Intervention Protocol Template



Title:Impact of a data-driven monitor alarm reduction strategy implemented in safety huddlesShort Title:Ward alarm huddles trialeIRB Number:IRB 15-011896Protocol Date:05/04/2015Amendment 1 Date: Amendment 3 Date: Amendment 2 Date:Amendment 4 Date:Sponsor:Academic Pediatric Association6728 Old McLean Village DriveMcLean, Virginia 22101 USAPhone: 703-556-9222info@Principal Investigator:Christopher P. Bonafide, MD, MSCEThe Children’s Hospital of Philadelphia3401 Civic Center BlvdMain Building Suite 12NW80Philadelphia, PA, 19104Phone 267-426-2901email: bonafide@chop.eduTable of Contents TOC \o "2-3" \t "Heading 1,1,Heading,4,Style1,1,Unnumber Heading 2,1" Table of Contents PAGEREF _Toc502155450 \h iiAbbreviations and Definitions of Terms PAGEREF _Toc502155451 \h ivAbstract PAGEREF _Toc502155452 \h iv1Background Information and Rationale PAGEREF _Toc502155453 \h 11.1Introduction PAGEREF _Toc502155454 \h 11.2Description of Intervention PAGEREF _Toc502155455 \h 11.3Relevant Literature and Data PAGEREF _Toc502155456 \h 11.4Compliance Statement PAGEREF _Toc502155457 \h 22Study Objectives PAGEREF _Toc502155458 \h 22.1Primary Objective (or Aim) PAGEREF _Toc502155459 \h 22.2Secondary Objectives (or Aim) PAGEREF _Toc502155460 \h 33Investigational plan PAGEREF _Toc502155461 \h 43.1General Schema of Study Design (see Figure 1) PAGEREF _Toc502155462 \h 53.1.1Baseline data collection (16 weeks) PAGEREF _Toc502155463 \h 53.1.2Phased intervention implementation (4-12 weeks) PAGEREF _Toc502155464 \h 53.1.3Post-implementation data collection (16 weeks) PAGEREF _Toc502155465 \h 53.2Allocation to Treatment Groups and Blinding PAGEREF _Toc502155466 \h 53.3Study Duration, Enrollment and Number of Sites PAGEREF _Toc502155467 \h 53.3.1Duration of Study Participation PAGEREF _Toc502155468 \h 53.3.2Total Number of Study Sites/Total Number of Subjects Projected PAGEREF _Toc502155469 \h 53.4Study Population PAGEREF _Toc502155470 \h 73.4.1Inclusion Criteria PAGEREF _Toc502155471 \h 73.4.2Exclusion Criteria PAGEREF _Toc502155472 \h 74Study Procedures PAGEREF _Toc502155473 \h 84.1Baseline data collection period (16 weeks) PAGEREF _Toc502155474 \h 84.1.1In final 6 weeks of baseline data collection period: PAGEREF _Toc502155475 \h 84.2Phased intervention implementation period (4-12 weeks) PAGEREF _Toc502155476 \h 84.2.1Implementation phases PAGEREF _Toc502155477 \h 84.3Post-implementation data collection period (16 weeks) PAGEREF _Toc502155478 \h 85Study Evaluations and Measurements PAGEREF _Toc502155479 \h 95.1Descriptions/Definitions PAGEREF _Toc502155480 \h 95.1.1Data collection only stage PAGEREF _Toc502155481 \h 95.1.2Active implementation stage PAGEREF _Toc502155482 \h 105.1.3Implementation support stage PAGEREF _Toc502155483 \h 135.2Safety Evaluation PAGEREF _Toc502155484 \h 146STATISTICAL CONSIDERATIONS PAGEREF _Toc502155485 \h 146.1Primary Endpoint (Aim 1) PAGEREF _Toc502155486 \h 146.2Secondary Endpoints PAGEREF _Toc502155487 \h 146.2.1Aim 1 PAGEREF _Toc502155488 \h 146.2.2Aim 2 PAGEREF _Toc502155489 \h 146.3Secondary Endpoints (Aim 3) PAGEREF _Toc502155490 \h 146.4Statistical Methods PAGEREF _Toc502155491 \h 156.4.1Aim 1 PAGEREF _Toc502155492 \h 156.4.2Aim 2 PAGEREF _Toc502155493 \h 156.4.3Aim 3 PAGEREF _Toc502155494 \h 156.5Sample Size and Power PAGEREF _Toc502155495 \h 157SAFETY MANAGEMENT PAGEREF _Toc502155496 \h 167.1Clinical Adverse Events PAGEREF _Toc502155497 \h 167.2Adverse Event Reporting PAGEREF _Toc502155498 \h 167.3Special Considerations PAGEREF _Toc502155499 \h 178STUDY ADMINISTRATION PAGEREF _Toc502155500 \h 178.1Treatment Assignment Methods PAGEREF _Toc502155501 \h 178.2Data Collection and Management PAGEREF _Toc502155502 \h 178.3Confidentiality PAGEREF _Toc502155503 \h 178.4Regulatory and Ethical Considerations PAGEREF _Toc502155504 \h 188.4.1Data and Safety Monitoring Plan PAGEREF _Toc502155505 \h 188.4.2Risk Assessment PAGEREF _Toc502155506 \h 188.4.3Potential Benefits of Study Participation PAGEREF _Toc502155507 \h 188.5Informed Consent/Assent and HIPAA Authorization PAGEREF _Toc502155508 \h 198.5.1Waiver of informed consent PAGEREF _Toc502155509 \h 198.5.2Waiver of HIPAA Authorization PAGEREF _Toc502155510 \h 198.6Publication PAGEREF _Toc502155511 \h 208.7Payment to Subjects/Families PAGEREF _Toc502155512 \h 209References PAGEREF _Toc502155513 \h 21Abbreviations and Definitions of TermsCR monitorCardiorespiratory monitorSpO2Peripheral oxygen saturation as measured by pulse oximetryAbstractPlease refer to abstract in eIRB.STUDY DIAGRAMInterventionUnit 1-iBaseline data collection(16 weeks)units paired and randomized to i/cPhased intervention implementation (4-12 weeks)Post-implementationdata collection(16 weeks)Unit 2-iUnit 3-iUnit 4-iControlUnit 1-cNo implementation(4-12 weeks)Post-implementationData collection(16 weeks)Unit 2-cUnit 3-cUnit 4-cFigure SEQ Figure \* ARABIC 1. Study diagram. i = intervention, c = control.Background Information and RationaleIntroductionHospital physiologic monitors can alert clinicians to early signs of physiologic deterioration, and thus have great potential to be life-saving. However, monitors generate frequent alarms, most of which are non-actionable. When clinicians become overburdened with alarms, they begin to exhibit alarm fatigue: responding more slowly to alarms or ignoring alarms entirely. In this protocol we outline the methods we will use to evaluate the impact of a safety huddle-based intervention on physiologic monitor alarm rates using a pragmatic, paired, cluster-randomized controlled trial with the intervention delivered at the unit level. This work is considered quality improvement research, and some of the approaches described in this protocol are from the field of quality improvement.Description of InterventionThe intervention consists of a monitor alarm dashboard that displays the numbers and types of alarms for each patient, and an accompanying huddle guide (part of appendix material) to guide data-driven discussion of 2-4 patients who had high alarm rates in the preceding 4 hours. Using the huddle guide, If the primary team confirms that the patient’s recent alarms were non-actionable, discussion will focus on a plan for reducing non-actionable alarms using interventions proven effective in other studies, such as adjusting alarm threshold values to actionable levels, ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"1bp7q2ir3v","properties":{"formattedCitation":"{\\rtf \\super 1\\nosupersub{}}","plainCitation":"1"},"citationItems":[{"id":302,"uris":[""],"uri":[""],"itemData":{"id":302,"type":"article-journal","title":"Impact of pulse oximetry surveillance on rescue events and intensive care unit transfers: a before-and-after concurrence study","container-title":"Anesthesiology","page":"282-287","volume":"112","issue":"2","source":"NCBI PubMed","abstract":"BACKGROUND: Some preventable deaths in hospitalized patients are due to unrecognized deterioration. There are no publications of studies that have instituted routine patient monitoring postoperatively and analyzed impact on patient outcomes.\nMETHODS: The authors implemented a patient surveillance system based on pulse oximetry with nursing notification of violation of alarm limits via wireless pager. Data were collected for 11 months before and 10 months after implementation of the system. Concurrently, matching outcome data were collected on two other postoperative units. The primary outcomes were rescue events and transfers to the intensive care unit compared before and after monitoring change.\nRESULTS: Rescue events decreased from 3.4 (1.89-4.85) to 1.2 (0.53-1.88) per 1,000 patient discharges and intensive care unit transfers from 5.6 (3.7-7.4) to 2.9 (1.4-4.3) per 1,000 patient days, whereas the comparison units had no change.\nCONCLUSIONS: Patient surveillance monitoring results in a reduced need for rescues and intensive care unit transfers.","ISSN":"1528-1175","note":"PMID: 20098128","shortTitle":"Impact of pulse oximetry surveillance on rescue events and intensive care unit transfers","journalAbbreviation":"Anesthesiology","language":"eng","author":[{"family":"Taenzer","given":"Andreas H."},{"family":"Pyke","given":"Joshua B."},{"family":"McGrath","given":"Susan P."},{"family":"Blike","given":"George T."}],"issued":{"date-parts":[["2010",2]]},"PMID":"20098128"}}],"schema":""} 1 changing delay time between when the threshold is crossed and when the alarm fires, ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"1ucpmrok8f","properties":{"formattedCitation":"{\\rtf \\super 2,3\\nosupersub{}}","plainCitation":"2,3"},"citationItems":[{"id":1572,"uris":[""],"uri":[""],"itemData":{"id":1572,"type":"article-journal","title":"Fact or artifact? Breakthroughs in pulse oximetry alarm management","container-title":"Neonatal Intensive Care","page":"51-53","volume":"16","issue":"4","source":"EBSCOhost","abstract":"DESCRIPTION OF SAT SECONDS","ISSN":"1062-2454","shortTitle":"Fact or artifact?","journalAbbreviation":"Neonatal Intensive Care","author":[{"family":"Sharkey","given":"T"}],"issued":{"date-parts":[["2003",7]]}}},{"id":401,"uris":[""],"uri":[""],"itemData":{"id":401,"type":"article-journal","title":"Improving alarm performance in the medical intensive care unit using delays and clinical context","container-title":"Anesth Analg","page":"1546-1552","volume":"108","archive_location":"19372334","abstract":"INTRODUCTION: In an intensive care unit, alarms are used to call attention to a patient, to alert a change in the patient's physiology, or to warn of a failure in a medical device; however, up to 94% of the alarms are false. Our purpose in this study was to identify a means of reducing the number of false alarms.METHODS: An observer recorded time-stamped information of alarms and the presence of health care team members in the patient room; each alarm response was classified as effective (action taken within 5 min), ineffective (no response to the alarm), and ignored (alarm consciously ignored or actively silenced).RESULTS: During the 200-h study period, 1271 separate entries by an individual to the room being observed were recorded, 1214 alarms occurred and 2344 tasks were performed. On average, alarms occurred 6.07 times per hour and were active for 3.28 min per hour; 23% were effective, 36% were ineffective, and 41% were ignored. The median alarm duration was 17 s. A 14-s delay before alarm presentation would remove 50% of the ignored and ineffective alarms, and a 19-s delay would remove 67%. Suctioning, washing, repositioning, and oral care caused 152 ignored or ineffective ventilator alarms.DISCUSSION: Introducing a 19-s alarm delay and automatically detecting suctioning, repositioning, oral care, and washing could reduce the number of ineffective and ignored alarms from 934 to 274. More reliable alarms could elicit more timely response, reduce workload, reduce noise pollution, and potentially improve patient safety.","note":"5","shortTitle":"Improving alarm performance in the medical intensive care unit using delays and clinical context","author":[{"family":"G?rges","given":"Matthias"},{"family":"Markewitz","given":"Boaz A."},{"family":"Westenskow","given":"Dwayne R."}],"issued":{"date-parts":[["2009"]]}}}],"schema":""} 2,3 and regularly changing leads. ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"288jecl6dr","properties":{"formattedCitation":"{\\rtf \\super 4\\nosupersub{}}","plainCitation":"4"},"citationItems":[{"id":1357,"uris":[""],"uri":[""],"itemData":{"id":1357,"type":"article-journal","title":"Daily electrode change and effect on cardiac monitor alarms: an evidence-based practice approach","container-title":"J Nurs Care Qual","page":"265-71","volume":"28","source":"NLM","archive_location":"23187092","abstract":"Frequent monitor alarms are distracting and interfere with clinicians performing critical tasks. This article describes a quality improvement rapid-cycle change approach to explore the benefits of changing electrodes daily on the number of cardiac monitor alarms. Eight days of baseline and intervention data were compared for 2 adult acute care units. Average alarms per bed per day were reduced by 46% on both units. Daily electrocardiogram electrode change reduces the number of cardiac monitor alarms.","ISSN":"1550-5065 (Electronic) 1057-3631 (Linking)","note":"3","shortTitle":"Daily electrode change and effect on cardiac monitor alarms: an evidence-based practice approach","journalAbbreviation":"Journal of nursing care quality","language":"eng","author":[{"family":"Cvach","given":"M. M."},{"family":"Biggs","given":"M."},{"family":"Rothwell","given":"K. J."},{"family":"Charles-Hudson","given":"C."}],"issued":{"date-parts":[["2013",7]]}}}],"schema":""} 4 Relevant Literature and DataHospital physiologic monitors can alert clinicians to early signs of physiologic deterioration, and thus have great potential to be life-saving. However, monitors generate frequent alarms, most of which are non-actionable. ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"dtu4jusV","properties":{"formattedCitation":"{\\rtf \\super 5\\uc0\\u8211{}7\\nosupersub{}}","plainCitation":"5–7"},"citationItems":[{"id":390,"uris":[""],"uri":[""],"itemData":{"id":390,"type":"article-journal","title":"Poor prognosis for existing monitors in the intensive care unit","container-title":"Critical Care Medicine","page":"614-619","volume":"25","issue":"4","source":"CrossRef","ISSN":"0090-3493","shortTitle":"Poor prognosis for existing monitors in the intensive care unit","author":[{"family":"Tsien","given":"Christine L."},{"family":"Fackler","given":"James C."}],"issued":{"date-parts":[["1997",4]]},"accessed":{"date-parts":[["2014",1,17]],"season":"18:31:57"}}},{"id":192,"uris":[""],"uri":[""],"itemData":{"id":192,"type":"paper-conference","title":"False physiologic monitor alarms and nurse response time in a children’s hospital","container-title":"NIH/NHLBI K Award Investigators Meeting","publisher-place":"Bethesda, MD","event":"NIH/NHLBI K Award Investigators Meeting","event-place":"Bethesda, MD","abstract":"In this study, we aimed to determine if increased rates of physiologic monitor false alarms were associated with nurses responding more slowly to subsequent alarms that could represent life-threatening conditions (“potentially critical alarms”). We used video to observe nurses caring for: (1) pediatric intensive care unit (PICU) patients requiring inotropic support and/or mechanical ventilation, and (2) medical ward patients. We defined true alarms as those that correctly identified physiologic status and were actionable. We evaluated the association between false alarm exposure in the preceding 120 minutes and response time using a log-rank test stratified by nurse.\nIn 210 hours of observation with 5070 alarms; 87.1% of PICU and 99.0% of ward clinical alarms were false. As nurses’ false alarm exposure increased, time-to-event curves (below) showed incremental increases in median response times to potentially critical alarms in PICU, and incremental increases in the 75th percentiles of response times in both PICU and ward settings. Alarm fatigue could explain these findings.","author":[{"family":"Bonafide","given":"Christopher P."},{"family":"Localio","given":"A. Russell"},{"family":"Nadkarni","given":"Vinay M"},{"family":"Lin","given":"Richard"},{"family":"Keren","given":"Ron"}],"issued":{"date-parts":[["2014",9,29]]}}},{"id":967,"uris":[""],"uri":[""],"itemData":{"id":967,"type":"article-journal","title":"Crying wolf: false alarms in a pediatric intensive care unit","container-title":"Crit Care Med","page":"981-5","volume":"22","issue":"6","source":"NLM","archive_location":"8205831","abstract":"OBJECTIVE: To determine the predictive value of patient monitoring alarms as a warning system in a pediatric intensive care unit (ICU). DESIGN: Prospective, observational study. SETTING: Pediatric ICU of a university affiliated children's hospital. INTERVENTIONS: During a 7-day period, ICU staff were asked to record the type and number of alarm soundings. Alarms were recorded as false, significant (resulted in change in therapy), or induced (by staff manipulations; not significant). MEASUREMENTS AND MAIN RESULTS: Sixty-six percent of nursing shifts (928 patient hours of care) responded. There were 2,176 alarms soundings: 1,481 (68%) false, 119 (5.5%) significant, and 576 (26.5%) induced. Alarm origins were: 44% pulse oximeter, 1% end-tidal PCO2, 31% ventilator, and 24% electrocardiograph (EKG). The positive predictive value of alarms were: 7% pulse oximeter, 16% end-tidal PCO2, 3% ventilator, and 5% EKG. The negative predictive value of all alarms were > 97%. More alarms sounded during the 7:00 am to 3:00 pm shift than during the 3:00 pm to 11:00 pm or 11:00 pm to 7:00 am shifts (167 +/- 19 vs. 64 +/- 39 vs. 75 +/- 43, p < .05, respectively). When corrected for number of patients/shift, the occurrence of soundings differed only between day and night (11.4 +/- 1.5/patient/shift vs. 6.1 +/- 1.0, p < .05). CONCLUSIONS: Over 94% of alarm soundings in a pediatric ICU may not be clinically important. Present monitoring systems are poor predictors of untoward events.","ISSN":"0090-3493 (Print) 0090-3493 (Linking)","shortTitle":"Crying wolf: false alarms in a pediatric intensive care unit","journalAbbreviation":"Critical care medicine","language":"eng","author":[{"family":"Lawless","given":"S. T."}],"issued":{"date-parts":[["1994",6]]}}}],"schema":""} 5–7 In a pilot study, our research team used video-based methods ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"16dn7taj48","properties":{"formattedCitation":"{\\rtf \\super 8\\nosupersub{}}","plainCitation":"8"},"citationItems":[{"id":1634,"uris":[""],"uri":[""],"itemData":{"id":1634,"type":"article-journal","title":"Video methods for evaluating physiologic monitor alarms and alarm responses","container-title":"Biomedical instrumentation & technology","page":"220-230","volume":"48","issue":"3","source":"NCBI PubMed","abstract":"False physiologic monitor alarms are extremely common in the hospital environment. High false alarm rates have the potential to lead to alarm fatigue, leading nurses to delay their responses to alarms, ignore alarms, or disable them entirely. Recent evidence from the U.S. Food and Drug Administration (FDA) and The Joint Commission has demonstrated a link between alarm fatigue and patient deaths. Yet, very little scientific effort has focused on the rigorous quantitative measurement of alarms and responses in the hospital setting. We developed a system using multiple temporarily mounted, minimally obtrusive video cameras in hospitalized patients' rooms to characterize physiologic monitor alarms and nurse responses as a proxy for alarm fatigue. This allowed us to efficiently categorize each alarm's cause, technical validity, actionable characteristics, and determine the nurse's response time. We describe and illustrate the methods we used to acquire the video, synchronize and process the video, manage the large digital files, integrate the video with data from the physiologic monitor alarm network, archive the video to secure servers, and perform expert review and annotation using alarm \"bookmarks.\" We discuss the technical and logistical challenges we encountered, including the root causes of hardware failures as well as issues with consent, confidentiality, protection of the video from litigation, and Hawthorne-like effects. The description of this video method may be useful to multidisciplinary teams interested in evaluating physiologic monitor alarms and alarm responses to better characterize alarm fatigue and other patient safety issues in clinical settings.","ISSN":"0899-8205","note":"PMID: 24847936","journalAbbreviation":"Biomed Instrum Technol","language":"eng","author":[{"family":"Bonafide","given":"Christopher P"},{"family":"Zander","given":"Miriam"},{"family":"Graham","given":"Christian Sarkis"},{"family":"Weirich Paine","given":"Christine M"},{"family":"Rock","given":"Whitney"},{"family":"Rich","given":"Andrew"},{"family":"Roberts","given":"Kathryn E"},{"family":"Fortino","given":"Margaret"},{"family":"Nadkarni","given":"Vinay M"},{"family":"Lin","given":"Richard"},{"family":"Keren","given":"Ron"}],"issued":{"date-parts":[["2014",6]]},"PMID":"24847936"}}],"schema":""} 8 to determine that 87.1% of PICU and 99.0% of general inpatient unit clinical alarms were non-actionable, meaning that they did not warrant clinical intervention or consultation. ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"2bbdb0dkam","properties":{"formattedCitation":"{\\rtf \\super 6\\nosupersub{}}","plainCitation":"6"},"citationItems":[{"id":192,"uris":[""],"uri":[""],"itemData":{"id":192,"type":"paper-conference","title":"False physiologic monitor alarms and nurse response time in a children’s hospital","container-title":"NIH/NHLBI K Award Investigators Meeting","publisher-place":"Bethesda, MD","event":"NIH/NHLBI K Award Investigators Meeting","event-place":"Bethesda, MD","abstract":"In this study, we aimed to determine if increased rates of physiologic monitor false alarms were associated with nurses responding more slowly to subsequent alarms that could represent life-threatening conditions (“potentially critical alarms”). We used video to observe nurses caring for: (1) pediatric intensive care unit (PICU) patients requiring inotropic support and/or mechanical ventilation, and (2) medical ward patients. We defined true alarms as those that correctly identified physiologic status and were actionable. We evaluated the association between false alarm exposure in the preceding 120 minutes and response time using a log-rank test stratified by nurse.\nIn 210 hours of observation with 5070 alarms; 87.1% of PICU and 99.0% of ward clinical alarms were false. As nurses’ false alarm exposure increased, time-to-event curves (below) showed incremental increases in median response times to potentially critical alarms in PICU, and incremental increases in the 75th percentiles of response times in both PICU and ward settings. Alarm fatigue could explain these findings.","author":[{"family":"Bonafide","given":"Christopher P."},{"family":"Localio","given":"A. Russell"},{"family":"Nadkarni","given":"Vinay M"},{"family":"Lin","given":"Richard"},{"family":"Keren","given":"Ron"}],"issued":{"date-parts":[["2014",9,29]]}}}],"schema":""} 6We know from the field of experimental psychology that humans rapidly learn to respond more slowly to alarms after being exposed to many false alarms, exhibiting “alarm fatigue.” ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"2ksutb9rjp","properties":{"formattedCitation":"{\\rtf \\super 9,10\\nosupersub{}}","plainCitation":"9,10"},"citationItems":[{"id":603,"uris":[""],"uri":[""],"itemData":{"id":603,"type":"article-journal","title":"System operator response to warnings of danger: a laboratory investigation of the effects of the predictive value of a warning on human response time","container-title":"J Exp Psychol-Appl","page":"19–33","volume":"1","shortTitle":"System operator response to warnings of danger: a laboratory investigation of the effects of the predictive value of a warning on human response time","author":[{"family":"Getty","given":"D. J."},{"family":"Swets","given":"J. A."},{"family":"Rickett","given":"R. M."},{"family":"Gonthier","given":"D."}],"issued":{"date-parts":[["1995"]]}}},{"id":687,"uris":[""],"uri":[""],"itemData":{"id":687,"type":"article-journal","title":"Human probability matching behaviour in response to alarms of varying reliability","container-title":"Ergonomics","page":"2300-12","volume":"38","archive_location":"7498189","abstract":"The goals of this research were to substantiate the existence of the cry-wolf effect for alarm responses, quantifying its effect on operator performance. A total of 138 undergraduate students performed two blocks of a cognitively demanding psychomotor primary task; at the same time, they were presented with alarms of varying reliabilities (25, 50 and 75% true alarms) and urgencies (green, yellow and red visual alarms presented concurrently with low-, medium- and high-urgency auditory civilian aircraft cockpit alarms). Alarm response frequencies were observed and analysed, and t-tests and repeated-measures MANOVAs were used to assess the effects of increasing alarm reliability on alarm response frequencies, speed and accuracy. The results indicate that most subjects (about 90%) do not respond to all alarms but match their response rates to the expected probability of true alarms (probability matching). About 10% of the subjects responded in the extreme, utilizing an all-or-none strategy. Implications of these results for alarm design instruction and further research are discussed.","ISSN":"0014-0139 (Print) 0014-0139 (Linking)","note":"11","shortTitle":"Human probability matching behaviour in response to alarms of varying reliability","journalAbbreviation":"Ergonomics","language":"eng","author":[{"family":"Bliss","given":"J. P."},{"family":"Gilson","given":"R. D."},{"family":"Deaton","given":"J. E."}],"issued":{"date-parts":[["1995",11]]}}}],"schema":""} 9,10 Our research team aimed to determine if this phenomenon existed in the hospital during actual patient care. Using video, we found that nurses had incrementally slower response time as the number of non-actionable alarms they experienced in the preceding 120 minutes increased, exhibiting behavior consistent with alarm fatigue. ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"22ch5skqn9","properties":{"formattedCitation":"{\\rtf \\super 6\\nosupersub{}}","plainCitation":"6"},"citationItems":[{"id":192,"uris":[""],"uri":[""],"itemData":{"id":192,"type":"paper-conference","title":"False physiologic monitor alarms and nurse response time in a children’s hospital","container-title":"NIH/NHLBI K Award Investigators Meeting","publisher-place":"Bethesda, MD","event":"NIH/NHLBI K Award Investigators Meeting","event-place":"Bethesda, MD","abstract":"In this study, we aimed to determine if increased rates of physiologic monitor false alarms were associated with nurses responding more slowly to subsequent alarms that could represent life-threatening conditions (“potentially critical alarms”). We used video to observe nurses caring for: (1) pediatric intensive care unit (PICU) patients requiring inotropic support and/or mechanical ventilation, and (2) medical ward patients. We defined true alarms as those that correctly identified physiologic status and were actionable. We evaluated the association between false alarm exposure in the preceding 120 minutes and response time using a log-rank test stratified by nurse.\nIn 210 hours of observation with 5070 alarms; 87.1% of PICU and 99.0% of ward clinical alarms were false. As nurses’ false alarm exposure increased, time-to-event curves (below) showed incremental increases in median response times to potentially critical alarms in PICU, and incremental increases in the 75th percentiles of response times in both PICU and ward settings. Alarm fatigue could explain these findings.","author":[{"family":"Bonafide","given":"Christopher P."},{"family":"Localio","given":"A. Russell"},{"family":"Nadkarni","given":"Vinay M"},{"family":"Lin","given":"Richard"},{"family":"Keren","given":"Ron"}],"issued":{"date-parts":[["2014",9,29]]}}}],"schema":""} 6 In response to mounting evidence, in 2013 the Joint Commission named alarm fatigue the most common contributing factor to alarm-related sentinel events in hospitals ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"1g0qiqtat9","properties":{"formattedCitation":"{\\rtf \\super 11,12\\nosupersub{}}","plainCitation":"11,12"},"citationItems":[{"id":479,"uris":[""],"uri":[""],"itemData":{"id":479,"type":"webpage","title":"Sentinel event alert: medical device alarm safety in hospitals","URL":" downloads/SEA_50_alarms.pdf","shortTitle":"Sentinel event alert: medical device alarm safety in hospitals","author":[{"family":"The Joint Commission","given":""}],"issued":{"date-parts":[["2013"]]},"accessed":{"date-parts":[["2014",10,9]]}}},{"id":253,"uris":[""],"uri":[""],"itemData":{"id":253,"type":"article-journal","title":"Joint commission warns of alarm fatigue: multitude of alarms from monitoring devices problematic","container-title":"JAMA","page":"2315-6","volume":"309","issue":"22","source":"NLM","archive_location":"23757063","ISSN":"1538-3598 (Electronic) 0098-7484 (Linking)","shortTitle":"Joint commission warns of alarm fatigue: multitude of alarms from monitoring devices problematic","journalAbbreviation":"JAMA : the journal of the American Medical Association","language":"eng","author":[{"family":"Mitka","given":"M."}],"issued":{"date-parts":[["2013",6,12]]}}}],"schema":""} 11,12 and the ECRI Institute, a non-profit patient safety organization, named clinical alarms the top health technology hazard. ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"pb5l5blv5","properties":{"formattedCitation":"{\\rtf \\super 13\\nosupersub{}}","plainCitation":"13"},"citationItems":[{"id":104,"uris":[""],"uri":[""],"itemData":{"id":104,"type":"article-journal","title":"Top 10 health technology hazards for 2014","container-title":"Health Devices","page":"354-380","volume":"42","issue":"11","source":"NCBI PubMed","ISSN":"0046-7022","note":"PMID: 24358513","journalAbbreviation":"Health Devices","language":"eng","issued":{"date-parts":[["2013",11]]},"PMID":"24358513"}}],"schema":""} 13 Our conceptual framework for the causal pathway between high rates of non-actionable alarms and patient harm, below, was informed by research and expert guidance. False (non-actionable) alarms lead to alarm fatigue, delaying responses to alarms that may represent true signs of deterioration, and interrupt error-prone tasks leading to potential patient harm.Figure SEQ Figure \* ARABIC 2. Conceptual framework.228601624965Figure SEQ Figure \* ARABIC 3. Unit alarm burden.Figure SEQ Figure \* ARABIC 3. Unit alarm burden.22860190500All patients do not contribute equally to the burden of non-actionable alarms. Most alarms are caused by a small proportion of patients. For example, the figure to the left displays data from one inpatient unit. Each bar is a patient, and the height of the bar represents the number of alarms in the previous 4 hours. Of the 14 monitored patients, 2 had extremely high alarm rates exceeding 100 alarms in 4 hours, or more than 1 alarm every 3 minutes. Currently, at most hospitals data like this on the numbers of alarms that patients generate are only available to researchers with the software tools needed to interrogate and record data from the monitor network. Our goal in this proposal is to bring this data to the safety huddles occurring daily on inpatient units in an accessible format to help teams make informed decisions about monitoring and minimize the potential of harm from alarm pliance StatementThis study will be conducted in full accordance with all applicable Children’s Hospital of Philadelphia Research Policies and Procedures and all applicable Federal and state laws and regulations including 45 CFR 46, 21 CFR Parts 50, 54, 56, 312, 314 and 812. All episodes of noncompliance will be documented.The investigators will perform the study in accordance with this protocol, will obtain consent and assent unless the requirements are waived, and will report unanticipated problems involving risks to subjects or others in accordance with The Children’s Hospital of Philadelphia IRB Policies and Procedures and all federal requirements. Collection, recording, and reporting of data will be accurate and will ensure the privacy, health, and welfare of research subjects during and after the study. Study ObjectivesPrimary Objective (or Aim)Specific Aim 1: To evaluate the impact of a safety huddle-based physiologic monitor alarm reduction intervention on unit-wide alarm rates. The following rates will be evaluated:1a. Alarms per patient-day1b. Alarms per monitored patient-day Secondary Objectives (or Aim)Specific Aim 2: To evaluate the impact of a safety huddle-based physiologic monitor alarm reduction intervention on the alarm rates of the individual patients whose alarms are discussed in the huddle.Specific Aim 3: To evaluate the adoption and implementation fidelity of the intervention.Investigational planThis study is on the pragmatic end of the pragmatic–explanatory trial continuum. Table SEQ Table \* ARABIC 1. Position of this study on the pragmatic–explanatory study continuum, adapted from Table 1 of Thorpe et al. ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"21aqm2sshs","properties":{"formattedCitation":"{\\rtf \\super 14\\nosupersub{}}","plainCitation":"14"},"citationItems":[{"id":7115,"uris":[""],"uri":[""],"itemData":{"id":7115,"type":"article-journal","title":"A pragmatic-explanatory continuum indicator summary (PRECIS): a tool to help trial designers","container-title":"Canadian Medical Association Journal","page":"E47-E57","volume":"180","issue":"10","source":"CrossRef","DOI":"10.1503/cmaj.090523","ISSN":"0820-3946, 1488-2329","shortTitle":"A pragmatic-explanatory continuum indicator summary (PRECIS)","language":"en","author":[{"family":"Thorpe","given":"K. E."},{"family":"Zwarenstein","given":"M."},{"family":"Oxman","given":"A. D."},{"family":"Treweek","given":"S."},{"family":"Furberg","given":"C. D."},{"family":"Altman","given":"D. G."},{"family":"Tunis","given":"S."},{"family":"Bergel","given":"E."},{"family":"Harvey","given":"I."},{"family":"Magid","given":"D. J."},{"family":"Chalkidou","given":"K."}],"issued":{"date-parts":[["2009",5,12]]},"accessed":{"date-parts":[["2015",4,9]]}}}],"schema":""} 14DomainFeatures of this studyLocation on the pragmatic–explanatory trial continuumParticipantsParticipant eligibilitycriteriaAll participants who have the condition of interest are included in the unit-wide analysis in the primary outcome. The patients discussed in huddles on the intervention units will be suggested by research team members based on the numbers of alarms over the preceding 4 hours, but ultimately the decision about who to discuss in the huddle falls to the clinicians present.more pragmatic than explanatoryInterventions and expertiseExperimental intervention — flexibilityThe huddle intervention will be incorporated into the safety huddles already occurring on each unit in a flexible way that takes into account the existing structure of the huddle and the preferences of the unit staff.more pragmatic than explanatoryExperimental intervention — practitioner expertiseThe experimental intervention will be applied by the full range of clinicians involved in the safety huddle, regardless of their expertise. Research assistants will be available for guidance but will not run the huddle or dictate exactly how the intervention will be implemented or carried out. more pragmatic than explanatoryComparison intervention—flexibilityThe comparison, or control units will perform their huddles according to their usual routines. Since alarm management is a current Joint Commission National Patient Safety Goal, there may be interventions that arise on units related to alarm management that are out of our control. The PI is a member of the hospital-wide team charged with addressing this goal. We will monitor for new interventions on the study units and track their activities if and when they arise so that those factors can be accounted for in the analysis.more pragmatic than explanatoryComparison intervention — practitioner expertiseThe comparison, or control units will perform their huddles according to their usual routines with the usual practitioners.more pragmatic than explanatoryFollow-up and outcomesFollow-up intensityFollow-up of individuals discussed in huddles will be intense as a measure of intervention effectiveness at the individual patient level.more explanatory than pragmaticPrimary trial outcomeThe primary outcome of unit-wide alarm rates is an objectively measured, clinically meaningful outcome to the study participants.more pragmatic than explanatoryCompliance/adherenceParticipant compliance with “prescribed” interventionHuddle participants’ use of the huddle dashboard and huddle guide will be monitored as a measure of fidelity. If unit compliance with the intervention is low, the research team will work with the unit to improve participation.more explanatory than pragmaticPractitioner adherence to study protocolAs above- practitioner is the participant.more explanatory than pragmaticAnalysisAnalysis of primary outcomeThe unit-wide primary analysis includes all patients and attempts to see if the intervention works under the usual conditions, with all the noise inherent therein.more pragmatic than explanatoryGeneral Schema of Study Design (see REF _Ref416336962 \h \* MERGEFORMAT Figure 1)This is a pragmatic, paired, cluster-randomized controlled trial with the intervention delivered at the unit level. The 8 participating medical units for the trial will be grouped into 4 pairs of units. Pairing will be based on (1) participation in hospital-wide alarm management quality improvement initiatives [2 of the 8 units that we will pair together], (2) baseline rates of alarms per patient-day, and (3) baseline monitoring practices including use of the mobile messaging gateway technology. One unit from each pair will be randomized to the intervention, and one will be randomized to control (no intervention). There are no patient- or staff-level exclusions.Baseline data collection (16 weeks)In this period, baseline alarm data will be collected from all participating units. This will provide data that will be compared to the post-implementation data collected later. The baseline data to inform the unit pairings will also be obtained during this period.Phased intervention implementation (4-12 weeks)During phased implementation, we will spend 1-3 weeks on each of the 4 intervention units intensively implementing the intervention, working closely with charge nurses and other staff to integrate alarm discussion into each unit’s existing safety huddles. The number of weeks we spend on implementation on each unit will depend on the needs and readiness of each unit as well as other factors that could delay readiness such as holidays.Post-implementation data collection (16 weeks)During post-implementation data collection, we will continue to work closely with staff on each intervention unit to continue integration of alarm discussion into each unit’s existing safety huddles. We will simultaneously collect data from all intervention and control units.Allocation to Treatment Groups and BlindingSee Section REF _Ref415146970 \w \h \* MERGEFORMAT 3.1 for the general schema of paired randomization. In order to keep the investigators blinded during the baseline data collection process, unit pairing and randomization within pairs will occur during the final 6 weeks of baseline data collection. Coin flip will determine which unit in the pair receives the intervention.Study Duration, Enrollment and Number of SitesDuration of Study ParticipationStudy participation will be at the unit level. Each unit will be involved for 16 weeks of baseline data collection, 4-12 weeks of phased implementation, and 16 weeks of post-implementation data collection (up to 44 weeks total). Total Number of Study Sites/Total Number of Subjects ProjectedThe study will be conducted on 8 medical inpatient units at The Children’s Hospital of Philadelphia only. The units are:4 West Seashore House5 East5 South5 West A5 West B8 South9 South7 West Medical Hospitalist TeamPrimary subjects (clinicians): Since we will be providing patient alarm information aimed at changing behavior of clinical staff and monitoring whether short-term behavior change in response to huddles occurred, nurses and providers (defined as physicians, nurse practitioners, and physician assistants) are the primary subjects. However, since the intervention is occurring at a unit-wide level and we are not collecting any identifiers on these primary subjects and all huddles will not be observed, it is not possible to accurately determine the actual number of nurses and providers whose behavior could potentially have been impacted by the intervention. It also will not be possible to identify when nurses and providers are exposed to the intervention repeatedly over time. In order to best estimate this number, we will “count” the involvement of 4 nurse and 1 provider subjects (5 clinical staff subjects) on each day that a huddle included discussion of alarms, acknowledging that this number will not be completely accurate but is a fair (over)estimate. Since the intervention is occurring on weekdays on 4 units over a 4-12 week implementation period and a 16 week post-implementation data collection period, and we will estimate that a huddle could occur up to 5 days/week on those days, we will estimate enrolling 28 weeks * 5 days per week * 4 intervention units * 5 clinical staff per huddle = 2800 clinical staff subjects (a combination of nurses and providers). When we report the “actual” number of primary subjects involved for continuing reviews and study closure, we will report the number of intervention huddles that we either attended or obtained a data collection sheet from the charge nurse on, multiplied by the estimated 5 clinical staff subjects, acknowledging that this may be an over estimate.Secondary subjects (patients): Aim 1a: For the Aim 1a unit-level outcome alarms per patient-day, we will, in a semi-automated fashion, collect bed occupancy (using patient-days) and alarm data from all patients hospitalized on the control and intervention units every day over the course of the 44 week study period in order to generate unit-level estimates. Estimating 24 occupied beds per unit * 8 units * 44 weeks * 7 days = 59,136 patient-days. This is an overestimate of the number of patients impacted because most patients will stay more than 1 day. Since our alarm data source features patient identifiers (name and MRN) as an optional field that is not reliably nor accurately completed by nurses on the inpatient units, we will only consistently have bed number and date/time as identifiers on most patients. So, we will not be able to accurately report the number of unique patients contributing to the entire dataset of alarms. Thus we will report the number of actual patient-days over the course of the study period as the number of secondary subjects in continuing reviews and for study closure.We will be collecting identifying data on each patient whose alarms are discussed in each huddle. We estimate discussing up to 4 patients per huddle * 4 intervention units * 5 huddles per week * up to (16+12=28 weeks of huddles) = 2240 patients actively discussed in huddles and tracked. These patients are nested within all the eligible patients who contribute alarm data described above.Aim 1b: Each pair of units will have one randomly-selected non-holiday weekday per week in the final 6 weeks of the baseline data collection period and one day per week in each week of the 16 week post-implementation data collection period that will be a “point prevalence of monitoring data collection day.” The random selection of dates will occur during the first week of each period. On these days, for the Aim 1b unit-level outcome alarms per monitored patient-day, we will, in a manual fashion, collect bed monitored status. These monitored patients are nested within the patient subjects described in Aim 1a above. This also will not involve collecting unique patient identifiers.Aim 2: Also nested within the patients described in Aim 1a are the patients used to determine the Aim 2 patient-level outcomes. These patients will be studied on 16 “intensive patient data collection days” in the post-implementation data collection period. On each intensive patient data collection day in the post-implementation data collection period, the 4 monitored patients currently hospitalized on each intervention and control unit with the highest number of “crisis” and “warning” alarms (the alarms that are generally high acuity and audible on the unit) at the time of the huddle— regardless of whether or not they were discussed in a huddle— will be identified for alarm data collection for patient-level comparison. We estimate that this will be 4 patients per unit * 8 units * 16 intensive patient data collection days = 512 patient subjects with identifiers collected, drawn from within the 59,136 potential subjects listed above.Aim 3: Uses the same patients as above.Study PopulationInclusion CriteriaPrimary subjects (all Aims): Any nurse, physician, nurse practitioner, or physician assistant caring for a patient whose alarms are discussed in a safety huddle on an intervention unit.Secondary subjects for Aim 1 analysis: All patients hospitalized on a control or intervention unit during the study period.Secondary subjects for Aim 2 analysis: the 4 monitored patients hospitalized on each intervention and control unit with the highest number of “crisis” and “warning” alarms (the alarms that are generally high acuity and audible on the unit) at the time of the huddle (typically around 11AM).Intervention patients: those patients discussed in a huddle on intervention unitsMonitored patients on intervention units whose alarms are not discussed in the huddle Monitored patients on control unitsExclusion CriteriaPrimary subjects (all Aims): none.Secondary subjects for Aim 1 analysis: none.Secondary subjects for Aim 2 analysis (patients discussed in huddle and their controls): Discharge anticipated for the same day (determined by “anticipated discharge” column in Epic and review of Epic progress notes).Subjects that do not meet all of the enrollment criteria may not be enrolled. Any violations of these criteria must be reported in accordance with IRB Policies and Procedures. Study Procedures Baseline data collection period (16 weeks)All intervention and control units performing data collection onlyIn final 6 weeks of baseline data collection period:Baseline data used to organize the units into matched pairsRandomized to control and intervention unit within each matched pairPhased intervention implementation period (4-12 weeks)Implementation phasesUnit 1-i implementation phase (1-3 weeks)Unit 1-i in active implementationAll other intervention and control units in data collection onlyUnit 2-i implementation phase (1-3 weeks)Unit 2-i in active implementationUnit 1-i in implementation supportAll other intervention and control units in data collection onlyUnit 3-i implementation phase (1-3 weeks)Unit 3-i in active implementationUnits 1-i and 2-i in implementation supportAll other intervention and control units in data collection onlyUnit 4-i implementation phase (1-3 weeks)Unit 4-i in active implementationUnits 1-i, 2-i, 3-i in implementation supportControl units in data collection onlyPost-implementation data collection period (16 weeks)All intervention units in implementation supportControl units in data collection onlyStudy Evaluations and MeasurementsDescriptions/DefinitionsIn each period described above, the terms below are used to describe a set of procedures, evaluations, and measurements which we have called stages. The descriptions below state exactly what is entailed for each stage.Data collection only stageNo interventionData collected every dayBed occupancy data comes from Epic and the CHOP financial dashboardData elements extracted from Epic:datetimeunitroombed occupied (yes/no)Data elements extracted from the CHOP financial dashboard:unitdatepatient-daysAlarm data comes from the GE monitor network, queried using Bedmaster software and/or the CHOP data warehouse (when available- incorporation into data warehouse is in progress at the time this protocol is being submitted). CHOP has been using Bedmaster in Biomedical Engineering for many years.Data elements extracted from the GE monitor network:datetimeunitroombed patient ID (a place where nurses can enter patient’s MRN; this practice varies by unit and is often unreliable; this element will therefore not be retained)patient name (a place where nurses can enter patient’s name; this practice varies by unit but is often unreliable this element will therefore not be retained)alarm start date and timealarm level (the acuity of the alarm)alarm message (the text of the alarm)alarm durationPoint prevalence of monitoring data collection days in data collection only phaseCollect all data described above, plus:Data for ascertainment of alarms per monitored patient-dayOccupied and monitored beds will be counted in the morning (6-9AM), afternoon (2-5pm), and evening (9PM-midnight)Monitored status will be assessed by remotely viewing the monitor using Bedmaster and/or GE software. As a backup method if software is experiencing a downtime (unlikely), a member of the research team will assess monitored status by briefly walking into each patient room and visualizing the monitor to see if it is in use.Active implementation stageThe interventionIdentify eligible patients for alarm huddle discussion and generate a corresponding alarm dashboard each weekdayEligible patients for whom an alarm dashboard will be generated include the 4 patients on each intervention unit with the highest number of crisis and warning alarms for that unit in the 4 hours preceding the huddle as determined using GE/BedMaster systems.The dashboard is available in paper and electronic formats. The electronic format uses QlikView and may be challenging for some units to access and use; we will therefore start with paper and then introduce electronic dashboard if there is interest from the units.Member of our team will attend and help facilitate alarm discussion in safety huddles each weekday using huddle guide Up to 4 patients per huddle will be discussed.Huddle guide is attached in the appendix. Given the pragmatic nature of this study ( REF _Ref416702877 \h \* MERGEFORMAT Table 1), the huddle guide may undergo minor modification or adaptation in a way that takes into account the existing structure of the unit huddle and the preferences of the staff. The “core components” ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"vl5bmbvfn","properties":{"formattedCitation":"{\\rtf \\super 15\\nosupersub{}}","plainCitation":"15"},"citationItems":[{"id":7089,"uris":[""],"uri":[""],"itemData":{"id":7089,"type":"article-journal","title":"Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science","container-title":"Implementation Science","page":"50","volume":"4","issue":"1","source":"CrossRef","DOI":"10.1186/1748-5908-4-50","ISSN":"1748-5908","shortTitle":"Fostering implementation of health services research findings into practice","language":"en","author":[{"family":"Damschroder","given":"Laura J"},{"family":"Aron","given":"David C"},{"family":"Keith","given":"Rosalind E"},{"family":"Kirsh","given":"Susan R"},{"family":"Alexander","given":"Jeffery A"},{"family":"Lowery","given":"Julie C"}],"issued":{"date-parts":[["2009"]]},"accessed":{"date-parts":[["2015",4,6]]}}}],"schema":""} 15 of the huddle guide are shown in the algorithm figure below. Any other aspects of the huddle guide not considered core components will be considered “adaptable periphery.” ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"1urpkus796","properties":{"formattedCitation":"{\\rtf \\super 15\\nosupersub{}}","plainCitation":"15"},"citationItems":[{"id":7089,"uris":[""],"uri":[""],"itemData":{"id":7089,"type":"article-journal","title":"Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science","container-title":"Implementation Science","page":"50","volume":"4","issue":"1","source":"CrossRef","DOI":"10.1186/1748-5908-4-50","ISSN":"1748-5908","shortTitle":"Fostering implementation of health services research findings into practice","language":"en","author":[{"family":"Damschroder","given":"Laura J"},{"family":"Aron","given":"David C"},{"family":"Keith","given":"Rosalind E"},{"family":"Kirsh","given":"Susan R"},{"family":"Alexander","given":"Jeffery A"},{"family":"Lowery","given":"Julie C"}],"issued":{"date-parts":[["2009"]]},"accessed":{"date-parts":[["2015",4,6]]}}}],"schema":""} 15 If the modification or adaptation represents changes to the core components (for example, a change to the core components would be discussing alarms from a different type of medical device), we will submit an IRB amendments. If the minor modifications or adaptations represent changes to the adaptable periphery only, we will not submit IRB amendments.Figure SEQ Figure \* ARABIC 4. Core components of huddle guide used to outline the discussion of monitor alarms.In response to the discussion, care team may choose to do nothing, or may choose to take an action to reduce non-actionable alarms if clinically appropriate, including but not limited to widening alarm threshold values, lengthening delay time between when threshold is crossed and alarm, changing leads, or discontinuing monitor entirelyCare team will be encouraged to enact the changes as soon as possible so they are not forgotten (e.g. place order, change settings)Charge nurse or designee will complete “huddle worksheets” (see appendix) with the following elements:UnitDatePatient names and room numbers of patients discussedRecommendations made for each patient (such as no changes, monitoring discontinuation or changes in intensity, specific changes to parameters, delay times, CR monitor lead changes, meticulous skin preparation, SpO2 lead changes, other)Whether or not the changes discussed were made, with a place to document the date and time the changes were madeData collection during huddlePatients discussed in huddle will have the following data collected by research staff at the time of the huddle and entered into REDCap:NameUnit/bedDate of birthMedical record numberIllness severity using pediatric early warning score (a score used in clinical care at CHOP) at the time of the huddle as a proxyPrimary diagnosisWas the patient’s bedside nurse present in huddle?Was a front line ordering clinician on the team caring for this patient present (resident physician, nurse practitioner, physician assistant, hospitalist physician)?Was this patient’s attending physician present?What recommendations and decisions were made regarding changes to monitoring?Data collection 4 hours after huddlePatients discussed in huddle will have the following data collected 4 hours after the time of the huddle (these are primarily focused on evaluating adoption and fidelity for Aim 3 and identifying barriers to high fidelity): Which changes that were discussed were enacted within 4 hours of the huddle (such as parameters changed, delay times introduced, leads changed)? This may require briefly walking into each patient room and visualizing the monitor and/or asking the patient/family/nurse if leads were changed.In situations where the changes were not enacted as discussed, a member of the research team will, in a non-confrontational way, ask the bedside nurse why they were not instituted in order to better understand and address barriers to the quality improvement intervention’s fidelity as early as possible (prior to Implementation support stage). Barriers identified will be recorded as field notes without clinicians’ identifiers and stored in a REDCap database.Unit-wide data collection Continues as in REF _Ref416702939 \r \h \* MERGEFORMAT 5.1.1 REF _Ref416702939 \h \* MERGEFORMAT Data collection only stageSurveillance for code blue events, critical assessment team activations, and near miss events on patients discussed in huddlesAt the beginning of each huddle guide, there is a prompt that screens for any recent critical assessment team activations, code blue calls, or near miss events related to monitoring or alarms (see appendix). The near miss definition is also on the huddle guide.In addition to prompting staff, we will, on a weekly basis, also screen the critical assessment team and code blue event hospital database maintained by the Resuscitation Committee. We will be looking for any events on patients previously discussed in huddles. The PI is a member of the Committee and has access to the database.Implementation support stageMember of our team will identify eligible patients for alarm huddle discussion and generate a corresponding alarm dashboard each weekday as described in section REF _Ref416867028 \r \h \* MERGEFORMAT 5.1.2.1, REF _Ref416867028 \h \* MERGEFORMAT The intervention.Member of our team will check in with charge nurse and provide alarm data to inform their alarm huddle discussion each weekday in person prior to huddleMember of our team will follow up with charge nurse after huddle to determine if a huddle occurred, collect the huddle worksheets documenting if alarms were discussed and in which patients, and to answer questions and provide feedback and guidanceData collection continues as in the baseline data collection period, see section REF _Ref416867107 \r \h \* MERGEFORMAT 5.1.1.1 REF _Ref416867107 \h \* MERGEFORMAT Data collected every day.Surveillance for code blue events, critical assessment team activations, and near miss events on patients discussed in huddles continues as in section REF _Ref416867159 \r \h \* MERGEFORMAT 5.1.2.5.Point prevalence of monitoring data collection days in implementation support stageCollect data for ascertainment of alarms per monitored patient-day as described in REF _Ref416959694 \r \h 5.1.1.2.1Intensive patient data collection days in implementation support stageResearch team member attends huddle and directly collects data described above in REF _Ref417305030 \r \h 5.1.2.2Collect additional data elements for fidelity evaluation on intensive patient data collection days as discussed in REF _Ref416867582 \r \h \* MERGEFORMAT 5.1.2.3 and REF _Ref416867583 \r \h \* MERGEFORMAT 5.1.2.4.Data for patient-level comparisonsThe 4 monitored patients hospitalized on each intervention and control unit with the highest numbers of “crisis” and “warning” alarms at the time of huddle will have the following data extracted into REDCap by research staff in addition to the data in the above sections. The focus is on evaluating changes in alarm rates between the 24h preceding the huddle time and the 24h following the huddle time.NameReference date/time of huddleUnit/Room/BedWas this patient in this bed for the 24h prior to the date/time of huddle?Date of birthMedical record numberAlarms discussed in a huddle (Y/N)Illness severity using pediatric early warning score (a score used in clinical care at CHOP) at the time of the huddle as a proxyPrimary diagnosisWas this patient in this bed for the 24h after the date/time of huddle?Safety EvaluationPlease refer to description of surveillance for code blue events, critical assessment team activations, and near miss events on patients discussed in huddles in section REF _Ref416867159 \r \h \* MERGEFORMAT 5.1.2.5.STATISTICAL CONSIDERATIONSPrimary Endpoint (Aim 1)The primary endpoint is:The change in the unit-wide rate of alarms per patient-day between the baseline data collection period and the post-implementation data collection period for intervention versus control units.Secondary Endpoints Aim 1The change in the unit-wide rate of alarms per monitored patient-day between the baseline data collection period and the post-implementation data collection period for intervention versus control units.The change in the unit-wide number of minutes of alarms per patient-day and per monitored patient-day between the baseline data collection period and the post-implementation data collection period for intervention versus control units.Aim 2The difference in the rate of alarms in individual patients in the 24 hours after discussion in a huddle compared with:Themselves in the 24 hours before the huddlePatients on the same unit as the huddle but whose alarms are not discussedPatients on units without the alarm huddle interventionSecondary Endpoints (Aim 3)AdoptionProportion of huddles in post-implementation data collection period that included discussion of at least 1 patient’s alarmsFidelityProportion of patients in whom lead changes were recommended who had leads changed within 4 hoursProportion of patients in whom parameter changes were recommended who had parameters changed in a direction consistent with the recommendations within 4 hoursProportion of patients in whom a change in monitor delay time was recommended who had delay time changed in a direction consistent with the recommendations within 4 hoursProportion of patients in whom monitoring discontinuation was recommended who were off monitor within 4 hoursStatistical MethodsAim 1We will first evaluate the primary endpoint using a 2-sample test of proportions. Next, we will perform an interrupted time series analysis using piecewise negative binomial regression accounting for the pairs of intervention and control units.Aim 2Using a negative binomial regression model, will compare alarm rates over time, making (1) within-subject comparisons evaluating the numbers of alarms and the trajectories of alarm rates in the 24 hours before and 24 hours after each huddle, (2) between-subject comparisons of the difference in alarm rates in the 24 hours before and after each huddle among intervention patients and control patients (a) from the same unit as the huddle intervention but whose alarms are not discussed, and (b) from units without the huddle intervention. We will account for clustering by unit and adjust for age group and illness severity using pediatric early warning score at the time of the huddle as a proxy.Aim 3We will analyze and report these rates as simple proportions stratified by unit and patient age. We will also generate a composite fidelity score that combines the numerators and denominators from all 4 of the fidelity outcome measures to summarize the total number of times the intervention was followed through divided by the number of opportunities. ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"2lssff2f88","properties":{"formattedCitation":"{\\rtf \\super 16\\nosupersub{}}","plainCitation":"16"},"citationItems":[{"id":6060,"uris":[""],"uri":[""],"itemData":{"id":6060,"type":"article-journal","title":"A team-based approach to reducing cardiac monitor alarms","container-title":"Pediatrics","page":"e1686-1694","volume":"134","issue":"6","source":"NCBI PubMed","abstract":"BACKGROUND AND OBJECTIVES: Excessive cardiac monitor alarms lead to desensitization and alarm fatigue. We created and implemented a standardized cardiac monitor care process (CMCP) on a 24-bed pediatric bone marrow transplant unit. The aim of this project was to decrease monitor alarms through the use of team-based standardized care and processes.\nMETHODS: Using small tests of change, we developed and implemented a standardized CMCP that included: (1) a process for initial ordering of monitor parameters based on age-appropriate standards; (2) pain-free daily replacement of electrodes; (3) daily individualized assessment of cardiac monitor parameters; and (4) a reliable method for appropriate discontinuation of monitor. The Model for Improvement was used to design, test, and implement changes. The changes that were implemented after testing and adaptation were: family/patient engagement in the CMCP; creation of a monitor care log to address parameters, lead changes, and discontinuation; development of a pain-free process for electrode removal; and customized monitor delay and customized threshold parameters.\nRESULTS: From January to November 2013, percent compliance with each of the 4 components of the CMCP increased. Overall compliance with the CMCP increased from a median of 38% to 95%. During this time, the median number of alarms per patient-day decreased from 180 to 40.\nCONCLUSIONS: Implementation of the standardized CMCP resulted in a significant decrease in cardiac monitor alarms per patient day. We recommend a team-based approach to monitor care, including individualized assessment of monitor parameters, daily lead change, and proper discontinuation of the monitors.","ISSN":"1098-4275","note":"PMID: 25384493","journalAbbreviation":"Pediatrics","language":"eng","author":[{"family":"Dandoy","given":"Christopher E."},{"family":"Davies","given":"Stella M."},{"family":"Flesch","given":"Laura"},{"family":"Hayward","given":"Melissa"},{"family":"Koons","given":"Connie"},{"family":"Coleman","given":"Kristen"},{"family":"Jacobs","given":"Jodi"},{"family":"McKenna","given":"Lori Ann"},{"family":"Olomajeye","given":"Alero"},{"family":"Olson","given":"Chad"},{"family":"Powers","given":"Jessica"},{"family":"Shoemaker","given":"Kimberly"},{"family":"Jodele","given":"Sonata"},{"family":"Alessandrini","given":"Evaline"},{"family":"Weiss","given":"Brian"}],"issued":{"date-parts":[["2014"]]},"PMID":"25384493"}}],"schema":""} 16 Sample Size and PowerWe have powered the study based on Aim 2 (patient-level outcomes) because it draws data only from the post-implementation data collection period.Since there have not been any studies on huddle interventions for alarm management before, we have little data to guide power calculation. However, Dandoy and colleagues evaluated the impact of a standardized cardiac monitor care process on a pediatric bone marrow transplant unit at Cincinnati Children’s and found a reduction from a median of 180 alarms per patient-day to 40 during the study period, a 78% reduction. ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"27gi5teepa","properties":{"formattedCitation":"{\\rtf \\super 16\\nosupersub{}}","plainCitation":"16"},"citationItems":[{"id":6060,"uris":[""],"uri":[""],"itemData":{"id":6060,"type":"article-journal","title":"A team-based approach to reducing cardiac monitor alarms","container-title":"Pediatrics","page":"e1686-1694","volume":"134","issue":"6","source":"NCBI PubMed","abstract":"BACKGROUND AND OBJECTIVES: Excessive cardiac monitor alarms lead to desensitization and alarm fatigue. We created and implemented a standardized cardiac monitor care process (CMCP) on a 24-bed pediatric bone marrow transplant unit. The aim of this project was to decrease monitor alarms through the use of team-based standardized care and processes.\nMETHODS: Using small tests of change, we developed and implemented a standardized CMCP that included: (1) a process for initial ordering of monitor parameters based on age-appropriate standards; (2) pain-free daily replacement of electrodes; (3) daily individualized assessment of cardiac monitor parameters; and (4) a reliable method for appropriate discontinuation of monitor. The Model for Improvement was used to design, test, and implement changes. The changes that were implemented after testing and adaptation were: family/patient engagement in the CMCP; creation of a monitor care log to address parameters, lead changes, and discontinuation; development of a pain-free process for electrode removal; and customized monitor delay and customized threshold parameters.\nRESULTS: From January to November 2013, percent compliance with each of the 4 components of the CMCP increased. Overall compliance with the CMCP increased from a median of 38% to 95%. During this time, the median number of alarms per patient-day decreased from 180 to 40.\nCONCLUSIONS: Implementation of the standardized CMCP resulted in a significant decrease in cardiac monitor alarms per patient day. We recommend a team-based approach to monitor care, including individualized assessment of monitor parameters, daily lead change, and proper discontinuation of the monitors.","ISSN":"1098-4275","note":"PMID: 25384493","journalAbbreviation":"Pediatrics","language":"eng","author":[{"family":"Dandoy","given":"Christopher E."},{"family":"Davies","given":"Stella M."},{"family":"Flesch","given":"Laura"},{"family":"Hayward","given":"Melissa"},{"family":"Koons","given":"Connie"},{"family":"Coleman","given":"Kristen"},{"family":"Jacobs","given":"Jodi"},{"family":"McKenna","given":"Lori Ann"},{"family":"Olomajeye","given":"Alero"},{"family":"Olson","given":"Chad"},{"family":"Powers","given":"Jessica"},{"family":"Shoemaker","given":"Kimberly"},{"family":"Jodele","given":"Sonata"},{"family":"Alessandrini","given":"Evaline"},{"family":"Weiss","given":"Brian"}],"issued":{"date-parts":[["2014"]]},"PMID":"25384493"}}],"schema":""} 16 A starting median of 180 is high, and their effect was the result of multiple interventions, so the estimates we use below are more conservative. To estimate sample size, we used a two-sample paired means test based on detecting the difference in differences between pre- and post-huddle alarms per 24 hours among control and intervention patients. CHOP’s baseline alarm rate on wards is approximately 90 alarms per monitored patient-day. We will be purposefully recruiting high alarm patients. We will therefore conservatively estimate a baseline mean of 100 alarms over the preceding 24 hours. We hypothesize that both intervention patients discussed in huddles as well as control patients not discussed in huddles will have fewer alarms in the 24 hours after huddles because most will be clinically improving, and some controls will have changes made to their alarm parameters to reduce non-actionable alarms even without having been discussed in a huddle. So, we will estimate that, in the 24 hours after the huddle, control patients will have a mean of 75 alarms. We hypothesize that intervention patients will have 20% fewer alarms than controls (mean of 60 in the 24 hours after the huddle). We have conservatively estimated the standard deviation of the difference between intervention and control over 24 hours to be 50-75 alarms. The number of intervention-control pairs of patients needed based on different combinations of the delta in alarms/24 hours and standard deviation are shown in the table below.Table. Sample size needed for 80% power to detect difference in differences, alpha = .05.Baseline mean number of alarms in 24h prior to huddleDiscussed in safety huddle?Mean number of alarms in 24h after huddleDifference compared to controls# of intervention-control pairs needed if SD of difference is 50 alarms# of intervention-control pairs needed if SD of difference is 75 alarmsControl100No75-reference--reference--reference-Intervention (Estimate #1)100Yes6810% lower403903Intervention (Estimate #2)100Yes6020% lower90199Intervention (Estimate #3)100Yes5330% lower4394Note: Calculations performed in Stata 13.1 using “power pairedmeans” command.In the post-implementation data collection period we will have:16 intensive patient data collection days per unit16 point prevalence of monitoring data collection days4 intervention-control unit pairsOn each intervention unit, on each intensive patient data collection day, at least 2 patients discussed in a huddleOn each control unit, on each intensive patient data collection day, at least 2 monitored patients to serve as controls for those discussed in huddlesTherefore we will have 16*4*2= 128 intervention-control pairs for the patient-level analysis. This anticipated sample size will be adequate to detect a 20% difference between controls and intervention patients as we hypothesize (see table above) assuming a standard deviation of 50.SAFETY MANAGEMENTClinical Adverse EventsClinical adverse events (AEs) will be monitored throughout the study. Adverse Event ReportingSince the study procedures are not greater than minimal risk, SAEs are not expected. If any unanticipated problems related to the research involving risks to subjects or others happen during the course of this study (including SAEs) they will be reported to the IRB in accordance with CHOP IRB SOP 408: Unanticipated Problems Involving Risks to Subjects. AEs that are not serious but that are notable and could involve risks to subjects will be summarized in narrative or other format and submitted to the IRB at the time of continuing review. Special ConsiderationsIn this study, we will regularly evaluate the safety of the huddle interventions, since the intervention may lead to less intensive monitoring and fewer alarms for some patients. As described above in Section REF _Ref416867159 \r \h \* MERGEFORMAT 5.1.2.5, REF _Ref416867159 \h \* MERGEFORMAT Surveillance for code blue events, critical assessment team activations, and near miss events on patients discussed in huddles, we will monitor for code blue events and critical assessment team activations in patients previously discussed in huddles and, if any are identified, we will discuss each event with unit staff to determine if the event was considered at least possibly related to the study intervention using the IRB’s scale of definitely related, probably related, possibly related, unlikely to be related, or unrelated. In addition, we will inquire at the start of each huddle about recent monitoring-related patient safety “near miss” events ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"21ppbtglu4","properties":{"formattedCitation":"{\\rtf \\super 17\\nosupersub{}}","plainCitation":"17"},"citationItems":[{"id":6058,"uris":[""],"uri":[""],"itemData":{"id":6058,"type":"webpage","title":"AHRQ Patient Safety Network: Definition of a near miss","URL":"","accessed":{"date-parts":[["2014",12,3]]}}}],"schema":""} 17 (situations that did not produce patient injury only because of a chance occurrence, such as a medical student walking into the room of a patient whose monitoring was discontinued and finding them cyanotic). All reported near misses and code blue and rapid response team activations in patients discussed will be considered, in IRB terms, Adverse Events, whether or not they are related to the research intervention. Each Adverse Event will be reviewed by the study team to determine if it meets criteria as an Unanticipated Problem Involving Risks to Subjects using definitions provided in the CHOP IRB’s Standard Operating Procedure #408. Adverse Events that meet the definition of Unanticipated Problem Involving Risks to Subjects will be reported promptly to the IRB and the study will be suspended pending review of the event by the IRB. Following the review, we will work with the IRB and staff from the inpatient units to determine if the study should be modified to improve the safety of patients. STUDY ADMINISTRATIONTreatment Assignment MethodsPlease see sections REF _Ref416868636 \r \h \* MERGEFORMAT 3.1 and REF _Ref416868641 \r \h \* MERGEFORMAT 3.2 for details of unit pairing, randomization, and blinding during baseline data collection.Data Collection and ManagementREDCap will be used as the primary source for data collection and management. Other electronic files generated as part of the study (such as BedMaster output) will be stored on the CHOP Research SAN on a server with access limited to the research team. Following final study publication or 3 years after study completion (whichever occurs first), the REDCap project will be moved to an inactive status and archived in that system. Any data files with PHI outside of REDCap will either be destroyed completely or stripped of PHI.ConfidentialityAll data and records generated during this study will be kept confidential in accordance with Institutional policies and HIPAA on subject privacy. The Investigator and other site personnel will not use such data and records for any purpose other than conducting the study. No identifiable data will be used for future study without first obtaining IRB approval. The investigator will obtain a data use agreement between the provider (the PI) of the data and any recipient researchers (including others at CHOP) before sharing a limited dataset (PHI limited to dates and zip codes). Regulatory and Ethical ConsiderationsData and Safety Monitoring PlanThe Principal Investigator will oversee the Data and Safety Monitoring Plan outlined above in Section REF _Ref416868856 \r \h \* MERGEFORMAT 7.3.Risk AssessmentThis study is minimal risk. Risks include:Delay in recognition of clinical deterioration The primary risk is a delay in recognition of clinical deterioration because an alarm threshold was changed or disabled following the intervention in such a way that an alarm did not fire or fired later than it would have prior to the intervention. Risk mitigationThe intervention consists primarily of a tool that structures discussion of monitor alarms and brings data on alarm frequency that was previously only available to researchers and biomedical engineers to clinicians who can use it to make more informed decisions about alarm management. Conversations about making changes to monitor settings occur on the wards daily, the only differences are that in current state they are not structured and are not informed by actual alarm data. To mitigate risk, we have designed the study such that the intervention does not involve the research team making any changes to the alarm orders or any changes on the actual devices. It is up to the primary medical team to determine if any adjustments will be made and if so, what the adjustments will be. Potential Benefits of Study ParticipationDirectPatients and the providers caring for them have the potential to benefit from fewer alarms. The patients may benefit from fewer interruptions from alarms, which may wake them from sleep and cause unnecessary anxiety in them and their loved ones. The patients also may have a lower risk of harm due to a reduction in the probability of the clinicians caring for them experiencing the alarm fatigue that can result from high alarm rates.The clinicians caring for the patients may also benefit from experiencing a lower burden of alarms, allowing them to spend more time completing important tasks such as medication administration and performing patient assessments that benefit from having fewer interruptions.IndirectThe study outlined in this proposal will generate important knowledge that will be applied to future projects aimed at addressing the burden of physiologic monitor alarm rates in centers across the country. Risk-Benefit AssessmentBased on the discussion above and the lack of deviation from standard clinical care, the benefits of this study far outweigh the minimal risks. Given that the risks of the study are minimal, it is reasonable to proceed with the project. Informed Consent/Assent and HIPAA AuthorizationWaiver of informed consent The study will seek a waiver of informed consent. This is an acceptable approach under 45 CFR 46.116(d), which states that the IRB may approve a consent procedure that leaves out or alters some or all of the elements of informed consent, provided that the following four criteria are met: (1) the research involves no more than minimal risk to the subjects, (2) the waiver or alteration will not adversely affect the rights and welfare of the subjects, (3) the research could not practicably be carried out without the waiver or alteration, and (4) whenever appropriate, the subjects will be provided with additional pertinent information after participation. The primary purpose of this study is quality improvement at CHOP that is generalizable (and thus constitutes QI research). This study is minimal risk.The primary subjects, the clinicians whose behavior we aim to impact (specifically the intervention may encourage them to make appropriate, safe changes in monitoring orders), will be informed and educated about the project during the existing huddles. Any physician or nurse practitioner can opt out of study participation at any time simply by not participating in huddle discussions about alarms and/or by not changing any monitor orders in response to recommendations discussed in the safety huddle, thus not adversely affecting their rights. A consent process prior to the safety huddle would not be practicable due to the large number of care providers who could be involved in the collaborative multidisciplinary care of the patients involved. In addition, the act of consenting the providers may change their behavior and make them more likely to change the order in the computer to please the researchers. Second, since no identifying information is being kept on the clinicians, there is minimal risk. For the secondary subjects, both the intervention and control patients, we seek a waiver of informed consent. The rights and welfare of the subjects would not be adversely affected because conversations about making changes to monitor settings occur on each of the study units daily, the only differences are that in current state they are not structured and are not informed by actual alarm data, thus this is usual care. Informed consent would not be practicable for these patients due to the following: The intervention patients will be chosen using rapidly changing alarm data, and then discussed minutes later. Based on clinical experience caring for patients on these units, It is unlikely that most parents would be available to consent during this time. Because we are performing research to evaluate a quality improvement intervention, we want to simulate as closely as possible the way that the intervention will be used in practice therefore the timeliness of alarm information is vital to the study. After participation, nurse and physician participants will be informed of the study’s findings in the following situation: If the results support that the intervention is effective, it is likely that the hospital will roll out a version of this intervention house-wide. If that occurs, the findings of this study will be key supporting information in the educational presentations associated with the roll out.Waiver of HIPAA AuthorizationNo personal health information will be collected on the primary subjects (the physicians and nurse practitioners) so no HIPAA authorization is necessary for the primary subjects. A waiver of HIPAA Authorization is requested for the secondary subjects for the same reasons as we are requesting waiver of consent as acquiring HIPAA authorization would present significant difficulties as it is unlikely that parents would be available to consent during this time. A waiver of HIPAA Authorization is sought for this study and is in agreement with 45 CFR 164.512(i.)(2)(ii) Since it fulfills all of the following: (A) The use or disclosure of protected health information involves no more than a minimal risk to the privacy of individuals, based on, at least, the presence of the following elements: (1) An adequate plan to protect the identifiers from improper use and disclosure; Please refer to Sections REF _Ref417558251 \r \h 8.2 and REF _Ref417558239 \r \h 8.3 above(2) an adequate plan to destroy the identifiers at the earliest opportunity consistent with conduct of the research, unless there is a health or research justification for retaining the identifiers or such retention is otherwise required by law; Please refer to Section REF _Ref417558251 \r \h 8.2 above(3) adequate written assurances that the protected health information will not be reused or disclosed to any other person or entity, except as required by law, for authorized oversight of the research project, or for other research for which the use or disclosure of protected health information would be permitted by this subpart; We confirm that PHI will not be reused or disclosed to any other person or entity, except: as required by law, for authorized oversight of the research project, or for other research for which the use or disclosure of PHI would be permitted by HIPAA(B) The research could not practicably be conducted without the waiver or alteration;Please see above reasons under Waiver of Informed Consent(C) The research could not practicably be conducted without access to and use of the protected health information In order to track potential alarm-associated adverse events in intervention patients discussed in the huddles as well as to link to clinical data (such as diagnosis, early warning score) documented in Epic, we need to collect PHI as outlined in Section REF _Ref417558766 \r \h 5.PublicationPeer-reviewed publication is planned. Payment to Subjects/FamiliesNone.References ADDIN ZOTERO_BIBL {"custom":[]} CSL_BIBLIOGRAPHY 1. Taenzer AH, Pyke JB, McGrath SP, Blike GT. Impact of pulse oximetry surveillance on rescue events and intensive care unit transfers: a before-and-after concurrence study. Anesthesiology. 2010;112(2):282-287.2. Sharkey T. Fact or artifact? Breakthroughs in pulse oximetry alarm management. Neonatal Intensive Care. 2003;16(4):51-53.3. G?rges M, Markewitz BA, Westenskow DR. Improving alarm performance in the medical intensive care unit using delays and clinical context. Anesth Analg. 2009;108:1546-1552.4. Cvach MM, Biggs M, Rothwell KJ, Charles-Hudson C. Daily electrode change and effect on cardiac monitor alarms: an evidence-based practice approach. J Nurs Care Qual. 2013;28:265-271.5. Tsien CL, Fackler JC. Poor prognosis for existing monitors in the intensive care unit. Crit Care Med. 1997;25(4):614-619.6. Bonafide CP, Localio AR, Nadkarni VM, Lin R, Keren R. False physiologic monitor alarms and nurse response time in a children’s hospital. In: NIH/NHLBI K Award Investigators Meeting. Bethesda, MD; 2014.7. Lawless ST. Crying wolf: false alarms in a pediatric intensive care unit. Crit Care Med. 1994;22(6):981-985.8. Bonafide CP, Zander M, Graham CS, et al. Video methods for evaluating physiologic monitor alarms and alarm responses. Biomed Instrum Technol. 2014;48(3):220-230.9. Getty DJ, Swets JA, Rickett RM, Gonthier D. System operator response to warnings of danger: a laboratory investigation of the effects of the predictive value of a warning on human response time. J Exp Psychol-Appl. 1995;1:19-33.10. Bliss JP, Gilson RD, Deaton JE. Human probability matching behaviour in response to alarms of varying reliability. Ergonomics. 1995;38:2300-2312.11. The Joint Commission. Sentinel event alert: medical device alarm safety in hospitals. 2013. downloads/SEA_50_alarms.pdf. Accessed October 9, 2014.12. Mitka M. Joint commission warns of alarm fatigue: multitude of alarms from monitoring devices problematic. JAMA. 2013;309(22):2315-2316.13. Top 10 health technology hazards for 2014. Health Devices. 2013;42(11):354-380.14. Thorpe KE, Zwarenstein M, Oxman AD, et al. A pragmatic-explanatory continuum indicator summary (PRECIS): a tool to help trial designers. Can Med Assoc J. 2009;180(10):E47-E57. doi:10.1503/cmaj.090523.15. Damschroder LJ, Aron DC, Keith RE, Kirsh SR, Alexander JA, Lowery JC. Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science. Implement Sci. 2009;4(1):50. doi:10.1186/1748-5908-4-50.16. Dandoy CE, Davies SM, Flesch L, et al. A team-based approach to reducing cardiac monitor alarms. Pediatrics. 2014;134(6):e1686-e1694.17. AHRQ Patient Safety Network: Definition of a near miss. . Accessed December 3, 2014. ................
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