Quantifying the effects of electronic health record systems in hospitals

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1 Quantifying the effects of electronic health record systems in hospitals Prof Johanna Westbrook Health Informatics Research & Evaluation Unit The University of Sydney

2 Approaches to answer four research questions Do pathology order entry systems deliver more efficient care? Do electronic medication management systems make health care safer? Do EHRs make clinical work more efficient and release clinicians to spend more time with patients? What is the role of mobile technologies in support clinical work in hospitals? Identifying unexpected consequences of ICT

3 Is care delivery more efficient? Few studies all specialised units Computerised test ordering all reported improved turnaround times. Turnaround time = Time from receipt of specimen in laboratory to report of result

4 AIMS 1. Do turnaround times decrease in the first 12 months following system introduction and are improvements sustained? 2. What is the impact on pathology staff?

5 Methods 650 teaching hospital Measurement of TAT pre & post CPOE -Cerner Millennium PowerChart Periods Jul Aug 2003 Jul Aug 2004 (post 1) Jul Aug 2005 (post 2)

6 Test turnaround time significantly declined Year 1 by 18.6%, Year 2 by 12.6% All tests Period No. tests Mean in minutes (95% C.I.) (35.11,35.59) (28.59,28.95) (24.99,25.29) Average number of tests per patient did not change: 92.5 assays/pt vs (P=0.23)

7 Changes in TAT post CPOE in four hospitals 2005 Before 2006 After 2007 After Hospital A - Median TAT % tests using CPOE 75% 80% Hospital B - Median TAT % tests using CPOE 0-44% 57% Hospital C- Median TAT % tests using CPOE 29-38% 53% Hospital D- Median TAT % tests using CPOE 56-71% 74% Kruskal- Wallis P<0.001 P<0.001 P<0.001 P<0.001

8 Effectiveness Does a reduction in TAT really matter? Regression analyses - TAT was a significant factor contributing to patients length of stay in ED (p<0.0001).

9 Qualitative studies to assess the impact pathology work Focus groups & interviews with management, pathology, clinical and IT department staff Observational video study of pathology staff over several months

10 Work practice changes

11 I don t have figures to prove this, but in my estimation it has made the turnaround time longer. (Senior scientist, 2004)

12 Implementing Systems Changes in roles & responsibilities Elimination of some tasks but creation of new tasks Required new procedures to replace established work practices eg Add on test requests. Failure of one group to use the system as expected impacts upon the work of others These elements of system impact are as important as quantitative indicators!

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14 Benefits realisation framework Efficiency Effectiveness Quality Test volumes Communication Test costs Redundant test rates Turn around times Work practices Patient safety Compliance with guidelines Patient management Length of stay Georgiou A, et al (2007) The impact of computerised physician order entry systems on pathology services: a systematic review. Intern J Med Informatics 76 (7), Georgiou et al. (2008) Electronic test management systems and hospital pathology services a framework for investigating their impact. Encyclopaedia of Healthcare Information Systems

15 Will electronic medication management systems make our health services safer?

16 Medication Errors In Australia 2% inpatients experience harm or death due to medication errors Estimated errors in 20% of all drug doses administered in hospital IV medications have error rates of 50-90% Very poor data on prescribing & medication administration error rates 2008 Review US Agency for Healthcare Research and Quality that the rate of medication administration errors is truly staggering.

17 Do e-prescribing systems reduce prescribing errors in hospital inpatients? 13 papers (US 6, UK 4, Europe 2, Israel 1) 9 showed significant decrease; 2 decrease in some categories 2 an increase in errors Limitations in study designs, eg only specific drugs Only 5 studies examined severity of errors 2 defined their scales e-prescribing effective in reducing errors related to the less severe errors eg unclear, incomplete orders Very limited evidence of effectiveness to reduce serious errors Reckmann, Westbrook et al (2009) Does computerized order entry reduce prescribing errors for hospital inpatients? A systematic review. Journal of American Medical Informatics Association. 16 (5) pre-print

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20 Controlled before & after study of prescribing errors Two hospitals, two different commerical e-prescribing systems 6 wards Daily Unclear, prospective incomplete, review ambiguous of medication charts orders Non compliance with legal or local requirements Pre Inappropriate (n=>8000) and drug post choice prescribing error rates Inappropriate, dose, freq, form, route Severity Duplication 5 point eg same scale drug class Type of errors Pt Allergic to drug Drugs required but not prescribed

21 Preliminary data intervention ward Prescribing errors significantly decreased 4.68 per patient (95% CI ) to 2.05 per patient (95%CI ) 56.2% reduction Large reductions in administrative errors unclear, and incomplete orders No change in clinical prescribing errors

22 Do electronic medication administration records reduce errors? Few studies all flawed methods Perceptions of staff Examination of voluntary incident reports

23 Observational Medication Administration Error Study Observe nurses as they prepare & administer medications Record interruptions & multi-tasking Compare observed data with patients charts to identify errors

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26 Baseline data (N=4271 medication administrations >500 hours observation) 74% procedural error/failure 25% clinical error 79% rated as insignificant 3.4% serious Will the medication system reduce the serious errors?

27 How does system use impact upon patterns of work? Will these systems save time? Do drs & nurses spend more time with patients?

28 Aim: To develop a reliable method for observing and recording time spent by clinicians in different work tasks Work Observation Method By Activity Timing (WOMBAT)

29 PDA data collection tool What task? With whom? With what? Interruptions Multi-tasking

30 Controlled before and after study 4 wards at baseline 1 intervention ward 2 control wards Completion date Dec 2009

31 Proportions of observed time in tasks across four wards (Before) Task Nurses N=52 Ward Drs N=19 ED Drs N=40 Hours of observation 250 hrs 151 hrs 210 hrs Direct Care 24% 15% 29% Professional 22% 33% 24% Communication Medication tasks 17% 7% 5% Indirect Care 13% 18% 26% Social Activities 13% 17% 6% In transit 9% 6% 3% Documentation 8% 14% 16% Supervision/education 3% 7% 2% Administration 3% 2% 2% Answering Pager 1% 1% <1%

32 Time with patients & interruptions (Baseline data) Nurses = 34.5%, interrupted 1/49mins, 12% multi-tasking Ward Drs = 15.0%, interrupted 1/21mins, 20% multi-tasking On average nurses spend 8.4 mins/shift talking with a Dr. Westbrook JI, et al (2008) Medical Journal of Australia. 188(9):

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34 What is the role of mobile technology in achieving benefits? COWs - Computers On Wheels

35 Paper is a highly mobile technology!

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37 Capturing what happens on a Available devices wardon each ward: Structured observations Two forms of COWs (n=5 & 6) Video observations Two Talking forms to of staff tablets (Motion computing C5 80 & hours LE1700) observation, (n=2/ward) 2 wards Aim: To measure which devices nurses and Fixed doctors PCs (n=7) select Andersen P, et al (2009) Is selection of hardware device related to clinical task?: A multimethod study of mobile and fixed computer use by doctors and nurses on hospital wards. Journal of Medical Internet Research. In press

38 Computers on wheels 82% of nurses tasks 3% of nurses work tasks

39 Doctors on ward rounds 57% of tasks completed using a generic COW 36% of tasks completed using a tablet Only 3% of tasks completed at the patient s bedside

40 Unexpected consequences of ICT in health

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43 Poor mobility workarounds may result in less safe practices

44 Conclusions Recognise the limitations of existing evidence-base Use explicit indicators & measure them Engagement of academics/clinicians/ vendors Feedback impact data to staff Create a market for evidence of impact Comparisons between systems, organisations

45 Acknowledgements HIREU Team Andrew Georgiou Joanne Callen Amanda Woods Margaret Reckmann Connie Lo Yvonne Koh Fiona Ray Nerida Creswick Marilyn Rob Mirela Prgomet Antonia Hordern Fiona McWhinney Pia Andersen Anne-Mette Lingaard Funding Bodies Australian Research Council NH & MRC NSW Health HCF Research Foundation SSWAHS Hospital staff at our study sites Publications available at:

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