How Can Information Technology Improve ICU Service provision€¦ · ICU Service Provision Kenny...

How Can Information

Technology Improve ICU

Service Provision Kenny CHAN King-chung

Evolution of Intensive Care

Big Data in an Individual

• Natural result from Velocity &Variety

• Artifacts in readings are common

• Physiology • Machine • Lab / Radiology

• Continuous data stream

• Frequent investigations

Velocity Variety

Volume Veracity

Safe & Effective Care in ICU

Early Detection

Appropriate Decision

Effective Intervention

Change in

Condition

Timely & Logical Presentation of Data

Clinical Decision Support

Computerized Order Entry

Work List System

ICU Informatics in HK

• 1996 - First neonatal ICU • 1997 - First adult ICU • Very limited connectivity

• BP/P/HR/SpO2 • Effort of individual ICU

Next Generation - 2007

• Increasing connectivity • Mechanical Ventilators • Laboratory Information System • Access to PACS and electronic

health record

• 2017 - Installed in all adult ICUs • 3 brands and 6 versions

• 2018 - A unified system for 5 ICUs

1Q06 2Q06 3Q06 4Q06 1Q07 2Q07 3Q07 4Q07 1Q08 2Q08 3Q08 4Q08OP 0 0 0 0 0 0 0 0 0 0 0 0IP 7 5 8 7 8 1 5 0 0 0 3 3

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ICU Medication Incidents

OPIP

Unified ICU Workstation

• Information gathering • Physiology data from monitors • Laboratory results • Radiology image & results • Physiological tests (ECG/EEG/etc) • Pathology results • Medication administration

record • Setting of life-support equipment • Past medical notes • Reference materials

• Documentation / Ordering • Clinical notes / Consultation

notes • Checklists / Structured forms • Prescription & Administration

(close loop) • Life support machine setting • Fluid / Blood products / Nutrition • Medication / Medication infusion • Nursing prescription • Ordering for investigations • Booking for endoscopy /

operative procedure • Admission / Discharge / Transfer

Level of Intelligence

Level Data Processing

Domain Knowledge Analytics Capability

Clerk + - - Collect comprehensive data

Secretary ++ + - Summarize data in a logical manner

Assistant +++ ++ + Remind clinicians on patient care

Partner ++++ +++ ++ Relied on for particular task

Teacher +++++ ++++ +++ Broaden medical knowledge

Types of Big Data Analytics

• Descriptive Analytics • Use data aggregation and data mining to provide insight into the past • “What has happened?”

• Predictive Analytics • Use statistical models and forecasts techniques to understand the future • “What could happen?”

• Prescriptive Analytics • Use optimization and simulation algorithms to advice on changing outcomes • “What should we do?”

Historical Data Present Data

Outcome Predictive Algorithm

Prescriptive Algorithm

Intervention Intervention

Intervention Intervention

Descriptive Analytics

Information System as Clerk or Secretary

• Descriptive Analytics • Administrative data • Process of Care • Outcome of Care

• Available form ICU’s system and Hospital’s system

• Relatively mature

Severity of Patient Admitted to ICU

ICU Readmission Rate

Use of Big-Gun Antibiotics in ICU

0

1

10

100

Day

s be

twee

n C

ases

Date of MRSA

g-Chart for ICU Acquired MRSA Run Length = 20

Cluster Analysis of MRSA Infection

Ventilator Associated Pneumonia

• Major morbidity for ICU patients • Incidence data difficult to obtain

• Ventilator Associated Events • Proposed by CDC • Stable for 2 days (baseline)

• Same or decreasing daily min FiO2 or PEEP • Worsening (above baseline)

• Rise of daily min FiO2 > 0.2 or • Rise of daily min PEEP > 3cmH2O

• Automatic capture

Information Overload (or Filter Failure)

• Automated Journalism • Create case summary • Handover between

clinicians • Communication with

patient and family

• Chatbot • Provide standardised

information • Update patient’s progress

(Smart Health Artificial Intelligence Lab Activity)

Predictive Analytics

• Available since 1980s for Hospital Mortality prediction • Different approach and versions available

• Mainly for quality assurance • Risk-adjustment

• Predict the expected outcome • Compare with the actual outcome • Observed / Expected (or the O/E ratio)

will provide information on quality of care

Hospital Mortality for ICU Patients

ICU Length-of-Stay Ratio

Prescriptive Analytics

• “Expert System” in use for many years

• Rule-based reminder • Blood transfusion • Nutritional intake

• Model-based therapeutics • Target-controlled infusion • “Smart ventilator modes”

• ?? Data-analytic based system

Over Reliance on Analytics

• Association ≠ Causation

• Black box algorithm

• Self fulling prophecy

• Discrimination in treatment

Our Way Forward • Unified ICU data and system • Enhance data analytic capability

• Platform for Doctors & Data Scientists