Book contents
- Quality Management in Intensive CareA Practical Guide
- Quality Management in Intensive Care
- Copyright page
- Contents
- Contributors
- Introduction
- Section 1 Quality management of patient care
- Section 2 Quality management of the ICU
- Section 3 Quality management on the national (and international) level
- Epilogue
- Index
- References
Section 2 - Quality management of the ICU
Published online by Cambridge University Press: 05 February 2016
Book contents
- Quality Management in Intensive CareA Practical Guide
- Quality Management in Intensive Care
- Copyright page
- Contents
- Contributors
- Introduction
- Section 1 Quality management of patient care
- Section 2 Quality management of the ICU
- Section 3 Quality management on the national (and international) level
- Epilogue
- Index
- References
- Type
- Chapter
- Information
- Quality Management in Intensive CareA Practical Guide, pp. 95 - 194Publisher: Cambridge University PressPrint publication year: 2016
References
References
Donchin, Y., Gopher, D., Olin, M., et al. A look into the nature and causes of human errors in the intensive care unit. Crit Care Med. 1995; 23(2): 294–300.CrossRefGoogle ScholarPubMed
Valentin, A., Capuzzo, M., Guidet, B., et al. Patient safety in intensive care: results from the multinational Sentinel Events Evaluation (SEE) study. Intensive Care Med. 2006; 32(10): 1591–1598.CrossRefGoogle ScholarPubMed
Vincent, C., Burnett, S., Carthey, J. Safety measurement and monitoring in healthcare: a framework to guide clinical teams and healthcare organisations in maintaining safety. BMJ Qual Saf. 2014; 23(8): 670–677.CrossRefGoogle ScholarPubMed
Donchin, Y., Seagull, F.J. The hostile environment of the intensive care unit. Curr Opin Crit Care. 2002; 8(4): 316–320.Google Scholar
Sevdalis, N., Brett, S.J. Improving care by understanding the way we work: human factors and behavioural science in the context of intensive care. Crit Care. 2009; 13(2): 139.CrossRefGoogle ScholarPubMed
Endacott, R. The continuing imperative to measure workload in ICU: impact on patient safety and staff well-being. Intensive Care Med. 2012; 38(9): 1415–1417.CrossRefGoogle ScholarPubMed
Penoyer, D.A. Nurse staffing and patient outcomes in critical care: a concise review. Crit Care Med. 2010; 38(7): 1521–1528.Google Scholar
Valentin, A., Capuzzo, M., Guidet, B., et al. Errors in administration of parenteral drugs in intensive care units: multinational prospective study. BMJ. 2009; 338: b814.CrossRefGoogle ScholarPubMed
Naveh, E., Katz-Navon, T., Stern, Z. The effect of safety management systems on continuous improvement of patient safety: the moderating role of safety climate and autonomy. Qual Manag J. 2011; 18: 54–67.CrossRefGoogle Scholar
Valentin, A., Schiffinger, M., Steyrer, J., Huber, C., Strunk, G. Safety climate reduces medication and dislodgement errors in routine intensive care practice. Intensive Care Med. 2013; 39(3): 391–398.Google Scholar
Rhodes, A., Moreno, R.P., Azoulay, E., et al. Prospectively defined indicators to improve the safety and quality of care for critically ill patients: a report from the Task Force on Safety and Quality of the European Society of Intensive Care Medicine (ESICM). Intensive Care Med. 2012; 38(4): 598–605.CrossRefGoogle ScholarPubMed
COBATRICE. International standards for programmes of training in intensive care medicine in Europe. Intensive Care Med. 2011 Mar; 37(3): 385–393.Google Scholar
Pronovost, P.J., Goeschel, C.A., Colantuoni, E., et al. Sustaining reductions in catheter related bloodstream infections in Michigan intensive care units: observational study. BMJ. 2010; 340: c309.CrossRefGoogle ScholarPubMed
Shekelle, P.G., Pronovost, P.J., Wachter, R.M., et al. The top patient safety strategies that can be encouraged for adoption now. Ann Intern Med. 2013; 158(5 Pt 2): 365–368.Google Scholar
Frengley, R.W., Weller, J.M., Torrie, J., et al. The effect of a simulation-based training intervention on the performance of established critical care unit teams. Crit Care Med. 2011; 39(12): 2605–2611.CrossRefGoogle ScholarPubMed
Manias, E., Williams, A., Liew, D. Interventions to reduce medication errors in adult intensive care: a systematic review. Br J Clin Pharmacol. 2012; 74(3): 411–423.CrossRefGoogle ScholarPubMed
Adapa, R.M., Mani, V., Murray, L.J., et al. Errors during the preparation of drug infusions: a randomized controlled trial. Br J Anaesth. 2012; 109(5): 729–734.CrossRefGoogle ScholarPubMed
Marsteller, J.A., Sexton, J.B., Hsu, Y.J., et al. A multicenter, phased, cluster-randomized controlled trial to reduce central line-associated bloodstream infections in intensive care units. Crit Care Med. 2012; 40(11): 2933–2939.Google Scholar
Pagnamenta, A., Rabito, G., Arosio, A., et al. Adverse event reporting in adult intensive care units and the impact of a multifaceted intervention on drug-related adverse events. Ann Intensive Care. 2012; 2(1): 47.Google Scholar
References
HIMSS Analytics. Healthcare Information and Management Systems Society. www.himssanalytics.org/home/index.aspx (accessed 15 May 2014).Google Scholar
Chan, K.S., Fowles, J.B., Weiner, J.P. Review: electronic health records and the reliability and validity of quality measures – a review of the literature. Med Care Res Rev. 2010; 67(5): 503–527.Google Scholar
Friedberg, M.W., Chen, P.G., Van Busum, K.R., et al. Factors Affecting Physician Professional Satisfaction and Their Implications for Patient Care, Health Systems, and Health Policy. Santa Monica, CA: Rand Corporation, 2013.Google Scholar
Häyrinen, K., Harno, K., Nykänen, P. Use of headings and classifications by physicians in medical narratives of EHRs: an evaluation study in a Finnish hospital. Appl Clin Inform. 2011; 2(2): 143–157.Google Scholar
SNOMED CT: The Global Language of Healthcare. The International Health Terminology Standards Development Organisation. www.ihtsdo.org/snomed-ct (accessed 13 November 2014).Google Scholar
Hanson, C.W., Marshall, B.E. Artificial intelligence applications in the intensive care unit. Crit Care Med. 2001; 29(2): 427–435.Google Scholar
Williams, C.N., Bratton, S.L., Hirshberg, E.L. Computerized decision support in adult and pediatric critical care. World J Crit Care Med. 2013; 2(4): 21–28.CrossRefGoogle ScholarPubMed
Moore, G. Integrated suites vs best of breed–advantage 2013. YouTube, 12 February 2014.Google Scholar
Ford, E.W., Huerta, T.R., Menachemi, N., Thompson, M.A., Yu, F. Health information technology vendor selection strategies and total factor productivity. Health Care Manage Rev. 2013; 38(3): 177–187.Google Scholar
Meystre, S., Shen, S., Hofmann, D., Gundlapalli, A. Can physicians recognize their own patients in de-identified notes? Stud Health Technol Inform. 2014; 205: 778–782.Google Scholar
Maat, B. Optimization of electronic prescribing in pediatric patients. Diss. U Medical Center Utrecht, 2014.Google Scholar
Han, Y.Y. Unexpected increased mortality after implementation of a commercially sold computerized physician order entry system. Pediatrics. 2005; 116(6): 1506–1512.CrossRefGoogle ScholarPubMed
Sarkar, S., Kak, A.C., Rama, G.M., Metrics for measuring the quality of modularization of large-scale object-oriented software. IEEE T Software Eng. 2008; 34(5): 700–720.Google Scholar
References
Donabedian, A. Evaluating the quality of medical care. 1966. Milbank Q. 2005; 83(4): 691–729.Google Scholar
Stratmann, WC, Goldberg, AS, Haugh, LD. The utility for audit of manual and computerized problem-oriented medical record systems. Health Serv Res. 1982; 17(1): 5–26.Google Scholar
Barnett, GO, Winickoff, R, Dorsey, JL,Morgan, MM, Lurie, RS. Quality assurance through automated monitoring and concurrent feedback using a computer-based medical information system. Med Care. 1978; 16(11): 962–970.Google Scholar
Kohn, LT, Corrigan, J, Donaldson, MS. To Err is Human: Building a Safer Health System. Washington, DC: National Academy Press, 2000.Google Scholar
Thompson, T, ed. Address of Tommy G. Thompson Secretary of Health and Human Services to the World Health Care Congress. World Healthcare Congress; 27 January 2004, Washington, DC.Google Scholar
Health Information Technology for Economic and Clinical Health (HITECH) Act, Pub. L. No. 111–115, Stat. 226 (2009).Google Scholar
Alsara, A, Warner, DO, Li, G, Herasevich, V, Gajic, O, Kor, DJ. Derivation and validation of automated electronic search strategies to identify pertinent risk factors for postoperative acute lung injury. Mayo Clinic Proceedings. 2011; 86(5): 382–388.CrossRefGoogle ScholarPubMed
Wright, A, Sittig, DF, McGowan, J, Ash, JS, Weed, LL. Bringing science to medicine: an interview with Larry Weed, inventor of the problem-oriented medical record. J Am Med Inform Assoc. 2014; 21: 964–968.CrossRefGoogle ScholarPubMed
Rector, A, Nowlan, W, Kay, S, Goble, C, Howkins, T. A framework for modelling the electronic medical record. Methods of Inf Med. 1993; 32(2): 109–119.Google ScholarPubMed
Shortliffe, EH, Buchanan, BG, Feigenbaum, EA. Knowledge engineering for medical decision making: a review of computer-based clinical decision aids. Proceedings of the IEEE. 1979; 67(9): 1207–1224.Google Scholar
Weiss, SM, Kulikowski, CA, Amarel, S, Safir, A. A model-based method for computer-aided medical decision-making. Artificial Intelligence. 1978; 11(1): 145–172.CrossRefGoogle Scholar
Pickering, BW, Herasevich, V, Ahmed, A, Gajic, O. Novel representation of clinical information in the ICU: developing user interfaces which reduce information overload. Appl Clin Inform. 2010; 1(2): 116–131.Google Scholar
Herasevich, V, Pickering, BW, Dong, Y, Peters, SG, Gajic, O. Informatics infrastructure for syndrome surveillance, decision support, reporting, and modeling of critical illness. Mayo Clinic Proceedings. 2010; 85(3): 247–254.CrossRefGoogle ScholarPubMed
Ahmed, A, Chandra, S, Herasevich, V, Gajic, O, Pickering, BW. The effect of two different electronic health record user interfaces on intensive care provider task load, errors of cognition, and performance. Crit Care Med. 2011; 39(7): 1626–1634.Google Scholar
Herasevich, V, Tsapenko, M, Kojicic, M, et al. Limiting ventilator-induced lung injury through individual electronic medical record surveillance. Crit Care Med. 2011; 39(1): 34–39.Google Scholar
McDonald, CJ. The barriers to electronic medical record systems and how to overcome them. J Am Med Inform Assoc. 1997; 4(3): 213–221.Google Scholar
Hunt, DL, Haynes, RB, Hanna, SE, Smith, K. Effects of computer-based clinical decision support systems on physician performance and patient outcomes: a systematic review. JAMA. 1998; 280(15): 1339–1346.Google Scholar
References
Reis Miranda, D., Langrehr, D.. National and regional organisation. In: Miranda, D.R., Williams, A., Loirat, P., eds. Management of Intensive Care: Guidelines for Better Use of Resources. Dordrecht and Boston, MA: Kluwer Academic Publishers, 1990; 83–102.Google Scholar
Reis Miranda, D., Ryan, D. W., Schaufeli, W. B., Fidler, V.. Organisation and Management of Intensive Care: A Prospective Study of 12 European Countries. Berlin, Heidelberg: Springer, 1998; 13–36.Google Scholar
Cullen, D.J., Civetta, J.M., Briggs, B.A., et al. Therapeutic Intervention Scoring System: a method for quantitative comparison of patient care. Crit Care Med 1974; 2: 57–60.Google Scholar
Ferdinande, P.. Recommendation on minimal requirements for intensive care departments: Members of the Task Force of the European Society of Intensive Care Medicine. Intensive Care Med 1997; 23: 226–232.Google Scholar
Moreno, R., Reis Miranda, D.. Nursing staff in intensive care in Europe: the mismatch between planning and practice. Chest 1998; 113: 752–758.Google Scholar
Iapichino, G., Radrizzani, D., Bertolini, G., et al. Daily classification of the level of care: a method to describe clinical course of illness, use of resources and quality of intensive care assistance. Intensive Care Med 2001; 27: 131–136.Google Scholar
Reis Miranda, D., Moreno, R., Iapichino, G.. Nine equivalents of nursing manpower use score (NEMS). Intensive Care Med 1997; 23: 760–765.Google Scholar
Valentin, A., Ferdinande, P., ESICM Working Group on Quality Improvement: recommendations on basic requirements for intensive care units – structural and organizational aspects. Intensive Care Med 2011; 37: 1575–1587.CrossRefGoogle Scholar
Endacott, R.. The continuing imperative to measure workload in ICU: impact on patient safety and staff well-being. Intensive Care Med 2012; 38: 1415–1417.Google Scholar
Reis Miranda, D., De Rijk, A., Schaufeli, W.. Simplified Therapeutic Intervention Scoring System: the TISS-28 items – results from a multicenter study. Crit Care Med 1996; 24: 64–73.CrossRefGoogle Scholar
Reis Miranda, D., Nap, R., de Rijk, A., et al. Nursing activity score. Crit Care Med 2003; 31: 374–382.Google Scholar
Italian Multicenter Group of ICU research (GIRTI). Time oriented score system (TOSS): a method for direct and quantitative assessment of nursing workload for ICU patients. Intensive Care Med 1991; 17: 340–345.Google Scholar
Campbell, T., Taylor, S., Callaghan, S., et al. Case-mix type as a predictor of nursing workload. J Nurs Manag 1997; 5: 237–240.Google Scholar
Ball, C., Walker, G., Harper, P., et al. Moving on from patient dependency and nursing workload to managing risk in critical care. Intensive and Critical Care Nursing 2004; 20: 62–68.CrossRefGoogle ScholarPubMed
Penoyer, D.A.. Nurse staffing and patient outcomes in critical care: a concise review. Crit Care Med 2010; 38: 1521–1528.Google Scholar
Campagner, A.O.M., Garcia, P.C.R., Piva, J.P.. Use of scores to calculate the nursing workload in a paediatric intensive care unit. Revista Brasilera de Terapia Intensiva 2014; 26: 36–43.Google Scholar
Debergh, D.P., Myny, D., van Herzeele, I., et al. Measuring the nursing workload per shift in the ICU. Intensive Care Med 2011; 38: 1438–1444.CrossRefGoogle Scholar
Reis Miranda, D., Jegers, M.. Monitoring costs in the ICU: a search for a pertinent methodology. Acta Anaesthesiol Scand 2012; 56: 1104–1113.Google Scholar
Hoonakker, P., Carayon, P., Gurses, A.P., et al. Measuring workload of ICU nurses with a questionnaire survey: the NASA task load index (TLX). IIE Trans Healthcare Syst Eng 2011; 1: 131–143.CrossRefGoogle ScholarPubMed
Iapichino, G., Radrizzani, D., Pezzi, A., et al. Evaluating daily nursing use and needs in the intensive care unit: a method to assess the rate and appropriateness of ICU resource use. Health Policy 2005; 73: 228–234.CrossRefGoogle ScholarPubMed
Iapichino, G., Radrizzani, D., Rossi, C., et al. Proposal of a flexible structural-organizing model for intensive care units. Minerva Anestesiol 2007; 73: 501–506.Google ScholarPubMed
Sprung, C.L., Danis, M., Iapichino, G., et al. Triage of intensive care patients: identifying agreement and controversy. Intensive Care Med 2013; 39: 1916–1924.Google Scholar
Gooch, R.A., Kahn, J.M., ICU bed supply, utilization, and healthcare spending: an example of demand elasticity. JAMA 2014; 311: 567–568.CrossRefGoogle ScholarPubMed
Needleman, J., Buerhaus, P., Pankratz, V.S., et al. Nurse staffing and inpatient hospital mortality. NEJM 2011; 364: 1037–1045.Google Scholar
Angus, D.C., Kelley, M.A., Schmitz, R.J., et al. The Committee on Manpower for Pulmonary and Critical Care Societies (COMPACCS): current and projected workforce requirements for care of the critically ill and patients with pulmonary disease. JAMA 2000; 284: 2762–2770.CrossRefGoogle Scholar
Garland, A., Gershengorn, H.B.. Staffing in ICUs: physicians and alternative staffing models. Chest 2013; 143: 214–221.CrossRefGoogle ScholarPubMed
References
Moreno, RP, Jardim, AL, Godinho de Matos, R, Metnitz, PGH. Principles of risk-adjustment in the critically ill patient. In: Kuhlen, R, Moreno, R, Ranieri, M, Rhodes, A, eds. 25 Years of Progress and Innovation in Intensive Care Medicine. Berlin: Medizinisch Wissenschaftliche Verlagsgesellschaft, 2007: 409–17.Google Scholar
Halpern, NA, Pastores, SM. Critical care medicine in the United States 2000–2005: an analysis of bed numbers, occupancy rates, payer mix, and costs. Crit Care Med 2010; 38: 65–71.Google Scholar
Committee on Quality of Healthcare in America IoM. Crossing the Quality Chasm: A New Health System for the 21st Century. Washington, DC: National Academy Press, 2001.Google Scholar
Howard, RJ. Missed opportunities: The Institute of Medicine report – organ donation. opportunities for action. Am J Transplant 2006; 6: 1–3.Google Scholar
James, JT. A new, evidence-based estimate of patient harms associated with hospital care. J Patient Saf 2013; 9: 122–128.Google Scholar
Rhodes, A, Ferdinande, P, Flaatten, H, Guidet, B, Metnitz, PG, Moreno, RP. The variability of critical care bed numbers in Europe. Intensive Care Med 2012; 38: 1647–1653.Google Scholar
Rhodes, A, Moreno, RP. Intensive care provision: a global problem. Braz J Intensive Care 2012; 24: 322–325.Google Scholar
Pearse, RM, Moreno, RP, Bauer, P, et al. Mortality after surgery in Europe: a 7 day cohort study. Lancet 2012; 380: 1059–1065.CrossRefGoogle ScholarPubMed
Rhodes, A, Cecconi, M. Can surgical outcomes be prevented by postoperative admission to critical care? Crit Care 2013; 17: 110.CrossRefGoogle ScholarPubMed
Rhodes, A, Moreno, RP, Azoulay, E, et al. Prospectively defined indicators to improve the safety and quality of care for critically ill patients: a report from the Task Force on Safety and Quality of the European Society of Intensive Care Medicine (ESICM). Intensive Care Med 2012; 38: 598–605.Google Scholar
Rothen, HU, Stricker, K, Einfalt, J, et al. Variability in outcome and resource use in intensive care units. Intensive Care Med 2007; 33: 1329–1336.Google Scholar
Randall Curtis, J, Cook, DJ, Wall, RJ, et al. Intensive care unit quality improvement: a “how-to” guide for the interdisciplinary team. Crit Care Med 2006; 34: 211–218.CrossRefGoogle Scholar
Breslow, MJ, Badawi, O. Severity scoring in the critically ill: part 1 – interpretation and accuracy of outcome prediction scoring systems. Chest 2012; 141: 245–252.Google Scholar
Breslow, MJ, Badawi, O. Severity scoring in the critically ill: part 2 – maximizing value from outcome prediction scoring systems. Chest 2012:141: 518–527.Google Scholar
Poole, D, Bertolini, G. Outcome-based benchmarking in the ICU part I: statistical tools for the creation and validation of severity scores. In: Chice, J-D, Moreno, R, Putensen, C, Rhodes, A, eds. Patient Safety and Quality of Care in Intensive Care Medicine. Berlin: Medizinisch Wissenschaftiche Verlagsgesellschaft, 2009: 141–150.Google Scholar
Poole, D, Bertolini, G. Outcome-based benchmarking in the ICU Part II: Use and limitations of severity scores in critical care. In: Chiche, J-D, Moreno, R, Putensen, C, Rhodes, A, eds. Patient Safety and Quality of Care in Intensive Care Medicine. Berlin: Medizinisch Wissenschaftiche Verlagsgesellschaft, 2009: 151–160.Google Scholar
Knaus, WA, Draper, EA, Wagner, DP, Zimmerman, JE. APACHE II: a severity of disease classification system. Crit Care Med 1985; 13: 818–829.Google Scholar
Le Gall, JR, Lemeshow, S, Saulnier, F. A new simplified acute physiology score (SAPS II) based on a European/North American multicenter study. JAMA 1993; 270: 2957–2963.Google Scholar
Moreno, RP. Outcome prediction in intensive care: why we need to reinvent the wheel. Curr Op Crit Care 2008; 14: 483–484.Google Scholar
Zimmerman, JE, Kramer, AA. Outcome prediction in critical care: the Acute Physiology and Chronic Health Evaluation models. Curr Op Crit Care 2008; 14: 491–497.CrossRefGoogle ScholarPubMed
Capuzzo, M, Moreno, RP, Le Gall, J-R. Outcome prediction in critical care: the Simplified Acute Physiology Score models. Curr Op Crit Care 2008; 14: 485–490.Google Scholar
Higgins, TL, Teres, D, Nathanson, B. Outcome prediction in critical care: the Mortality Probability Models. Curr Op Crit Care 2008; 14: 498–505.Google Scholar
Harrison, DA, Rowan, KM. Outcome prediction in critical care: the ICNARC model. Curr Op Crit Care 2008; 14: 506–512.Google Scholar
Moreno, RP, Afonso, S. Building and using outcome prediction models: should we be lumpers or splitters? In: Kuhlen, R, Moreno, R, Ranieri, M, Rhodes, A, eds. Controversies in Intensive Care Medicine. Berlin: Medizinisch Wissenschaftiche Verlagsgesellschaft, 2008: 415–419.Google Scholar
Poole, D, Rossi, C, Anghileri, A, et al. External validation of the Simplified Acute Physiology Score (SAPS) 3 in a cohort of 28,357 patients from 147 Italian intensive care units. Intensive Care Medicine 2009; 35: 1916–1924.CrossRefGoogle Scholar
Rowan, K. The reliability of case mix measurements in intensive care. Curr Op Crit Care 1996; 2: 209–213.Google Scholar
Black, NA, Jenkinson, C, Hayes, JA, et al. Review of outcome measures used in adult critical care. Crit Care Med 2001; 29: 2119–2124.Google Scholar
Bosman, RJ, Oudemane van Straaten, HM, Zandstra, DF. The use of intensive care information systems alters outcome prediction. Intensive Care Med 1998; 24: 953–958.Google Scholar
Suistomaa, M, Kari, A, Ruokonen, E, Takala, J. Sampling rate causes bias in APACHE II and SAPS II scores. Intensive Care Med 2000; 26: 1773–1778.Google Scholar
Moreno, RP, Metnitz, PG, Almeida, E, et al. SAPS 3 Investigators: SAPS 3 – from evaluation of the patient to evaluation of the intensive care unit. Part 2: development of a prognostic model for hospital mortality at ICU admission. Intensive Care Med 2005; 31: 1345–1355.Google Scholar
Higgins, TL, Teres, D, Copes, WS, Nathanson, BH, Stark, M, Kramer, AA. Assessing contemporary intensive care unit outcome: an updated Mortality Probability Admission Model (MPM0-III). Critical Care Med 2007; 35: 827–835.CrossRefGoogle ScholarPubMed
Levy, M. Including families in quality measurement in critical care. Critical Care Med 2007; 35: 324–325.Google Scholar
Wall, RJ, Engelberg, RA, Gries, CJ, Glavan, B, Randall Curtis, J. Spiritual care of families in the intensive care unit. Critical Care Med 2007; 35: 1084–1090.Google Scholar
Sprung, CL, Maia, P, Bulow, H-H, et al., The importance of religious affiliation and culture on end-of-life decisions in European intensive care units. Intensive Care Med 2007; 33: 1732–1739.Google Scholar
Bertolini, G, Boffelli, S, Malacarne, P, et al. End-of-life decision-making and quality of ICU performance: an observational study in 84 Italian units. Intensive Care Med 2010; 36: 1495–1504.Google Scholar
Azoulay, E, Metnitz, B, Sprung, C-L, et al. End-of-life practices in 282 intensive care units: data from the SAPS 3 database. Intensive Care Med 2009; 35: 623–630.Google Scholar
Randall Curtis, J. End-of-life care for patients in the intensive care unit. In: Kuhlen, R, Moreno, R, Ranieri, M, Rhodes, A, eds. 25 Years of Progress and Innovation in Intensive Care Medicine. Berlin: Medizinisch Wissenschaftliche Verlagsgesellschaft, 2007: 469–479.Google Scholar
Zimmerman, JE, Kramer, AA, McNair, DS, Malila, FM, Shaffer, VL. Intensive care unit length of stay: benchmarking based on Acute Physiology and Chronic Health Evaluation (APACHE) IV. Critical Care Med 2006; 34: 2517–2529.Google Scholar
Rapoport, J, Teres, D, Lemeshow, S, Gehlbach, S. A method for assessing the clinical performance and cost-effectiveness of intensive care units: a multicenter inception cohort study. Critical Care Med 1994; 22: 1385–1391.CrossRefGoogle ScholarPubMed
Nathanson, BH, Higgins, TL, Teres, D, Copes, WS, Kramer, A, Stark, M. A revised method to assess intensive care unit clinical performance and resource utilization. Critical Care Med 2007; 35: 1853–1862.Google Scholar
Moreno, RP, Hochrieser, H, Metnitz, B, Bauer, P, Metnitz, PGH. Characterizing the risk profiles of intensive care units. Intensive Care Med 2010; 36: 1207–1212.Google Scholar
Metnitz, PG, Moreno, RP, Almeida, E, et al., SAPS 3 Investigators: SAPS 3 – from evaluation of the patient to evaluation of the intensive care unit. Part 1: objectives, methods and cohort description. Intensive Care Med 2005; 31: 1336–1344.Google Scholar
References
Cochrane, AL. Effectiveness and efficiency. In Random Reflections on Health Services. London: Taylor and Francis, 1999.Google Scholar
The Acute Respiratory Distress Syndrome Network. Ventilation with lower tidal volumes as compared with traditional tidal volumes for acute lung injury and the acute respiratory distress syndrome. NEJM. 2000; 342(18): 1301–1308.CrossRefGoogle Scholar
Rubenfeld, GD, Cooper, C, Carter, G, Thompson, BT, Hudson, LD. Barriers to providing lung-protective ventilation to patients with acute lung injury. Crit Care Med. 2004; 32(6): 1289–1293.Google Scholar
Umoh, NJ, Fan, E, Mendez-Tellez, PA, et al. Patient and intensive care unit organizational factors associated with low tidal volume ventilation in acute lung injury. Crit Care Med. 2008; 36(5): 1463–1468.Google Scholar
Sinuff, T, Cook, D, Giacomini, M, Heyland, D, Dodek, P. Facilitating clinician adherence to guidelines in the intensive care unit: a multicenter, qualitative study. Crit Care Med. 2007; 35(9): 2083–2089.Google Scholar
Sinuff, T, Muscedere, J, Adhikari, NK, et al. Knowledge translation interventions for critically ill patients: a systematic review. Crit Care Med. 2013; 41(11): 2627–2640.Google Scholar
D’Andreamatteo, A, Ianni, L, Lega, F, Sargiacomo, M. Lean in healthcare: a comprehensive review. Health Policy. 2015. doi: 10.1016/j.healthpol.2015.02.002.Google Scholar
de Koning, H, Verver, JP, van den Heuvel, J, Bisgaard, S, Does, RJ. Lean Six Sigma in healthcare. J Healthc Qual. 2006; 28(2): 4–11.Google Scholar
Nicolay, CR, Purkayastha, S, Greenhalgh, A, et al. Systematic review of the application of quality improvement methodologies from the manufacturing industry to surgical healthcare. Brit J Surg. 2012; 99(3): 324–335.Google Scholar
Scales, DC, Dainty, K, Hales, B, et al. A multifaceted intervention for quality improvement in a network of intensive care units: a cluster randomized trial. JAMA. 2011; 305(4): 363–372.Google Scholar
Wallis, L. Barriers to implementing evidence-based practice remain high for U.S. nurses: getting past “we’ve always done it this way” is crucial. Am J Nurs. 2012; 112(12): 15.Google Scholar
Grant, HS, Stuhlmacher, A, Bonte-Eley, S. Overcoming barriers to research utilization and evidence-based practice among staff nurses. JNSD. 2012; 28(4): 163–165.Google Scholar
Dainty, KN, Scales, DC, Sinuff, T, Zwarenstein, M. Competition in collaborative clothing: a qualitative case study of influences on collaborative quality improvement in the ICU. BMJ Qual Saf. 2013; 22(4): 317–323.Google Scholar
Services VCfH. ICU delirium and Cognitive Impairment Study Group www.icudelirium.org 2013 (accessed 1 March 2015).Google Scholar
Halpern, SD, Becker, D, Curtis, JR, et al. An official American Thoracic Society/American Association of Critical-Care Nurses/American College of Chest Physicians/Society of Critical Care Medicine policy statement: the Choosing Wisely(R) Top 5 list in Critical Care Medicine. Am J Respir Crit Care Med. 2014; 190(7): 818–826.Google Scholar
Ranieri, VM, Thompson, BT, Barie, PS, et al. Drotrecogin alfa (activated) in adults with septic shock. NEJM. 2012; 366(22): 2055–2064.Google Scholar
Finfer, S, Chittock, DR, Su, SY, et al. Intensive versus conventional glucose control in critically ill patients. NEJM. 2009; 360(13): 1283–1297.Google Scholar
Niven, DJ, Rubenfeld, GD, Kramer, AA, Stelfox, HT. Effect of published scientific evidence on glycemic control in adult intensive care units. JAMA. 2015; 175: 801–809.Google Scholar
References
Sanchez, J, Barach, P. High reliability organizations and surgical microsystems: re-engineering surgical care. Surgical Clinics of North America, 2012; DOI: 10.1016/j.suc.2011.12.005.Google Scholar
Rothschild, JM, Landrigan, CP, Cronin, JW, et al. The Critical Care Safety Study: the incidence and nature of adverse events and serious medical errors in intensive care. Crit Care Med. 2005; 33(8): 1694–1700.Google Scholar
Valentin, A, Capuzzo, M, Guidet, B, et al. Patient safety in intensive care: results from the multinational Sentinel Events Evaluation (SEE) study. Intensive Care Med. 2006; 32(10): 1591–1598.Google Scholar
Urbach, DR, Govindarajan, A, Saskin, R, et al. Introduction of surgical safety checklists in Ontario, Canada. NEJM. 2014; 370: 1029–1038.Google Scholar
Glasby, J. Hospital Discharge: Integrating Health and Social Care. Oxford: Radcliffe Publishing, 2003.Google Scholar
Hesselink, G, Vernooij-Dassen, M, Barach, P,et al. Organizational culture: an important context for addressing and improving hospital to community patient discharge. Medical Care, 2012; doi: 10.1097/MLR.0b013e31827632ec.Google Scholar
Mohr, J, Batalden, P, Barach, P. Integrating patient safety into the clinical microsystem. Qual Saf Healthc. 2004; 13: 34–38.Google Scholar
Quinn, JB. Intelligent Enterprise: A Knowledge and Service Based Paradigm for Industry. New York: Free Press, 1992.Google Scholar
Nelson, E, Batalden, P, Huber, T, et al. Microsystems in health care: part 1. Learning from high-performing front-line clinical units. Jt Comm J Qual Improv. 2002; 28: 472–493.Google Scholar
Eccles, M, Grimshaw, J, Walker, A, et al. Changing the behaviour of healthcare professionals: the use of theory in promoting the uptake of research findings. J Clin Epidem 2005; 58(2): 107–112.Google Scholar
National Institute of Health and Clinical Excellence. Behaviour Change at Population, Community and Individual Levels. London: National Institute of Health and Clinical Excellence, 2007. www.nice.org.uk/PH006 (accessed 29 May 2015).Google Scholar
Lilford, R, Chilton, PJ, Hemming, K, Brown, C, Girling, A, Barach, P. Evaluating policy and service interventions: framework to guide selection and interpretation of study end points. BMJ 2010; 341: c4413.Google Scholar
Greenhalgh, T, Robert, G, Bate, P, et al. Diffusion of Innovations in Health Service Organisations: A Systematic Literature Review. Oxford: Blackwell BMJ Books, 2005.Google Scholar
Grol, R, Wensing, M, Eccles, M, eds. Improving Patient Care: The Implementation of Change in Clinical Practice. Oxford: Elsevier, 2004.Google Scholar
van Bokhoven, MA, Kok, G, van der Weijden, T. Designing a quality improvement intervention: a systematic approach. Qual Saf Healthc. 2003; 12: 215–220.Google Scholar
Hesselink, G, Zegers, M, Vernooij-Dassen, M, et al. Improving patient discharge and reducing hospital readmissions by using Intervention Mapping. BMC Health Serv Res. 2014; 14: 389.Google Scholar
Vaughan, D. The dark side of organizations: mistake, misconduct and disaster. Annu Rev Sociol 1999; 25: 271–305.Google Scholar
Ashforth, DE, Anand, V. The normalization of corruption in organizations. Res Organ Behav. 2003; 25: 1–52.Google Scholar
Langley, G, Nolan, T, Provost, L, eds. The Improvement Guide, second edition. San Francisco, CA: Jossey-Bass, 2009.Google Scholar
References
Salas, E., Rosen, M.A., King, H. Managing teams managing crises: principles of teamwork to improve patient safety in the Emergency Room and beyond. Theor Issues Ergonomics Sci. 2007; 8(5): 381–394.Google Scholar
Tuckman, B.W. Developmental sequence in small groups. Psychol Bull. 1965; 63: 384–399.Google Scholar
Baggs, J.G., Ryan, S.A., Phelps, C.E., Richeson, J.F., Johnson, J.E. The association between interdisciplinary collaboration and patient outcomes in a medical intensive care unit. Heart Lung. 1992; 21(1): 18–24.Google Scholar
Wheelan, S.A., Burchill, C.N., Tilin, F. The link between teamwork and patients’ outcomes in intensive care units. Am J Crit Care. 2003; 12(6): 527–534.Google Scholar
Schmutz, J., Manser, T. Do team processes really have an effect on clinical performance? A systematic literature review. Brit J Anaesth. 2013; 110: 529–544.Google Scholar
Manser, T. Teamwork and patient safety in dynamic domains of healthcare: a review of the literature. Acta Anaesthesiologica Scandinavica. 2009; 53(2): 143–151.Google Scholar
Guzzo, R.A., Shea, G.P. Group performance and intergroup relations in organizations. In Handbook of Industrial and Organizational Psychology. 3. Palo Alto, CA: Consulting Psychologists Press, 1992: 269–313.Google Scholar
Helmreich, R.L., Foushee, H.C. Why crew resource management? Empirical and theoretical bases of human factors training in aviation. In: Weiner, E.L., Kanki, B.G., Helmreich, R.L., eds. Cockpit Resource Management. San Diego, CA: Academic Press; 1993: 3–46.Google Scholar
Anderson, N.R., West, M.A. Measuring climate for work group innovation: development and validation of the team climate inventory. Journal of Organizational Behavior. 1998; 19(3): 235–258.Google Scholar
Kolbe, M., Burtscher, M.J., Wacker, J., et al. Speaking up is related to better team performance in simulated anesthesia inductions: an observational study. Anesth Analg. 2012; 115(5): 1099–1108.Google Scholar
Edmondson, A.C. Speaking up in the operating room: how team leaders promote learning in interdisciplinary action teams. J Manage Stud. 2003; 40(6): 1419–1452.Google Scholar
Singer, S., Lin, S., Falwell, A., Gaba, D., Baker, L. Relationship of safety climate and safety performance in hospitals. Health Serv Res. 2009; 44(2 Pt 1): 399–421.CrossRefGoogle ScholarPubMed
Reader, T.W., Flin, R., Mearns, K., Cuthbertson, B.H. Interdisciplinary communication in the intensive care unit. Brit J Anaesth. 2007; 98(3): 347–352.Google Scholar
Zwarenstein, M., Bryant, W. Interventions to promote collaboration between nurses and doctors. Cochrane Database of Systematic Reviews. 2000; (2): CD000072.Google Scholar
Thomas, E.J., Sexton, J.B., Helmreich, R.L. Discrepant attitudes about teamwork among critical care nurses and physicians. Crit Care Med. 2003; 31(3): 956–959.Google Scholar
Martin, J.S., Ummenhofer, W., Manser, T., Spirig, R. Interprofessional collaboration among nurses and physicians: making a difference in patient outcome. Swiss Med Wkly. 2010; 140: w13062.Google Scholar
Manser, T., Foster, S. Effective handover communication: an overview of research and improvement efforts. Best Prac Res Clin Anaesthesiol. 2011; 25(2): 181–191.Google Scholar
Iedema, R. Creating safety by strengthening clinicians’ capacity for reflexivity. BMJ Quality Saf. 2011; 20(Suppl 1): i83–i86.Google Scholar
Broekhuis, M., Veldkamp, C. The usefulness and feasibility of a reflexivity method to improve clinical handover. J Eval Clin Prac. 2007; 13(1): 109–115.Google Scholar
Brown, M.S., Ohlinger, J., Rusk, C., Delmore, P., Ittmann, P. Implementing potentially better practices for multidisciplinary team building: creating a neonatal intensive care unit culture of collaboration. Pediatrics. 2003; 111(4): 7.Google Scholar
West, M., Lyubovnikova, J.R. Real teams or pseudo teams? The changing landscape needs a better map. Ind Organ Psychol. 2012; 5(1): 25–28.Google Scholar
References
Dreu, CKW de, Gelfand, MJ, eds. The Psychology of Conflict and Conflict Management in Organizations. New York: Lawrence Erlbaum Associates, 2008.Google Scholar
Norton, SA, Tilden, VP, Tolle, SW, Nelson, CA, Eggman, ST. Life support withdrawal: communication and conflict. Am J Crit Care Off Publ Am Assoc Crit-Care Nurses. 2003; 12(6): 548–555.Google Scholar
Studdert, DM, Mello, MM, Burns, JP, et al. Conflict in the care of patients with prolonged stay in the ICU: types, sources, and predictors. Intensive Care Med. 2003; 29(9): 1489–1497.Google Scholar
Azoulay, E, Timsit, J-F, Sprung, CL, et al. Prevalence and factors of intensive care unit conflicts: the Conflicus study. Am J Respir Crit Care Med. 2009; 180(9): 853–860.Google Scholar
Breen, CM, Abernethy, AP, Abbott, KH, Tulsky, JA. Conflict associated with decisions to limit life-sustaining treatment in intensive care units. J Gen Intern Med. 2001; 16(5): 283–289.Google Scholar
Way, J, Back, AL, Curtis, JR. Withdrawing life support and resolution of conflict with families. BMJ. 2002; 325(7376): 1342–1345.Google Scholar
Abbott, KH, Sago, JG, Breen, CM, Abernethy, AP, Tulsky, JA. Families looking back: one year after discussion of withdrawal or withholding of life-sustaining support. Crit Care Med. 2001; 29(1): 197–201.Google Scholar
Ferrand, E, Lemaire, F, Regnier, B, et al. Discrepancies between perceptions by physicians and nursing staff of intensive care unit end-of-life decisions. Am J Respir Crit Care Med. 2003; 167(10): 1310–1315.Google Scholar
Rusinova, K, Kukal, J, Simek, J, Cerny, V. Limited family members/staff communication in intensive care units in the Czech and Slovak Republics considerably increases anxiety in patients’ relatives. BMC Psychiatry. 2014; 14: 21.Google Scholar
Danjoux Meth, N, Lawless, B, Hawryluck, L. Conflicts in the ICU: perspectives of administrators and clinicians. Intensive Care Med. 2009; 35(12): 2068–2077.Google Scholar
Aiken, LH, Clarke, SP, Sloane, DM, Sochalski, J, Silber, JH. Hospital nurse staffing and patient mortality, nurse burnout, and job dissatisfaction. JAMA. 2002; 288(16): 1987–1993.Google Scholar
Poghosyan, L, Clarke, SP, Finlayson, M, Aiken, LH. Nurse burnout and quality of care: cross-national investigation in six countries. Res Nurs Health. 2010; 33(4): 288–298.CrossRefGoogle ScholarPubMed
Embriaco, N, Azoulay, E, Barrau, K, et al. High level of burnout in intensivists: prevalence and associated factors. Am J Respir Crit Care Med. 2007; 175(7): 686–692.Google Scholar
Tarnow-Mordi, WO, Hau, C, Warden, A, Shearer, AJ. Hospital mortality in relation to staff workload: a 4-year study in an adult intensive-care unit. Lancet. 2000; 356(9225): 185–189.Google Scholar
Manser, T. Teamwork and patient safety in dynamic domains of healthcare: a review of the literature. Acta Anaesthesiol Scand. 2009; 53(2): 143–151.Google Scholar
Strack van Schijndel, RJM, Burchardi, H. Bench-to-bedside review: leadership and conflict management in the intensive care unit. Crit Care Lond Engl. 2007; 11(6): 234.Google Scholar
Hawryluck, LA, Espin, SL, Garwood, KC, Evans, CA, Lingard, LA. Pulling together and pushing apart: tides of tension in the ICU team. Acad Med J Assoc Am Med Coll. 2002; 77(10 Suppl): S73–76.Google Scholar
Lingard, L, Espin, S, Evans, C, Hawryluck, L. The rules of the game: interprofessional collaboration on the intensive care unit team. Crit Care Lond Engl. 2004; 8(6): R403–408.Google Scholar
Fassier, T, Azoulay, E. Conflicts and communication gaps in the intensive care unit. Curr Opin Crit Care. 2010; 16(6): 654–665.Google Scholar
Chiarchiaro, J, Schuster, RA, Ernecoff, NC, Barnato, AE, Arnold, RM, White, DB. Developing a simulation to study conflict in ICUs. Ann Am Thorac Soc. 2015; 12(4): 526–532.Google Scholar
Burns, JP, Mello, MM, Studdert, DM, Puopolo, AL, Truog, RD, Brennan, TA. Results of a clinical trial on care improvement for the critically ill. Crit Care Med. 2003; 31(8): 2107–2117.Google Scholar
Curtis, JR, White, DB. Practical guidance for evidence-based ICU family conferences. Chest. 2008; 134(4): 835–843.Google Scholar
Anderson, WG, Cimino, JW, Ernecoff, NC, et al. A multicenter study of key stakeholders’ perspectives on communicating with surrogates about prognosis in intensive care units. Ann Am Thorac Soc. 2015; 12(2): 142–152.Google Scholar
Ury, W. Getting Past No: Negotiating With Difficult People. New York: Bantam Books, 1991.Google Scholar
Sulmasy, DP, Sood, JR, Ury, WA. Physicians’ confidence in discussing do not resuscitate orders with patients and surrogates. J Med Ethics. 2008; 34(2): 96–101.Google Scholar
References
Lassen, H.C.A.. A preliminary report on the 1952 epidemic of poliomyelitis in Copenhagen: with special reference to the treatment of acute respiratory insufficiency. Lancet 1953; 1(6749): 37–41.Google Scholar
Hillman, K., Jones, D., Chen, J.. Rapid response systems. Med J Aust 2014; 201: 519–521.Google Scholar
Goldhil, D.R., Sumner, A.. Outcome of intensive care patients in a group of British intensive care units. Crit Care Med 1998; 26: 1337–1343.Google Scholar
Donaldson, L.J., Panesar, S. S., Darzi, A.. Patient-safety-related hospital deaths in England: thematic analysis of incidents reported to a national database, 2010–2012. PLoS Med 2014; 11: e10016667.Google Scholar
Lee, A., Bishop, G., Hillman, K. M., Daffurn, K.. The medical emergency team. Anaesth Intensive Care 1995; 23: 183–186.Google Scholar
Buist, M., Bernard, S., Nguyen, T. V., et al. Association between clinically abnormal observations and subsequent in-hospital mortality: a prospective study. Resuscitation 2004; 62: 137–141.Google Scholar
Chan, P.S., Jain, R., Nallmothu, B.K., Berg, R.A., Sasson, C.. Rapid response teams: a systematic review and meta-analysis. Arch Intern Med 2010; 170: 18–26.Google Scholar
Chen, J., Bellomo, R., Flabouris, A., et al. The relationship between early emergency team calls and serious adverse events. Crit Care Med 2009; 37: 148–153.Google Scholar
Chen, J., Ou, L., Hillman, K.M., et al. Cardiopulmonary arrest and mortality trends and their association with rapid response system expansion. Med J Aust 2014; 201: 167–170.Google Scholar
Bellomo, R., Hillman, K., Flabouris, A., et al. Triggers for emergency team activation: a multicenter assessment. J Crit Care 2010; 25: e1–7.Google Scholar
Chen, J., Hillman, K., Bellomo, R., et al. The impact of introducing medical emergency team system on the documentation of vital signs. Resuscitation 2009; 80: 35–43.Google Scholar
Hillman, K., Chen, J., Cretikos, M., et al. Introduction of the medical emergency team (MET) system: a cluster-randomised controlled trial. Lancet 2005; 365: 2091–2097.Google Scholar
Pawar, P. Jones, P.V., van Beijnum, B.-J.F., Hermens, H.. A framework for the comparison of mobile patient monitoring systems. J Biomed Inform 2012 45: 544–556.Google Scholar
Esmonde, L., McDonnell, A., Ball, C., et al. Investigating the effectiveness of critical care outreach services: a systematic review. Intensive Care Med 2006; 32: 1713–1721.Google Scholar
Gerber, D.R.. Structural models for intermediate care areas: one size does not fit all. Crit Care Med 1999; 27: 2321–2322.Google Scholar
Angus, D.C., Barnato, A.E., Linde-Zwirble, W.T., et al. Use of intensive care at the end of life in the United States: an epidemiologic study. Crit Care Med 2004; 32: 638–643.Google Scholar
Jones, D., Bagshaw, S.M., Barrett, J., et al. The role of the medical emergency team in end-of-life care: a multicentre prospective observational study. Crit Care Med 2012; 40: 98–103.Google Scholar
Parr, M.J.A., Hadfield, J.H., Flabouris, A., Bishop, G., Hillman, K.. The Medical Emergency Team: 12 month analysis of reasons for activation, immediate outcome and not-for-resuscitation orders. Resuscitation 2001; 50: 39–44.Google Scholar
Flaatten, H.. Mental and physical disorders after ICU discharge. Curr Opin Crit Care 2010; 16: 510–551.Google Scholar
Herridge, M.S., Batt, J., Dos Santos, C.. ICU-acquired weakness, morbidity and death. Am J Resp Crit Care 2014; 190: 360–361.Google Scholar
References
Wise, J.. Part of hospitals’ funding will depend on patient satisfaction ratings from 2010–11. BMJ 2009; 339: b5451.Google Scholar
Rothen, H.U., Stricker, K.H., Heyland, D.K.. Family satisfaction with critical care: measurements and messages. Curr Opin Crit Care 2010; 16: 623–631.Google Scholar
Heyland, D.K., Rocker, G.M., Dodek, P.M..Family satisfaction with care in ICU: results of a multi-centre study. Crit Care Med 2002; 30: 1413–1418.Google Scholar
Molter, N.C.. Needs of relatives of critically ill patients: a descriptive study. Heart Lung 1979; 8: 332–339.Google Scholar
Latour, J.M., Hazelzet, A.J., van Heijden, A.J.. Parent satisfaction in paediatric intensive care: a critical appraisal of the literature. Pediat Crit Care Med 2005; 6: 578–584.Google Scholar
Azoulay, E., Chevret, S., Leleu, G., et al. Half the families of intensive care unit patients experience inadequate communication with physicians. Crit Care Med 2000; 28: 3044–3049.Google Scholar
Khalaila, R.. Meeting the needs of patients’ families in intensive care units. Nursing Standard 2014; 28 (43): 37–44.Google Scholar
Hinkle, L.J. Bosslet, G.T., Torke, A.M.. Factors associated with family satisfaction with end of life care in the ICU: a systematic review. Chest 2014; doi:10.1378/chest.14-1098.Google Scholar
Scheunemann, L.P., McDevitt, M., Carson, S.S., Hanson, L.C.. Randomized, controlled trials of interventions to improve communication in intensive care. Chest 2011; 139: 543–554.Google Scholar
Stricker, K.H., Kimberger, O., Schmidlin, K., Zwahken, M., Mohr, U, Rothen, H.U. Family satisfaction in the intensive care unit: what makes the difference? Intens Care Med 2009; 35: 2051–2059.Google Scholar
Hunziker, S., McHugh, W., Sarnoff-Lee, B., , S. et al. Predictors and correlates of dissatisfaction with intensive care. Crit Care Med 2012; 40: 1554–1561.Google Scholar
Wall, R.J., Curtis, J.R., Cooke, C.R.. Family satisfaction in ICU: differences between families of survivors and nonsurvivors. Chest 2007; 132: 1425–1433.Google Scholar
Schwarzkopf, D., Behrend, S., Skupkin, H. et al. Family satisfaction in the intensive care unit: a quantitative and qualitative analysis. Intensive Care Med. 2013; 39: 1671–1679.Google Scholar
Boev, C.. The relationship between nurses’ perception of work environment and patient satisfaction in adult critical care. J Nurs Sch 2012; 44: 368–375.Google Scholar
Sundararajan, K., Sullivan, T.S., Chapman, M.. Determinants of family satisfaction in the intensive care unit. Anaesth Intensive Care 2012; 40: 159–165.Google Scholar
Jongerdan, I.P., Slooter, A.J., Peelen, L.M., et al. Effect of intensive care environment on family and patient satisfaction: a before–after study. Intens Care Med 2013; 39: 1626–1634.Google Scholar
Stricker, K.H., Kimberger, O., Brunner, L., Rothen, H.U.. Patient satisfaction with care in the intensive care unit: can we rely on proxies? Acta Anaesthesiol Scand 2011; 55: 149–156.Google Scholar
Osborn, T.R., Curtis, J.R., Neilsen, E.L. Back, A.L., Shannon, S.E., Engleberg, R.A.. Identifying elements of ICU care that families report as important but unsatisfactory: decision-making, control and ICU atmosphere. Chest 2012; 142: 1185–1192.Google Scholar
Dhillon, A., Tardini, F., Bittner, E., Schmidt, U., Allain, R., Bigatello, L.. Benefit of using a ‘bundled’ consent for intensive care unit procedures as part of an early family meeting. J Crit Care 2014; 29: 919–922.Google Scholar
Higginson, I.J., Koffman, J., Hopkins, P., et al. Development and evaluation of the feasibility and effects on staff, patients, and families of a new tool, the Psychosocial Assessment and Communication Evaluation (PACE), to improve communication and palliative care in intensive care and during clinical uncertainty. BMC Medicine 2013; 11: 213.Google Scholar
References
Nouwen, MJ, Klijn, FA, van den Broek, BT, Slooter, AJ. Emotional consequences of intensive care unit delirium and delusional memories after intensive care unit admission: a systematic review. J Crit Care. 2012; 27(2): 199–211.Google Scholar
Ridley, S. Non-mortality outcome measures. In Outcomes in Critical Care. Oxford: Butterworth-Heinemann; 2002, 120–138.Google Scholar
van den Boogaard, M, Schoonhoven, L, Evers, AW, van der Hoeven, JG, van Achterberg, T, Pickkers, P. Delirium in critically ill patients: impact on long-term health-related quality of life and cognitive functioning. Crit Care Med. 2012; 40(1): 112–118.Google Scholar
Wade, DM, Howell, DC, Weinman, JA, et al. Investigating risk factors for psychological morbidity three months after intensive care: a prospective cohort study. Crit Care. 2012; 16(5): R192.Google Scholar
Dowdy, DW, Bienvenu, OJ, Dinglas, VD, et al. Are intensive care factors associated with depressive symptoms 6 months after acute lung injury? Crit Care Med. 2009; 37(5): 1702–1707.Google Scholar
Herridge, MS, Tansey, CM, Matte, A, et al. Functional disability 5 years after acute respiratory distress syndrome. NEJM. 2011; 364(14): 1293–1304.Google Scholar
Ulvik, A, Kvåle, R, Wentzel-larsen, T, Flaatten, H. Sexual function in ICU survivors more than 3 years after major trauma. Intensive Care Med. 2008; 34: 447–453.Google Scholar
Berkius, J, Engerstrom, L, Orwelius, L, et al. A prospective longitudinal multicentre study of health related quality of life in ICU survivors with COPD. Crit Care. 2013; 17(5): R211.Google Scholar
Bowling, A. Measuring Health: A Review of Quality of Life Measurement Scales. third edition. Milton Keynes: Open University Press, 2005.Google Scholar
Angus, DC, Carlet, J. Surviving intensive care: a report from the 2002 Brussels Roundtable. Intensive Care Med. 2003; 29(3): 368–377.Google Scholar
Niskanen, M, Kari, A, Halonen, P. Five-year survival after intensive care: comparison of 12,180 patients with the general population. Finnish ICU patients. Crit Care Med. 1996; 24: 1962–1967.Google Scholar
The EuroQol Group. EuroQol: a new facility for the measurement of health-related quality of life. Health Policy. 1990; 16: 199–208.Google Scholar
Ware, J, Snow, K, Kosinski, M, Gandek, B. SF-36 Health Survey: Manual and Interpretation Guide Boston, MA: The Health Institute, 1993.Google Scholar
Wehler, M, Geise, A, Hadzionerovic, D, et al. Health-related quality of life of patients with multiple organ dysfunction: individual changes and comparison with normative population. Crit Care Med. 2003; 31(4): 1094–1101.Google Scholar
Scales, DC, Tansey, CM, Matte, A, Herridge, MS. Difference in reported pre-morbid health-related quality of life between ARDS survivors and their substitute decision makers. Intensive Care Med. 2006; 32(11): 1826–1831.Google Scholar
Granja, C, Amaro, A, Dias, C, Costa-Pereira, A. Outcome of ICU survivors: a comprehensive review. The role of patient-reported outcome studies. Acta Anaesthesiol Scand. 2012; 56(9): 1092–1103.Google Scholar
Orwelius, L, Nordlund, A, Nordlund, P, et al. Pre-existing disease: the most important factor for health related quality of life long-term after critical illness: a prospective, longitudinal, multicentre trial. Crit Care. 2010; 14: R67.Google Scholar
Orwelius, L, Willebrand, M, Gerdin, B, Ekselius, L, Fredrikson, M, Sjoberg, F. Long term health-related quality of life after burns is strongly dependent on pre-existing disease and psychosocial issues and less due to the burn itself. Burns. 2013; 39(2): 229–235.Google Scholar
References
Lighthall, GK, Poon, T, Harrison, TK. Using in situ simulation to improve in-hospital cardiopulmonary resuscitation. Jt Comm J Qual Patient Saf 2010; 36: 209–216.Google Scholar
Walker, ST, Sevdalis, N, McKay, A, et al. Unannounced in situ simulations: integrating training and clinical practice. BMJ Qual Saf 2013; 22: 453–458.Google Scholar
Patterson, MD, Blike, GT, Madkarni, VM. In situ simulation: challenges and results. In: Henriksen, K, Battles, JB, Keyes, MA, et al. eds. Advances in Patient Safety: New Directions and Alternative Approaches, Vol. 3: Performance and Tools). Rockville, MD: Agency for Healthcare Research and Quality (US), 2008. www.ncbi.nlm.nih.gov/books/NBK43682/ (accessed 11 September 2014).Google Scholar
Geis, GL, Pio, B, Pendergrass, TL, et al. Simulation to assess the safety of new healthcare teams and new facilities. Simul Healthc 2011; 6: 125–133.Google Scholar
Meurling, L, Hedman, L, Sandahl, C, et al. Systematic simulation-based team training in a Swedish intensive care unit: a diverse response among critical care professions. BMJ Qual Saf 2013; 22: 485–494.Google Scholar
Allan, CK, Thiagarajan, RR, Beke, D, et al. Simulation-based training delivered directly to the pediatric cardiac intensive care unit engenders preparedness, comfort, and decreased anxiety among multidisciplinary resuscitation teams. J Thorac Cardiovasc Surg 2010; 140: 646–652.Google Scholar
Hansen, SS, Arafeh, J. Implementing and sustaining in situ drills to improve multidisciplinary health care training. J Obstet Gynecol Neonatal Nurs 2012; 41: 559–571.Google Scholar
Reader, TW, Flin, R, Cuthbertson, BH. Communication skills and error in the intensive care unit. Curr Opin Crit Care 2007; 13: 732–736.Google Scholar
Wayne, D, Didwania, A, Feinglass, J, et al. Simulation-based education improves quality of care during cardiac arrest team responses at an academic teaching hospital. Chest 2008; 133: 56–61.Google Scholar
McGaghie, WC, Issenberg, SB, Cohen, MER, et al. Does simulation-based medical education with deliberate practice yield better results than traditional clinical education? A meta-analytic comparative review of the evidence. Acad Med 2011; 86: 706.Google Scholar
Cook, DA, Hatala, R, Brydges, R, et al. Technology-enhanced simulation for health professions education. JAMA 2011; 306: 978–988.Google Scholar
Flin, R, Maran, N. Identifying and training non-technical skills for teams in acute medicine. Qual Saf Health Care 2004; 13: 80–84.Google Scholar
Kohn, LT, Corrigan, JM, Donaldson, MS. To Err is Human: Building a Safer Health System. Washington, DC: Institute of Medicine, National Academy Press, 1999.Google Scholar
Salas, E, Paige, JT, Rosen, MA. Creating new realities in healthcare: the status of simulation based training as a patient safety improvement strategy. BMJ Qual Saf 2013; 22: 449–452.Google Scholar
Patterson, MD, Geis, GL, Falcone, RA, et al. In situ simulation: detection of safety threats and teamwork training in a high risk emergency department. BMJ Qual Saf 2013; 22: 468–477.Google Scholar
Arora, S, Cox, C, Davies, S, et al. Towards the next frontier for simulation-based training: full-hospital simulation across the entire patient pathway. Ann Surg 2013; 260: 252–258.Google Scholar
Langley, JG, Nolan, KM, Nolan, TW, et al. The Improvement Guide: A Practical Approach to Enhancing Organizational Performance. New York: Jossey-Bass, 1996.Google Scholar
Grol, R. Successes and failures in the implementation of evidence-based guidelines for clinical practice. Med Care 2001; 39: 46–54.Google Scholar
Weinstock, PH, Kappus, LJ, Garden, A, Burns, JP.Simulation at the point of care: reduced-cost, in situ training via a mobile cart. Pediat Crit Care Med 2009; 10: 176–181.Google Scholar