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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