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Klinische studies en referenties

Als Itémedical leveren we graag een bijdrage aan klinisch onderzoek naar het verbeteren van de kwaliteit van leven en zorg van patiënten. Dit doen we via diverse medische software oplossingen en diensten. We verwachten in de toekomst dat klinisch onderzoek een toenemend belang krijgt. Hieronder staan een aantal referenties van Itémedical onderzoeksthema’s zoals alarmveiligheid, kwaliteit, werklast en interessante links. 

Lees meer

Alarmveiligheid

  • AAMI Foundation 2015, Clinical Alarm Management Compendium.
  • Bach et al. 2018.  “Managing alarm systems for quality and safety in the hospital setting.” BMJ Open Qual 7.3: e000202.
  • Borowski et al. 2011. “Medical device alarms.” Biomedizinische Technik/Biomedical Engineering, 56(2), 73-83.
  • Cvach 2012. “Monitor alarm fatigue: An integrative review.” Biomed Instrum Technol. 2012;46(4):268-277.
  • Johns Hopkins, 2012. Using Data to Drive Alarm System Improvement Efforts, The Johns Hopkins Hospital Experience. Association for the Advancement of Medical Instrumentation (AAMI) Foundation/Healthcare Technology Safety Institute (HTSI).
  • Hravnak et al. 2018. “A call to alarms: Current state and future directions in the battle against alarm fatigue.” Journal of electrocardiology. Nov-Dec 2018;51(6S):S44-S48.
  • Lewis & Oster 2019. “Research Outcomes of Implementing CEASE: An Innovative, Nurse-Driven, Evidence-Based, Patient-Customized Monitoring Bundle to Decrease Alarm Fatigue in the Intensive Care Unit/Step-down Unit.” Dimensions of Critical Care Nursing 38.3 (2019): 160-173.
  • Özkan et al. 2018. A Holistic and Collaborative Approach to Audible Alarm Design. 52(6):422-432.
  • Paine et al. 2016. “Systematic review of physiologic monitor alarm characteristics and pragmatic interventions to reduce alarm frequency.” Journal of hospital medicine 11.2: 136-144.
  • Pater et al. 2020. Time series evaluation of improvement interventions to reduce alarm notifications in a paediatric hospital. BMJ Quality and Safety, 29(9), 717–726.
  • Ruppel et al. 2018. “Testing physiologic monitor alarm customization software to reduce alarm rates and improve nurses’ experience of alarms in a medical intensive care unit.” PloS one 13.10: e0205901.
  • Sanz-Segura et al. 2019. Alarm Compliance in Healthcare: Design Considerations for Actionable Alarms (In Intensive Care Units). Proceedings of the 22nd International Conference on Engineering Design (ICED19).
  • Sendelbach & Funk 2013. “Alarm fatigue: A patient safety concern.” AACN Adv Crit Care. 2013;24(4):378-386.
  • Ten Caat 2020. Alarmveiligheid objectief gemeten – een stappenplan.
  • Welch et al. 2016. “Framework for alarm management process maturity. “Biomed Instrum Technol.50(3):165-179.
  • Winters et al. 2018. “Technological distractions (part 2): a summary of approaches to manage clinical alarms with intent to reduce alarm fatigue.” Critical care medicine 46.1: 130-137.

Medische alarmfiltering

  • 1. Winters BD, Cvach MM, Bonafide CP, et al. Technological Distractions (Part 2): A Summary of Approaches to Manage Clinical Alarms with Intent to Reduce Alarm Fatigue. Crit Care Med. 2018;46(1):130-137. doi:10.1097/CCM.00000000000028032. Görges M, Markewitz BA, Westenskow DR. Improving alarm performance in the medical intensive care unit using delays and clinical context. Anesth Analg. 2009;108(5):1546-1552. doi:10.1213/ane.0b013e31819bdfbb
  • 2. Funk M, Clark JT, Bauld TJ, Ott JC, Coss P. Attitudes and practices related to clinical alarms. Am J Crit Care [Internet]. 2014 May 1;23(3):e9–18.
  • 3. Johnson KR, Hagadorn JI, Sink DW. Alarm Safety and Alarm Fatigue. Clin Perinatol. 2017;44(3):713-728. doi:10.1016/j.clp.2017.05.005Ruppel H, Funk M, Whittemore R. Measurement of physiological monitor alarm accuracy and clinical relevance in intensive care units. Am J Crit Care [Internet]. 2018 Jan 1;27(1):11–21.
  • 4. Cvach M. Monitor alarm fatigue: An integrative review. Biomed Instrum Technol. 2012;46(4):268-277. doi:10.2345/0899-8205-46.4.268
  • 5. Bach TA, Berglund L-M, Turk E. Managing alarm systems for quality and safety in the hospital setting. BMJ Open Qual. 2018;7(3):e000202. doi:10.1136/bmjoq-2017-000202Carra G, Salluh JIF, da Silva Ramos FJ, Meyfroidt G. Data-driven ICU management: Using Big Data and algorithms to improve outcomes. J Crit Care [Internet]. 2020;60:300–4.
  • 6. Schmid F, Goepfert MS, Franz F, et al. Reduction of clinically irrelevant alarms in patient monitoring by adaptive time delays. J Clin Monit Comput. 2017;31(1):213-219. doi:10.1007/s10877-015-9808-2
  • 7. Cosper P, Zellinger M, Enebo A, Jacques S, Razzano L, Flack MN. Improving clinical alarm management: Guidance and strategies. Biomed Instrum Technol. 2017;51(2):109-115. doi:10.2345/0899-8205-51.2.109
  • 8. Ruppel H, De Vaux L, Cooper D, Kunz S, Duller B, Funk M. Testing physiologic monitor alarm customization software to reduce alarm rates and improve nurses’ experience of alarms in a medical intensive care unit. PLoS One. 2018;13(10):1-16. doi:10.1371/journal.pone.0205901
  • 9. Pater CM, Sosa TK, Boyer J, et al. Time series evaluation of improvement interventions to reduce alarm notifications in a paediatric hospital. 2020:717-726. doi:10.1136/bmjqs-2019-010368
  • 10. Carra G, Salluh JIF, da Silva Ramos FJ, Meyfroidt G. Data-driven ICU management: Using Big Data and algorithms to improve outcomes. J Crit Care. 2020;60:300-304. doi:10.1016/j.jcrc.2020.09.002
  • 11. Olive MK, Owens GE. Current monitoring and innovative predictive modeling to improve care in the pediatric cardiac intensive care unit. 2018;7(2):120-128. doi:10.21037/tp.2018.04.03
  • 12. Petersen JA. Early warning score challenges and opportunities in the care of deteriorating patients. Dan Med J. 2018;65(2):16-19.
  • 13. Koomen E, Webster CS, Konrad D, et al. Reducing medical device alarms by an order of magnitude : A human factors approach. 2021. doi:10.1177/0310057X20968840
  • 14. IEC 60601-1-8 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. 2012;(Edition 2.1, 2012-11).

Datagedreven alarmfiltering

  • 1. Winters BD, Cvach MM, Bonafide CP, Hu X, Konkani A, O’Connor MF, et al. Technological Distractions (Part 2): A Summary of Approaches to Manage Clinical Alarms with Intent to Reduce Alarm Fatigue. Crit Care Med [Internet]. 2018 Jan;46(1):130–7.
  • 2. Funk M, Clark JT, Bauld TJ, Ott JC, Coss P. Attitudes and practices related to clinical alarms. Am J Crit Care [Internet]. 2014 May 1;23(3):e9–18.
  • 3. Ruppel H, Funk M, Whittemore R. Measurement of physiological monitor alarm accuracy and clinical relevance in intensive care units. Am J Crit Care [Internet]. 2018 Jan 1;27(1):11–21.
  • 4. Carra G, Salluh JIF, da Silva Ramos FJ, Meyfroidt G. Data-driven ICU management: Using Big Data and algorithms to improve outcomes. J Crit Care [Internet]. 2020;60:300–4.
  • 5. Shillan D, Sterne JAC, Champneys A, Gibbison B. Use of machine learning to analyse routinely collected intensive care unit data: A systematic review. Crit Care. 2019;23(1):1–11.
  • 6. van de Sande D, van Genderen ME, Huiskens J, Gommers D, van Bommel J. Moving from bytes to bedside: a systematic review on the use of artificial intelligence in the intensive care unit. Intensive Care Med [Internet]. 2021;47(7):750–60.
  • 7. Mousavi S, Fotoohinasab A, Afghah F. Single-modal and multi-modal false arrhythmia alarm reduction using attentionbased convolutional and recurrent neural networks. PLoS One [Internet]. 2020;15(1):1–15.
  • 8. Koomen E, Webster CS, Konrad D, van der Hoeven JG, Best T, Kesecioglu J, et al. Reducing medical device alarms by an order of magnitude: A human factors approach. Anaesth Intensive Care [Internet]. 2021 Jan 2;49(1):52–61.
  • 9. Hravnak M, Pellathy T, Chen L, Dubrawski A, Wertz A, Clermont G, et al. A call to alarms: Current state and future directions in the battle against alarm fatigue. J Electrocardiol [Internet]. 2018;51(6):S44–8.
  • 10. Joshi R, Van Pul C, Atallah L, Feijs L, Van Huffel S, Andriessen P. Pattern discovery in critical alarms originating from neonates under intensive care. Physiol Meas. 2016;37(4):564–79.
  • 11. Schmid F, Goepfert MS, Kuhnt D, Eichhorn V, Diedrichs S, Reichenspurner H, et al. The wolf is crying in the operating room: Patient monitor and anesthesia workstation alarming patterns during cardiac surgery. Anesth Analg. 2011;112(1):78–83.
  • 12. Imhoff M, Kuhls S. Alarm algorithms in critical monitoring. Anesth Analg. 2006;102(5):1525–37.
  • 13. Imhoff M, Kuhls S, Gather U, Fried R. Smart alarms from medical devices in the OR and ICU. Best Pract Res Clin Anaesthesiol. 2009;23(1):39–50.
  • 14. Konkani A, Oakley B. Noise in hospital intensive care units-a critical review of a critical topic. J Crit Care [Internet]. 2012 Oct;27(5):522.e1-522.e9.
  • 15. Walsh BK, Waugh JB. Alarm strategies and surveillance for mechanical ventilation. Respir Care. 2020;65(6):820–31.
  • 16. Pater CM, Sosa TK, Boyer J, Cable R, Egan M, Knilans TK, et al. Time series evaluation of improvement interventions to reduce alarm notifications in a paediatric hospital. BMJ Qual Saf [Internet]. 2020 Sep;29(9):717–26.
  • 17. Flohr L, Beaudry S, Johnson KT, West N, Burns CM, Ansermino JM, et al. Clinician-Driven Design of VitalPAD-An Intelligent Monitoring and Communication Device to Improve Patient Safety in the Intensive Care Unit. IEEE J Transl Eng Heal Med [Internet]. 2018;6:3000114.
  • 18. Su J, Liu S, Sun Z, Sun B, Ye W, Rajagopalan C, et al. Real-time Fusion of ECG and SpO2 Signals to Reduce False Alarms. In: Computing in Cardiology [Internet]. 2018. p. 1–4.
  • 19. Liu C, Zhao L, Tang H, Li Q, Wei S, Li J. Life-threatening false alarm rejection in ICU: Using the rule-based and multi-channel information fusion method. Physiol Meas. 2016;37(8):1298–312.
  • 20. Krasteva V, Jekova I, Leber R, Schmid R, Abacherli R. Real-time arrhythmia detection with supplementary ECG quality and pulse wave monitoring for the reduction of false alarms in ICUs. Physiol Meas. 2016;37(8):1273–97.
  • 21. Phillips JA, Sowan A, Ruppel H, Magness R. Educational Program for Physiologic Monitor Use and Alarm Systems Safety: A Toolkit. Clin Nurse Spec. 2020;34(2):50–62.
  • 22. Smith MEB, Chiovaro JC, O’Neil M, Kansagara D, Quiñones AR, Freeman M, et al. Early warning system scores for clinical deterioration in hospitalized patients: A systematic review. Ann Am Thorac Soc [Internet]. 2014 Nov;11(9):1454–65.
  • 23. Mestrom E, De Bie A, van de Steeg M, Driessen M, Atallah L, Bezemer R, et al. Implementation of an automated early warning scoring system in a surgical ward: Practical use and effects on patient outcomes. Serra R, editor. PLoS One [Internet]. 2019 May 8;14(5):e0213402.
  • 24. Hollis RH, Graham LA, Lazenby JP, Brown DM, Taylor BB, Heslin MJ, et al. A role for the early warning score in early identification of critical postoperative complications. Ann Surg [Internet]. 2016 May;263(5):918–23.
  • 25. Zaidi H, Bader-El-Den M, McNicholas J. Using the National Early Warning Score (NEWS/NEWS 2) in different Intensive Care Units (ICUs) to predict the discharge location of patients. BMC Public Health [Internet]. 2019 Dec 5;19(1):1231.
  • 26. Rothman MJ, Rothman SI, Beals J. Development and validation of a continuous measure of patient condition using the Electronic Medical Record. J Biomed Inform [Internet]. 2013;46(5):837–48.
  • 27. Klepstad PK, Nordseth T, Sikora N, Klepstad P. Use of national early warning score for observation for increased risk for clinical deterioration during post-ICU care at a surgical ward. Ther Clin Risk Manag. 2019;15:315–22.
  • 28. Smith GB, Prytherch DR, Meredith P, Schmidt PE, Featherstone PI. The ability of the National Early Warning Score (NEWS) to discriminate patients at risk of early cardiac arrest, unanticipated intensive care unit admission, and death. Resuscitation [Internet]. 2013 Apr;84(4):465–70.
  • 29. Finlay GD, Rothman MJ, Smith RA. Measuring the modified early warning score and the Rothman Index: Advantages of utilizing the electronic medical record in an early warning system. J Hosp Med. 2014;9(2):116–9.
  • 30. Gotur DB, Masud F, Paranilam J, Zimmerman JL. Analysis of Rothman Index Data to Predict Postdischarge Adverse Events in a Medical Intensive Care Unit. J Intensive Care Med. 2020;35(6):606–10.
  • 31. Thoral P, Fornasa M, Bruin D, Hovenkamp H, Driessen R, Girbes A, et al. Developing a Machine Learning prediction model for bedside decision support by predicting readmission or death following discharge from the Intensive Care unit. preprint. 2020;1–29.

Medicatieveiligheid

  • Bakker et al. 2021. Clinically Relevant Potential Drug-Drug Interactions in Intensive Care Patients: A Large Retrospective Observational Multicenter Study. Journal of Critical Care, 62, 124–30.
  • Bakker et al., 2020. Improving medication safety in the Intensive Care by identifying relevant drug-drug interactions – Results of a multicenter Delphi study, Journal of Critical Care, 57, 134–140.
  • Doel van deze studie is om de relevante medicatie interactie een intensive care te bepalen: Bakker et al. 2019. The effect of ICU-tailored drug-drug interaction alerts on medication prescribing and monitoring: protocol for a cluster randomized stepped-wedge trial. BMC Med Inform Decis Mak. 19(1):1-10.
  • Ongering et al. 2019. Effect van beslissingsondersteuning op het verminderen van interacterende medicatiecombinaties op de intensive care. Nederlands Platform voor Farmaceutisch Onderzoek 4:a1703.
  • Z-index vernieuwt de G-standaard met extra Medisch Farmaceutische Beslisregels (MFB’s).

Kwaliteit en werklast

  • Stichting Nationale Intensive Care Evaluatie (NICE).
  • Epimed is gespecialiseerd in verzamelen van klinische gegevens, die verbeteringen in de effecientie van ziekenhuiszorg en patiëntveiligheid mogelijk maken.
  • Meynaar et al. 2012. Long term survival after ICU treatment. Minerva Anestesiologica 78:1324-32.
  • Meynaar et al. 2013. Red cell distribution width as predictor for mortality in critically ill patients. The Netherlands Journal of Medicine 71(9):488-93.
  • van Beusekom et al. 2020. The Influence of Clinical Variables on the Risk of Developing Chronic Conditions in ICU Survivors. Journal of Critical Care 55: 134–39.
  • Review from the IMIA Technology Assessment & Quality Development in Health Informatics Working Group and the EFMI Working Group for Assessment of Health Information Systems: Magrabi et al. 2019. Artificial Intelligence in Clinical Decision Support: Challenges for Evaluating AI and Practical Implications. Yearb Med Inform. 28(1):128-134.
  • Huijben et al 2019. Development of a Quality Indicator Set to Measure and Improve Quality of ICU Care for Patients With Traumatic Brain Injury. Critical care. 23(1):95.
  • Verburg et al. 2018. The Association Between Outcome-Based Quality Indicators for Intensive Care Units. PLoS One; 13(6):e0198522.
  • Van der Sluijs et al. 2019. Reducing errors in the administration of medication with infusion pumps in the intensive care department: A lean approach. SAGE Open Med. 7: 2050312118822629.
  • Hoogendoorn et al 2020. Workload scoring systems in the Intensive Care and their ability to quantify the need for nursing time: A systematic literature review. International Journal of Nursing Studies, 101:103408.
  • Margadant et al 2020. The Nursing Activities Score Per Nurse Ratio Is Associated With In-Hospital Mortality, Whereas the Patients Per Nurse Ratio Is Not. Crit Care Med. 48(1):3-9.
  • NICE Zorgzwaarte.
  • Gude et al. 2019. Facilitating Action Planning Within Audit and Feedback Interventions: A Mixed-Methods Process Evaluation of an Action Implementation Toolbox in Intensive Care. Implement Sci. 14(1):90.
  • Roos-Boom et al. 2019. Measuring quality indicators to improve pain management in critically ill patients. Journal of Critical Care. 49:136-142.
  • COVID-19 IC-patiëntinformatie: https://nlcovid-19-esrinl-content.hub.arcgis.com/.

Incidentieonderzoek Lijnen-Sepsis RIVM

  • Manniën et al 2007. Validation of Surgical Site Infection Surveillance in The Netherlands. Infection Control & Hospital Epidemiology, 28(1), 36-41.
  • Kuindersma et al. 2019. Central venous catheter associated infections in the ICU: A Dutch approach. Netherlands Journal of Critical Care. 28:80-85.
  • Koek et al 2017. Adhering to a National Surgical Care Bundle Reduces the Risk of Surgical Site Infections. PLoS One. 12(9):e0184200.

IC Nazorg / Post-Intensive Care Syndroom (PICS)

  • Van Beusekom et al. 2018. Lessons learnt during the implementation of a web-based triage tool for Dutch intensive care follow-up clinics. BMJ Open. 8(9): e021249.
  • Richtlijnwerkgroep Nazorg en revalidatie van IC-patiënten 2020, LEIDRAAD Nazorg voor IC-patiënten met COVID-19. Federatie Medisch Specialisten.

Behandelingsoptimalisatie COVID-19

  • Door middel van kunstmatige intelligentie het beloop van COVID-19 beter kunnen voorspellen en de behandeling van COVID-19 optimaliseren: https://covidpredict.org/.

Overige studies

  • Intensive care ontslagalgoritme maakt gebruik van MetaVision-data: https://pacmed.ai/nl/projects/ic.
  • Cardiology Decision Support System (CARDSS) Engen-Verheul et al 2013. A Web-Based System to Facilitate Local, Systematic Quality Improvement by Multidisciplinary Care Teams: Development and First Experiences of CARDSS Online. Stud Health Technol Inform. 192:248-52.
  • Inspectie Gezondheidszorg en Jeugd (IGJ): Kwaliteitsregistratie NICE incl. beademing en hypoglycemie van kinderen op een IC-afdeling.

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