Address the alarm problem by redesigning, reorganizing, and reprioritizing to better discriminate alarm sounds and displays in a hospital.
Background
Alarms in hospitals are frequently misunderstood, disregarded, and overridden.
Method
Discovery-oriented, intervention, and translational studies were conducted. Study objectives and measures varied, but had the shared goals of increasing positive predictive value (PPV) of critical alarms by reducing low-PPV alarms in the background, prioritizing alarms, redesigning alarm sounds to increase information content, and transparently conveying who initiated alarms. An alarm ontology was iteratively generated and refined until consensus was achieved.
Results
The ontology distinguishes five levels of urgency that incorporate likely PPV, three categories for who initiates the alarm (hospital staff, patient, or machine), whether it is clinical or technical, and clinical functions.
Conclusion
This unique collaboration allowed us to make progress on the alarm problem by making unintuitive leaps, avoiding common missteps, and refuting conventional healthcare approaches.
Application
Hospitals can consistently redesign, reorganize, reprioritize, and better discriminate alarms by priority, PPV, and content to reduce nurse response times.
BennettC.DudarykR.CrenshawN.EdworthyJ.McNeerR. (2019). Recommendation of new medical alarms based on audibility, identifiability, and detectability in a randomized, simulation-based study. Critical Care Medicine, 47, 1050–1057.doi:10.1097/CCM.000000000000380231135498
3.
BoltonM. L.EdworthyJ. R.BoydA. D. (2020). Masking between reserved alarm sounds of the IEC 60601-1-8 international medical alarm standard: A systematic, formal analysis. Human Factors, 18720820967596.doi:10.1177/001872082096759633350873
4.
BoltonM. L.ZhengX.LiM.EdworthyJ. R.BoydA. D. (2020). An experimental validation of masking in IEC 60601-1-8: 2006-compliant alarm sounds. Human Factors, 62, 954–972.doi:10.1177/001872081986291131411902
5.
BruderA. L.EdworthyJ.SchlesingerJ.RothwellC. (2019). The multisensory benefit of informative sound in visual task performance. The Journal of the Acoustical Society of America, 146, 2834.doi:10.1121/1.5136825
EdworthyJ.HellierE.TitchenerK.NaweedA.RoelsR. (2011). Heterogeneity in auditory alarm sets makes them easier to learn. International Journal of Industrial Ergonomics, 41, 136–146.doi:10.1016/j.ergon.2010.12.004
8.
EdworthyJ.LoxleyS.DennisI. (1991). Improving auditory warning design: Relationship between warning sound parameters and perceived urgency. Human Factors, 33, 205–231.doi:10.1177/0018720891033002061860703
9.
EdworthyJ.ReidS.McDougallS.EdworthyJ.HallS.BennettD.KhanJ.PyeE. (2017). The recognizability and localizability of auditory alarms: Setting global medical device standards. Human Factors, 59, 1108–1127.doi:10.1177/001872081771200428574734
10.
EdworthyJ.ReidS.PeelK.LockS.WilliamsJ.NewburyC.FosterJ.FarringtonM. (2018). The impact of workload on the ability to localize audible alarms. Applied Ergonomics, 72, 88–93.doi:10.1016/j.apergo.2018.05.00629885730
11.
EdworthyJ. R.McNeerR. R.BennettC. L.DudarykR.McDougallS. J. P.SchlesingerJ. J.BoltonM. L.EdworthyJ. D. R.ÖzcanE.BoydA. D.ReidS. K. J.RayoM. F.WrightM. C.OsbornD. (2018). Getting better hospital alarm sounds into a global standard. Ergonomics in Design, 26, 4–13.doi:10.1177/1064804618763268
FitzgeraldM.RayoM. F.ReynoldsM. E.Abdel-RasoulM.Moffatt-BruceS. D. (2019). The importance of testing sets of clinical alarm sounds and navigating tradeoffs to translate research findings into implementation. Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care, 8, 67–71.doi:10.1177/2327857919081015
14.
FriedmanB.FuckertD.JahrsdoerferM.MagnessR.PattersonE. S.SyedR.ZaleskiJ. R. (2019). Identifying and monitoring respiratory compromise: Report from the rules and algorithms working group. Biomedical Instrumentation & Technology, 53, 110–123.doi:10.2345/0899-8205-53.2.11030901250
HaasE. C.EdworthyJ. (1996). Designing urgency into auditory warnings using pitch, speed and loudness. Computing & Control Engineering Journal, 7, 193–198.
17.
HellierE. J.EdworthyJ.DennisI. (1993). Improving auditory warning design: Quantifying and predicting the effects of different warning parameters on perceived urgency. Human Factors, 35, 693–706.doi:10.1177/0018720893035004088163282
18.
HeverG.CohenL.O’ConnorM. F.MatotI.LernerB.BitanY. (2020). Machine learning applied to multi-sensor information to reduce false alarm rate in the ICU. Journal of Clinical Monitoring and Computing, 34, 339–352.doi:10.1007/s10877-019-00307-x30955160
19.
HorwoodC. R.Moffatt-BruceS. D.FitzgeraldM.RayoM. F. (2018). A qualitative analysis of clinical decompensation in the surgical patient: Perceptions of nurses and physicians. Surgery, 164, 1311–1315.doi:10.1016/j.surg.2018.06.00630061038
20.
HorwoodC. R.Moffatt-BruceS. D.RayoM. F. (2019). Continuous cardiac monitoring policy implementation: Three-year sustained decrease of hospital resource utilization. InMoffatt-BruceS. D. (Ed.), Structural approaches to address issues in patient safety (advances in health care management (Vol. 18, pp.159–171). Emerald Publishing Limited, pp.
21.
HorwoodC. R.RayoM. F.FitzgeraldM.BalkinE. A.Moffatt-BruceS. D. (2018). Gaps between alarm capabilities and decision-making needs: An observational study of detecting patient decompensation. InProceedings of the International Symposium on Human Factors and Ergonomics in Health Care (Vol. 7, pp.112–116). SAGE Publications.doi:10.1177/2327857918071028
22.
IEC 60601-1-8 Ed. 2.2. EN. (2020). Medical electrical equipment - Part 1-8: General requirements for basic safety and essential performance - Collateral Standard: General requirements, tests and guidance for alarm systems in medical electrical equipment and medical electrical systems.
23.
LacherezP.SeahE. L.SandersonP. (2007). Overlapping melodic alarms are almost indiscriminable. Human Factors, 49, 637–645.doi:10.1518/001872007X21571917702215
24.
McFarlaneD. C.DoigA. K.AgutterJ. A.BrewerL. M.SyroidN. D.MittuR. (2018). Faster clinical response to the onset of adverse events: A wearable metacognitive attention aid for nurse triage of clinical alarms. Plos One, 13, 1–35.doi:10.1371/journal.pone.019715729768477
25.
McFarlaneS. J.GarciaJ. E.VerhagenD. S.DyerA. G. (2020). Alarm tones, music and their elements: Analysis of reported waking sounds to counteract sleep inertia. Plos One, 15, e0215788.doi:10.1371/journal.pone.021578831990906
26.
McGrathS. P.TaenzerA. H.KaronN.BlikeG. (2016). Surveillance monitoring management for general care units: Strategy, design, and implementation. The Joint Commission Journal on Quality and Patient Safety, 42, 293–302.doi:10.1016/S1553-7250(16)42040-427301832
27.
McNeerR. R.HornD. B.BennettC. L.EdworthyJ. R.DudarykR. (2018). Auditory icon alarms are more accurately and quickly identified than current standard melodic alarms in a simulated clinical setting. Anesthesiology, 129, 58–66.doi:10.1097/ALN.000000000000223429698253
MumawR. J.RothE. M.PattersonE. S. (2021). Lessons from the glass cockpit: Innovation in alarm systems to support cognitive work. Biomedical Instrumentation & Technology, 55, 29–40.doi:10.2345/0899-8205-55.1.2933662139
30.
OrlovskyM.MeyerJ.MitelpunktA.Weiss-MeilikA. (2021). Patient monitoring as a predictor of blood culture results in a tertiary neonatal intensive care unit. Applied Ergonomics, 90, 103233.doi:10.1016/j.apergo.2020.10323332858394
31.
PatersonE.SandersonP. M.SalisburyI. S.BurgmannF. P.MohamedI.LoebR. G.PatersonN. A. B. (2020). Evaluation of an enhanced pulse oximeter auditory display: A simulaion study. British Journal of Anaesthesia, 125, 826–834.doi:10.1016/j.bja.2020.05.03832682554
32.
PattersonE. S. (2018). Workarounds to intended use of health information technology: A narrative review of the human factors engineering literature. Human Factors, 60, 281–292.doi:10.1177/001872081876254629533682
33.
PattersonE. S.HansenC. J.AllenT. T.YangQ.Moffatt-BruceS. D. (2019). Predicting mortality with applied machine learning: Can we get there?InProceedings of the International Symposium on Human Factors and Ergonomics in Health Care (Vol. 8, pp.115–119). SAGE Publications.doi:10.1177/2327857919081026
34.
PattersonE. S.HansenC. J.RayoJ. F.YamokoskiT.Abdel-RasoulM.AllenT. T.SochaJ. J.Susan Moffatt-BruceS. (in review, Human Factors). Perceptually discriminating the highest priority alarms reduces response time: A retrospective pre-post study at four hospitals.
35.
PattersonE. S.HritzC.GebruL.PatelK.YamokoskiT.Moffatt-BruceS. D. (2018). Use preferences for continuous cardiac and respiratory monitoring systems in hospitals: a survey of patients and family caregivers. InProceedings of the International Symposium on Human Factors and Ergonomics in Health Care (Vol. 7, pp.123–128). SAGE Publications, pp..doi:10.1177/2327857918071032
36.
PattersonE. S.HritzC.Moffatt-BruceS. (2019). Reducing alert fatigue for comfort care and palliative care hospital patients. InProceedings of the International Symposium on Human Factors and Ergonomics in Health Care (Vol. 8, pp.1–3). SAGE Publications.doi:10.1177/2327857919081000
37.
RayoM. F.MansfieldJ.EifermanD.MigneryT.WhiteS.Moffatt-BruceS. D. (2016). Implementing an institution-wide quality improvement policy to ensure appropriate use of continuous cardiac monitoring: A mixed-methods retrospective data analysis and direct observation study. BMJ Quality & Safety, 25, 796–802.doi:10.1136/bmjqs-2015-00413726566801
38.
RayoM. F.Moffatt-BruceS. D. (2015). Alarm system management: Evidence-based guidance encouraging direct measurement of informativeness to improve alarm response. BMJ Quality & Safety, 24, 282–286.doi:10.1136/bmjqs-2014-00337325734193
39.
RayoM. F.PattersonE. S.Abdel-RasoulM.Moffatt-BruceS. D. (2019). Using timbre to improve performance of larger auditory alarm sets. Ergonomics, 62, 1617–1629.doi:10.1080/00140139.2019.167647331587607
40.
RussA. L.FairbanksR. J.KarshB.-T.MilitelloL. G.SaleemJ. J.WearsR. L. (2013). The science of human factors: Separating fact from fiction. BMJ Quality & Safety, 22, 802–808.doi:10.1136/bmjqs-2012-00145023592760
41.
SandersonP. M.WeeA.LacherezP. (2006). Learnability and discriminability of melodic medical equipment alarms. Anaesthesia, 61, 142–147.doi:10.1111/j.1365-2044.2005.04502.x16430567
42.
ShanmughamM.StrawdermanL.Babski-ReevesK.BianL. (2018). Alarm-related workload in default and modified alarm settings and the relationship between alarm workload, alarm response rate, and care provider experience: Quantification and comparison study. JMIR Human Factors, 5, e11704.doi:10.2196/1170430355550
43.
ShefferJ.CvachM.EdworthyJ. R.PattersonE. S. (2018). A roundtable discussion: Improving the ‘alarm problem’ will require much more than just reducing the number of alarms. Biomedical Instrumentation & Technology, 52, 454–461.doi:10.2345/0899-8205-52.6.45430479154
44.
SorkinR. D. (1988). Why are people turning off our alarms?The Journal of the Acoustical Society of America, 84, 1107–1108.
45.
VicenteK. I. M. J.RothE. M.MumawR. J. (2001). How do operators monitor a complex, dynamic work domain? The impact of control room technology. International Journal of Human-Computer Studies, 54, 831–856.doi:10.1006/ijhc.2001.0463
46.
WeeA. N.SandersonP. M. (2008). Are melodic medical equipment alarms easily learned?Anesthesia & Analgesia, 106, 501–508.doi:10.1213/01.ane.0000286148.58823.6c18227306
47.
WatkinsT.WhismanL.BookerP. (2016). Nursing assessment of continuous vital sign surveillance to improve patient safety on the medical/surgical unit. Journal of Clinical Nursing, 25, 278–281.doi:10.1111/jocn.1310226531215
48.
WoodsD. D. (1995). The alarm problem and directed attention in dynamic fault management. Ergonomics, 38, 2371–2393.
49.
WrightM. C.RadcliffeS.JanzenS.EdworthyJ.ReeseT.SegallN. (2020). Organizing audible alarm sounds in the hospital: A card-sorting study. IEEE Transactions on Human-Machine Systems, 50, 623–627.doi:10.1109/THMS.2020.301936333777543
