Case Study: Acute PREDICT

Transcription

1 Case Study: Acute PREDICT Cardiovascular Prevention Program and Acute Coronary Syndrome database Andrew Kerr and Andrew McLachlan, Cardiology Dept Middlemore Hospital

2 Themes Motivation Team approach Willingness to learn and adapt Data collection as part of the clinical work-flow ideally to drive decision support Electronic data collection, decision support and reporting Program development driven by results and data

3 The Treatment Gap Discrepancy between ideal CVD risk management and what happens in real life

4 Counties Manukau

5 Management Indicators in High Risk Patients 80% 70% Sinclair and Kerr, NZMJ 2006 Percentage achieved 60% 50% 40% 30% 20% 55% 40% 62% 36% 50% 22% 10% 0% Prescribed Aspirin Prescribed Statin Systolic BP< 130 CVD or Equivalent Risk > 15%

6 CCU audit highlights an issue Increasing CCU staff turnover/ less experienced nurses Shorter but more intensive in patient stays Poor uptake of cardiac rehabilitation Primary care input on discharge 100% unknown/variable. 80% 100% 80% 70% 60% 50% 40% 30% 20% 10% 0% 60% 40% 20% 78% 64% Documented lifestyle interventions 52% Assessment documented 24% 4% 0% 0% 0% Smoking addressed Weight Height BMI Waist circumference Smoking Cardiac Rehab Diet Activity Weight loss 0% BP/lipid lowering

7 The golden 24 hours Robinson (2001) argues that health funding for health promotion is targeted at primary health care and nurses in acute care have slowly developed a belief that health education is no longer their role.

8 Acute Predict Project To implement an electronic clinical decision support system (Acute Predict ) to more systematically manage peoples cardiovascular disease (CVD) risk factors in acute care. Run by nursing and junior medical staff To collect and analyse data to help strategically plan a considered response.

9 The PREDICT CVD ECDS Program ECDS vision shared by multiple stakeholders» ProCare» CMDHB/WDHB» University of Auckland» Enigma» Ministry of Health» National Heart Foundation» National Cardiovascular Advisory Group» New Zealand Guidelines Group

10 Predict Electronic Program Use Information Technology to provide on-line CVD risk assessment and management advice which is: Fast User Friendly Guideline and evidence based Patient Individualised Using the PREDICT tool integrated with PMS PROMPT CMDHB CCM Acute Predict CVD/DM Predict

11 Who carries responsibility for managing your information? The Acute CVD PREDICT Administrator, who can be contacted C/O CCU, Middlemore Hospital, Pr ivate Bag 94052, South Auckland Mail Centre, if you have any c Monthly user group identify issues and develop action plans Celebrate Success s Frequent training sessions including one on one support to develop CHAMPIONS 2004 Monthly audit and feedback results to CCU team Goal 1 - Increase use of PREDICT The Acute PREDICT implementation process A Plan, Do, Study, Act quality improvement cycle. Goal 2- Improve workflow process Goal 3- Close loop- Get PREDICT info to GP Regular marketing to keep Predict in peoples thoughts % 70% 60% % of total 50% CCU/SDU 40% population 30% screened 20% 10% 0% Feb May July Aug Sept Oct Nov Dec Jan Feb March Patient resources and information month Nurse surveys and act on feedback Ongoing development of software by Enigma publishing Ltd acting on user feedback The things you can do to reduce your risk of heart or stroke problems in the future include: Stay smokefree Keep as active as you can, on as many days a week as you are able. Try and keep your weight stable or lose weight if you are too heavy. Follow a healthy eating plan Take the medications you have been prescribed regularly. Talk to someone if you are stressed or feeling low Your doctor and nurses are happy to help you keep as well as you can. Together we can make a difference. Cardiovascular disease (CVD) includ ing heart attacks and strokes and diabetes are major problem in New Zealand today. The good news is that there are many things that both you and your doctors and nurses can do to reduce your risk of heart attack or stroke or the complications of diabetes.

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38 ACS data base within Acute Predict

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42 KPIs Finalised patients on Statin Finalised patients on Aspirin Finalised IHD/CABG/PTCA patients on Beta Finalised patients on ACE and/or ATII blocker Current smokers who got smoking cessation advice Door to balloon < 90 min for primary PCI STEMI Door to needle < 30 min for thrombolysed STEMI Revascularisation (PCI or CABG referral) Aspirin< 24h ACE and/or ATII blocker in patients with EF <40% at D/C Beta-blocker in patients with EF <40% at D/C

43 Cardiac out patients

44 Innovations increasing screening COP monthly PREDICT screenings CVD risk questionnaire introduced 12/06. 1/07. 2/07. 3/07. 4/07. 5/07. 6/07. 7/07. DM non DM

45 Gout clinics Over 50% >15%/High risk Patients with >15% CVD risk on management n = % 80% 60% 40% non DM n=26 DM n=16 20% 0% Aspirin Statin BP therapy Significant treatment gaps

46 Incidence of ACS admission to CCU per head of CMDHB population y y y NZ Euro/Other NZ Maori Pacific All Asian

47 Modifiable risk: Smoking Smoking (%) Other Maori Pacific Indian

48 Modifiable risk: BMI Mean BMI Other Maori Pacific Indian

49 Modifiable risk: Diabetes Diabetes (%) Other Maori Pacific Indian

50 GP practice RA and management advice Linkage of risk to outcomes approved by Multicentre Ethics Committee Predict database NHI + Predict data Encrypted NHI + baseline data University of Auckland Combines Predict data with NZHIS outcomes NHI & enhi enhi + outcomes data NZHIS enhi is linked to outcomes data (hospital admissions, deaths)

51 Results: events in risk groups in first 30,878 patients Hx CVD 12% 47% Risk 15+% 9% 27% 26% Risk <10% 68% Risk 10-<15% 11% 74% of events occur in 32% of the people, 26% in low-risk people

52 The Framingham score & NZ Guideline adjustments: useful for CVD risk prediction in NZ? Work in progress: Preliminary results J B Broad, R J Marshall, S Wells, A J Kerr, T Riddell, R Jackson on behalf of HRC-Predict Co-Investigators

53 Results: est. 5-year incidence 60 Cumulative incidence (%) of CVD event in 5 years (95%CI) for Hx CVD no Hx CVD 0 <5 5-<10 10-<15 15-< Framingham score (5-year risk, %) Mean est. 5-year incidence for Hx CVD is 28.4% (95%CI 26.3 to 30.4) For prior CVD 5-year risk is: *Framingham score

54 Gotta keep moving

55 Where to next? Support CVD risk screening in other areas of the hospital Support development of nursing workforce to assess and manage CVD risk as part of an advanced role Next audit loop use data reports/exception reporting to identify problem areas and design programs to address these Move towards electronic integration of PREDICT with primary care Develop HF/AF ecds? Electronic care planning

56 Acknowledgements School of Population Health particularly Rod Jackson, Sue Wells, Tania Riddell, Jo Broad Many many GPs, practice nurses and clinical medical/nursing specialists Primary Healthcare organisations esp ProCare, HealthWest, Enigma Publishing New Zealand Guidelines Group National Cardiovascular Advisory Group Maori Cardiovascular Group Ministry of Health Clinical Services Directorate National Heart Foundation Diabetes NZ Counties Manukau District Health Board esp Chronic Care Management programme, Middlemore Hospital Cardiology and Diabetes Services Medtech Global Ltd Health Research Council