Intensive care unit providers more quickly and accurately assess risk of multiple harms using an engineered safety display

Project Emerge took a systems engineering approach to reduce avoidable harm in the intensive care unit. We developed a socio-technology solution to aggregate and display information relevant to preventable patient harm. We compared providers’ efficiency and ability to assess and assimilate data associated with patient-safety practice compliance using the existing electronic health record to Emerge, and evaluated for speed, accuracy, and the number of mouse clicks required. When compared to the standard electronic health record, clinicians were faster (529 ± 210 s vs 1132 ± 344 s), required fewer mouse clicks (42.3 ± 15.3 vs 101.3 ± 33.9), and were more accurate (24.8 ± 2.7 of 28 correct vs 21.2 ± 2.9 of 28 correct) when using Emerge. All results were statistically significant at a p-value < 0.05 using Wilcoxon signed-rank test (n = 18). Emerge has the potential to make clinicians more productive and patients safer by reducing the time and errors when obtaining information to reduce preventable harm.

[1]  M. Makary,et al.  Medical error—the third leading cause of death in the US , 2016, British Medical Journal.

[2]  D. Schoenfeld,et al.  Ventilation with lower tidal volumes as compared with traditional tidal volumes for acute lung injury and the acute respiratory distress syndrome. , 2000, The New England journal of medicine.

[3]  N. Cowan The magical number 4 in short-term memory: A reconsideration of mental storage capacity , 2001, Behavioral and Brain Sciences.

[4]  Robert H. Brook,et al.  Factors Affecting Physician Professional Satisfaction , 2013 .

[5]  Y. Donchin,et al.  Patient safety in intensive care medicine: the Declaration of Vienna , 2009, Intensive Care Medicine.

[6]  C. Anandan,et al.  The Impact of eHealth on the Quality and Safety of Health Care: A Systematic Overview , 2011, PLoS medicine.

[7]  B. Hales,et al.  A multifaceted intervention for quality improvement in a network of intensive care units: a cluster randomized trial. , 2011, JAMA.

[8]  C. Holzmueller,et al.  A multicenter, phased, cluster-randomized controlled trial to reduce central line-associated bloodstream infections in intensive care units* , 2012, Critical care medicine.

[9]  Todd Dorman,et al.  Building safety into ICU care. , 2002, Journal of critical care.

[10]  P. Pronovost,et al.  Quality Management in Intensive Care: Use of checklists , 2016 .

[11]  Farah Magrabi,et al.  Problems with health information technology and their effects on care delivery and patient outcomes: a systematic review , 2017, J. Am. Medical Informatics Assoc..

[12]  S. Asch,et al.  Sins of omission , 2005, Journal of General Internal Medicine.

[13]  P. Pronovost,et al.  Preventing patient harms through systems of care. , 2012, JAMA.

[14]  Peter J Pronovost,et al.  Enhancing the quality of care in the intensive care unit: a systems engineering approach. , 2013, Critical care clinics.

[15]  Peter J. Pronovost,et al.  Developing a Comprehensive Model of Intensive Care Unit Processes: Concept of Operations , 2015, Journal of patient safety.

[16]  P. Pronovost,et al.  Lung protective mechanical ventilation and two year survival in patients with acute lung injury: prospective cohort study , 2012, BMJ : British Medical Journal.

[17]  A. Jamal,et al.  The Impact of Health Information Technology on the Quality of Medical and Health Care: A Systematic Review , 2009, Health information management : journal of the Health Information Management Association of Australia.

[18]  Mica R. Endsley,et al.  Toward a Theory of Situation Awareness in Dynamic Systems , 1995, Hum. Factors.

[19]  Peter Griffiths,et al.  ‘Care left undone’ during nursing shifts: associations with workload and perceived quality of care , 2013, BMJ quality & safety.

[20]  P. Pronovost,et al.  Sustaining Reductions in Central Line–Associated Bloodstream Infections in Michigan Intensive Care Units , 2016, American journal of medical quality : the official journal of the American College of Medical Quality.

[21]  Christine A. Sinsky,et al.  Relationship Between Clerical Burden and Characteristics of the Electronic Environment With Physician Burnout and Professional Satisfaction. , 2016, Mayo Clinic proceedings.

[22]  Michael A. Barnes,et al.  Comparison of accuracy of physical examination findings in initial progress notes between paper charts and a newly implemented electronic health record , 2017, J. Am. Medical Informatics Assoc..

[23]  Ann Hendrich,et al.  A 36-hospital time and motion study: how do medical-surgical nurses spend their time? , 2008, The Permanente journal.

[24]  William W. Stead,et al.  Assessing Data Quality: From Concordance, through Correctness and Completeness, to Valid Manipulatable Representations , 2000, J. Am. Medical Informatics Assoc..

[25]  Ashish K. Jha,et al.  Patient Engagement Remain Low In Office Settings Despite Substantial Progress In EHR Adoption , Health Information Exchange And , 2014 .

[26]  P. Pronovost,et al.  An intervention to decrease catheter-related bloodstream infections in the ICU. , 2006, The New England journal of medicine.

[27]  Marie T. Egan,et al.  Clinical Dashboards: Impact on Workflow, Care Quality, and Patient Safety , 2006, Critical care nursing quarterly.