Engineering better Health


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Engineering better Health

  1. 1. Engineering Better Health 19 November 2008 Patient monitoring in and out of hospital Prof. Lionel Tarassenko Chair of Electrical Engineering Director, Institute of Biomedical Engineering
  2. 2. The changing landscape in healthcare • The WHO predicts that chronic diseases (long-term conditions) such as diabetes, asthma or hypertension will be the leading cause of disability by 2020.
  3. 3. Long-term conditions In the UK there are 17.5 million people with a long-term condition (mainly diabetes, hypertension, asthma or Chronic Obstructive Pulmonary Disease). Diabetes is the fastest growing disease in the Western world as a result of poor diet and obesity. £5.8 billion is spent per year by the NHS on diabetes and its related complications (2002 figures). Asthma affects 3.7 million adults and 1.5 million children in the UK (70,000 hospital admissions for asthma in 2002).
  4. 4. Long-term conditions 80% of primary care consultations relate to long-term conditions and patients with such conditions or their complications use over 60% of hospital days. The key to minimising long-term complications is to empower patients to take more responsibility for the management of their condition. The economic driver is reduction in unplanned hospital admissions.
  5. 5. The costs of long-term conditions Unplanned hospital admissions Repeated visits to primary care physician (GP) 60% of hospital bed days 80% of GP visits 125 million people in US 15 million people in UK Typical Annual Care Plan for a Patient with a LTC 45 minutes 8,759 hours 15 minutes alone Managed Care 41.5% of UK diabetic population have an HbA1C greater than the 7.5% target* *2007 National Review of Diabetes - DH
  6. 6. Technology for self-management Wilson et al. (BMJ, 2005): “The evidence backing the use of disease-specific self-management programmes like diabetes is strong. The challenge is how to move to a programme that can support the many millions of patients who might benefit.” Focus on mobile phone: – Equality of care – 90% of UK population owns a mobile phone – Real-time feedback, with two-way information flow – Communication with remote carer based on shared data – Economic model based on reduction in unplanned hospital admissions makes mobile phone solution a financially viable proposition
  7. 7. Patient monitoring out of hospital Telehealth using mobile phone technology Readings automatically transmitted by the phone Mobile phone BG meter Immediate feedback Intelligent software internet analyses incoming data Prioritisation algorithms for effective disease SERVER management Interactive tool to promote self-management Regular support from remote nurse (based on real-time data)
  8. 8. Delivering the telehealth vision / Healthcare telehealth Provider Patient Patient Team Any network Server Mobile Health Tool Prioritisation of patients • Intelligent algorithms • Colour coded feedback • Red Alert responses • Messaging • HbA1C prediction • Compliance monitoring • Education/coaching delivery • Weather forecasts • Medicines optimization • Carer Alerts • Admissions avoidance programmes
  9. 9. Personalised feedback screens
  10. 10. Web-based tools (for the telehealth nurse)
  11. 11. Summary of clinical studies and trials Asthma 3 published clinical studies, 1 recruiting for Asthma UK COPD 1 trial at Bristol Royal Infirmary published in Thorax Diabetes Type 1 1 RCT at OCDEM published in Diabetes Care 4 trials in progress in Dundee, Eire, Dubai and Oxford 2 studies pending with UK NHS and Singhealth in Singapore Diabetes Type 2 1 published clinical study for Lloyds Pharmacy Cystic Fibrosis 1 published clinical trial (data submitted to NICE) Cancer 1 study at Churchill Hospital published in Annals of Oncology Drug Titration 1 study at Corbeilles-Essone presented at Alfadiem 1 trial recruiting in Oxfordshire GP Practices Hypertension 1 trial recruiting in Oxfordshire GP Practices Health Economics 1 RCT in process with the UK Department of Health 1 RCT recruiting with Matria Inc 1 RCT recruiting with SHPS Inc
  12. 12. Clinical evidence 20 clinical trials or studies with e-health disease management system (type 1 & type 2 diabetes, asthma, COPD, cystic fibrosis, chemotherapy) Diabetes: • 0.62% reduction in HbA1c in people with Type 1 diabetes (after 9 months) • 0.7% reduction in HbA1c in people with Type 2 diabetes (after 6 months) – Mean age of patients: 58 years Asthma: • 31% reduction in uncontrolled use of reliever inhaler COPD: • Reduction in hospitalisation rate from 1.64 per annum to 0.70 per annum
  13. 13. Commissioning telehealth services in the NHS The following have all signed up to the t+ Medical disease management service: Walsall • Oxfordshire • Norfolk & Norwich • Newham • Southampton • Leicester • North-East Essex • Calderdale • t+ Medical is also supplying telehealth services to the Newham Whole-System Demonstrator (WSD) Project and is involved in the Cornwall WSD Project. 13
  14. 14. Patient monitoring in hospital In August 2007, the National Patient Safety Agency (NPSA) reported that one of the two most important actions which could be taken to improve patient safety in hospitals was “to identify patients who are deteriorating and act early”.
  15. 15. The deteriorating patient UK statistics 20,000 unscheduled ICU admissions per annum 23,000 avoidable in-hospital cardiac arrests per annum Between 5 and 24% of patients survive to discharge Vital sign abnormalities observed up to 8 hours beforehand in >50% of cases
  16. 16. The clinical need Early detection of patients at risk followed by • intervention and stabilisation can prevent adverse events such as a cardiac arrest, unscheduled admission to ICU or death. Why is patient deterioration so often missed? •
  17. 17. The clinical need: identifying at-risk patients All acutely ill patients (Level 2 and upper end of Level 1 in • NHS) have their vital signs (heart rate, breathing rate, oxygen levels, temperature, blood pressure) continuously monitored but… Patient monitors generate very high numbers of false alerts • (e.g. 86% of alerts in 1997 MIT study). Nursing staff mostly ignore alarms from monitors (“alarm • noise”), apart from the apnoea alarm, and tend to focus on checking the vital signs at the time of the 4-hourly observations.
  18. 18. Vital sign monitoring of in-hospital patients Heart rate Heart rate Respiratory rate Respiratory rate Single representation Single representation Oxygen saturation Oxygen saturation Fusion Fusion of patient status of patient status Blood Pressure Blood Pressure Temperature Temperature Vital sign monitoring requires data fusion technology to deal with problem of false alerts Data fusion technology already developed within Oxford University for monitoring of jet engines
  19. 19. Vital sign monitoring of in-hospital patients Heart rate Heart rate Respiratory rate Respiratory rate Single representation Single representation Oxygen saturation Oxygen saturation Fusion Fusion of patient status of patient status Blood Pressure Blood Pressure Temperature Temperature Data fusion system relies on having learnt a model of normality for the vital signs using a comprehensive training of thousands of hours of vital sign data When the data fusion system is used to monitor a high- risk patient, an alert is generated whenever the patient state is about to go outside the boundaries of normality
  20. 20. Data fusion model of normality The model of normality has been trained on a data set acquired from a representative sample of patients • The model of normality is an estimate of the unconditional probability density function (pdf) of the normal vital sign data (c.f. “5-D histogram”) • The unconditional pdf of the data is estimated using Parzen windows with a number of prototype patterns: || x – xm ||2 P ∑ { } _1 1 exp ———— ———— m=1 p(x) = σ2 σ 2 d/2 d P (2π)
  21. 21. Data fusion model of normality These prototype patterns define the data fusion model of normality
  22. 22. Detecting patient deterioration Data fusion software (Visensia) is connected to patient monitors via a standard interface. When an alert is generated, the pie chart indicates the “most abnormal vital sign(s)” or the trend mode shows changes prior to the alert.
  23. 23. Validation trials 1. John Radcliffe Hospital (Oxford) 440 high-risk elective/emergency surgery or medical patients • September 2003 to July 2005 • 2. Clarian Methodist (Indianapolis) 220 patients from upper end of general floor or Progressive • Care Unit (PCU) January 2006 to June 2007 • 3. University of Pittsburgh Medical Center (UPMC): 1000 patients from 24-bed Step-Down Unit (SDU) • November 2006 to August 2007 •
  24. 24. False alert rate during UPMC trial There were 0.94 false alerts per 100 hours of monitoring. This corresponds to a false alert rate of 0.23 per patient per day. The Visensia data fusion model automatically switches to a lower-dimensional model when a parameter is artifactual or missing. This makes the technology usable by the nursing team.
  25. 25. Phase 3 trial of data fusion system Hravnak et al, MET Conference (2008) Three-fold reduction in the number of patients becoming critically unstable and needing an emergency call: 17.8% in Phase 1, 5.2% in Phase 3 (p < 0.0001). Data fusion system was not withdrawn from the SDU at the end of the 6-month trial. No cardiac arrests in last 18 months (compared with 50 in previous 18 months, prior to introduction of data fusion technology).
  26. 26. “The hospital of the future” project Wireless monitoring and data fusion Vital signs and data fusion alerts from • all patients on Central Station Vital signs/alerts from any patient • relayed to (m)any “nurse display” Hospital wired and WiFi network used •
  27. 27. The future: Home monitoring of vital signs? Technology will gradually move into home monitoring context from the hospital setting Acute Care (Step- Level 2 Down Unit, High- Dependency Unit) Upper end of general Level 1 ward Lower end of general Level 0 ward • Combination of wireless sensors Home monitoring of and data fusion technology Level -1 vital signs • Early discharge from hospital