Corresponding author: Leandro Pecchia. [email protected] 15 November 2013
AHP
Algorithms and tools to support medical decision making
-Case studies-
Leandro Pecchia
AHP
Corresponding author: Leandro Pecchia. [email protected] 2/32
RESEARCH BACKGROUNDRESEARCH BACKGROUNDMe
2005 2009 2011 2013
RF1UNINA
RF2NOTT
Ass. Prof.PHD
2007
Career path:2013-2016: Assistant Professor, University of Warwick
2011-2013: Research Fellow (RF2), University of Nottingham
2009-2011: Research Fellow (RF1), UNINA*
2005-2009: PhD in Biomedical Engineering, UNINA*
May 2005: BSc+MSc in Electronic Eng., UNINA*
*UNINA= University Federico II of Naples, Italy
Research interests: Biomedical signal processing and second level pattern recognition/data-mining
Early stage Health Technology Assessment (HTA) and User Need Elicitation methods
My main applications: active/healthy ageing: chronic cardiovascular diseases and falls in elderly
Disease Management Programs, patient pervasive monitoring and Telemedicine
Ω≈f(stress, fat, salary, ↓ free-t,…)
AHP
Corresponding author: Leandro Pecchia. [email protected] 3/32
Contributions and outputs (1/2)Contributions and outputs (1/2)Me
Signal processing and pattern recognition for Cardiovascular disease (CVD)to identify Congestive Heart Failure (CHF) [early diagnosis]
2011, “Long-term HRV & CHF detection ”, Med & Biol Engin & Computing, 49 (1):67- 74
2011, “Short-term HRV & CHF detection”, IEEE T Inf Technolog in Biomed, 15 (1):40-46.
to manage chronic CVD monitoring its damages and severity…[early detection of risks]2012, “HRV &Organ Damage in Hypertension”, BMC Cardiovascular Disorders, 12:105
2013, “Long-term HRV & CHF severity assessment”, IEEE J. of Biom. and Health Informatics, 17(3): 727-733
…also in remote monitoring applications: [telemedicine]2011, “Remote Health Monitoring of CHF”, IEEE T Bio-Med Eng, 58 (3):800-804
2011, “A feasibility study on telemedicine”, Biomedical Engineering Online, 10: 49
Other signal processing and pattern recognition applications2011, “Nonlinear HRV for real-life stress detection”, Biomedical Engineering Online 10: 96
2012, “Pupillometric analysis for assessment of gene therapy”, Biomedical Engineering Online,11(1):40
2013, “Infant cry analysis for early detection of Autism”, ICHI2013 (+ submitting 2 journal papers)
AHP
Corresponding author: Leandro Pecchia. [email protected] 4/32
Contributions and outputs (2/2)Contributions and outputs (2/2)Me
Medical decision making is complex and multidisciplinary and needs:Quantitative knowledge: from the best available evidence (RCT, meat-analyses, network meta-analyses)
Qualitative knowledge: to interpret the top of the EBM pyramid into everyday clinical practice
Methods for the “impact” of BME researches: quantify qualitative knowledgeUser need elicitation using the Analytic Hierarchy Process (AHP) method
2013, “User needs elicitation via AHP”, BMC Medical Informatics and Decision Making, 3(1):2
2013, “AHP & auto-injection of epinephrine”, HIS2013, 25-27 March 2013 in London.
2011, “Factors affecting wellbeing in elderly”, ISAHP 2011, Sorrento, Naples, Italy.
Health Technology Assessment (HTA), especially for early stages of technology development2013, “HTA & AHP”. In Studies in Fuzziness and Soft Computing, ed. Springer, Volume 305,
2013, “ HTA, Telemedicine, CHF”. In Telehealthcare Computing and Engineering: Principles and Design. Science Publishers. ISBN 978-1-57808-802-7 (book chapter)
2013. “Enhanced Remote Health Monitoring.. In Telehealthcare Computing and Engineering: Principles and Design, ed. Science Publishers. ISBN 978-1-57808-802-7 (book chapter)
2012, “Network meta-analysis & mini-invasive surgery”, Surgical Endoscopy, 2012 Jun 16, [Epub ahead of print]
2012, “RCT for innovative biological drug”, Hernia, 20 November 2012 Nov, [Epub ahead of print]
2011, “Meta-analysis & minimally invasive surgery”, Minimally Invasive Therapy & Allied Technologies, 21(3):150-60
June 2012, Treasurer of the HTA Division of International Federation of BME, IFMBE
Risk factors for falls in elderly home dwellingMany intrinsic risk factors are related to physiological condition that can be detected
2011, “Risk factors for falls”, Methods of Information in Medicine, 50 (5):435
2010, “Risk factors for falls”, International Journal of the Analytic Hierarchy Process, 2 (2)
Corresponding author: Leandro Pecchia. [email protected] 15 November 2013
AHP
-Case study 1-SHARE Project*:
home monitoring for patients suffering from Congestive Heart Failure (CHF)
Leandro Pecchia
*Smart Health and Artificial intelligence for Risk Estimation grant PON04a3_00139 to PM; 2007-2013
Italian National Operational Programme for Research and Competitiveness.
PI: Dr Paolo Melillo
AHP
Corresponding author: Leandro Pecchia. [email protected] 6/32
INTROINTRO
• Congestive Heart Failure (CHF) is • leading cause of hospitalization among the elderly in developed countries • up to 50% of patients are rehospitalized within 3 months.• Mortality ranges from 10% in patients with mild HF to 40% in severe cases• COSTS: direct treatment costs of HF represent 2–3% of the total healthcare budget
• In literature there are three main models of care:• Usual care (UC): outpatient follow-up (GP guided) as recommended by guideline• Disease Man. Programs (DMP): UC + specialized doctors/nurses proactively at home• Home Monitoring (HM): DMP + Information and Communication Technologies (ICT)
• The goal of HM, DMP and UC for CHF is to:• ↑QoL (or at least maintain stable)• ↓mortality (for CHF and for all causes),• ↓NHS costs by: ↓ bed days (CHF/all causes); ↓readmissions (CHF/all causes)
INTROCS1
AHP
Corresponding author: Leandro Pecchia. [email protected] 7/32
GOALSGOALS
• Not all the HM is equally important and independent contribution of ICT is unclear
• Thus, the goals of the SHARE project are:• To identify those elements that make HM more effective than DMP and UC (WP1)• To design the most effective HM program • To develop the ICT system to support such a HM• To test its cost-effectiveness with 2 clinical trials
GOALS
N° Mese 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
Anno
Durata
Giu
gn
o
Lu
gli
o
Ag
ost
o
Set
tem
bre
Ott
ob
re
No
vem
bre
Dic
emb
re
Gen
nai
o
Feb
bra
io
Mar
zo
Ap
rile
Mag
gio
Giu
gn
o
Lu
gli
o
Ag
ost
o
Set
tem
bre
Ott
ob
re
No
vem
bre
Dic
emb
re
Gen
nai
o
Feb
bra
io
Mar
zo
Ap
rile
Mag
gio
Giu
gn
o
Lu
gli
o
Ag
ost
o
Set
tem
bre
Ott
ob
re
No
vem
bre
Dic
emb
re
Gen
nai
o
Feb
bra
io
Mar
zo
Ap
rile
Mag
gio
WP1 6 mesi
WP2 15 mesi
WP3 15 mesi
WP4 14 mesi
WP5 12 mesi
WP6 -
2012 2013 2014 2015
WP1: HTA of HMWP2: HM and ICT platform R&D
WP3: Algorithms R&D WP4: Observational Study
WP5: Prospective studyWP6: PM/Dissemination
CS1
AHP
Corresponding author: Leandro Pecchia. [email protected] 8/32
METHODS 1/2METHODS 1/21. Meta-analyses were performed comparing DMPvsUC & HMvsUC
• Only well designed RCT were included• Outcome considered:
o ↑QoL (or at least maintain stable)o ↓mortality (for CHF and for all causes),o ↓ bed days (CHF/all causes); o ↓readmissions (CHF/all causes)
2. Classified all the HM RCT according to: patients’ severity and HM complexity
3. We studied the correlations between the last 6 outcomes and • patients’ severity• HM complexity
4. According to these, the clinical trial and the ICT platform were designed
5. Algorithms for early detection of CHF worsening were • Developed using public DB available• Adapted using the information collected during the WP4 [now]• Integrated into the ICT platform
[2014]
• Assessment of cost-effectiveness in WP5 [2016]
METHODSCS1
AHP
Corresponding author: Leandro Pecchia. [email protected] 9/32
METHODS 2/2METHODS 2/2• HM RCT were classified according to patients’ severity and HM complexity
• Correlations effectiveness-severity and effectiveness-complexity were computed
METHODS
PATIENTSEVERITY
NYHA CLASSES
MEAN PATIENTS
AGE
75 -
70 -
EJECTION FRACTION
FREQUECY OF THE MONITORING
Parameters &
symptoms
HM COMPLEXITY
OnlySymptoms
Signal & Parameters
& symptoms
DATA MONITORED
CS1
AHP
Corresponding author: Leandro Pecchia. [email protected] 10/32
RESULTSRESULTS
• Study selection:
RESULTS
314 papers
23 DMP vs UC8 HM vs UC1DMP & HM vs UC
32 papers
Full paper selection:1 Not heart failure7 Editorial or review16 not an RCT40 Other heart failure intervention or research10 Study design4 Other languages5Short Follow-up
115 papers
Title and abstract seletion:8 Not heart failure18 Invasive hemodynamic monitoring43 Editorial or review70 not an RCT41 Other heart failure intervention or research19 Study design
CS1
AHP
Corresponding author: Leandro Pecchia. [email protected] 11/32
RESULTSRESULTSRESULTS
•There is evidence that DMP are more effective that UC•Reducing All-causes mortality•Reducing readmission (All-causes and HF-related)
•It seems, but there is not evidence that DMPs reduce bed-days
CS1
AHP
Corresponding author: Leandro Pecchia. [email protected] 12/32
RESULTSRESULTS
• Mortality: HM vs UC
All causes HF mortality
RESULTS
The HM RCT seems (no statistically significant result) more effective than UC
CS1
AHP
Corresponding author: Leandro Pecchia. [email protected] 13/32
RESULTSRESULTS
• Readmission: HM vs UC
All causes HF mortality
RESULTS
The HM RCT seems (no statistically significant result) more effective than UC
CS1
AHP
Corresponding author: Leandro Pecchia. [email protected] 14/32
RESULTSRESULTS
• Bad days: HM vs UC
All causes HF mortality
RESULTS
The HM RCT seems (no statistically significant result) more effective than UC
CS1
AHP
Corresponding author: Leandro Pecchia. [email protected] 15/32
RESULTSRESULTS
• HM RCT classification: Patients’ severity
RESULTS
NYHA Ejection Fraction Mean AgePatient Severity
Sherr 2008 3 <40 73 8Dendale, 2011 3 <40 76 7Dar, 2009 3 >40 72 6Koehler,2011 2&3 <35 67 5Antonicelli, 2010 2&3 <40 78 4Soran, 2008 2&3 <40 76 4Kulshreshtha, 2010 2&3 <40 68 3Giordano, 2010 2 <40 70 2Mortara, 2009 2 <40 60 1
CS1
AHP
Corresponding author: Leandro Pecchia. [email protected] 16/32
RESULTSRESULTS
• HM RCT classification: HM protocol complexity
RESULTS
Simptomps Parameters Signals Frequency Complexity
KOEHLER, 2011 x ECG Daily 6
KULSHRESHTHA, 2010 X Daily 5
DAR, 2009 X X Daily 4
SORAN, 2008 X X 4
GELLIS, 2012 X Daily 4
SHERR, 2008 X Daily 3
DENDALE, 2011 X Daily 3
ANTONICELLI, 2010 X X ECG Weekly2
MORTARA, 2009 X X ECG Weekly2
GIORDANO, 2010 X ECG Each 15 days1
CS1
AHP
Corresponding author: Leandro Pecchia. [email protected]
RESULTSRESULTS
• HM outcomes vs Pz complexity and HM complexity:
RESULTSPA
TIEN
T SE
VERI
TYH
M
COM
PLEX
ITY
Survival Saved Bed days Reospedalization
No significant correlation
No significant correlation
No significant correlation
• Evidence:• HM is more effective for more severe patients
(SHARE now focus on NYHA >2)• More complex HM are more effective (SARE
is designed accordingly)17/32
CS1
significant correlation
significant correlation
significant correlation
AHP
Corresponding author: Leandro Pecchia. [email protected]
RESULTSRESULTS
• How this informed the SHARE the clinical protocol?o Enrolling a proper number of severe cases [about 300 subjects in 12 months]o Acquiring daily useful symptoms, parameters and signalso These info will be daily reviewed to early detect patients’ worsening
• …and how the ICT platform?
• The DSS (WP3) will support clinician in modulating patient therapyo What these algorithms does and how they look like?
RESULTS
18/32
NEXUS10(4), MindMedia-sensors: - EXG: ECG, EMG, EOG, EEG- SpO2, BVP, Body Temperature, Breathing acts, GSR
-Communication: Bluetooth-Memory: Up to 7 days recording memory-Costs: from £5k to £12k, according to the sensors
BioHarmess3, Zephyr-sensors: ECG, 3axial accellerations, Breathing-up to 3 days recording memory-Communication: Bluetooth/ZigBee-Memory: Up to 7 days recording memory-Cost: £350 (≤10) or £200 (>10)
Healthcare Professionals
(App)
BIOMEDICAL SIGNALPROCESSING
(wearable devices)
REMOTE PROCESSING
(DDS)
WARNING
APPROPRIATE INTERVENTION
CS1
AHP
Corresponding author: Leandro Pecchia. [email protected]
RESULTSRESULTSRESULTS
19/32
• Autonomous Nervous System (ANS) controls human equilibrium (homeostasis)• Normal subjects show a good degree of variability in body functionalities, reflecting a continuous state of unstable equilibrium • Unstable equilibrium is complex to control, but allows faster state changes• This allow humans to react promptly to:
– internal changes (i.e. emotions, stress)– external treads (i.e. the lion…)
• Monitoring these changes we can estimate the status of a subject and how stable it is…
CS1
AHP
Corresponding author: Leandro Pecchia. [email protected]
RESULTSRESULTSRESULTS
20/32
Signal processing: features extractionin time-, frequency-, nonlinear- domain
Pattern recognition: signal/patient classifications“normal vs CHF”, “mild vs severe”, “damage vs sane”
Signal pre-processing: filtering, beat recognition (normal vs abnormal),…
Peculiarities of these signals:Bandwidth (0- few tens of Hz)Low S/N ratio in bandNo stationary (FFT cannot be used!)Strong non linearity and high dependence from parameters (chaos?)
Problems for pattern recognitionLimited casesNatural patterns (no human-generated)Last but not least…
…our methods/results are needed by clinicians that cold be not skilled in mathematical methods!
CS1
AHP
Corresponding author: Leandro Pecchia. [email protected]
RESULTSRESULTSRESULTS
21/32
ECGPREPROCESSING
HRVDETECTION
HRVFEATURES EXTRAC.
CARTTRAIN/TEST
FEATURESCOMBINATION
PERFORMANCEASSESSMENT
Node 1TOTPWR<451
Node 2TOTPWR<212
Node 3LF/HF<1.35
Node 4RMSSD <12.4
Node 5LF/HF<2.44
Node 6RMSSD <16.3
Node 7RMSSD <28.9
Node 8LF/HF<1.42
Node 9HF < 115
Leaf 1Class = 1
Leaf 2Class = 1
Leaf 6Class = 1
Leaf 3Class = 1
Leaf 4Class = 0
Leaf 7Class = 0
Leaf 8Class = 0
Leaf 9Class = 1
Leaf 10Class = 0
Leaf 5Class = 0
Detection: long & short HRV(Guidelines says that 12-leads ECG is not enough to diagnoses
HF)
Severity (NYHA) assessment
HRV to identify Congestive Heart Failure (CHF)A) 2011, Med & Biol Engin & Computing 49 (1):67- 74
B) 2011, IEEE T Inf Technolog in Biomed 15 (1):40-46.
HRV to manage CHF monitoring its severity…C) IEEE J. of Biom. and Health Informatics, 17(3): 727-733
D) BMC Cardiovascular Disorders, 12:105 [organ damages]
A) B) C)
CS1
AHP
Corresponding author: Leandro Pecchia. [email protected] 22/32
CONCLUSIONSCONCLUSIONSCONCLUSIONS
• Not all the HM strategies are equally effective:• HM is more effective on more severe patients• more complex HM interventions seems more effective than less complex ones
• However, the increased quantity of information requires:• Reliable technological solution• Smart algorithms to extract the useful information• Integrated management strategies
• The preliminary results of the algorithms developed are promising on public DB
• These SHARE trials will generate reliable databases for the adaptation of these algorithms.
Corresponding author: Leandro Pecchia. [email protected] 15 November 2013
AHP
-Case Study 2-A software tool to support
the Health Technology Assessment (HTA) and the user need elicitation of medical devices via the Analytic Hierarchy Process (AHP)
L. Pecchia1, F. Crispino2, S. Morgna3
1 University of Warwick, The United Kingdom2 Business Engineering, Avellino, Italy3 University of Nottingham, The United Kingdom
AHP
Corresponding author: Leandro Pecchia. [email protected] 24/32
AHP for HTA & User Need Elic.AHP for HTA & User Need Elic.HTA/UNE
MULTIDIMENSIONAL EVALUATION
IDENTIFY EXISTINGTECHNOLOGIES
CLINICALEPIDEMIOLOGICAL
ECONOMICAL …ETHIC/SOCIAL
DATA ANALYSIS
RELATIVE ASSESSMENT
NEED ANALYSIS
PRIORITIZATIONINDIVIDUATION CLASSIFICATION
PERFORMANCEEFFICACY EFFICIENCY
How to prioritize the needs?
How to measure the fitting between MD performance and needs??
How to measure the MD performance in non-clinical domains?
INTRODUCTION METHOD RESUTLS COMCLUSIONS
CS2
AHP
Corresponding author: Leandro Pecchia. [email protected]
Developing (or selecting) the health technology for a clinical problem(i.e. congestive heart failure)
Developing (or selecting) the health technology for a clinical problem(i.e. congestive heart failure)
25/32
↓ mortality↓ mortality ↓ worsening↓ worsening ……usabilityusability educationeducation serviceservice …… Initial costInitial cost ReadmissionC.ReadmissionC.↑ qaly↑ qaly ……
Technological domain(services/spare parts/ Human F)
[Medical Eng.]
Technological domain(services/spare parts/ Human F)
[Medical Eng.]
Economical domain(costs)
[Hosp. Managers]
Economical domain(costs)
[Hosp. Managers]
AHP for HTAHierarchy via an exempla
Clinical domain(effectiveness/utility)
[clinicians/cardiologists/ger.]
Clinical domain(effectiveness/utility)
[clinicians/cardiologists/ger.]
ALTERNATIVE 1Disease Management Program
ALTERNATIVE 1Disease Management Program
ALTERNATIVE 3ALTERNATIVE 3Active Implantable DeviceActive Implantable Device
ALTERNATIVE 3ALTERNATIVE 3Active Implantable DeviceActive Implantable Device
ALTERNATIVE 2ALTERNATIVE 2TelemedicineTelemedicine
ALTERNATIVE 2ALTERNATIVE 2TelemedicineTelemedicine
AHP
How important is each need for the assessment? [needs prioritization]How each alternative satisfy each factor? [MD performance]How each alternative fit with the goal? [MD/Goal fitting]
INTRODUCTION METHOD RESUTLS COMCLUSIONS
CS2
AHP
Corresponding author: Leandro Pecchia. [email protected] 26/32
N1 N2 N3N1 1 I21 I31
N2 I12=1/I21 1 I32
N3 I13=1/I31 I23=1/I32 1
N1 N2 N3N1 1 I21 I31
N2 I12=1/I21 1 I32
N3 I13=1/I31 I23=1/I32 1
AHP method pairwise comparisons Process
Much less important
Less important
Equally important
More important
Much more
Numerical values
N1>N2 & N2>N3 =>
N1 >> N3
N1 > N3
N1 < N3
(5)
(3)
(1)
(1/3)
(1/5)
NEEDS’ INDIVIDUATION
JUDGEMENTS MATRIX (J)
TREE OF NEEDS
DATA POOLING
QUESTIONNAIRES
RELATIVE IMPORTANCE OF NEEDS
ALTERNATIVES’PERFORMANCE ASSESSMENT
ALTERNATIVES’ PRIORITIZATION
CONSISTENCY RATIO(CR)
IFCR >0.1 Eigenvector
(priorities)
Eigen value(coherence)
AHP
EXPERTS
INTRODUCTION METHOD RESUTLS COMCLUSIONS
RELATIVE IMPORTANCE OF NEEDS’ CATEGORIES
Need 1(↓mortality)
important henmuch less
lessequallymoremuch more
is :Need 3
(↑QALY)
Need 3(↑QALY)
important henmuch less
lessequallymoremuch more
is:Need 2
(↓Pz. worsening)
Need 2(↓Pz. worsening)
important henmuch less
lessequallymoremuch more
Is:Need 1
(↓mortality)
Need 1(↓mortality)
important henmuch less
lessequallymoremuch more
is :Need 3
(↑QALY)
Need 3(↑QALY)
important henmuch less
lessequallymoremuch more
is:Need 2
(↓Pz. worsening)
Need 2(↓Pz. worsening)
important henmuch less
lessequallymoremuch more
Is:Need 1
(↓mortality)
According to your experience, how important is each need on the left compared with each one on the right?
CS2
AHP
Corresponding author: Leandro Pecchia. [email protected] 27/32
AHP method Analytic needs prioritization
Method
↓ worsening↓ worsening ↓ mortality↓ mortalityusabilityusability educationeducation serviceservice Initial costInitial cost ReadmissionC.ReadmissionC.↑ qaly↑ qaly
Developing (or selecting) the health technology for a clinical problem(i.e. congestive heart failure)
Developing (or selecting) the health technology for a clinical problem(i.e. congestive heart failure)
Technological domain(services/spare parts/ Human F)
[Medical Eng.]
Technological domain(services/spare parts/ Human F)
[Medical Eng.]
Economical domain(costs)
[Hosp. Managers]
Economical domain(costs)
[Hosp. Managers]
Clinical domain(effectiveness/utility)
[clinicians/cardiologists/ger.]
Clinical domain(effectiveness/utility)
[clinicians/cardiologists/ger.]
11
Cat
SCW1
2Cat
SCW1
3Cat
SCW
21
Cat
SCW2
2Cat
SCW2
3Cat
SCW3
1Cat
SCW3
2Cat
SCW
1Cat
CW2Cat
CW3Cat
CW
1GW 2GW 3GW 4GW 5GW 6GW 7GW 8GW
11
111
1 *
CatCatCat
SC SCWCWGWGW
INTRODUCTION METHOD RESUTLS COMCLUSIONS
ALTERNATIVE 1Disease Management Program
ALTERNATIVE 1Disease Management Program
ALTERNATIVE 3Active Implantable Device
ALTERNATIVE 3Active Implantable Device
ALTERNATIVE 2Telemedicine
ALTERNATIVE 2Telemedicine
CS2
AHP
Corresponding author: Leandro Pecchia. [email protected]
AHP for HTAAHP
28/32
ALTERNATIVE 1DMP
ALTERNATIVE 1DMP
ALTERNATIVE 2Telemedicine
ALTERNATIVE 2Telemedicine
↓ worsening↓ worsening
↓ mortality↓ mortality
usabilityusability
educationeducation
serviceservice
Initial costInitial cost
ReadmissionC.ReadmissionC.
↑ qaly↑ qaly
ALTERNATIVE 3Active Implantable D.
ALTERNATIVE 3Active Implantable D.
Global importance
Global importance
1GW
2GW
3GW
4GW
5GW
6GW
7GW
8GW
11
AP
12AP
13AP
14AP
15AP
16AP
17AP
18AP
21
AP
22AP
23AP
24AP
25AP
26AP
27AP
28AP
31
AP
32AP
33AP
34AP
35AP
36AP
37AP
38AP
8
1
*i
Ajiij PGWeAlternativPerfomanceGlobal
INTRODUCTION METHOD RESUTLS COMCLUSIONS
CS2
AHP
Corresponding author: Leandro Pecchia. [email protected]
• Method: AHP for HTA/User need elicitation• Applications: Publication in Healthcare whit the App• Models: to be downloaded and adapted in your study• Community: experts willing be involved
The systema web tool with App
AHP
29/32
http://www.ahpapp.net/
INTRODUCTION METHOD RESUTLS COMCLUSIONS
CS2
AHP
Corresponding author: Leandro Pecchia. [email protected] 30/32
AHP for HTAHierarchy via an exempla
AHP
INTRODUCTION METHOD RESUTLS COMCLUSIONS
• Users:• The elicitor:
• design/pilot the hierarchy and the questionnaires, • invites domain experts and final responders, • pool the results; • generate the report;• publish the results on the web portal.
• The domain expert: • review the hierarchy/questionnaires, • suggest final responders or other domain experts;
• The final responder:• under invitation, • download the hierarchy• answer the questions.
CS2
AHP
Corresponding author: Leandro Pecchia. [email protected] 31/32
AHP for HTAHierarchy via an exempla
AHP
• Two possible scenarios:• S1: Local
• elicitor, domain experts and the final responders in the same place • Using the APP to speed-up the process and find consensus
• S2: Remote• elicitor, domain experts and the final responders NOT in the same place, • Using the APP and the portal to cooperate to the study via the web.
• Functionalities:• Create the Hierarchy: problem definition/hierarchy draft• Download an existing hierarchy: to be used as starting model• (only S2) Invite domain experts: study piloting• (only S2) Amend the hierarchy • (only S2) Invite responders• (only S2) Participate to the study• Analyse and Pool results• Generate a report• Publish: upload on the portal hierarchy ¦¦ results ¦¦ reports¦¦papers
INTRODUCTION METHOD RESUTLS COMCLUSIONS
CS2
AHP
Corresponding author: Leandro Pecchia. [email protected] 32/32
CONCLUSIONSCONCLUSIONSConcluding
INTRODUCTION METHOD RESUTLS COMCLUSIONS
• This is the first tool specifically designed to:• perform shared decision making in healthcare• involve lay-users into the decisional process (paramount important for HTA)• applying the AHP to the HTA/the user need elicitation in healthcare
• Medical Decision making is complex (…not necessary difficult!)
• Methods have to be:• Reliable• Well tested according to clinical practices• Intelligible (no black boxes)• Easy to use/understand for people not skilled in maths• Traceable (you may have to prove that you did the best you could after years)
AHP
Corresponding author: Leandro Pecchia. [email protected] 34/33
HTA HTA standard methods
HTA
MULTIDIMENSIONAL EVALUATIONMULTIDIMENSIONAL EVALUATION
IDENTIFY EXISTINGIDENTIFY EXISTINGTECHNOLOGIESTECHNOLOGIES
CLINICALCLINICALEPIDEMIOLOGICALEPIDEMIOLOGICALECONOMICALECONOMICAL ……ETHIC/ETHIC/
SOCIALSOCIAL
DATA ANALYSISDATA ANALYSIS
DISSEMINATION DISSEMINATION OF INFORMATIONOF INFORMATION
MONITORINGMONITORING
RELATIVE ASSESSMENT (VERSUS BENCHMARK)RELATIVE ASSESSMENT (VERSUS BENCHMARK)
NEED ANALYSISNEED ANALYSIS
SCORINGSCORINGINDIVIDUATIONINDIVIDUATION CLASSIFICATIONCLASSIFICATION
PERFORMANCEPERFORMANCEEFFICACYEFFICACY EFFICIENCYEFFICIENCY
Example 3 – not cost effectiveExample 5 – highlighting data requirementsExample 1 – cost effectiveExample 4 – price optimisationExample 4 – price optimisation
EFFECT DIFFERENCE(QALY?)
COST DIFFERENCE+
+--
red
uce
pri
ce
Reduce data uncertainty
* NHS NICE willingness-to-pay ‘threshold’ range of £20,000-£30,000 per QALY.
*
AHP
Corresponding author: Leandro Pecchia. [email protected] 35/33
HTA HTA Limits of standard methods
HTA limits
VS
L Pecchia, MP Craven, “Early stage Health Technology Assessment (HTA) of biomedical devices. The MATCH experience” . World Congress on Medical Physics and Biomedical 2012, 26-31 May 2012, Beijing, China.
AHP
Corresponding author: Leandro Pecchia. [email protected] 36/33
Headroom AnalysisHeadroom Analysis
Method 1
p1
30k£/QALY
QALY
p3
DEVICE Production Costs
OTHER NHS Costs
HEADROOMp2
U
C
p’2
http://www.match.ac.uk/
AHP
Corresponding author: Leandro Pecchia. [email protected]
4 states Markov Models 4 states Markov Models disease/worsening/exacerbation/dead
MM
HAVE DISEASE(C1,U1)
DEAD(C2,U2)
1-pd
-pw-p
e
WORSE DISEASE(C3,U3)
pwpwb
↓ worsening (p’w < pw)IN
NO
VA
TE
DE
VIC
E
ped
1-peb-p
ew-ped
EXACERBATION
(C4,U4)
pwe
pwe
pe
pd
pwd
peb
HAVE DISEASE(C’1,U1’)
DEAD(C2’,U2’
)
WORSE DISEASE(C’3,U3’)
p’wpwb ped
EXACERBATION
(C4’,U4’)
pwe
P’we
p’e
pd
pwd
peb
↓ exacerbation (p’e< pe & p’we < pwe)
U
CC/U =30K/QALY
1-pw
b-pw
d-pw
e
1-pw
b-pw
d-p’w
e1-
pd-p
’w-p
’e
1-peb-p
ew-ped
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AHP
Corresponding author: Leandro Pecchia. [email protected]
Markov Models & AHPMarkov Models & AHP
MM&AHP
HAVE DISEASE(C1,U1)
DEAD(C2,U2)
1-pd
-pw-p
e
WORSE DISEASE(C3,U3)
pwpwb
INN
OV
AT
E D
EV
ICE
ped
1-peb-p
ew-ped
EXACERBATION
(C4,U4)
pwe
pwe
pe
pd
pwd
peb
HAVE DISEASE(C’1,U1’)
DEAD(C2’,U2’
)
WORSE DISEASE(C’3,U3’)
pwpwb ped
EXACERBATION
(C4’,U4’)
pwe
pwe
pe
pd ± p
pwd
peb
1-pw
b-pw
d-pw
e
1-pw
b-pw
d-pw
e1-
pd-p
w-p
e
1-peb-p
ew-ped
What if some information are missing (eHTA)?Missing data can be estimated using AHP...
…and using sensitivity analysisto estimate worst/best cases.
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