaluated the appropriateness of alerts and physicians’ responses through a detailed medical record review of the medication-related passive alert system. An excessive number of unnecessary alerts are generated, because the algorithm operates as a rule base without reflecting the individual condition of the patient. It is important to maximize the value of the CDSS by comprehending physicians’ responses.
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Affiliation(s)
- Hyunjung Park
- Department of Digital Health, Samsung Advanced Institute of Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea
| | - Minjung Kathy Chae
- Department of Digital Health, Samsung Advanced Institute of Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea
| | - Woohyeon Jeong
- Department of Digital Health, Samsung Advanced Institute of Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea
| | - Jaeyong Yu
- Department of Digital Health, Samsung Advanced Institute of Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea
| | - Weon Jung
- Department of Digital Health, Samsung Advanced Institute of Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea
| | - Hansol Chang
- Department of Digital Health, Samsung Advanced Institute of Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea
- Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Won Chul Cha
- Department of Digital Health, Samsung Advanced Institute of Health Sciences & Technology, Sungkyunkwan University, Seoul, Republic of Korea
- Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
- Digital Innovation Center, Samsung Medical Center, Seoul, Republic of Korea
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12
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Chaparro JD, Beus JM, Dziorny AC, Hagedorn PA, Hernandez S, Kandaswamy S, Kirkendall ES, McCoy AB, Muthu N, Orenstein EW. Clinical Decision Support Stewardship: Best Practices and Techniques to Monitor and Improve Interruptive Alerts. Appl Clin Inform 2022; 13:560-568. [PMID: 35613913 PMCID: PMC9132737 DOI: 10.1055/s-0042-1748856] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022] Open
Abstract
Interruptive clinical decision support systems, both within and outside of electronic health records, are a resource that should be used sparingly and monitored closely. Excessive use of interruptive alerting can quickly lead to alert fatigue and decreased effectiveness and ignoring of alerts. In this review, we discuss the evidence for effective alert stewardship as well as practices and methods we have found useful to assess interruptive alert burden, reduce excessive firings, optimize alert effectiveness, and establish quality governance at our institutions. We also discuss the importance of a holistic view of the alerting ecosystem beyond the electronic health record.
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Affiliation(s)
- Juan D Chaparro
- Division of Clinical Informatics, Nationwide Children's Hospital, Columbus, Ohio, United States.,Departments of Pediatrics and Biomedical Informatics, The Ohio State University College of Medicine, Columbus, Ohio, United States
| | - Jonathan M Beus
- Department of Pediatrics, Emory University School of Medicine, Atlanta, Georgia, United States.,Children's Healthcare of Atlanta, Atlanta, Georgia, United States
| | - Adam C Dziorny
- Department of Pediatrics, University of Rochester School of Medicine, Rochester, New York, United States
| | - Philip A Hagedorn
- Department of Pediatrics, University of Cincinnati, College of Medicine, Cincinnati, Ohio, United States.,Division of Hospital Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, United States
| | - Sean Hernandez
- Center for Healthcare Innovation, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States.,Department of General Internal Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States
| | - Swaminathan Kandaswamy
- Department of Pediatrics, Emory University School of Medicine, Atlanta, Georgia, United States
| | - Eric S Kirkendall
- Center for Healthcare Innovation, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States.,Department of Pediatrics, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States.,Center for Biomedical Informatics, Wake Forest School of Medicine, Winston-Salem NC, United States
| | - Allison B McCoy
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, United States
| | - Naveen Muthu
- Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, United States.,Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States
| | - Evan W Orenstein
- Department of Pediatrics, Emory University School of Medicine, Atlanta, Georgia, United States.,Children's Healthcare of Atlanta, Atlanta, Georgia, United States
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13
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Bakken S. Celebrating Randolph A. Miller, MD, 2021 Morris F. Collen Award winner and pioneer in clinical decision support. J Am Med Inform Assoc 2021; 28:2549-2550. [PMID: 34791297 PMCID: PMC8633661 DOI: 10.1093/jamia/ocab249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 11/01/2021] [Indexed: 11/12/2022] Open
Affiliation(s)
- Suzanne Bakken
- School of Nursing, Department of Biomedical Informatics, Data Science Institute, Columbia University, New York, New York, USA
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