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Testing Asbru Guidelines and Protocols for Neonatal Intensive Care Christian Fuchsberger, Jim Hunter and Paul McCue.

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Presentation on theme: "Testing Asbru Guidelines and Protocols for Neonatal Intensive Care Christian Fuchsberger, Jim Hunter and Paul McCue."— Presentation transcript:

1 Testing Asbru Guidelines and Protocols for Neonatal Intensive Care Christian Fuchsberger, Jim Hunter and Paul McCue

2 Acknowledgements Clinical collaborators: Christian Popow, Neil McIntosh and Yvonne Freer Silvia Miksch UK Research Councils – ESRC and EPSRC Austrian Research Council

3 Intensive Care Intensive! –Patients with multiple problems –High rate of intervention –Increasing sophistication of available treatments –Increased levels of monitoring Errors do happen –Majority are unimportant –Some are significant Missed symptoms and signs Attentional overload

4 Intensive Care is Expensive In 2002 expenditure on health care as % of GDP: –from: 10.7% (Germany) to 6.7% (Ireland) – say 8% overall expenditure on intensive care as % of health care costs: –from 2.6% (Netherlands) to 1% in UK – say 1.5% overall European GDP ~ €10,000,000 M so ~ €12,000 M spent on intensive care every 1% saved ~ €120 M

5 Solutions? Display the data: Not demonstrated to help junior nurses and doctors. Decision support …

6 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

7 Computerised Guidelines Formal representation of a guideline Languages: –Guide, Prodigy, GLIF, SAGE, EON, ProForma, Asbru Automatic application to electronic data (EPR) Often envisaged as operation in ‘encounters’ with patient –10’s or 100’s of data items –daily or weekly –possibility of clinician data input

8 Data volume –continuous physiological data (heart rate, oxygen, carbon dioxide, blood pressures) as often as every second – 100,000’s of data items –sporadic data – lab results, blood gases, … –‘paperless’ ICU – data input from nurses and doctors Complex abstractions –bridge the gap between raw data and guideline –some data not available electronically (sight, touch, …) Automatic application –medical staff have no time to answer questions Continuous advice provision –system often has access to actions taken by staff Computerised Guidelines in Intensive Care

9 What do we need for development? Formal language: Asbru (Shahar, Miksch and Johnson,1998) Guideline (protocol) Translation of guideline Visualisation of guideline Data Abstraction Execution Engine: AsbruRTM Test Data Infrastructure Evaluation NB: All testing is off-ward

10 Architecture Data Abstraction Execution Engine Test Data Visualisation Guideline Recommendations

11 Guideline Data Abstraction Execution Engine Test Data Visualisation Guideline Recommendations

12 Guideline Maintain suitable oxygen (O 2 ) level in the blood (as measured) … … by adjusting the fraction of inspired oxygen (FiO 2 ) on the ventilator IF O2 > O2-High THEN Rec_FiO2 = FiO2 - 5 IF O2-High> O2 > O2-Low THEN Rec_FiO2 = FiO2 IF O2-Low > O2 THEN Rec_FiO2 = FiO O2-High O2-Low O2 FiO2 - FiO2 + 8 kPa 6 kPa

13 Guideline Coded by hand IF O2 > O2-High THEN Rec_FiO2 = FiO2 - 5

14 Guideline Also simple guideline to maintain suitable oxygen (CO 2 ) level in the blood (as measured) … … by adjusting respiration rate on the ventilator

15 Test Data Data Abstraction Execution Engine Test Data Visualisation Guideline Recommendations

16 Test Data Taken from the ‘Neonate’ database (Hunter, Ferguson, Freer, Ewing, Logie, McCue and McIntosh, 2003) Continuous monitoring of physiological variables –1 second; heart rate, blood pressure, O 2, CO 2 … Discontinuous numerical data –Ventilator settings, blood gas, laboratory results Discontinuous symbolic data –Observations of physical state –Actions taken by the staff

17 ‘Neonate’ Database 407 hours 31 individual babies Background information –sex, gestation, weight at birth, … Anonymised Microsoft Access Available

18 Discontinuous Data Individual records Actions19,610 Observations 1,831 Settings 4,512 Laboratory results 1,343 Blood gas 2,187 Medication 148 Comments 2,443 Observer present 403 TOTAL32,477

19 Subset Used measured O 2 (OX) sampled 1/second measured CO 2 (CO) sampled 1/second FiO 2 setting when changed respiration rate when changed actions taken

20 Data Abstraction Execution Engine Test Data Visualisation Guideline Recommendations

21 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

22 Execution Engine Data Abstraction Execution Engine Test Data Visualisation Guideline Recommendations

23 Execution Engine Based on AsbruRTM (Fuchsberger and Miksch, 2003)

24 Execution Engine CORBA Simplified and data abstraction done externally

25 Detailed Architecture Rec_Resp_Rate MD[OX]+C MD[OX] OX CO Median ArtiDetector AsbruRTM MD[CO] Rec_FiO 2 FiO2 Resp_Rate Guideline MD[CO]+C

26 Infrastructure Data Abstraction Execution Engine Test Data Visualisation Guideline Recommendations Time Series Workbench (Delphi) Asbru RTM (Java) CORBA

27 Results

28 Where now? Can we derive abstractions for more complex guidelines which may refer to data which is not available electronically? How do we deliver advice in a ‘continuous’ environment when the guideline can “see” what the clinical staff are doing? How do we integrate the work of different groups?

29 Distributed Infrastructure Clients Test Data Filters (Data Abstraction) Guidelines Servers Plots Execution Engines Guideline Visualisation Data and Recommendation Visualisation Test Management Internet

30


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