WOMBAT case studies

WOMBAT case studies

The use of WOMBAT, as a reliable method for quantifying the work and communication patterns of health professionals, continues to grow. WOMBAT has been used by research teams from several countries including: Australia, United States, United Kingdom, Canada, New Zealand, South Africa, Italy, Finland, Sweden and Norway.

As the flexibility of the WOMBAT tool allows researchers to devise their own work task classification template, it can be used to undertake observational studies of different health professionals in different settings. Examples of published studies include:

  • examining hospital ward nurses’ time in medication related tasks [1]
  • assessing hospital doctors’ and nurses’ patterns of work and communication [2, 3] and measuring the impact of health information technologies [4-6]
  • quantifying how and with who doctors on hospital wards spend their time [7]
  • examining intensive care unit nurse workflow during shift change [8]
  • quantifying the work patterns of doctors in intensive care units [9-12]
  • assessing the rate of interruptions and multitasking by intensive care doctors and nurses [13-15]
  • measuring the work patterns of hospital pharmacists’ [16] and the impact of electronic medication management systems [17, 18]
  • quantifying junior doctors’ work practices after hours and on weekends [19, 20]
  • evaluating the impact of a drug monitoring system on nurses’ work in ambulatory care [21]
  • assessing interruptions and multitasking by doctors in emergency departments [22-24]
  • examining work patterns and the use of electronic health records in ambulatory care [25]

Alternatively, research groups can elect to use the same work task classification template as applied in a previous study to allow findings from different studies to be compared.

Below are two recent case study examples using WOMBAT: one where a new work task classification template was devised to look at physician time in ambulatory care settings in the U.S.; and one where a research team in Australia used an existing work task classification template (previously applied in studies looking at doctors on wards and in emergency department) to examine the work of intensive care doctors.

Case study – physician time in ambulatory care

Following the results of a large survey [26], which revealed signs of burnout and growing dissatisfaction with work-life balance amongst U.S. physicians, the American Medical Association were interested in quantifying how physicians in ambulatory care spend their time. In collaboration with the Dartmouth-Hitchcock Medical Center and the Centre for Health Systems and Safety Research, Macquarie University, the researchers undertook a WOMBAT study of 57 physicians working in 4 specialties (family medicine, internal medicine, cardiology, and orthopaedics) across 4 states (Illinois, New Hampshire, Virginia, and Washington).WOMBAT data collector

A physician work task classification for ambulatory care was devised, which included 12 broad work task categories grouped under four key activities: direct clinical face time; electronic health record (EHR) and desk work; administrative tasks; and other tasks. Ten medical students were trained regarding the classification definitions and how to use WOMBAT. The students undertook 430 hours of observation of ambulatory physicians’ work.

The findings showed thatduring office hours, ambulatory care physicians spent nearly half their time on EHR and desk work activities and less than one third on direct clinical face time with patients; in other words, for every hour of direct clinical face time with patients, physicians spent almost 2 hours on EHR and desk work. Such findings provide critical information in characterising the ambulatory care domain regarding what work is done and for how long. Using WOMBAT allowed the researchers to describe ambulatory physicians’ time distribution objectively, capturing work and interactions both with and without electronic devices, and provided a broader view of the role and use of the EHR in the ambulatory environment.

The study was published in Annals of Internal Medicine

Case study – work patterns of intensive care unit doctors

Work Patterns of Intensive Care Unit Doctors

The intensive care unit (ICU) is known to be a complex environment, where clinicians care for some of the most acutely ill patients in hospital. Decision making in intensive care is also multifaceted and involves collaboration between ICU doctors, medical teams from other specialties, as well as multipleinformation elements including flow charts, patient notes, medication charts, vital signs, test results, and images. The aim of this study was to measure how ICU doctors they spend their time, what information resources they use to assist their work, with whom they work, and how often they multitaskor are interrupted. A secondary aim was to compare the work patterns of ICU doctors with those of doctors on general wards and in emergency departments from previously published WOMBAT studies.WOMBAT in use

The study was conducted in two ICUs at two major teaching hospitals in Sydney and involved 26 doctors. Two observers used an existing WOMBAT work task classification template to record over 160 hours of observation. The findings showed that ICU doctors spent 69% of time working at patients’ bedsides, 50% of time in professional communication, and 39% of time accessing information resources. Over half (54%) of their time was spent with other ICU doctors and 29% with nurses. ICU doctors had a high multitasking rate (40 times per hour) and were interrupted 4 times per hour.

Compared with doctors on general wards and emergency departments, ICU doctors spent more time in professional communication, more time with patients, and more time with nurses. ICU doctors were also more likely to multitask. By using a work task classification template used in previous WOMBAT studies, the researchers were able to demonstrate how ICU doctors manage their time and work demands compared with their colleagues on general wards and in emergency departments. Using WOMBAT allowed the researchers to quantify the more complex environment of the ICU, which necessitates high levels of multitasking and interdisciplinary collaboration to care for acutely ill patients.

The study was published in Critical Care and Resuscitation.

References

  1. Ampt A, Westbrook JI. Measuring nurses' time in medication related tasks prior to the implementation of an electronic medication management system. Studies in Health Technology and Informatics. 2007;130:157–67.
  2. Westbrook JI, Ampt A. Design, application and testing of the Work Observation Method by Activity Timing (WOMBAT) to measure clinicians' patterns of work and communication. International Journal of Medical Informatics. 2009;78 Suppl 1:S25–33.
  3. Westbrook J, Duffield C, Li L, et al. How much time do nurses have for patients? A longitudinal study quantifying hospital nurses' patterns of task time distribution and interactions with health professionals. BMC Health Services Research. 2011;11:319.
  4. Westbrook JI, Ampt A, Williamson M, et al. Methods for measuring the impact of health information technologies on clinicians' patterns of work and communication. Proceedings of the 12th World Congress on Health (Medical) Informatics: Building Sustainable Health Systems. 2007;1083–1087.
  5. Westbrook JI, Creswick NJ, Duffield C, et al. Changes in nurses’ work associated with computerised information systems: opportunities for international comparative studies using the revised Work Observation Method By Activity Timing (WOMBAT). Proceedings of the 11th International Congress on Nursing Informatics. 2012;448.
  6. Westbrook JI, Li L, Georgiou A, et al. Impact of an electronic medication management system on hospital doctors’ and nurses’ work: a controlled pre–post, time and motion study. Journal of the American Medical Informatics Association. 2013;20:1150–8.
  7. Westbrook JI, Ampt A, Kearney L, et al. All in a day's work: an observational study to quantify how and with whom doctors on hospital wards spend their time. The Medical Journal of Australia. 2008;188:506–9.
  8. Shaw NT, Ballermann MA, Hagtvedt R, et al. Intensive care unit nurse workflow during shift change prior to the introduction of a critical care clinical information system. eJounral of Health Informatics. 2011;6:5.
  9. Hefter Y, Madahar P, Eisen LA, et al. Relationship of ICU strain factors and allocation of physician time in the ICU. Proceedings of the American Thoracic Society 2015 International Conference: Optimizing Limited ICU Resources. 2015;A5233.
  10. Yosefa H, Purnema M, Lewis AE, et al. A time motion study to describe workflow of attendings and residents in medical and surgical ICUs. Proceedings of the American Thoracic Society 2015 International Conference: High Impact Trials in Critical Care. 2015;A5126.
  11. Hefter Y, Madahar P, Eisen LA, et al. A time-motion study of ICU workflow and the impact of strain. Critical Care Medicine. 2016;44:1482–9.
  12. Li L, Hains I, Hordern T, et al. What do ICU doctors do?: A multisite time and motion study of the clinical work patterns of registrars. Critical Care and Resuscitation. 2015;17:159.
  13. Ballermann M, Shaw N, Mayes D, et al. Intensive care unit health care providers spend less time multitasking after the introduction of a critical care clinical information system. Proceedings of the 18th Annual Health Informatics Conference: Informing the Business of Healthcare. 2010;6.
  14. Ballermann MA, Shaw NT, Arbeau KJ, et al. Impact of a critical care clinical information system on interruption rates during intensive care nurse and physician documentation tasks. Studies in Health Technology and Informatics. 2010;160:274–8.
  15. Ballermann M, Shaw NT, Mayes DC, et al. Impact of a clinical information system on multitasking in two intensive care units.eJournal of Health Informatics. 2011;7:2.
  16. Lehnbom E, Li L, Prgomet M, et al. Little things matter: a time and motion study of pharmacists' activities in a paediatric hospital. Digital Health Innovation for Consumers, Clinicians, Connectivity and Community: Selected Papers from the 24th Anustralian National Health Informatics Conference. 2016;227:80.
  17. Lo C, Burke R, Westbrook JI. Electronic medication management systems' influence on hospital pharmacists' work patterns. Journal of Pharmacy Practice and Research. 2010;40:106–10.
  18. Schofield B, Cresswel K, Westbrook J, et al. The impact of electronic prescribing systems on pharmacists’ time and workflow: protocol for a time-and-motion study in English NHS hospitals. BMJ Open. 2015;5:e008785.
  19. Arabadzhiyska PN, Baysari MT, Walter S, et al. Shedding light on junior doctors' work practices after hours. Internal Medicine Journal. 2013;43:1321–6.
  20. Richardson LC, Lehnbom EC, Baysari MT, et al. A time and motion study of junior doctor work patterns on the weekend: a potential contributor to the weekend effect? Internal Medicine Journal. 2016;46:819–25.
  21. Callen J, Hordern A, Gibson K, et al. Can technology change the work of nurses? Evaluation of a drug monitoring system for ambulatory chronic disease patients. International Journal of Medical Informatics. 2013;82:159–67.
  22. Walter SR, Li L, Dunsmuir WTM, et al. Managing competing demands through task-switching and multitasking: a multi-setting observational study of 200 clinicians over 1000 hours. BMJ Quality and Safety. 2014;23:231–41.
  23. Westbrook JI, Coiera E, Dunsmuir WTM, et al. The impact of interruptions on clinical task completion. Quality and Safety in Health Care. 2010;19:284–9.
  24. Raban MZ, Walter SR, Douglas HE, et al. Measuring the relationship between interruptions, multitasking and prescribing errors in an emergency department: a study protocol. BMJ Open. 2015;5:e009076.
  25. Sinsky C, Colligan L, Li L, et al. Allocation of physician time in ambulatory practice: a time and motion study in 4 specialties. Annals of Internal Medicine. 2016;165:753–760.
  26. Shanafelt TD, Boone S, Tan L, et al. Burnout and satisfaction with work-life balance among US physicians relative to the general US population. Archives of Internal Medicine. 2012;172:1377–1385.

Content owner: Australian Institute of Health Innovation Last updated: 07 Sep 2018 2:48pm

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