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Prof. Johanna Westbrook, Director, Centre for Health Systems and Safety Research - How Doctors’ and Nurses’ Patterns of Work and Communication Change Following the Introduction of an Electronic Medication Management System

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Johanna Westbrook delivered this presentation at the 3rd Annual Electronic Medication Management Conference 2014. This conference is the nation’s only event to look solely at electronic prescribing …

Johanna Westbrook delivered this presentation at the 3rd Annual Electronic Medication Management Conference 2014. This conference is the nation’s only event to look solely at electronic prescribing and electronic medication management systems.

For more information, please visit http://www.healthcareconferences.com.au/emed14

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  • 1. Centre for Health Systems and Safety Research Professor Johanna Westbrook Centre for Health Systems & Safety Research How doctors’ and nurses’ patterns of work and communication change following eMMS
  • 2. Centre for Health Systems & Safety Research To produce world-class evidence which informs policy and practice, focusing on patient safety and the evaluation of information and communication technologies (ICT) in the health sector
  • 3. Effectiveness Impact of eMMS on medication error rates? Efficiency How do eMMS impact work of health professionals? Why should we care about study design? What are some of the measurement challenges and how can we design useful evaluation studies? Outline
  • 4. Results – Post eMMS Prescribing errors declined by > 50% from: 406 (95% CI 374-437) 185 (95% CI 160-210) per 100 admissions; p<0.0001 No significant change in errors on control wards Greatest reduction from orders which were illegible, incomplete or illegal National inpatient medication chart 5-10%
  • 5. Serious prescribing errors Intervention wards – significant 44% (p=0.0002) reduction in serious prescribing error rate 25/100 admissions 14/100 admissions (95%CI 21-29) (95%CI 10-18) No significant change on the control wards (p=0.4)
  • 6. MAE Post eMMS Across all clinical error categories - a significant reduction on the intervention wards of 4.24 clinical errors/100 administrations (95%CI: 0.15-8.32, p=0.04) compared to control wards. Wrong timing errors had the greatest decline by 3.35 /100 administrations (95%CI: 0.01-6.69, p<0.05) compared with control wards.
  • 7. Change in serious medication administration errors Significant reduction in serious (ie potential ADEs) MAEs on the intervention wards compared to the control wards 4.20% 1.83% (95%CI 3.25, 5.15%) (95%CI 1.20, 2.46%) Pre Post
  • 8. This eMMS is going to take too long
  • 9. Systems are promoted for their ability to improve work efficiency and safety - Less time on administrative tasks - More time for patient care
  • 10. Research Evidence Qualitative accounts - both significantly hinders and assists work efficiency Quantitative evidence is sparse Most studies on doctors’ work in ambulatory care and critical care Know little about the impact on nurses on general wards No Australian studies
  • 11. Use of an eMMS
  • 12. Aim: To measure changes in how nurses and doctors distributed their time across work tasks pre and post eMMS Changes in time spent on: Medication tasks Direct Care Professional Communication
  • 13. Controlled Pre and Post Study Control Control Intervention Intervention Control Control eMMS eMMS 4 Wards Pre Year 1 4 Wards Post Year 3
  • 14. Direct Observations Nurses & Doctors 70 nurses observed for 276.9 hours 59 doctors observed for 356.3 hours
  • 15. Work Observation Method By Activity Timing - Where? With what? With whom? What task? Interruptions
  • 16. Results Did nurses/doctors on the eMMS wards spend more or less time on direct care, medication tasks and professional communication compared to colleagues on the control wards?
  • 17. No Significant Differences Nurses Comparison of time distribution control and eMMS Wards % Time P Value Direct Care Control 22.1 0.23 eMMS 26.1 Medication Control 23.7 0.28 eMMS 22.6 Prof Comm. Control 20.1 0.57 eMMS 22.8
  • 18. Doctors Comparison of time distribution control and eMMS Wards % time P Value Direct Care Control 19.7 0.08 eMMS 25.7 Medication Control 7.4 0.4 eMMS 8.5 Prof Comm. Control 36.6 0.8 eMMS 37.6 No Significant Differences
  • 19. Time Nurses Spent with Others Baseline 33% of nurses time is spent with patients 50% spent with other nurses 5% with doctors 4% with Relatives
  • 20. Changes Following eMMS  Nurses on the eMMS wards spent less time with doctors (p=0.0001).  4.2% less time than nurses on the control wards.  Due to both fewer interactions (tasks per hour) and shorter interactions (mean task time).
  • 21. Time Doctors Spent with Others Baseline 18% of doctors’ time is spent with patients 63% spent with other doctors 10% with nurses 4% with relatives
  • 22. Changes Following eMMS for Doctors  Doctors on the eMMS wards spent more time with other doctors (p=0.003).  6% more time than doctors on the control wards.  Doctors spent more time with patients (p=.009)  6% more time than doctors on the control wards.
  • 23. Available at JAMIA.BMJ.Com
  • 24. Why should we care about study design? Before and after Versus Controlled before and after
  • 25. Controlled Pre and Post Study Control Control Intervention Intervention Control Control eMMS eMMS 4 Wards Pre Year 1 4 Wards Post Year 3 Before After
  • 26. Differences in controlled versus uncontrolled studies Control Control Intervention Intervention Control Control eMMS eMMS 4 Wards Pre Year 1 4 Wards Post Year 3 Comparison over time Comparison with & without eMMS taking account of how both have changed from baseline
  • 27. How did nurses’s work change from year 1 (before) to year 3 (after)? Nurses now spending significantly more time on medication tasks (p=0.001) 20.2% year 1 23.1% year 3  Nurses now spending more time on direct care (p=0.003) 20.2% year 1 24.2% year 3  These changes were experienced by all nurses regardless of the eMMS
  • 28. Looking for the expected and unexpected Selective attention
  • 29. The Gorilla Strikes Again! – Drew and colleagues presented 24 radiologists with typical lung cancer screening CT scans
  • 30. 20/24 radiologists (83%) missed the gorilla 25 non-trained reviewers all missed the gorilla “It’s important to be willing to look for more than one thing, to set yourself up for success.” Drew et al Psychol Science 2013
  • 31. Context Matters Understanding how systems impact work will be influenced by context Example: Decision support When, who and how it will impact
  • 32. What impact does decision support have during ward rounds?
  • 33. Study – Teaching Hospital 58.5 hours direct observation of 14 teams on ward rounds 48% of medication orders triggered alerts 17% of alerts were read No prescriber read the entire content of an alert. No prescriptions were changed
  • 34. Senior clinicians during ward-rounds are the decision-makers but do not receive the alerts No junior doctor was observed questioning a senior doctor’s decision following the triggering of an alert JAMIA, 2011
  • 35. Junior doctors at night 16:30-22:30 Observational study - 65 hours 78% of those alerts were read ≈ 50% read completely 5% resulted in a change in prescribing
  • 36. 2 hospital wards Ward A 47 staff Ward B 54 staff How often do you seek advice from this person about medication decisions/tasks?
  • 37. • Each shape is a staff member • Each line is a medication advice seeking connection • Arrow indicate the direction of the advice sought • Networks of medication advice seeking at least weekly ie they sought medication advice weekly or more frequently
  • 38. Junior Drs are hubs of medication information
  • 39. Senior Drs were isolates
  • 40. Prescribing error rates 19.4 / 100 patient days Sample of 240 admissions 9.0/100 patient days Sample of 428 admissions
  • 41. Conclusions regarding impact of eMMS on work  eMMS not associated with significant redistribution of time  Some interactions change - implications of these for safety and quality should be investigated  On wards with eMMS there were significant reductions in both prescribing and medication administration errors  Only one aspect of work patterns – ie time distribution  eMMS will influence work practice and workflow in a multitude of expected and unexpected ways requiring investigation  Study design may have a significant impact on the results.  What’s next ? – Cost-effectiveness study
  • 42. Team of researchers and hospital staff who made this work possible  Andrew Georgiou  Ling Li  Margaret Reckmann  Melissa Baysari  Nerida Creswick  Joanne Callen  Ric Day  Jeffrey Braithwaite  William Runciman  Richard Paoloni  Katherine Gibson  John Cullen  Louise Robertson  Rosemary Burke  Connie Lo  Kate Richardson  Maureen Heywood  Fiona McWhinnie  Amanda Woods  Naomi Malouf  Margaret Williamson  Jackie Laurens  Silvia Fazekas  Rosemary Richman  Joanne Villaret  Natasha Smith  Amanda Ampt  Melissa Pignone  David Roffe  Clinical ward staff  Pharmacy staff  ISD Staff
  • 43. Thank You J.Westbrook@UNSW.edu.au Centre for Health Systems & Safety Research Australian Institute of Health Innovation UNSW Medicine This program of research has been supported by funding from the NHMRC & ARC