e-Health

e-Health

Jim Hunter

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Outline

• What is e-Health?• Stuart Scott

GP/Clinical Director e-Health NHS GrampianTuesday 5th December

• e-Health research at Aberdeen

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e-Health - Definitions

EUThe use of modern information and communication technologies to meet needs of citizens, patients, healthcare professionals, healthcare providers, as well as policy makers.

World Health Organisation (WHO):

eHealth is the cost-effective and secure use of information and communications technologies in support of health and health-related fields, including health-care services, health surveillance, health literature, and health education, knowledge and research.

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NHS – Connecting for Health

National Programme for Information Technology (2002-2010)• CRS: "a secure, shared national Care Records Service for England".

Designed to replace existing electronic and paper based systems for all patients in England.

• Choose and Book - electronic appointment booking system for GPs/hospitals and patients

• ETP - Electronic Transmission of Prescriptions: a system designed to support the electronic transmission of prescriptions between GPs (General Practitioners), pharmacies and the Prescription Pricing Authority (PPA)

• HealthSpace: a web service allowing patients to access their own NHS care records

• Picture Archiving and Communications Systems (PACS)

• Quality Management and Analysis System (QMAS) (for primary care)

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NHS – Connecting for Health

• N3: a new national network to provide broadband network capacity and support interoperability between above applications and activities such as telemedicine and telecare.

• the Spine: the "core data storage and messaging system" underlying and integrating the CRS and associated functionality. Standard terminologies will include SNOMED CT. Health Language Inc.'s language engine technology will support interoperability, including integrating and sharing patient data.

• Decision support: the electronic prescribing programme (support for prescribing in different care settings); online knowledge and library systems: integrated care pathways; NICE clinical recommendations; National Service Frameworks (NSFs); e-referral support; support for ordering clinical investigations; "accredited protocols of care, procedures and clinical guidance".

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Healthcare

• Pervasive – involves everyone• I’ve never used eBay but …

• Large • NHS employs about 1 M people

• Diverse• different rates of change

• Political

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Healthcare - Costs

Expensive

EU - 2002

• expenditure on total health care as % of GDP: from: 10.7% (Germany) to 6.7% (Ireland) –

say 8% overall

• European GDP ~ €10,000 B (1000 M)

• so EU health care expenditure ~ €800 B (£570 B)

UK

2002/3 2003/4 2004/5 2005/6 £56 B £64 B £69 B £76 B (estimated)

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Healthcare - People

• Patient • Doctor• Nurse• Other health care professional

• Pharmacist, Radiographer, Pathologist, …

• Administrator• Finance, Human resources, Appointments,

…

• Researchers• Students

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Healthcare - Professionals

Professionals• Long Training (6+ years for doctors)

• Large body of knowledge – constantly changing

• Medicine is not an absolute science – uncertainty

• Time pressures

• Dealing with patients (not insurance quotes)

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Healthcare - Locations

• Home• Primary care and local clinic• District general hospital

many departments

• Tertiary hospital+ more specialised departments

• Research laboratory• Classroom• Everywhere! …

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Healthcare - Sensitivity

• Need for• Privacy• Security• Access

Right people Right time

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Healthcare - Change

Not all organisations at the same stage.

Distinguish• What is all pervasive

• What has been adopted by some but not by others

• What is ready to be deployed

• What is still ‘blue skies’ research

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Informatics

Input

Transmit

Store

Process

Retrieve

Transmit

Output

Home

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Healthcare Informatics

Input

Transmit

Store

Process

Retrieve

Transmit

Output

Agent

Location

PatientDoctor

Nurse…

HomeSurgery

…Hospital

Home

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Computers vs Humans

Research and development

Treatment

Reasoning (diagnosis and treatment planning)

Processing

Data Acquisition, Storage and Retrieval

Data Transmission

Computer

Human

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Data Transmission

Use ‘standard’ technologies• fax• phone• email• intra-net• web• etc.

Encryption

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Telemedicine

Patient (home)

GP

Local Hospital

Remote Hospital

National serviceRemote monitoring for chronic illnesses

• Diabetes• Renal failure• Cardiac problems•…

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Telemedicine

Patient (home)

GP

Local Hospital

Remote Hospital

National service

24 hour call centres• NHS Direct (England & Wales)• NHS 24 (Scotland)

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Telemedicine

Patient (home)

GP

Local Hospital

Remote Hospital

National service

Sharing expertise• Video conferencing• Data sharing

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Data Acquisition

• Human input

• Unstructured - e.g. word documents

• Semi-structured - e.g. free text in an input box

• Structured (coded) – selection from a fixed list, check boxes, etc

• Machine input (often with analogue/digital conversion)

• Single point in time Laboratory results Images

• Continuous Physiological data

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X-Rays

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CT Scans

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ECG

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ICU

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Data Storage and Retrieval

• ‘Standard’ database technologies

• Can be very large • especially if picture achieving is involved

(terabytes – 1012 bytes)

• Coding is a real problem• signs (what the doctor can see and feel)

• symptoms (what the patient says)

• diagnoses

• treatments

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Formal medical languages

• Name objects and events in the external world

• Need for sharing

• Computerisation increases the need for precision• communication

• audit

• research

• resource management

• decision support

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• Need for structuring• retrieval

• abstraction

• Hierarchies – dimensions/attributes/axes

part-whole

body

arm

hand

finger

kind-of

infection

hepatitis

viral hepatitis

hepatitis-A

causal

plaque

thrombosis

infarction

arrhythmia

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Enumerative

• List all the possibilities in advance (and structure them)

• International Classification of Diseases (ICD) 9/10

Compositional

• Agree on a set of primitives which are combined acute bacterial septicemia

• Knowledge which controls the way in which terms can be combined can’t say: a fractured lung can say: a fracture of the second bone in the third toe of the left

foot

Common

• SNOMED, UMLS (USA)

• Read (UK)

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Processing

Repetitious and formalised computations• construction of CT scans

• 3-D reconstruction

• image analysis

• ECG analysis

• radiation dose calculation

• finance and accounting

• administration

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ReasoningDiagnostic/Therapeutic Cycle

Patient

Observation

Diagnosis(Interpretation)

TherapyPlan

Observe

Reason Reason

Treat

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Clinical Guidelines and Protocols

Clear statements of the optimal management for a specific group of patients which, when properly applied, will improve the quality of the care they receive.

Guideline• often formulated nationally or internationally• often evidence-based• widely disseminated

Protocol• more detailed• local (one clinician or group of clinicians)• often mandatory

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Computerised Protocols

Represent the protocol in a formal language

Apply the protocol automatically to the electronic patient record (EPR)

Present the advice from the protocol to the doctor or nurse

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Aberdeen Research

• Neonatal Intensive Care

• BabyTalk

• Computerised Guidelines

• TSNet

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A Neonatal ICU

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Data in intensive care

Continuous:

Monitor (one second resolution) : heart rate, blood pressures, O2, CO2,

temperatures ... (i.e. 86,400 samples/channel/patient/day)

Sporadic:

Ventilator : Mode, pressures, FiO2, respiration rate …

Incubator : O2, temperature, humidity

On-ward blood gases : pH, pO2, pCO2 ...

Laboratory : Haemoglobin, Na, K, Urea …

Manual: Notes, medication, ...

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Complex high volume data

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BabyTalk

Textual summarisation of Neonatal ICU data

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Experiment: Graphs vs. Text

To compare the effects of • different presentations – graphical and textual

• of the physiological history of a neonate

• on decision-making

• in terms of 'accuracy‘ response time

Hypothesis• clinical staff will make more accurate decisions when informed by

graphical displays than by textual summaries.

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Graphs

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Text

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Appropriate Actions

Proportion of total appropriate actions identified

Mean; Whisker: Mean-SE, Mean+SE

Graph Text

JN IN SN JD SD

GROUP

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

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BabyTalk

EPSRC funded project• 4 years

• 2 research fellows and 2 research students

BT- 45: Replicate the original experiment with automatically generated text

BT- Doc: Summarise several hours for clinical decision support

BT- Nurse: Generate 12 hour nurse shift summary

BT- Family: Reports for family members

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BabyTalk Architecture

Data Abstraction

Pattern Recognition

Content Determination

Text Generation

Numerical and Interval

Interval

Interval ++

Interval --

Text

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AutomaticMedical staff have no time to answer questions

Protocols in the ICU

Clinical autonomy• advice must always be advisory, not mandatory• must cater for differences in practice

between units within units

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Timing

• many medical decisions made at a daily or weekly ‘encounter’ with the patient

• ICU: continuous WHAT to do WHEN to do it

• advice provision system often has access to actions taken by staff


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Access to data

• humans and computers have different ‘windows’ on to the patient

• computers have: monitor data, lab results, etc.

• humans have this plus: sight, touch, sound, (smell, (taste)) actions taken


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Data abstraction

• protocols expressed in higher level clinical terms than the raw patient data

• try to ‘reconstruct’ sensory input

• deal with artefacts


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Run-time Architecture

Abstraction Execution Engine

Patient Data

Visualisation

Formal Protocol

Recommendations

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Protocol

Acquisition

• published guidelines

• local manuals

• knowledge-acquisition interviews with clinicians

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Example Protocol

Maintain suitable oxygen (O2) level in the blood … by adjusting the fraction of inspired oxygen (FiO2) on the ventilator as follows:

O2-High

O2-Low

O2

FiO2 -

FiO2 +

8 kPa

6 kPa

if the O2 is above 8 kPa then reduce the FiO2 by 5%

if the O2 is below 6 kPa then increase the FiO2 by 10%

otherwise do nothing

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Formal Languages for Guidelines and Protocols

Guide

Prodigy

GLIF

SAGE

EON

ProForma

Asbru (Shahar, Miksch and Johnson,1998)

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Protocol Translation

Difficult and time consuming

Various approaches to (semi) automatic translation

Need for verification:• graphical presentations

• ? text

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Example Protocol in Asbru

<if-then-else> <simple-condition> <comparison type="greater-than"> <left-hand-side> <parameter-ref name="PtcO2"/> </left-hand-side> <right-hand-side> <numerical-constant value="8"/> </right-hand-side> </comparison> </simple-condition> <then-branch> <variable-assignment variable="REC_SETTING:VENTILATOR:Rec_FiO2"> <operation operator="subtract"> <parameter-ref name="VENTILATOR:FiO2"/> <numerical-constant value="5"/> </operation> </variable-assignment> </then-branch></if-then-else> Coded by hand

if the O2 is above 8 kPa then reduce the FiO2 by 5%

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Data Abstraction

Compression – median value every 60 seconds

Artefact removal (Cao et al., 1999)• limit-based detector

flags as artefact values outside extreme centiles

• deviation-based detector flags as artefact values which cause the standard deviation to exceed a limit

• correlation-based detector: uses lower standard deviation limits when a ‘correlated’ channel is flagged

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Detailed Architecture

Rec_Resp_Rate

MD[OX]+CMD[OX]OX

CO

Median

Median

ArtiDetector AsbruRTM

MD[CO]

Rec_FiO2

FiO2

Resp_Rate

Guideline

MD[CO]+C

channel

filter

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Results

TSNet

A Distributed Architecture for Time Series Analysis

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Need for collaborationand sharing

abstraction of complex time series is difficult

need to combine experience

demonstration of generality to help acceptance and standardisation

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Realities

the ‘not invented here’ syndrome

people use different programming languages and are unwilling to re-write complex software- hence need to accommodate different languages

people may be unwilling to release source (or even compiled versions) - hence need to take the data to the algorithm

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Distribution

Internet

Group S1:

Raw data

Group S2:

Filter (Java)

Group S3:

Filter (MatLab, S+, …)

Group C1:

Management and display

Client

Servers

Group C2:


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Channels

A channel is a named data stream• equi-sampled

numerical (floating point) boolean enumerated (0, 1, ...) spectrum

• a set of intervals start and end date/time attribute and value zero length interval = event

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Examples of channels

Heart rate: equi-sampled numerical channel

Qualitative heart rate [low, normal, high]:equi-sampled enumerated channel

FiO2 (ventilator setting – fraction of inspired oxygen):interval channel

Artefact present:interval channel

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Filters

A filter is anything that processes data in channels• 0, 1, 2, .... input channels

• 0, 1, 2, .... output channels

• data source (no input channels – data from files, database, ...)

• data sink (no output channels) plot to screen write to file, database, ...

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Examples of filters

moving window: mean, median, slope, ...

segmentation

clinical guideline

equi-sampled numerical equi-sampled numerical

equi-sampled numerical interval

equi-sampled numerical interval

interval

equi-sampled numerical

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Plots and displaysA plot is a user defined visual representation of one or more channels.

A display is a user-defined collection of plots.

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Networks

F1

F2

F3

F4

F5

Data

source

P1

P2

Display

user-configurable

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Distribution

Internet

Group S1:

Raw data

Group S2:

Filter (Java)

Group S3:

Filter (MatLab, S+, …)

Group C1:


Client

Servers

Group C2:


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Client-Server architecture

Data management

User interface

External filter manager Internal filters

SOAP

CLIENT

SERVER

INTERNET

SOAP

Tomcat

Filter manager

Filters

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Demo scenario

Asbru guideline to advise on the control of blood pressure using saline and then dopamine

• mean blood pressure (1 sec)

• smooth with a moving window median filter (1 min)

• remove artefacts (use data on HR, OX and CO2 if available)

• run Asbru guideline using the Asbru interpreter

• output is an interval channel containing recommendations

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Demo at IDAMAP (Verona)Raw data source

Median filter

Artefact removal

Guideline application (Asbru)

BM

MD[BM]

MD[BM]+C

ABL(MD[BM]+C)

equi-sampled numerical (1/sec)

interval

equi-sampled numerical (1/min)

equi-sampled numerical (1/min)

ABERDEEN

internal

internal

LILLE

Plotinternal

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Demo at IDAMAP

Aberdeen

Lille

Verona

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Demo at IDAMAP

BM

MD[BM]

MD[BM]+C

ABL(MD[BM]+C)

e-Health

Documents

Transcript of e-Health