Ying Wang1, Enrico Coiera2, Blanca Gallego2, Oscar Perez Concha2, Mei-Sing Ong3, Guy Tsafnat2, David Roffe4, Graham Jones5, Farah Magrabi2. 1. Centre for Health Informatics, Australian Institute of Health Innovation, Faculty of Medicine and Health Sciences, Macquarie University, Sydney, Australia. Electronic address: ying.wang@mq.edu.au. 2. Centre for Health Informatics, Australian Institute of Health Innovation, Faculty of Medicine and Health Sciences, Macquarie University, Sydney, Australia. 3. Centre for Health Informatics, Australian Institute of Health Innovation, Faculty of Medicine and Health Sciences, Macquarie University, Sydney, Australia; Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, United States. 4. Information Technology Service Centre, St Vincent's Hospital, Sydney, Australia. 5. Department of Chemical Pathology, SydPath, St Vincent's Hospital, Sydney, Australia; Faculty of Medicine, The University of New South Wales, Sydney Australia.
Abstract
OBJECTIVE: To introduce and evaluate a method that uses electronic medical record (EMR) data to measure the effects of computer system downtime on clinical processes associated with pathology testing and results reporting. MATERIALS AND METHODS: A matched case-control design was used to examine the effects of five downtime events over 11-months, ranging from 5 to 300min. Four indicator tests representing different laboratory workflows were selected to measure delays and errors: potassium, haemoglobon, troponin and activated partial thromboplastin time. Tests exposed to a downtime were matched to tests during unaffected control periods by test type, time of day and day of week. Measures included clinician read time (CRT), laboratory turnaround time (LTAT), and rates of missed reads, futile searches, duplicate orders, and missing test results. RESULTS: The effects of downtime varied with the type of IT problem. When clinicians could not logon to a results reporting system for 17-min, the CRT for potassium and haemoglobon tests was five (10.3 vs. 2.0days) and six times (13.4 vs. 2.1days) longer than control (p=0.01-0.04; p=0.0001-0.003). Clinician follow-up of tests was also delayed by another downtime involving a power outage with a small effect. In contrast, laboratory processing of troponin tests was unaffected by network services and routing problems. Errors including missed reads, futile searches, duplicate orders and missing test results could not be examined because the sample size of affected tests was not sufficient for statistical testing. CONCLUSION: This study demonstrates the feasibility of using routinely collected EMR data with a matched case-control design to measure the effects of downtime on clinical processes. Even brief system downtimes may impact patient care. The methodology has potential to be applied to other clinical processes with established workflows where tasks are pre-defined such as medications management.
OBJECTIVE: To introduce and evaluate a method that uses electronic medical record (EMR) data to measure the effects of computer system downtime on clinical processes associated with pathology testing and results reporting. MATERIALS AND METHODS: A matched case-control design was used to examine the effects of five downtime events over 11-months, ranging from 5 to 300min. Four indicator tests representing different laboratory workflows were selected to measure delays and errors: potassium, haemoglobon, troponin and activated partial thromboplastin time. Tests exposed to a downtime were matched to tests during unaffected control periods by test type, time of day and day of week. Measures included clinician read time (CRT), laboratory turnaround time (LTAT), and rates of missed reads, futile searches, duplicate orders, and missing test results. RESULTS: The effects of downtime varied with the type of IT problem. When clinicians could not logon to a results reporting system for 17-min, the CRT for potassium and haemoglobon tests was five (10.3 vs. 2.0days) and six times (13.4 vs. 2.1days) longer than control (p=0.01-0.04; p=0.0001-0.003). Clinician follow-up of tests was also delayed by another downtime involving a power outage with a small effect. In contrast, laboratory processing of troponin tests was unaffected by network services and routing problems. Errors including missed reads, futile searches, duplicate orders and missing test results could not be examined because the sample size of affected tests was not sufficient for statistical testing. CONCLUSION: This study demonstrates the feasibility of using routinely collected EMR data with a matched case-control design to measure the effects of downtime on clinical processes. Even brief system downtimes may impact patient care. The methodology has potential to be applied to other clinical processes with established workflows where tasks are pre-defined such as medications management.
Authors: Dean F Sittig; Adam Wright; Enrico Coiera; Farah Magrabi; Raj Ratwani; David W Bates; Hardeep Singh Journal: Health Informatics J Date: 2018-12-11 Impact factor: 2.681