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Andrew Georgiou, Australian Institute of Health Innovation - Improving Health Information and Data Management – the Evidence of e-Health’s Impact
 

Andrew Georgiou, Australian Institute of Health Innovation - Improving Health Information and Data Management – the Evidence of e-Health’s Impact

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Associate Professor Andrew Georgiou, Senior Research Fellow, Centre for Health Systems & Safety Research, Australian Institute of Health Innovation, UNSW delivered this presentation at the 15th Annual ...

Associate Professor Andrew Georgiou, Senior Research Fellow, Centre for Health Systems & Safety Research, Australian Institute of Health Innovation, UNSW delivered this presentation at the 15th Annual Health Congress 2014. This event brings together thought leaders and leading practitioners from across the Australian health system to consider the challenges, implications and future directions for health reform.

For more information, please visit http://www.informa.com.au/annualhealthcongress14

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    Andrew Georgiou, Australian Institute of Health Innovation - Improving Health Information and Data Management – the Evidence of e-Health’s Impact Andrew Georgiou, Australian Institute of Health Innovation - Improving Health Information and Data Management – the Evidence of e-Health’s Impact Presentation Transcript

    • Centre for Health Systems and Safety Research Improving Health Information and Data Management – the Evidence of e-Health’s Impact Associate Professor Andrew Georgiou Senior Research Fellow
    • Outline • Background o Existing evidence of the impact of health Information Technology • Aim and Method o Key performance indicators of laboratory performance • Results o The impact on efficiency, effectiveness and patient outcomes and safety? o The challenge of safe test result follow-up • Conclusion
    • Evidence of the impact of health information technology • 257 studies (24% from 4 US centres, all home grown systems)* • Only 4% (n=9) studies examined the impact of commercial systems • 8** years later - increase in number and scope of studies (13% per year <2007, 25% >2007) • 56% report uniformly positive results, 21% mixed-positive effects • Poor reporting of context and implementation details *Chaudhry et al (2006) Ann Intern Med ** Jones et al (2014) Ann Intern Med.
    • Evidence of the impact of health IT • Most lab studies showed decreases in ordering including a 27% reduction in redundant lab tests • Most lab and imaging studies showed improved adherence to guidelines and improved efficiency (up to 50% for labs) • Few studies across multiple sites • Lack of outcome measures
    • The aged care informatics challenge • A fragmented service • The delivery of “seamless” care • Integration of services • ICT “laggard” • Lack of solid research evidence of the contextual and holistic functioning and requirements of aged care
    • How aged care staff spend their time? • A median of six forms completed each day per staff member • 69% of staff spend time transferring information from paper to computer (30 mins/shift) • Median of 3.5 faxes and 3.5 phones calls to GPs/pharmacy per day • 35.4% reported that they always had access to residents’ hospital information after discharge Gaskin et al. BMC Geriatrics (2012)
    • Research question What is the impact of the Electronic Medical Record on pathology services, their work processes and relationships with other departments, and on key performance indicators?
    • Key performance metrics Georgiou et al. Int J Med Info 2006 Test order Test processing Test result application Costs Work practices Test volumes Redundant test rates Guideline compliance Turnaround times Doctor-lab communication Patient management Length of stay Patient safety
    • Average turnaround time in minutes Before implementation (95% CI) After implementation (95% CI) t test results* All test assays 73.8 (72.2-95.4) 58.3 (57.1-59.4) t=15.6 (df 184257) p=0.000 Prioritised tests 44.6 (42.4-46.8) 40.1 (38.7-41.6) t=3.3 (df 37830) p=0.001 Non-prioritised tests 81.5 (79.6-83.5) 65.9 (64.4-67.4) t=12.6 (df 148493) p=0.000 Tests in business hours 81.8 (80.1-83.5) 69.0 (67.4-70.6) t=10.7 (df 141219) p=0.000 Tests outside business hours 54.0 (50.6-57.4) 39.2 (37.8-40.5) t=7.9 (df 37524) p=0.000 Tests in control ward 68.7 (63.9-73.5) 64.7 (60.4-69.0) t=1.2 (df 12993) p=0.218 Westbrook et al. (2006) J Clin Pathol
    • TAT pre & post EMR in four hospitals 2005 Before 2006 After 2007 After Kruskal- Wallis Hospital A - Median TAT 77 68 66 P<0.001 % tests using EMR 75% 80% Hospital B - Median TAT 145 129 108 P<0.001 % tests using EMR 0-44% 57% Hospital C- Median TAT 138 135 113 P<0.001 % tests using EMR 29-38% 53% Hospital D- Median TAT 141 139 128 P<0.001 % tests using EMR 56-71% 74% Median TAT in minutes
    • Volume of tests and specimens* Average number of test assays per patient did not change 92.5 assays/patient versus 103.2 (P=0.23) Average number of specimens per patient did not change 10.8/patient versus 11.7 (P=0.32) *Westbrook et al. (2006) J Clin Pathol
    • Cumulative percentages of repeat testing, as a proportion of all tests ordered, within one-hour to 35- hours of the previous test, for tests orders using the paper-based (dashed line) and electronic ordering system (solid line).
    • Quality of pathology ordering Specification of gentamycin specimens Before 16% of gentamicin and 13% of vancomycin samples specified as peak or trough. After significant increase - 73% for gentamicin and 77% for vancomycin. Westbrook et al. J Clin Pathol 2006
    • The impact of electronic ordering on information exchange Wound specimens with a request specifying source and body site Before electronic ordering (2005) 578 (69.6%) One year later (2006) 774 (92.9%) Two years later (2007) 814 (95.3%) Three years later (2008) 877 (95.6%)
    • Incident Information Management System (IIMS) reported errors EMR Paper Mislabelled specimen 0.1 (n=39) 0.31 (n=56) p<.001 Mismatched specimen 0.49 (n=200) 1.42 (n=255) p<.001 Unlabelled specimen 1.37 (n=559) 1.65 (n=296) p<.01
    • Missed test results • Critical safety issue – increases the risk of missed or delayed diagnoses World Alliance for Patient Safety, WHO, 2008; Schiff, 2006 • Clinicians are concerned that their test management practices are not systematic Poon et al. Arch Int Med 2004 • Medico-legal concerns Berlin, AJR, 2009 • Impact on patient outcomes Roy et al. Ann Intern Med, 2005
    • How many results are missed for hospital patients? • Hospital inpatients 20% - 62% of tests are missed • ED patients (discharged) 1% - 75% of tests are missed Callen et al. BMJ Qual Saf 2011;20;194-199 • Ambulatory patients 7% - 62% laboratory tests missed 1% - 36% imaging tests missed Callen et al. Jnl Gen Int Med, 2012
    • Study methods Survey design (17 questions) 1 metropolitan ED; senior ED doctors Significantly abnormal results – not life threatening but need short-term follow-up (e.g., chest x-ray with new shadow, abnormal PSA) Automatic patient notification methods – Patient portal, Email, SMS, fax, mail or phone
    • What types of tests were missed? (%)
    • Are there standard policies and procedures for patient notification of results?
    • Perceptions of missed test results 19.2 26.9 53.9 In the past year I have missed an abnormal result that led to delayed patient care Yes (%) No (%) Don't know (%) 38.5 11.5 50 In the past year a colleague has missed an abnormal results that led to delayed patient care Yes (%) No (%) Don't know (%)
    • • Mater Mothers’ Hospital (Brisbane) • IP Health Verdi software which allowed clinicians to electronically document review and acknowledgement of test results (2010) • Hospital data (Aug ’11 – Aug ‘12) involving 27,354 inpatient tests for 6855 patients • All test results were acknowledged • 60% of laboratory and 44% of imaging results acknowledged within 24h An electronic safety net to enhance test result management
    • Safety considerations with health IT implementation • Solutions need to be multipronged • Policies, procedures and responsibilities • Role of patients, doctors, nurses, clerical staff and laboratories in the follow-up process • Evaluation of information and communication technology (ICT) solutions • Integrate solutions with work practices of health professionals
    • Acknowledgements Australian Research Council (ARC) Linkage Grant (LP0347042) to evaluate the impact of information and communication technologies on organisational processes and outcomes: a multi- disciplinary, multi-method approach (2003 – 2007) ARC Linkage Grant (LP0989144) to investigate the use of information and communication technologies to support effective work practice innovation in the health sector (2008 – 2012) ARC Discovery Grant (DP120100297) to evaluate an electronic test management system in health care (2012 – 2014) Department of Health Quality Use of Pathology Program grant (2008- 2009), (2011-2012)
    • Thank you Email: a.georgiou@unsw.edu.au Website: www.aihi.unsw.edu.au Twitter: @AGeorgiouUNSW