This document discusses patient monitoring both in and out of hospitals. It summarizes that (1) chronic diseases will be a major cause of disability and healthcare costs going forward, (2) most hospital resources are used for patients with long-term conditions, and (3) remote patient monitoring using mobile phones and other wireless technologies can help manage conditions and reduce costs. Remote monitoring systems have shown reductions in HbA1c levels for diabetes and decreases in hospitalization rates for other conditions. The document advocates for expanding telehealth monitoring both within hospitals and to patients' homes.

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. 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. 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. 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. 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. 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. 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. 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. Personalised feedback screens

10. Web-based tools (for the telehealth nurse)

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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Data fusion model of normality
These prototype patterns define the
data fusion model of normality

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. 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. 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. 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. “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. 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