The Austin Health Diabetes Discovery: Using technology to support the implementation of standardised clinical care
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Libby Owen-Jones, HiNZ 2015 Conference, Christchurch
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The Austin Health Diabetes Discovery: Using technology to support the implementation of standardised clinical care
- 1. Diabetes Discovery Project Using technology to support the implementation of standardised clinical care Libby Owen-Jones Project Director Dr Nic Woods Physician Executive, Cerner
- 2. Overview Austin Health EMR Strategy & implementation Diabetes Discovery Project Change management and Clinical Adoption Outcomes
- 3. Major Tertiary Health Provider in Northeast Melbourne 3 Campuses - The Austin Hospital - Heidelberg Repatriation Hospital - Royal Talbot Rehabilitation Centre Major Services - Liver and Gastro-Intestinal Transplantation - Spinal Cord Injuries - Oncology - Victorian Respiratory Services - Olivia Newton John Cancer Centre
- 4. 93,000 Inpatient Admissions 900 Beds 73,000 Emergency Attendances 57,000 Placement Days for Entry Level to Practice Students in 17 disciplines 176,000 Outpatients 176,000 Outpatients (360 clinics) 8,000 staff >26,000 Surgical Operations Large Nursing and Medical Post Graduate Education Program
- 5. Austin Health IT and EMR Strategy
- 6. Austin Health – Strategic Priorities Build Capacity in Systems Redesign to Improve Quality, Value and Efficiency Provide Contemporary Clinical and Business Information Systems that Support Excellence in Decision Making, Patient Care and Accountability Continually Enhance Information Technology and Communication Systems
- 7. From paper ……to mobile computing
- 8. EMR Journey 2011 Rad/Lab orders ePrescribing Disch Summary Results 2012 E-Meds Management Fluid balance chart E-Referrals 2013 E-Meds Management Diabetes Discovery 2014 ED - FirstNet Physician Handover 2015 -2016 Vital Signs & Obs Theatres -Surginet Oncology system BI/Data warehousing Nursing documentation Progress notes
- 9. Transformational Change More than 70% of all major transformation efforts fail. Why? Because organisations do not take a consistent, holistic approach to changing themselves, nor do they engage their workforce effectively. Kotter 1995
- 10. Prevalence of Diabetes Prevalence of diabetes in Australia is estimated at 7% (AusDiab)1 23% for people older than 75 years2 40% of diabetes undiagnosed3 1 Diabetes Care 2002; 25: 829-834 2 Dunstan DW, Zimmet PZ, Welborn TA, et al. Diabetes Care 2002; 25: 829-834 3 Diabetes Care 32:287–294, 2009 .
- 11. Background – Inpatient hyperglycaemia 0 2 4 6 8 10 12 Known diabetes New hyperglycaemia (FPG >7mmol/L) Uncertain glycaemic status (FPG, 5.6– 6.9mmol/L) Normoglycaemia (FPG, < 5.6mmol/L) Mortality(%) p=0.04
- 12. Diabetes in the Surgical Units • Comparison of Length of Stay data for Surgical patients from 2009 to 2013: – Ave LOS is 6.91 days – Patients with a coded diagnosis of Diabetes 10.61 days – Diabetes patients stay 53% longer • Comparison of Readmission rates – Diabetes patients have higher readmission rate – but may be due to other reasons
- 13. Goal: patient safety & consistent practice Clinical System supports Clinical practice Clinician Led Use evidence based protocols BUT Who needs the intervention? Diabetes Management Project
- 14. Diabetes Discovery Project • Aim – To investigate the prevalence of diabetes (diagnosed and undiagnosed) at Austin Health via routine HbA1c testing in inpatients using the CERNER Millennium Health IT System – To identify inpatients with poor glycaemic control (HbA1c≥ 8.5%, 69 mmol/mol) • Hypothesis – Information technology tools such as CERNER Millennium aid the identification of patients with undiagnosed and patients with poor glycaemic control
- 15. Change Management Workflow for Medical staff Who sees which patients? How do they know who they need to see ? Alerts and Notifications Tools to support Medical staff workflow HbA1c Results Extract Report Improve communication with GPs via discharge summary documentation Clinical Guideline translated to a PowerPlan Nursing Workflows Patient Access List : Referrals , Meds to be administered, Path to be collected Diabetes education team – e-referral workflows Task List Reports Documenting outcomes
- 16. Automated ordering of HbA1c Austin Health Admissions (July 2013 to Jan 2014) Inclusion criteria: ≥ 54 years Acute admissions Austin campus Exclusion criteria: Day cases Palliative care Psychiatry
- 17. Automated ordering of HbA1c Austin Health Admissions (July 2013 to Jan 2014) Inclusion criteria: ≥ 54 years Acute admissions Austin campus Exclusion criteria: Day cases Palliative care Psychiatry Automated CERNER order for HbA1c% generated if no result within 3 months
- 18. Adding an Alert and notification – automated process
- 19. Adding an Alert and notification – automated process
- 21. Clinical Guideline – e referrals
- 22. Clinical Guideline – e referrals
- 25. Outcomes – Diabetes Discovery Known Diabetes 28% No Diabetes 67% Undiagnosed Diabetes 5% Previous diabetes diagnosis and HbA1c≥6.5% No previous diabetes diagnosis and HbA1c<6.5% Nil previous diabetes diagnosis and HbA1c≥6.5% 8892 Admissions analysed 6721 HbA1C orders (70% autogenerated) 1791 patients had HbA1C > 6.5% 380 (21% ) was not previously known 34 Type 1 Diabetes 1,295 Type 2 Diabetes 10 Other (gestational diabetes, etc)
- 26. Outcomes – LOS by HbA1c and Unit 0 2 4 6 8 10 Medical Surgical Days HbA1c <6.5 HbA1c ≥ 6.5% 8.2 ±10.8 6.8 ±8.8 7.2 ±8.3 6.9 ±9.7 p=0.35 p=0.03 %
- 27. Outcomes – Readmits < 6 months p=0.007 22% 23% 26% 0% 10% 20% 30% No Diabetes New Diabetes Known Diabetes %ofpatientsreadmittedwithinsix months Undiagnosed Diabetes
- 28. Outcomes – Rates by Specialty 0 200 400 600 800 1000 1200 No Diabetes Known Diabetes New Diabetes
- 29. Conclusions Higher HbA1c is associated with • increased admission rates • Longer length of stay in surgical patients Routine inpatient HbA1c testing using CERNER addresses a currently missed opportunity to identify patients with newly diagnosed diabetes and poor glycaemic control. .
- 30. Evolving Changes to practice Inclusion of Mental Health patients – with different auto ordering criteria Refinement of parameters – who sees which patients General medicine Outpatient Clinic- follow-up of poorly controlled patients post discharge Ongoing education in diabetes management to junior medical staff Research in ICU – using HbA1c results – changes to protocols The impact of early identification and treatment of poor glycaemic control on patient outcomes requires further study
- 31. Acknowledgements Cerner Corporation University of Melbourne – Endocrinology Unit at Austin Health Austin Health - Clinical Systems Projects Unit & Business Intelligence Unit Health Shared Services BMJ – Action Sets
Editor's Notes
- Austin Health has a clear strategic Direction to become a leading health Serivce in eHealth. The Boar see this as an important strategy to improve quality, and improve consistency of practice. The Electronic medical record is one of the main projects the organisation has undertaken over the last 5 years.
- Austin Health Named as Lead Agency in 2009 Aim to Implement a Common System Design Victorian State Build Undertaking the Project as Part of a Group, with each Organisation’s project running in parallel and working to a common schedule with shared domain with multiple Health Services UK Experience – One size will NOT necessarily fit all ▪▪▪▪Brennan 2009 Austin health has implemented the initial StateBuild, plus has now extended this to include many other intitiative – see next slide We have used the EMR adioption model as the basis to guide the strategy and work through the ‘gaps’ in the initial statebuild. EMR Adoption Model Structure Ensures Objectivity 2012 Self Assessment for Austin Health = Stage 2 2014 Self Assessment for Austin Health = Stage 5
- It has been a graduyal process with some of the major changes occurring over a number of years. Ordering of pathology, radiology and direct interface into the systems that receive the orders Results viewing and endorsement Discharge summaries Discharge prescriptions Alerts and allergy management Medication ordering and administration management Medication reconciliation Drug interaction alerts Fluid balance charts E-referral Once the basis was there, with eMedications and electronic ordering, we were in a position to look at how we delivered chhages to Clinical practive – hence the Doabetes discovery project.
- This is not viewed as an IT Project – rather it is an Organisation Change project designed to bring about Transformational change in the way we deliver healthcare in the 21st century Austin has a ‘Can Do’ Organisational culture and has made these projects work well for the organisation Governing principle - Patient and patient Safety First But What was difficult was that clinical adoption was a slow process. There are always people who resist change and avoid any of the usual change management messages and efforts. Clinicans were required to enter lots of data – but they could not necessarily see all the benefits of this initially. It just seemed like lots more work Engaging clinicians and finding something of interest to them to be the catalyst for change was a prioirity Linking the implementation to more tangible benefits in the clinical setting like improving patient outcomes was critical.
- As everyone in this room is well aware diabetes poses an increasing challenge to healthcare provision. In 2011 366 million people reported to have diabetes and is estimated to increase to 552 million by 2030(1). The prevalence of diabetes in the Australian community is 7.4%, rising to 23% for those aged above 75 years.
- Previous audits, including from Austin, estimate prevalence of inpatient diabetes at ≈20%* Inpatient data This is likely to be an underestimated as many are undiagnosed at the time. AIHW data indicate that people with diabetes have longer lengths of stay, being about 2 days longer than people without diabetes. Inpatient hyperglycaemia is associated with poor hospital outcomes. In several settings, hyperglycaemia has been associated with increased morbidity and mortality Reasons for increased morbidity and mortality may be related to poor immune response, delayed healing, inflammation and thrombosis associated with hyperglycaemia as well as a higher rate of co-morbidities in this patient group
- If we examine the data in more detail, it is interesting to see the impact of diabetes on surgical admission. Patients coming for a surgical procedure eg a knee replacement etc. may not necessarily have an extensive presurgery work up if they appear young and healthy. The average length of stay is 6.91 days for patients without diabetes diagnosis, and 10.61 days for patients with diabetes. The relative length of stay for diabetes patients is 153% (i.e. they stay, on average 53% longer). This is highly significant. See Elif’s slides in the other presentation for more background. Unplanned Readmissions within 30 days shows the rate on unplanned readmissions (defined as emergency admissions via ED) within 30 days of the hospital discharge for the surgical admission including any subacute component. Diabetes patients have a significantly higher readmission rate, but the readmission reason may not be related to the original surgical admissio Refer to the other presentation for lots more detail on the pre Diabetes discovery data. There is probably not enough time to go into that. I just wanted to highlight the main points.
- The Diabetes Discovery Project then, became a Catalyst for change in that it provided a strategy to manage one of them main public health issues in our region. It met the criteria to have a goal focussed on Patient safety and developing a clear clinical guideline that is supported by the system. Most importantly it was Clinian led – by the proffessor of Endocrinology at austin health. It was based on evidence based protocols – at the time they were those provided by the BMJ, who had a ‘partnership’ with the Clinical system provider (Cerner Millenium) to integrate eveidence based medicine into the Clinical system. BUT, while the aim (see next page) was to apply an evidence based guideline, How could we identify more quickly who the protocol needed to be applied to? The
- The normal workflow is to train junior doctors to order the correct tests in ED or on admission to identify what needs to be treated. If a patient is admitted for another reason eg an elective procedure, or via emergency for something completely unrelated, it may not be recognised initially that tests to diagnose diabetes need to be ordered. By the time they were ordered, reveiwed and a plan determined, a number of days could be lost in treating the secondary condition. For this project to work we needed to improve the early identification of the cohort of patients who were most at risk of underlying diabetes. This lead to both system changes in the Technical build of the Clinical System and to Change Management – ie business process changes.
- The Change management components cannot be underestimated. Business process need to define the technical requirements. While this list looks relatively simple, it did require a considerable amount of negotiation between Units about who saw which patients. If you identify new Diabetes patients, who sees these patients ? How will they know who has to be seen and where they are located, What treatment should be applied, How will we let the GP know. Whos should be refereed to the Dibetes educatiors – and how will anyone know this has happened? How do the Diabetes Educators manage their referrals?
- A summary of all the technical components is listed below. Fololowing slides show each of these components in more detail. See the RUG presentation for more detial on the technical Cerne aspects of al l of these.
- 1Diabetes Problems = 200+ Snomed Terms evaluated during rule processing Evaluation of Abnormal HbA1c% Results via Discern Rule Outcome 1 - Results 6.5- 8.0% w/out ‘Diabetes’ Documented in Medical Hx “Possible New Diabetes Pt” Alert applied Message sent to Generic Gen-Med Diabetes Inbox Generic Inbox managed by Gen Med Clinical Unit Outcome 2 - Results >=8.1% ‘Possible Poorly Controlled Diabetes’ Alert applied Managed by Endocrine Unit via Alerts or HbA1c Report Auto applied alerts part of Patient’s on-going Problems/Alerts Profile “Possible New Diabetes Patient” Alert Inactivated & replaced with Snomed CT ‘Diabetes’ Medical History if pertinent after patient assessment Medical History(Snomed CT & alerts) created for all future care Inactive alerts are still auditable on General Alerts report “Possible Poorly Controlled” Alert Not inactivated unless incorrect Snomed CT ‘Diabetes’ Medical History if pertinent after patient assessment Medical History(Snomed CT & alerts) created for all future care Auditable on General Alerts report
- 1Diabetes Problems = 200+ Snomed Terms evaluated during rule processing Evaluation of Abnormal HbA1c% Results via Discern Rule Outcome 1 - Results 6.5- 8.0% w/out ‘Diabetes’ Documented in Medical Hx “Possible New Diabetes Pt” Alert applied Message sent to Generic Gen-Med Diabetes Inbox Generic Inbox managed by Gen Med Clinical Unit Outcome 2 - Results >=8.1% ‘Possible Poorly Controlled Diabetes’ Alert applied Managed by Endocrine Unit via Alerts or HbA1c Report Auto applied alerts part of Patient’s on-going Problems/Alerts Profile “Possible New Diabetes Patient” Alert Inactivated & replaced with Snomed CT ‘Diabetes’ Medical History if pertinent after patient assessment Medical History(Snomed CT & alerts) created for all future care Inactive alerts are still auditable on General Alerts report “Possible Poorly Controlled” Alert Not inactivated unless incorrect Snomed CT ‘Diabetes’ Medical History if pertinent after patient assessment Medical History(Snomed CT & alerts) created for all future care Auditable on General Alerts report
- BMJ Action Sets built as PowerPlans where possible within the licencing Home Unit to use PowerPlan to guide treatment of patients with HbA1C readings > 6.4% and < 8.5% NB Extracts only: Power Plans include different medication regimes and are quite extensive Link to the Action set is within the PowerPlan
- Implemented Diabetes Referrals April 2014 Utilised same ‘Referrals’ template used for many disciplines at Austin health Diabetes Edu Referral and Review Orders/Tasks Referrals placed by any clinician or self referred Reviews placed by Diabetes Edu CNCs Discipline designed list of referral and review reasons Referral/Review Task list (MPTL) managed Diabetes Edu CNC ‘Phone” icon on Patient Access List (PAL) for all users indicating referrals/reviews are outstanding for patients Link to the Action set is within the PowerPlan
- Implemented Diabetes Referrals April 2014 Utilised same ‘Referrals’ template used for many disciplines at Austin health Diabetes Edu Referral and Review Orders/Tasks Referrals placed by any clinician or self referred Reviews placed by Diabetes Edu CNCs Discipline designed list of referral and review reasons Referral/Review Task list (MPTL) managed Diabetes Edu CNC ‘Phone” icon on Patient Access List (PAL) for all users indicating referrals/reviews are outstanding for patients Link to the Action set is within the PowerPlan
- Implemented Diabetes Referrals April 2014 Utilised same ‘Referrals’ template used for many disciplines at Austin health Diabetes Edu Referral and Review Orders/Tasks Referrals placed by any clinician or self referred Reviews placed by Diabetes Edu CNCs Discipline designed list of referral and review reasons Referral/Review Task list (MPTL) managed Diabetes Edu CNC ‘Phone” icon on Patient Access List (PAL) for all users indicating referrals/reviews are outstanding for patients Link to the Action set is within the PowerPlan
- Implemented Diabetes Referrals April 2014 Utilised same ‘Referrals’ template used for many disciplines at Austin health Diabetes Edu Referral and Review Orders/Tasks Referrals placed by any clinician or self referred Reviews placed by Diabetes Edu CNCs Discipline designed list of referral and review reasons Referral/Review Task list (MPTL) managed Diabetes Edu CNC ‘Phone” icon on Patient Access List (PAL) for all users indicating referrals/reviews are outstanding for patients Link to the Action set is within the PowerPlan
- All acute admissions, >1 day, 6 months (7/2013 to 1/2014), age>54 years 6716 admissions in total 4388 medical, 28% HbA1c ≥6.5% (n=1234) 2328 surgical, 21% HbA1c ≥ 6.5% (n=483)
- All acute admissions, >1 day, 6 months (7/2013 to 1/2014), age>54 years 6716 admissions in total 4388 medical, 28% HbA1c ≥6.5% (n=1234) 2328 surgical, 21% HbA1c ≥ 6.5% (n=483)
- All acute admissions, >1 day, 6 months (7/2013 to 1/2014), age>54 years 6716 admissions in total 4388 medical, 28% HbA1c ≥6.5% (n=1234) 2328 surgical, 21% HbA1c ≥ 6.5% (n=483)
- All acute admissions, >1 day, 6 months (7/2013 to 1/2014), age>54 years 6716 admissions in total 4388 medical, 28% HbA1c ≥6.5% (n=1234) 2328 surgical, 21% HbA1c ≥ 6.5% (n=483)
- 34% of all inpatients ≥54 years have diabetes 29% known diabetes 5% undiagnosed diabetes Higher HbA1c is associated with increased readmission rates Higher HbA1c is associated with longer length of stay in surgical patients