Patient monitoringin and out of hospital

Prof. Lionel TarassenkoChair of Electrical Engineering

Director, Institute of Biomedical Engineering

Engineering Better Health 19 November 2008

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.

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).

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.

Long-term conditions

The costs of long-term conditions

Unplanned hospital admissionsRepeated visits to primary care physician (GP)

8,759 hours 15 minutes alone 45 minutesManaged Care

Typical Annual Care Plan for a Patient with a LTC

41.5% of UK diabetic population have an HbA1C greater than the 7.5% target*

*2007 National Review of Diabetes - DH

125 million people in US15 million people in UK

60% of hospital bed days80% of GP visits

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

Mobile phone BG meter

Prioritisation algorithms for effective disease

management

Intelligent software analyses incoming data

Readings automatically transmitted by the phone

internet

SERVER

Immediate feedback

Interactive tool to promote self-management Regular support from remote nurse (based on real-time data)

Patient monitoring out of hospital Telehealth using mobile phone technology

Delivering the telehealth vision

Prioritisation of patients• Red Alert responses• Compliance monitoring• Education/coaching delivery• Medicines optimization• Admissions avoidance programmes

Mobile Health Tool• Colour coded feedback• HbA1C prediction• Weather forecasts• Carer Alerts

telehealthPatient Team Patient

Server • Intelligent algorithms• Messaging

/ Healthcare Provider

Anynetwork

Personalised feedback screens

Web-based tools (for the telehealth nurse)

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 Care4 trials in progress in Dundee, Eire, Dubai and Oxford2 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 Alfadiem1 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 Health1 RCT recruiting with Matria Inc1 RCT recruiting with SHPS Inc

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

1313

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.

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”.

The deteriorating patient UK statistics

20,000 unscheduled ICU admissions per annum23,000 avoidable in-hospital cardiac arrests per annumBetween 5 and 24% of patients survive to dischargeVital sign abnormalities observed up to 8 hours beforehand in >50% of cases

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?

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.

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

Heart rateHeart rateRespiratory rateRespiratory rate

Oxygen saturationOxygen saturationBlood PressureBlood PressureTemperatureTemperature

FusionFusion Single representationof patient status

Single representationof patient status

Vital sign monitoring of in-hospital patients

Heart rateHeart rateRespiratory rateRespiratory rate

Oxygen saturationOxygen saturationBlood PressureBlood PressureTemperatureTemperature

FusionFusion Single representationof patient status

Single representationof patient status

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

Vital sign monitoring of in-hospital patients

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:

p(x) = 1 P (2π)d/2 σd

exp{ 12

_∑m=1

P || x – xm ||2

————σ2 }————

Data fusion model of normality

These prototype patterns define the data fusion model of normality

Data fusion model of normality

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.

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

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.

False alert rate during UPMC trial

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).

Phase 3 trial of data fusion systemHravnak et al, MET Conference (2008)

“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

• Combination of wireless sensors and data fusion technology

• Early discharge from hospital

Level 2

Level 1

Level 0

Level -1

Acute Care (Step-Down Unit, High-Dependency Unit)

Upper end of general ward

Lower end of general ward

Home monitoring of vital signs

Technology will gradually move into home monitoring context from the hospital setting

The future: Home monitoring of vital signs?