David Roberson1, Michael Connell2, Shay Dillis3, Kimberlee Gauvreau4, Rebecca Gore5, Elaina Heagerty6, Kathy Jenkins7, Lin Ma8, Amy Maurer9, Jessica Stephenson10, Margot Schwartz11. 1. Senior Associate in the Department of Otolaryngology at Boston Children's Hospital and Associate Professor in the Department of Otology and Laryngology at Harvard Medical School in MA. david.roberson@childrens.harvard.edu. 2. Cognitive Psychology Consultant at the Institute for Knowledge Design, LLC, in Arlington, MA. mikeroe@msn.com. 3. Transplant Coordinator in the Department of Cardiology at Boston Children's Hospital in MA. shay.dillis@cardio.chboston.org. 4. Biostatistician in the Department of Cardiology at Boston Children's Hospital in MA. kimberlee.gauvreau@cardio.chboston.org. 5. Administrative Assistant III in the Department of Otolaryngology and Communication Enhancement at Boston Children's Hospital in MA. rebecca.gore@childrens.harvard.edu. 6. Program Manager in Quality Improvement for the United Hospital Fund in New York, NY. elaina1026@gmail.com. 7. Senior Vice President and Chief Safety and Quality Officer for the Department of Patient Safety and Quality and the Department of Cardiology for Boston Children's Hospital in MA. kathy.jenkins@cardio.chboston.org. 8. Biostatistician in the Departments of Clinical Science, Epidemiology, and Research for Fresenuis Medical Care North America in Waltham, MA. linma424@yahoo.com. 9. Program Manager for the Division of Interventional Cardiology at Boston Scientific Corporation in Natick, MA. amy.britt@bsci.com. 10. Medical Student at Tufts University School of Medicine in Boston, MA. stephenson.jessica@gmail.com. 11. Public Health Analyst for the Program for Health Care Quality and Outcomes at RTI International. mschwartz@rti.org.
Abstract
CONTEXT: Patients in tertiary care hospitals are more complex than in the past, but the implications of this are poorly understood as "patient complexity" has been difficult to quantify. OBJECTIVE: We developed a tool, the Complexity Ruler, to quantify the amount of data (as bits) in the patient’s medical record. We designated the amount of data in the medical record as the cognitive complexity of the medical record (CCMR). We hypothesized that CCMR is a useful surrogate for true patient complexity and that higher CCMR correlates with risk of major adverse events. DESIGN: The Complexity Ruler was validated by comparing the measured CCMR with physician rankings of patient complexity on specific inpatient services. It was tested in a case-control model of all patients with major adverse events at a tertiary care pediatric hospital from 2005 to 2006. MAIN OUTCOME MEASURES: The main outcome measure was an externally reported major adverse event. We measured CCMR for 24 hours before the event, and we estimated lifetime CCMR. RESULTS: Above empirically derived cutoffs, 24-hour and lifetime CCMR were risk factors for major adverse events (odds ratios, 5.3 and 6.5, respectively). In a multivariate analysis, CCMR alone was essentially as predictive of risk as a model that started with 30-plus clinical factors. CONCLUSIONS: CCMR correlates with physician assessment of complexity and risk of adverse events. We hypothesize that increased CCMR increases the risk of physician cognitive overload. An automated version of the Complexity Ruler could allow identification of at-risk patients in real time.
CONTEXT: Patients in tertiary care hospitals are more complex than in the past, but the implications of this are poorly understood as "patient complexity" has been difficult to quantify. OBJECTIVE: We developed a tool, the Complexity Ruler, to quantify the amount of data (as bits) in the patient’s medical record. We designated the amount of data in the medical record as the cognitive complexity of the medical record (CCMR). We hypothesized that CCMR is a useful surrogate for true patient complexity and that higher CCMR correlates with risk of major adverse events. DESIGN: The Complexity Ruler was validated by comparing the measured CCMR with physician rankings of patient complexity on specific inpatient services. It was tested in a case-control model of all patients with major adverse events at a tertiary care pediatric hospital from 2005 to 2006. MAIN OUTCOME MEASURES: The main outcome measure was an externally reported major adverse event. We measured CCMR for 24 hours before the event, and we estimated lifetime CCMR. RESULTS: Above empirically derived cutoffs, 24-hour and lifetime CCMR were risk factors for major adverse events (odds ratios, 5.3 and 6.5, respectively). In a multivariate analysis, CCMR alone was essentially as predictive of risk as a model that started with 30-plus clinical factors. CONCLUSIONS: CCMR correlates with physician assessment of complexity and risk of adverse events. We hypothesize that increased CCMR increases the risk of physician cognitive overload. An automated version of the Complexity Ruler could allow identification of at-risk patients in real time.
Authors: Jeffrey M Rothschild; Christopher P Landrigan; John W Cronin; Rainu Kaushal; Steven W Lockley; Elisabeth Burdick; Peter H Stone; Craig M Lilly; Joel T Katz; Charles A Czeisler; David W Bates Journal: Crit Care Med Date: 2005-08 Impact factor: 7.598
Authors: Tamara D Simon; Jay Berry; Chris Feudtner; Bryan L Stone; Xiaoming Sheng; Susan L Bratton; J Michael Dean; Rajendu Srivastava Journal: Pediatrics Date: 2010-09-20 Impact factor: 7.124
Authors: Katherine H Burns; Patrick H Casey; Robert E Lyle; T Mac Bird; Jill J Fussell; James M Robbins Journal: Pediatrics Date: 2010-09-20 Impact factor: 7.124