2/20/2017
1
Integrating data for drug utilisation evaluation
in the big data era
Li-Chia Chen
Senior Lecturer
Division of Pharmacy and Optometry, School of Health Sciences,
Faculty of Biology, Medicine and Health, The University of Manchester
1
• Airport 30 minutes from campus
• London only 2 hours by train
3
• 38,000 - Largest student community in the UK
• 10,000 - Most international students of any UK university
• More than 1,000 degree programmes
• £886m Annual income
• More than £297m in research income
• £300m bond issue to support Campus Masterplan
25 Nobel
Prize winners
The Manchester Pharmacy School
The Drug Usage and Pharmacy Practice Division
Centre for Pharmacy Postgraduate Education
The largest single site University in the UK
In 2004, the Victoria University of Manchester
merged with the University of Manchester Institute
of Science and Technology (UMIST).
A leading international centre for research and
education in medicine and a spectrum of health-
related professions including nursing, midwifery,
social work, pharmacy, dentistry, psychology,
audiology and speech and language therapy.
The University of Manchester, Central Manchester
University Hospitals NHS Foundation Trust,
Manchester Mental Health and Social Care Trust,
Salford Clinical Commissioning Group, Salford
Royal NHS Foundation Trust, The Christie NHS
Foundation Trust and University Hospital of South
Manchester NHS Foundation Trust.
The University of Manchester
The Faculty of Medical and
Human Sciences (FMHS)
The Faculty of Biology,
Medicine and Health (FBMH)
Manchester Academic Health
Science Centre (MAHSC)
Division of Pharmacy and
Optometry
2/20/2017
2
My clinical practice, research and teaching experiences
7
Learner
Pharmacists Patients Outcomes Physicians
• Patient-physician
communication
• Accessibility • Initiation of treatments • Maintenance of treatment • Outcome monitoring
Affordability
Engagement
Evidence-based practice Adherence • Safety
• Clinical effectiveness
• Cost-effectiveness
• Quality of life
Clinical Pharmacy Pharmacoepidemiology
Health economics
Social Pharmacy
Pharmaco-informatics
Healthcare
demand Prescribing Dispensing
Outline
• Drug utilisation research (DUR)
• Why, what and how?
• Different data sources the I used for DUR
• Issues for secondary database analysis
• Future work
• Take home message
Global trend of increasing medicine spending
• In 2012, it has been predicted that
Spending on medicines will reach
nearly $1,205 Bn in 2016.
• In 2015, Spending on prescription
drugs in the U.S. rose 12 percent to a
record $425 billion before discounts
last year.
9
http://www.bloomberg.com/news/articles/2016-04-
14/prescription-drug-spending-hits-record-425-billion-in-u-s
Pharmaco-emerging countries
• All of the BRIC countries (Brazil, Russia, India and China) will be top 10
markets in 2016 and poised for further advances.
10
Factors attribute to increasing medicine spending
11
Pharmacists Patients Outcomes Physicians
Dispensing Prescribing
Life style
Demographics
Innovation
Defensive medicine
Duplication and waste Salary cost
Expectation
Non-adherence
Pharmaceutical wastes
• The total amount of annual avoidable costs estimated in the report is almost
$500 billion, or about 8 percent of total global healthcare spending.
Advancing the responsible use of medicines, IMS Institute for Healthcare Informatics, October 2012. 12
57%
9%
13%
11% 6%
4%
2/20/2017
3
Drug utilisation research
• Rational drug utilisation
– Right drug, for the right patient, at the right dose at acceptable costs.
(WHO 1985, Nairobi)
– Considering safety, efficacy and economic
• Drug utilisation research
– “An eclectic collection of descriptive and analytical methods for the
quantification, the understanding and the evaluation of the processes
of prescribing, dispensing and consumption of medicines, and for the
testing of interventions to enhance the quality of these processes.”
13
Wettemark et al. In Pharmacoepidemiology and Risk Management, Hartaema (ed) 2008
Conceptual framework for DUR
Quantify Understand Evaluate
Test
intervention to
enhance
quality
Prescribing
Dispensing
Consumption
14
Cross national
comparisons
Health survey
Drug choice
process
Prescribing
quality indicators
Individual
prescription
feedback
Patient
education
Risk
management Drug policy
research
Patient compliance studies
Health services research
Data sources for drug utilisation research
15
Literature
• PROMs prospectively collected by
wearable device and mobile app
Patient-reported outcomes
Government statistic or
health survey
• Geographic variation
• Demographics, social economic status and
epidemiology
• Mortality and other performance indicators
e.g. UK Office for National Statistics, Office for
National Statistics, NHS Digital, NHS Wales
Shared Services Partnership
Disease or product related registry
• Outcome research
• Adverse drug events
e.g. Breast Cancer Registry for Older Women
at Nottingham City Hospital in England
Reimbursement data
• Medicine utilisation and pattern
• Adherence
• Policy evaluation
• Cost-effectiveness
e.g. National Health Institute Research Database
Hospital medical chart or
electronic medical records
• Drug utilisation research
• Adherence
• Outcome research
• Adverse drug events
e.g. Clinical Practice Research Datalink D
ata
ext
raction
D
ata
lin
kage
Government statistics and publically available data
sources
16
Data source Dataset Country
NHS Wales Shared
Services
Partnership
Annual Prescription Cost
Analysis16 Wales
NHS Digital Annual Prescription Cost
analysis England
NHS Digital Monthly practice-level
dispensing data England
NHS Digital Numbers of patients
registered at a GP practice England
NHS Digital Quality and Outcomes
Framework - 2014-15 England
Department of
Communities and
Local Government
All ranks, deciles and
scores for the indices of
deprivation, and population
denominators
England
Office for National
Statistics
Annual number of mi-year
population estimates
Deaths registration
England
Amount of opioid
prescriptions
Data linkage
NHS Digital
London practice
identification
British National
Formulary code of
opioids
Data set
Amount of opioid
prescriptions in London
Characteristics of
population
Index of multiple
deprivation
Lower-Layer Super
Output Areas (LOSA) Local Authority
Monthly practice-level
dispensing data
Practice code
Post
code
Data sources, country, variables, frequency
and duration, restricted use
Bing J-H, Chen T-C, Chen L-C, Knaggs R. The role of socioeconomic status in regional variation of opioid utilisation in the Greater London area. Pharmacoepidmiology and Drug Safety, 2016;25(S3):19
Geographical variation and utilisation trend of opioids
17
050
10
015
0
All
Opio
ids
DD
D p
er
10
00
inh
ab
itan
ts p
er
da
y
Birmingham London Manchester Newcastle
Regional Variance in Opioid Utilisation
0
50
100
150
200
250
300
10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9
2010 2011 2012 2013 2014 2015
Defin
ed
d
aily d
ose/1
000 in
hab
itan
ts/m
on
th
Monthly tramadol utilisation
Best fit of monthly tramadol utilisation trend before tramadol classification
Best fit of monthly tramadol utilisation trend after tramadol classification
Tramadol classification
050
100
150
All
Opi
oids
DD
D p
er
1000
inh
abita
nts
pe
r da
y
0 20 40 60 80Index of Multiple Deprivation (IMD) Score
London London
Birmingham Birmingham
Manchester Manchester
Newcastle Newcastle
Chen T-C, Chen L-C, Knaggs RD. A fifteen-year overview of increasing tramadol utilisation and associated deaths and the impact of tramadol classification in the UK. Pharmacoepidemiology and Drug Safety
(submitted on 1/2/2017)
Comparing cost-effectiveness of initial primary endocrine therapy
with sugary using a registry of older women with primary breast
cancer
18
-4
-3
-2
-1
0
1
2
3
4
5
-6 -4 -2 0 2 4
Incre
men
tal c
osts
(10
3£)
Incremental LYGs (years)
0
0.1
0.2
0.3
0.4
0.5
Pro
bab
ilit
ies o
f co
st-
eff
ecti
ven
ess
WTP (£) Mousa R, Chen L-C, Cheung K-L. Cost-effectiveness of primary endocrine therapy against surgery for older women with primary breast cancer. Value in Health 2016;19(3):A152
2/20/2017
4
Taiwan National Health Insurance Research Database
19
http://w3.nhri.org.tw/nhird/en/Background.html
Divisions
of BNHI BNHI
Scrambled
17 research-related files
NHRI
National
Health
Insurance
Research
Database
Registration
files
Original
claim data
BNHI Storage
Claim data from
healthcare providers
Oracel database
Large computerized databases derived from
this system by the Bureau of National Health
Insurance, Taiwan (BNHI) and maintained by
the National Health Research Institutes,
Taiwan, are provided to scientists in Taiwan
for research purposes.
encrypted 17 subsets
150-220 G annually
Largest dataset in Taiwan
Taiwan National Health Insurance Data
20
Registry for board-certified specialists (DOC)
Registry for medical personnel (PER)
Registry for contracted beds (BED)
Registry for contracted specialty services (DETA)
Registry for contracted medical facilities (HOSB)
Supplementary registry for contracted medical
facilities (HOSX)
Registry for beneficiaries (ID)
Registry for catastrophic illness patients (HV)
Physicians
Medical facilities
Patients
Inpatient expenditures by
admissions (DD)
Ambulatory care
expenditures by visits
(CD)
Details of inpatient orders (DO)
Details of ambulatory care orders
(OO)
Expenditures for prescriptions
dispensed at contracted
pharmacies (GD)
Details of prescriptions
dispensed at contracted
pharmacies (GO)
Medical Orders Costs Basic Data
Taiwan NHI copayment policy change in 2015
21
Co-payment
Tier
General outpatient
visit
Estimated out-of-pocket cost to patients per outpatient
visit
Registration
fee
Co-payment
for drugs(c)
Total out-of-pocket
payment (d)
Referral Direct (a) Referral Direct (a)
Medical centres 210 360* 100-150 0-200 310-560
(310-360)
460-710
(460-510)
Regional hospitals 140 240* 30-100 0-200 170-440
(170-240)
270-540
(270-340)
Local community
hospitals 50 80* 0-100 0-200
50-350
(50-150)
80-380
(80-180)
Physician clinics 50 50* 0-50 (b) 0-200 50-300
(50-100)
50-300
(50-100)
Chen L-C, Schafheutle EI, Noyce PR. The impact of nonreferral outpatient co-payment on medical care utilization and expenditure in Taiwan. Research in Social and Administrative Pharmacy 2009; 5(3): 211-24
0
100
200
300
400
500
600
0
20
40
60
80
100
120
140
160
1 14 27 40 53 66 79 92 105 118
Nu
mb
er
of
vis
its
to
ph
ys
ica
in c
lin
ics
(1
00
0)
Nu
mb
er
of
vis
its
(1
00
0)
Weeks
Medical centres Regional hospitals Local community hospitals Physician clinics
Impacts of Taiwan NHI’s copayment policy on medical
utilisation and prescriptions
Number of outpatient visits Number of prescriptions
22
0
10
20
30
40
50
60
1 14 27 40 53 66 79 92 105 118
Nu
mb
er
of
pre
sc
rip
tio
ns
(1
00
0)
Weeks
Medical centres Regional hospitals Local community hospitals
Physician clinics Community pharmacies
Chen L-C, Schafheutle EI, Noyce PR. The impact of nonreferral outpatient co-payment on medical care utilization and expenditure in Taiwan. Research in Social and Administrative Pharmacy 2009; 5(3): 211-24
Persistence of endocrine therapy on mortality of breast
cancer patients in Taiwan
23
Kaplan-Meier Survival Curve
Early-stage Treatment
Interruption vs. Early-stage
Treatment Persistence
Hazard ratio (HR) of all cause
mortality comparing gap(+)
vs. gap(-) at the fist year:
Crude HR: 1.16 (1.32-1.96),
p <0.0001;
Adjusted HR: 1.17 (0.96-1.43),
p=0.1258
Kun-Pin Hsieh, Li-Chia Chen, Kwok-Leung Cheung, Chao-Sung Chang, Yi-Hsin Yang. Interruption and non-adherence to long-term adjuvant hormone therapy is associated with adverse survival outcome of breast cancer women - an Asian population-based study. PLoS ONE 2014; 9(2): e87027.
OP(+) CT(+)
Time (year)
0 2 4 6 8 10
Pro
ba
bil
ity o
f s
urv
iva
l
0.6
0.7
0.8
0.9
1.0
Gap(-)
Gap(+)
OP(+) CT(+)
Log-Rank: p=0.0004
OP(+) CT(-)
Time (year)
0 2 4 6 8 10
Pro
bab
ilit
y o
f su
rviv
al
0.6
0.7
0.8
0.9
1.0 OP(+) CT(-)
Log-Rank: p=0.0971
OP(-) CT(+)
Time (year)
0 2 4 6 8 10
Pro
bab
ilit
y o
f su
rviv
al
0.6
0.7
0.8
0.9
1.0 OP(-) CT(+)
Log-Rank: p=0.0296
OP(-) CT(-)
Time (year)
0 2 4 6 8 10
Pro
bab
ilit
y o
f su
rviv
al
0.6
0.7
0.8
0.9
1.0OP(-) CT(-)
Log-Rank: p=0.4975
Adherence of endocrine therapy on mortality of breast
cancer patients in Taiwan
24
Time (year)
0 2 4 6 8 10
Pro
ba
bil
ity o
f s
urv
iva
l
0.6
0.7
0.8
0.9
1.0
OP(+) CT(+) MPR<80%
OP(+) CT(+) MPR>=80%
Time (year)
0 2 4 6 8 10
Pro
ba
bil
ity o
f s
urv
iva
l
0.6
0.7
0.8
0.9
1.0
OP(+) CT(-) MPR<80%
OP(+) CT(-) MPR>=80%
Time (year)
0 2 4 6 8 10
Pro
bab
ilit
y o
f su
rviv
al
0.6
0.7
0.8
0.9
1.0
OP(-) CT(+) MPR<80%
OP(-) CT(+) MPR>=80%
Time (year)
0 2 4 6 8 10
Pro
bab
ilit
y o
f su
rviv
al
0.6
0.7
0.8
0.9
1.0
OP(-) CT(-) MPR<80%
OP(-) CT(-) MPR>=80%
Log-Rank: p<.0001
Log-Rank: p=0.682
Log-Rank: p=0.6403
Log-Rank: p=0.0003
Kun-Pin Hsieh, Li-Chia Chen, Kwok-Leung Cheung, Chao-Sung Chang, Yi-Hsin Yang. Interruption and non-adherence to long-term adjuvant hormone therapy is associated with adverse survival outcome of breast cancer women - an Asian population-based study. PLoS ONE 2014; 9(2): e87027.
Hazard ratio (HR) of all cause
mortality comparing nonadherence
vs. adherence:
Crude HR: 1.28 (1.16-1.42),
p<0.0001;
Adjusted HR: 1.23 (1.11-1.37),
p=0.1258
OP(+) CT(+) OP(+) CT(+)
OP(-) CT(+) OP(-) CT(+)
2/20/2017
5
Clinical Practice Research Datalink
25
Information for each consultation
stored in different record tables
Deprivation score, urban/rural
location, NHS region
Age, gender and deprivation score
Consultation data
Practice data
Patients data
Read code for symptom and/or
diagnosis
Clinical records
Height, weight, smoking …etc.
Additional clinical
Code for prescribed products
Prescription records
Read code for symptom and/or
diagnosis plus speciality
Referral records
Read code for test type +/- results
Test record
Implementation of prescribing indicator – Better Care Better
Value for drugs affecting the renin-angiotensin system
• The policy had no instant impact on the level of all drugs
• The policy significant reduction in the trend of all drugs after its implementation in April
2009
0
5
10
15
20
25
4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3
2006 2007 2008 2009 2010 2011 2012
Nu
mb
er
of p
res
cri
pti
on
s (1
04)
Diuretics ACEIs ARBs
CCBs BBs Others
β1 β2 β3
ACEI 1703.9 --- -1920.4
ARB 753.3 --- -835.1
Diuretic 673.5 --- -1219.1
CCB 1286.4 --- -1302
BB 88.3 --- ---
“Others” 186.8 --- -281.1
BCBV policy
Baker A, Chen LC, Elliott RA, Godman B. The impact of the 'Better Care Better Value' prescribing
policy on the utilisation of angiotensin-converting enzyme inhibitors and angiotensin receptor
blockers for treating hypertension in the UK primary care setting: longitudinal quasi-experimental
design. Health Services Research. 2015; 15(1): 367
Switching ARBs to ACEIs and blood pressure
• Switching of ARBs to ACEIs was associated with significantly lower systolic/diastolic BP
• Stratification by the two study groups
The significant difference was only found in ACEIs-combined group
This suggested that reduction in BP was not associated with the switching
27
Total (n=470) ACEIs-combined (n=369) ACEIs-monotherapy (101)
Before switching After switching Before switching After switching Before switching After switching
SBP 143.2* 141.3* 144.2* 141.9* 139.8 138.8
Mean Differ -2.3* -2.2* -2.0
DBP 84.1* 82.5* 84.6* 82.6* 82.4 81.9
Mean Differ -1.9* -2.1* -1.0*
Baker A, Chen L-C, Elliott RA. Switching of Angiotensin Receptor Blockers to Angiotensin-Converting Enzyme Inhibitors in patients with hypertension: Is it a cost-saving strategy? Pharmacoepidmiology and Drug Safety, 2016;25(S3): 576
Opioids utilisation in chronic non-cancer pain
• Number of patients prescribed with opioids • Number of strong opioid users stratified by
annual OMEQ dose per patient per day
28
0
20
40
60
80
100
120
140
160
0
5
10
15
20
25
30
35
40
45
50
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
Cancer patient Non-cancer patient
An
nu
al d
ays o
f su
pp
ly p
er p
atie
nt (d
ays)
Nu
mb
er
of
pati
en
ts (
10
3)
≤50 mg 51-100 mg
101-200 mg >200 mg
Annual days of supply per patient
http://www.bbc.co.uk/programmes/b06nzl6d Che S. Zin, Li-Chia Chen, Roger D. Knaggs. Changing Patterns and Trends of Strong Opioid Prescribing in Primary Care. European Journal of Pain 2014; 18(9): 1343-51
Tramadol utilisation and mortality
• Increasing tramadol utilisation coincidently
matches the increasing reported deaths, and
tramadol was classified as Schedule 3 Control
Substance. (Office of National Statistics)
• 21.2% tramadol users persistent uses in the 1st
year. The prevalence of persistent tramadol
users increased to 49.6% from the second
patient year.
29
0
20
40
60
80
100
120
140
160
180
200
0
2
4
6
8
10
12
14
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Nu
mb
er
of
pati
en
ts
Defi
ne d
aily d
oses (
10
6)
Tramadol annual usage
Number of reported deaths related to tramadol
0
10
20
30
40
50
60
0
5
10
15
20
25
30
35
1 2 3 4 5 6 7
Pre
vale
nce o
f p
ers
iste
nt u
sers
(%
)
Nu
mb
er
of
pati
en
ts (
10
4)
Patient year
Number of persistent tramadol users
Number of non-persistent tramadol users
Prevalence of persistent tramadol users
Chen T-C, Chen L-C, Knaggs R. Patient characteristics associated with persistent tramadol use for patients with chronic non-cancer pain in U.K. general practices. Poster presentation. The 32nd Anniversary International Conference on Pharmacoepidemiology and Therapeutic Risk Management, 25-28 August
2016 at the Convention Center Dublin, Dublin, Ireland. Pharmacoepidmiology and Drug Safety, 2016;(S1):
Persistency of tramadol utilisation
• From the 2nd p-yr, more than 80% of persistent
tramadol users who ever used tramadol
persistently in previous patient year. (40%, at
the end of the 7th p-yr).
• Baseline characteristics of patients associated
with persistent use of tramadol in 1st p-yr
30
0
20
40
60
80
100
2 3 4 5 6 7
Pro
po
rtio
n o
f p
ati
en
ts (
%)
Patient year
Number of patients within wide definition group in specific year
Prevalence of patients with history of within wide definition group in previous patient year
Prevalence of patients continuing in wide definition group in specific year
0
1
2
3
4
5
6
7
8
9
1
Nu
mb
er
of
pati
en
ts (
10
4)
Demographic characteristics
OR (95%CI)
Gender Female 1
Male 1.25 (1.17, 1.33)
Age 18≤age<40 1
40≤age<65 1.21 (1.07, 1.36)
Smoking Non 1
Current 1.22 (1.08, 1.37)
Townsend
score
1 1
2 1.16 (1.05, 1.29)
3 1.16 (1.05, 1.28)
5 1.18 (1.06, 1.31)
Disease history
Chronic pain Back pain 1
Arthritis 1.35 (1.23, 1.48)
* Only significant covariates were listed
Medication history
OR (95%CI)
Strong
opioids
0 1
0<-≤1125 1.49 (1.04, 2.12)
1125<-≤2250 2.36 (1.07, 5.18)
2250< 2.11 (1.07, 4.16)
Weak
opioids
0 1
0<-≤1125 1.56 (1.37, 1.78)
1125<-≤2250 4.27 (2.68, 6.82)
2250< 5.59 (2.52, 12.4)
SSRI/SNRI 0 1
180< 1.81 (1.25, 2.62)
TCA
0 1
0<-≤90 1.32 (1.22, 1.43)
90<-≤180 1.91 (1.62, 2.26)
180< 2.04 (1.6, 2.61)
Chen T-C, Chen L-C, Knaggs. R. Patient characteristics associated with persistent tramadol use for patients with chronic non-cancer pain in U.K. general practices. Poster presentation. Pharmacoepidmiology and Drug Safety, 2016;25(S3):293
2/20/2017
6
Issues for secondary database analysis
31
Data management and
validation
Content and structure
Ethic review and cost
• Duplicates (complete copies , copies on
same day with different information)
• Missing information (e.g. dose)
• Outliers (e.g. large and small quantities)
• Coding for identifying measures and
ascertainment
• Population
• Duration
• Data collection
• Data structure and coding
• Quality of data
• Cost and procedure to access data
• Ethical or methodological review
• Data storage and management
Extend database
• Other datasets for linkage
• Study cohort
• Identifiers
• Linkage process
Motheral, B., et al., A checklist for retrospective database studies--report of the ISPOR Task Force on Retrospective Databases. Value Health, 2003. 6(2): p. 90-7.
Berger, M.L., et al., Good research practices for comparative effectiveness research: defining, reporting and interpreting nonrandomized studies of treatment effects using secondary data sources: the ISPOR Good Research Practices for Retrospective Database Analysis Task Force Report--Part I. Value Health, 2009. 12(8): p. 1044-52.
Common statistical methods and issues
32
Statistical methods Issues
Propensity score Selection bias
Charleston chronic disease score Severity of disease state
Time-series analysis (ARIMA) Secular trend / effect
Principal component analysis Identify key predictors or key patterns
Cox-proportional hazard regression (survival analysis) Time to event
Meta-analysis Evidence-based medicine
Poisson regression Poisson (non-normal) distribution outcomes
Quasi-experimental comparison Intervention effect
Structural equation modelling Latent variable
Marginal cost measurement Different in different related to intervention
Cox, E., et al., Good research practices for comparative effectiveness research: approaches to mitigate bias and confounding in the design of nonrandomized studies of treatment effects using secondary data sources: the International Society for Pharmacoeconomics and Outcomes Research Good Research Practices for Retrospective Database Analysis
Task Force Report--Part II. Value Health, 2009. 12(8): p. 1053-61.
Johnson, M.L., et al., Good research practices for comparative effectiveness research: analytic methods to improve causal inference from nonrandomized studies of treatment effects using secondary data sources: the ISPOR Good Research Practices for Retrospective Database Analysis Task Force Report--Part III. Value Health, 2009. 12(8): p. 1062-73.
Integration of patient-reported outcomes and electronic medical
records to optimise the use of opioids in patients with chronic non-
cancer pain
33
Patients Physicians
• Patient-physician communication platform
• Patient reported outcomes
• Drug utilisation journey
• Electronic medical records
• Prescriptions
Timely inform pain management decisions
• Modify pain management strategies
• Adjust opioid prescriptions
Summary of pain
management and
opioid utilisation
Improve self-management of pain
• Understand benefits of treatment
• Identify drug utilisation problem
• Improve medicine adherence
• Optimise opioids utilisation
• Enhance outcomes of chronic non-cancer pain management
Paradigm shift – going digital
34
• Regulation on risk management
• Planning for drug development
• Not only pharmaceuticals
• Patients involve in health monitoring
• EMR proliferation
- Translational HER infrastructure
- Public data bank
• Using database linkage
• Proliferation of standardising data
• Privacy of data
• Consensus of analysis
• Advance analytical methods
• Geographical variations
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