open access research quality indicators for responsible ... · three main terms: ‘quality...

1FujitaK, etal. BMJ Open 2018;8:e020437. doi:10.1136/bmjopen-2017-020437

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Quality indicators for responsible use of medicines: a systematic review

Kenji Fujita, Rebekah J Moles, Timothy F Chen

To cite: FujitaK, MolesRJ, ChenTF. Quality indicators for responsible use of medicines: a systematic review. BMJ Open 2018;8:e020437. doi:10.1136/bmjopen-2017-020437

Prepublication history and additional material for this paper are available online. To view these files, please visit the journal online (http:// dx. doi. org/ 10. 1136/ bmjopen- 2017- 020437).

Received 6 November 2017Revised 26 April 2018Accepted 13 June 2018

School of Pharmacy, Faculty of Medicine and Health, The University of Sydney, Sydney, New South Wales, Australia

Correspondence toKenjiFujita; kfuj2522@ uni. sydney. edu. au

Research

Author(s) (or their employer(s)) 2018. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

AbstrACtObjective All healthcare systems require valid ways to evaluate service delivery. The objective of this study was to identify existing content validated quality indicators (QIs) for responsible use of medicines (RUM) and classify them using multiple frameworks to identify gaps in current quality measurements.Design Systematic review without meta-analysis.setting All care settings.search strategy CINAHL, Embase, Global Health, International Pharmaceutical Abstract, MEDLINE, PubMed and Web of Science databases were searched up to April 2018. An internet search was also conducted. Articles were included if they described medication-related QIs developed using consensus methods. Government agency websites listing QIs for RUM were also included.Analysis Several multidimensional frameworks were selected to assess the scope of QI coverage. These included Donabedians framework (structure, process and outcome), the Anatomical Therapeutic Chemical (ATC) classification system and a validated classification for causes of drug-related problems (c-DRPs; drug selection, drug form, dose selection, treatment duration, drug use process, logistics, monitoring, adverse drug reactions and others).results 2431 content validated QIs were identified from 131 articles and 5 websites. Using Donabedians framework, the majority of QIs were process indicators. Based on the ATC code, the largest number of QIs pertained to medicines for nervous system (ATC code: N), followed by anti-infectives for systemic use (J) and cardiovascular system (C). The most common c-DRPs pertained to drug selection, followed by monitoring and drug use process.Conclusions This study was the first systematic review classifying QIs for RUM using multiple frameworks. The list of the identified QIs can be used as a database for evaluating the achievement of RUM. Although many QIs were identified, this approach allowed for the identification of gaps in quality measurement of RUM. In order to more effectively evaluate the extent to which RUM has been achieved, further development of QIs may be required.

IntrODuCtIOnResponsible use of medicines (RUM) is an essential element in achieving quality of care for patients and the community. According to the WHO, RUM implies that the activi-ties, capabilities and existing resources of health system stakeholders are aligned to

ensure patients receive the right medicines at the right time, use them appropriately and benefit from them.1 RUM, however, is not easily achievable, and if medicines are used inappropriately, negative consequences for both patients and/or the society may occur. It is reported that worldwide more than 50% of all medicines are prescribed, dispensed or sold inappropriately, while 50% of patients fail to take them correctly.2 In addition, it has been reported that one-third of preventable drug-related admissions are associated with medication non-adherence, 31% are related to prescribing problems and 22% are related to monitoring problems.3 The frequency of these medication errors varies depending on the specific medicine. For example, previous systematic reviews have found that prevent-able drug-related admissions to hospital accounted for 3.7% of all admissions, of which four groups of drugs, antiplatelets, diuretics, non-steroidal anti-inflammatory and antico-agulants accounted for more than 50% of the drug groups associated with those prevent-able drug-related hospitalisations.3 From the economic perspective, globally, the cost associated with medication errors has been estimated at US$42 billion annually or almost

strengths and limitations of this study

A comprehensive literature search was undertaken across seven databases and government agency websites without restriction of disease categories and care settings.

The classification of quality indicators (QIs) was based on multiple frameworks (eg, Donabedians framework, the Anatomical Therapeutic Chemical classification system and a validated classification for causes of drug-related problems) for maximum understanding and profiling of the included QIs.

Content validated QIs that were developed using consensus methods were only included, and there-fore valid QIs might have been excluded during the screening process.

Although 5% of this review processes were verified by multiple authors to check for accuracy, most of the classification was undertaken by one author.

on 3 February 2019 by guest. P

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2 FujitaK, etal. BMJ Open 2018;8:e020437. doi:10.1136/bmjopen-2017-020437

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1% of total global health expenditure.4 Given the health concerns and the economic burden associated with medi-cation errors, the achievement of RUM underpinned by an evidence-based approach has become increasingly important worldwide.

One critical element for any healthcare system or organisation is how to measure and evaluate RUM. A widely used method to do this is the use of quality indi-cators (QIs).5 6 QIs are explicitly defined and measurable items referring to the structures, processes or outcomes of care are usually described with a denominator and a numerator.7 The denominator is the total number of cases in the intended population, and the numerator is the number of cases that fulfil a predetermined crite-rion, and the calculated QI score indicates the quality of care.8 QIs can be used to monitor the quality of care provided by healthcare professionals in a single institu-tion, to promote quality improvement activities, to make comparisons over time between institutions or to support consumers to choose healthcare providers.5 For QIs to be useful, they must be developed with scientific rigour, and all quality dimensions of care must be measured to capture a comprehensive landscape of healthcare quality.5

To achieve RUM using QIs, it is first necessary to iden-tify existing QIs for RUM, independent of disease cate-gories and care settings. Additionally, in the light of the concept of RUM, multifaceted assessment is required to gain full understanding of the breadth of coverage by QIs. To our knowledge, however, previously conducted system-atic reviews have been restricted to setting (eg, hospital),9 disease state (eg, HIV/AIDS),10 specific to a healthcare group (eg, nursing sensitive QIs) or indicator name (eg, clinical indicators)11 and have only been classified based on Donabedians framework or implicit frameworks such as quality dimensions defined by the Institute of Medi-cine.12 Hence, the main purpose of this systematic review was to identify existing content validated QIs for RUM independent of disease category and care settings, and then classify them using multiple frameworks in order to identify gaps in current quality measurements.

MethODsData sourcesThis systematic review was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement (see online supplementary table S1).13 Two approaches were used to identify relevant QIs.

First, CINAHL, Embase, Global Health, International Pharmaceutical Abstract, MEDLINE, PubMed and Web of Science databases were searched to identify relevant articles published up to 5 April 2018. No restriction on year of study was applied. Search strategies comprised keywords and, when available, controlled vocabulary such as Medical Subject Headings/EMTREE based on three main terms: quality indicators, development and consensus. Since quality indicators are referred to by wide variety of terms such as clinical indicators, or

performance measures, the finalised search strategies were developed using an iterative development process during which citations identified by various search terms were screened for relevance. We chose consensus as a main term because QIs are recommended to be devel-oped using expert panels based on rigorous evidence in order to ensure high face validity and content validity.14 Exact search dates for each database with the search strat-egies are included in online supplementary table S2.

Second, using Google, an internet search was also conducted (search terms: quality indicators, clinical indi-cators, performance indicator or performance measures) to capture additional QIs listed in the websites of rele-vant organisations responsible for quality improvement. Potentially relevant organisations websites, found in the process of literature review,9 12 1517 were also searched (see online supplementary table S3).

stuDy seleCtIOnInclusion criteriaArticles were included if they fulfilled the following criteria: (A) the article was peer reviewed and published in English, (B) numerators and denominators were defined for the QIs, or they could be directly deduced from the descriptions of the QIs, (C) the publication contained at least one medication-related QI, (D) the development of QIs was one of the objectives and (E) QIs were developed using consensus methods in order to confirm content validity. Furthermore, relevant organisations QIs found from websites were included if the organisation was a government agency for ensuring quality in healthcare, and at least one QI for RUM was reported with a clear description, as detailed above (B).

Given the concept of QIs and RUM mentioned above, we regarded a measurement tool as a QI for RUM when the definition of the QI referred to a medication. In addi-tion, if publications concerned the same project/QIs set, the descriptions of the QIs in the most recent publication were used for data extraction.

exclusion criteriaArticles were excluded if the consensus results for QI development were unclear, if QI lists were obtainable only by purchase or if QIs were for monitoring the effective-ness of national policies.

This study selection process was performed using a purposed designed screening proforma (see online supplementary table S4). The retrieved articles were transferred into Endnote to remove duplicates, then initial screening of journal names, titles and abstracts was conducted to remove irrelevant articles.

DAtA extrACtIOnOne researcher (KF) extracted the following data from the full text of included articles or websites: publication year, country or other targeted location in which QIs were



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intended to be used, name of measurement tools, total number of QIs, the number of relevant QIs for RUM, scope of the QIs and definition of QIs (numerator and denominator, if available). A data extraction proforma was designed, pilot-tested on five included studies, then refined accordingly.

AnAlysIsDescriptive statistics were computed for the results of the present review based on counts and proportions where relevant. Since the components of RUM are multidimen-sional, multiple frameworks were used to understand the breadth of coverage by QIs. That is, we used four types of classification: (1) problem type; (2) Donabedians frame-work; (3) the Anatomical Therapeutic Chemical (ATC) classification system; and (4) causes of drug-related prob-lems (c-DRPs) classification system.

Problem typeThe first step of a structured QI development process is to identify the problem for which measurement is needed.18 Classifying QIs according to problem type can highlight prioritised problems for QI development. Therefore, QI sets described in each source were classified into the following six problem types proposed by Evans et al18:1. Disease based: problems relevant to diseases, illnesses,

conditions, injuries or procedures for which the quali-ty of care needs to be measured.

2. Patient based: problems related to patient groups, such as vulnerable elders and paediatric patients.

3. Treatment modality based: problems relevant to ser-vice providing areas, such as intensive care units or pal-liative care settings.

4. Organisation based: problems relevant to organisation-al issues, such as whether organisations have effective structures in place at an organisational level to support quality and safety.

5. Generic problems: problems relevant to issues that are multidisciplinary in nature and relevant to any form of healthcare delivery in multiple physical settings, such as falls prevention, or pain management.

6. Profession based: problems unique to the different healthcare professions and include availability and competence of healthcare personnel.

If a QI set related to more than one problem type, they were classified accordingly (eg, an article about QIs for nursing practice in the operating room fell into treat-ment modality-based and profession-based problem).

Donabedians frameworkQIs were classified according to the widely used Donabe-dians framework of structure (referred to the factors that designate the conditions under which care is provided, such as material or human resources), process (referred to the actions of healthcare professionals, such as prescribing or monitoring) or outcome (referred to the changes in individuals that can be attributed to care

provided), irrespective of the category defined in the original source.19 Online supplementary table S5 lists examples of QIs classified into these three categories.

the AtC classification systemQIs were first classified into medicine class specific indi-cators or general medication indicators, depending on whether the definition of the QI described a specific class of medicines. For example, a QI numerator: patients with acute myocardial infarction (AMI) received aspirin within 3 hours of hospital arrival/denominator: AMI patients without aspirin contraindications20 was clas-sified as a medicine class specific indicator, while a QI numerator: number of patients aged 65 years and older whose current medications are documented and recon-ciled at admission/denominator: number of patients aged 65 years and older in sample21 was classified as a general medication indicator. After this process, medi-cine class specific indicators were classified using the first and second levels of the ATC code.22 A single QI was sometimes allocated into more than one ATC code. For example, a QI, percentage of patients using opioids with concomitant laxatives,23 represented A06 (drugs for constipation) and N02 (analgesics).

c-DrPs classification systemSince minimising the factors that contribute to drug-re-lated problems (ie, causes of DRPs) is closely linked to achieving RUM, the extracted QIs were classified using a comprehensive taxonomy of the causes of DRPs.24 This taxonomy divides c-DRPs into the following nine categories.1. Drug selection, for example, whether appropriate

drugs are selected by healthcare professionals.2. Drug form, for example, whether appropriate drug

forms are selected by healthcare professionals.3. Dose selection, for example, whether appropriate drug

dosages are selected by healthcare professionals.4. Treatment duration, for example, whether drugs are

being prescribed or dispensed for an appropriate du-ration by healthcare professionals.

5. Drug use process, for example, whether drugs are tak-en properly by patients.

6. Logistics, for example, whether necessary drugs are properly delivered to the patients.

7. Monitoring, for example, monitoring for the effect/adverse effects of drugs.

8. Adverse drug reactions, for example, the occurrence of adverse drug reactions.

9. Other.Note that a single QI was sometimes allocated into

more than one c-DRP category. Online supplementary table S6 illustrates how QIs were classified using the c-DRP taxonomy.

All processes were conducted independently by one author (KF), and 5% of these processes were verified by TFC and RJM. Any issues that arose during the process were resolved by discussion between the research team



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(KF, RJM and TFC). Meta-analysis was not applicable due to heterogeneity in interventions, methods and reported outcomes. We believed that it was not necessary to assess the quality of the content validated QIs included in our studies such as their feasibility, and reliability because problems affecting QIs (eg, feasibility of data collection, reliability of calculating QI scores and opportunities for gaming) vary depending on the healthcare infrastructure and healthcare remuneration system in each country.

PAtIent AnD PublIC InvOlveMentAs this was a literature review, there was no patient and public involvement in this study.

resultsstudy selectionInitially, a total of 39 430 articles were obtained. The sample included 17 822 duplicate records, which were removed. After the initial screening, 973 full texts were assessed for eligibility with 842 excluded based on the inclusion and exclusion criteria. Eventually 131 articles met all inclusion criteria and were included in our review. Additionally, through the internet search, five relevant

websites were identified and included in our review (figure 1).

study characteristicsOf the 131 articles, 78 articles (60%) developed QIs for use in three countries: USA (n=36),2560 Canada (n=26)6186 and Netherlands (n=16).23 87101 The remaining 53 arti-cles developed QIs for use in 16 other countries20 21 102145 and 4 other targeted locations (such as the Organisation for Economic Co-operation and Development (OECD) countries)146152 (figure 2). Of the five relevant websites, three were Australian organisations,153155 one was a UK organisation156 and the other was USA organisation.157 The three Australian and UK organisations developed QIs at the organisation level, while the American website, National Quality Measures Clearinghouse, sponsored by the Agency for Healthcare Research and Quality, stored QIs developed by various countries. Of 7750 QIs listed in the 131 articles and 5 websites, we identified 2431 QIs for RUM: 1947 QIs from journal articles and 484 QIs from the web.

While there were 21 different ways of labelling the measurement tools, quality Indicators (n=80, 59%) was the most commonly used term in our included articles

Figure 1 Study flow diagram.QI,quality indicator; RUM, responsible use of medicines.



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and websites, followed by quality measures (n=11, 8%), quality of care indicators (n=8, 6%) and indicators (n=7, 5%).

In terms of the problem type, 43% of QI sets pertained to disease-based problems (n=89, eg, knee osteoarthritis), then 27% for treatment modality-based problems (n=55, eg, primary care), 21% for patient-based problems (n=44, eg, geriatric care), 5% for profession based problems (n=11, eg, community pharmacists), 3% generic prob-lems (n=6, eg, long-term prescribing) or 1% organisa-tion-based practice (n=2, eg, centralised intake systems). The majority of QIs (n=2289, 94%) were process indica-tors, while structure (n=80) and outcome (n=62) indica-tors accounted for only 3% each (table 1).

Of 2431 QIs, 247 QIs (10%) were general medication indicators, and 2184 QIs (90%) were medicine class specific indicators. Some of the 2184 QIs represented more than one ATC code resulting in 2613 first level of ATC classifications. Of these, the most number of QIs covered medicines for nervous system (N, n=407, 16%), followed by the anti-infectives for systemic use (J, n=397, 15%), cardiovascular system (C, n=364, 14%) and blood and blood forming organs (B, n=345, 13%) (figure 3). Dermatological medicines (D) were covered by the least number of QIs (n=19, 0.7%) aside from antiparastic prod-ucts, insecticides and repellents (P, n=7, 0.3%).

The distribution of the QIs across the second level of ATC code and c-DRPs classification system is presented in table 2. General medication indicators were only clas-sified using c-DRPs category. Because some QIs repre-sented more than one ATC code and/or c-DRPs category, the total number of the QIs contained within each cell

of the matrix was 3666. Of these, when investigating the number of QIs in each c-DRPs category, the largest number of QIs for drug selection pertained to antibac-terials for systemic use (J01, 176 of 2117, 8%), followed by antithrombotic agents (B01, 172 of 2117, 8%). Anti-thrombotic agents (B01) also contributed the largest number of QIs for dose selection (20 of 142, 14%) and the drug use process (52 of 439, 12%) and monitoring (52 of 574, 9%). Likewise, the most number of QIs for treatment duration (13 of 85, 15%) pertained to psycho-analeptics (N06).

With regard to the c-DRPs classification system, the most common c-DRPs pertained to drug selection (n=2117, 58%), followed by monitoring (n=574, 16%) and the drug use process (n=439, 12%). The remaining six c-DRPs categories accounted for only 14% of the QIs. Interestingly, only QIs for analgesics (N02) covered all nine c-DRPs categories. In terms of general medication indicators, the largest number of QIs covered Logistics (n=73, 29%) among the c-DRPs category, which mainly focus on medication reconciliation problems during tran-sitions of care, such as hospital admission and discharge.

A complete list of 2431 QIs is available in online supple-mentary table S7.

DIsCussIOnThe RUM is important for almost every healthcare setting in every country across the globe. Knowledge of whether medicines are being used in an optimal manner there-fore presents a significant international challenge. In this systematic review, we identified 2431 QIs evaluating RUM

Figure 2 The number of publications by country and other target location.



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Tab

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20

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Typ

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P, P

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133

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dic

ator

sG

ener

al p

ract

ice

T33

/125

(26)

030

333

0

Had

orn

et a

l25

1996

US

AR

evie

w c

riter

iaH

eart

failu

reD

4/8

(50)

04

03

1

Asc

h et

al2

620

01U

SA

Qua

lity

of c

are

ind

icat

ors

Wom

en w

ith h

yper

tens

ion

D, P

6/13

(46)

06

05

1

Mik

uls

et a

l27

2004

US

AQ

ualit

y of

car

e in

dic

ator

sG

out

D9/

10 (9

0)0

90

90

Sal

iba

et a

l28

2004

US

AQ

ualit

y in

dic

ator

sN

ursi

ng h

ome

resi

den

tsT

54/1

14 (4

7)1

530

540

Tab

le 1

C

ontin

ued

Con

tinue

d



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Ref

eren

ceYe

ar

Co

untr

y/o

ther

targ

et

loca

tio

nQ

I nam

e

Sco

pe

of

QIs

(set

ting

, co

ndit

ion,

tar

get

pat

ient

g

roup

, occ

upat

ion)

Pro

ble

m

typ

e*Q

Is f

or

RU

M

(%)

Do

nab

edia

ns

fram

ewo

rk

QI t

ype

SP

OM

eG

e

Kru

mho

lz e

t al

2920

06U

SA

Per

form

ance

mea

sure

sS

T-el

evat

ion

and

non

-ST-

elev

atio

n m

yoca

rdia

l inf

arct

ion

D8/

11 (7

3)0

80

80

McG

ory

et a

l30

2006

US

AQ

ualit

y in

dic

ator

sP

atie

nts

und

ergo

ing

colo

rect

al c

ance

r su

rger

yD

20/9

2 (2

2)0

200

191

Mul

arsk

i et

al31

2006

US

AQ

ualit

y m

easu

res

Pal

liativ

e ca

reT

1/18

(6)

01

01

0

Man

gion

e-S

mith

et

al32

2007

US

AQ

ualit

y in

dic

ator

sP

aed

iatr

ic o

utp

atie

nts

P, T

58/1

75 (3

3)0

580

580

Sm

ith e

t al

3320

07U

SA

Qua

lity

ind

icat

ors

Hom

e-b

ased

prim

ary

care

T71

/200

(36)

170

059

12

Wen

ger

et a

l34

2007

US

AQ

ualit

y in

dic

ator

sVu

lner

able

eld

ers

P14

6/39

2 (3

7)0

146

012

917

Est

es e

t al

3520

08U

SA

Per

form

ance

mea

sure

sO

utp

atie

nt a

dul

ts w

ith n

on-v

alvu

lar

atria

l fib

rilla

tion

or a

tria

l flut

ter

D, P

, T2/

3 (6

7)0

20

20

Bili

mor

ia e

t al

3620

09U

SA

Qua

lity

ind

icat

ors

Pan

crea

tic c

ance

rD

3/43

(7)

12

03

0

Lore

nz e

t al

3720

09U

SA

Qua

lity

mea

sure

sS

upp

ortiv

e ca

ncer

car

eD

36/9

2 (3

9)0

360

351

McG

ory

et a

l38

2009

US

AQ

ualit

y in

dic

ator

sP

erio

per

ativ

e ca

re fo

r el

der

ly s

urgi

cal

pat

ient

sP,

T17

/91

(19)

017

013

4

Yazd

any

et a

l39

2009

US

AQ

ualit

y in

dic

ator

sS

yste

mic

lup

us e

ryth

emat

osus

D14

/20

(70)

014

014

0

Che

ng e

t al

4020

10U

SA

Qua

lity

ind

icat

ors

Mul

tiple

scl

eros

isD

19/7

6 (2

5)0

190

172

Kan

wal

et

al41

2010

US

AQ

ualit

y in

dic

ator

sN

onva

ricea

l up

per

gas

troi

ntes

tinal

ha

emor

rhag

eD

6/26

(23)

06

06

0

Kan

wal

et

al42

2010

US

AQ

ualit

y in

dic

ator

sC

irrho

sis

D12

/41

(29)

012

012

0

Sch

enck

et

al43

2010

US

AQ

ualit

y m

easu

res

Hos

pic

e an

d p

allia

tive

care

T7/

34 (2

1)0

70

70

Kha

nna

et a

l44

2011

US

AQ

ualit

y in

dic

ator

sS

yste

mic

scl

eros

isD

10/3

2 (3

1)0

100

100

Soo

Hoo

et

al45

2011

US

AQ

ualit

y of

car

e in

dic

ator

sP

atie

nts

und

ergo

ing

tota

l hip

or

tota

l kn

ee r

epla

cem

ent

D8/

68 (1

2)0

80

53

Wan

g et

al4

620

11U

SA

Qua

lity

of c

are

ind

icat

ors

Chi

ldre

n w

ith s

ickl

e ce

ll d

isea

seD

, P13

/41

(32)

013

013

0

Ang

er e

t al

4720

13U

SA

Qua

lity

of c

are

ind

icat

ors

Wom

en w

ith u

rinar

y in

cont

inen

ceD

, P7/

27 (2

6)0

70

70

Jack

son

et a

l48

2013

US

AQ

ualit

y in

dic

ator

sC

olor

ecta

l can

cer

D11

/34

(32)

011

011

0

Mel

med

et

al49

2013

US

AQ

ualit

y in

dic

ator

sIn

flam

mat

ory

bow

el d

isea

seD

8/21

(38)

05

38

0

Wan

g et

al5

020

13U

SA

Qua

lity

of c

are

ind

icat

ors

Infa

ntile

sp

asm

sD

, P10

/21

(48)

010

010

0

Yad

lap

ati e

t al

5120

15U

SA

Qua

lity

mea

sure

sG

astr

o-oe

sop

hage

al r

eflux

dis

ease

D15

/25

(60)

015

015

0

Faro

et

al52

2016

US

AQ

ualit

y in

dic

ator

s

Follo

w-u

p c

are

for

ind

ivid

uals

with

p

ositi

ve s

cree

ns fo

r si

ckle

cel

l dis

ease

an

d t

rait

D2/

9 (2

2)

02

02

0

Vila

et

al53

2016

US

AQ

ualit

y m

easu

res

Ad

ult

coch

lear

imp

lant

cen

tres

D, P

, T2/

8 (2

5)0

11

20

Yazd

any

et a

l54

2016

US

AQ

ualit

y m

easu

res

Rhe

umat

oid

art

hriti

sD

1/4

(25)

01

01

0

Cho

wd

hury

et

al55

2017

US

AQ

ualit

y m

etric

sA

dul

t co

ngen

ital h

eart

dis

ease

and

p

aed

iatr

ic c

ard

iolo

gy c

are

D, P

5/27

(19)

04

15

0

Tab

le 1

C

ontin

ued

Con

tinue

d



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and classified them using multiple frameworks. The large number of QIs reflects the multidimensional compo-nents of RUM and the different perspectives of multidis-ciplinary stakeholders involved in the RUM. The QI list presented in this review can be used as a comprehensive database and reference for existing content validated QIs pertaining to RUM. All stakeholders involved in quality assurance for RUM, for example, healthcare profes-sionals, researchers and decision makers, can select QIs from the multicategorised QI list for their own purpose. Since healthcare systems and medication guidelines may vary between countries when using the QIs at the local setting, it is important for users to critically review the QIs for their acceptability, feasibility of acquiring neces-sary data, reliability, sensitivity to change, work load and validity.8 14

The vast majority of the QIs for RUM identified were intended to be used in only a few high-income countries. Low-income and middle-income countries, however, are estimated to have similar rates of medication-related adverse events, and the impact has been reported to be about twice as much in terms of the number of years of healthy life lost.4 Since feasibility of data collection for calculating QI scores in low-income settings remain a concern,151 further efforts for improving the data collec-tion method might need to be made. We found that even though the role of all measurement tools (ie, QIs) rele-vant to RUM have the goal of quality improvement, the terminology used to describe QIs varied significantly. About 20 name variations were found, which reflects the absence of a universally accepted definition for such tools. For example, Campbell et al8 distinguished QIs from performance indicators, arguing that QIs infer a judge-ment about the quality of care provided, while perfor-mance indicators are statistical devices for monitoring care provided to populations without any necessary infer-ence about quality. However, we found that these terms, quality and performance, were used interchangeably. Hence, further research for standardising the definition that distinguishes these measurement tools is warranted.

We also found a significant gap in terms of the problem type (eg, disease-based problems (43%), treatment modality-based problems (27%) and profession-based problems (5%)). Since RUM is facilitated by collabo-ration in multidisciplinary teams, all healthcare profes-sionals involved in medication treatment should take responsibility for quality assurance, regardless of diseases, care settings and professions. When using Donabedians framework, about 94% of the identified QIs related to processes of care. This could be because processes of care are easier to measure, and because process indicators can provide interpretable feedback about care provided.158 In contrast, there was a paucity of outcome indicators. This may be because multiple factors influence health outcomes, many of which are outside the control of indi-vidual healthcare professionals. In addition, the diffi-culty of obtaining sufficient information for assessing outcomes, requiring the linkage of multiple data sources, R

efer

ence

Year

Co

untr

y/o

ther

targ

et

loca

tio

nQ

I nam

e

Sco

pe

of

QIs

(set

ting

, co

ndit

ion,

tar

get

pat

ient

g

roup

, occ

upat

ion)

Pro

ble

m

typ

e*Q

Is f

or

RU

M

(%)

Do

nab

edia

ns

fram

ewo

rk

QI t

ype

SP

OM

eG

e

Hep

ner

et a

l56

2017

US

AQ

ualit

y m

easu

res

Unh

ealth

y al

coho

l use

D3/

25 (1

2)0

30

30

Ingr

aham

et

al57

2017

US

AQ

ualit

y in

dic

ator

sE

mer

genc

y ge

nera

l sur

gery

car

eT

3/25

(12)

21

02

1

Man

gion

e-S

mith

et

al58

2017

US

AQ

ualit

y in

dic

ator

sH

osp

ital-

bas

ed c

are

for

com

mon

p

aed

iatr

ic r

esp

irato

ry il

lnes

ses

D, P

, T43

/76

(57)

043

043

0

Od

etol

a et

al5

920

17U

SA

Qua

lity

mea

sure

sIn

hosp

ital c

are

of p

aed

iatr

ic s

epsi

s sy

ndro

me

D, P

, T3/

7 (4

3)1

20

30

NQ

MC

157

2018

US

AQ

ualit

y m

easu

res

Vario

us c

are

All

412/

2525

(16)

037

834

376

36

Par

ast

et a

l60

2018

US

AQ

ualit

y m

easu

res

Hos

pita

l-b

ased

car

e fo

r su

icid

al y

outh

D, P

, T4/

4 (1

00)

04

02

2

Tota

l24

31/7

750

(31)

6022

8962

2184

247

*Pro

ble

m t

ype:

D, d

isea

se b

ased

; G, g

ener

ic; O

, org

anis

atio

n b

ased

; P, p

atie

nt b

ased

; Pr,

pro

fess

ion

bas

ed; T

, tre

atm

ent

mod

ality

bas

ed.

Don

abed

ian

s fr

amew

ork:

S, s

truc

ture

; P, p

roce

ss; O

,out

com

e.Q

I typ

e: M

e, m

edic

ine

clas

s sp

ecifi

c; G

e, g

ener

al m

edic

atio

n.G

over

nmen

t ag

ency

web

site

. A

CS

QH

C, T

he A

ustr

alia

n C

omm

issi

on o

n S

afet

y an

d Q

ualit

y in

Hea

lthca

re; N

ICE

, Nat

iona

l Ins

titut

e fo

r H

ealth

and

Clin

ical

Exc

elle

nce;

NQ

MC

, Nat

iona

l Qua

lity

Mea

sure

s C

lear

ingh

ouse

; QI,

qua

lity

ind

icat

or; R

UM

,res

pon

sib

le u

se o

f med

icin

es.

Tab

le 1

C

ontin

ued



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could be another reason of the limited number of outcome indicators. For outcome indicators to become more useful, multiple confounders such as patient demo-graphic characteristics, and severity of illness, may need to be considered.159 Similarly, there was a low proportion of structural indicators. This may be because they are not sufficiently sensitive for monitoring ongoing perfor-mance and they have traditionally been used to monitor standards of healthcare facilities, not RUM.160 It is note-worthy that there is no set requirement for equal propor-tions of structural, process and outcome indicators in quality measurement. Instead, it is important to recognise the interconnectedness of these measures. For example, high structure indicator scores increase the likelihood of good process indicator scores, which in turn, may lead to higher outcome indicator scores.161 Further research is

needed to investigate the associations between the iden-tified QIs in each framework within healthcare settings.

We found large differences in the degree to which c-DRPs categories were covered by the identified QIs. Not surprisingly, Drug selection accounted for more than half of the QIs, as choosing an inappropriate drug is the main cause of DRPs.3 162 Since focusing on limited c-DRPs categories may divert attention and resources away from other factors contributing to DRPs,163 164 users of QIs should be aware of what c-DRPs categories are not being measured. Like Donabedians framework, we do not expect that QIs should be evenly distributed across each of the c-DRPs categories or ATC groups. We do, however, expect that there will be greater QIs in areas of greatest need. These clinical areas may include common areas of practice suspected to be associated with inappropriate

Figure 3 The number of QIs by first-level ATC code.ATC,Anatomical Therapeutic Chemical; QIs, quality indicators. on 3 February 2019 by guest. P


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Tab

le 2

D

istr

ibut

ion

of Q

Is fo

r R

UM

by

the

ATC

cod

e (ro

ws)

and

the

c-D

RP

s ca

tego

ry (c

olum

ns)*

AT

C c

od

e 1.

Dru

g

sele

ctio

n2.

Dru

g

form

3. D

ose

se

lect

ion

4.

Trea

tmen

t d

urat

ion

5. D

rug

us

e p

roce

ss6.

Lo

gis

tics

7.

Mo

nito

ring

8.

Ad

vers

e d

rug

re

acti

on

9. O

ther

Tota

l, n

(%)

A: a

limen

tary

tra

ct a

nd m

etab

olis

m

A

01: S

tom

atol

ogic

al p

rep

arat

ions

7

5 12

(0.3

)

A

02: D

rugs

for

acid

rel

ated

dis

ord

ers

373

28

151

(1.4

)

A

03: D

rugs

for

func

tiona

l gas

troi

ntes

tinal

d

isor

der

s35

34

42 (1

.1)

A

04: A

ntie

met

ics

and

ant

inau

sean

ts23

45

32 (0

.9)

A

06: D

rugs

for

cons

tipat

ion

201

32

26 (0

.7)

A

07: A

ntid

iarr

heal

s, in

test

inal

an

tiinfl

amm

ator

y/an

tiinf

ectiv

e ag

ents

141

21

12

21 (0

.6)

A

08: A

ntio

bes

ity p

rep

arat

ions

, exc

l. d

iet

pro

duc

ts1

1 (0

)

A

10: D

rugs

use

d in

dia

bet

es40

14

193

192

88 (2

.4)

A

11: V

itam

ins

281

11

132

(0.9

)

A

12: M

iner

al s

upp

lem

ents

241

15

334

(0.9

)

B: B

lood

and

blo

od fo

rmin

g or

gans

B

01: A

ntith

rom

bot

ic a

gent

s17

220

852

652

19

320

(8.7

)

B

02: A

ntih

emor

rhag

ics

21

3 (0

.1)

B

03: A

ntia

nem

ic p

rep

arat

ions

91

13

21

17 (0

.5)

B

05: B

lood

sub

stitu

tes

and

per

fusi

on

solu

tions

331

92

41

555

(1.5

)

C: C

ard

iova

scul

ar s

yste

m

C

01: C

ard

iac

ther

apy

301

44

415

58 (1

.6)

C

02: A

ntih

yper

tens

ives

505

12

1068

(1.9

)

C

03: D

iure

tics

576

23

231

92 (2

.5)

C

04:P

erip

hera

l vas

odila

tors

51

6 (0

.2)

C

05: V

asop

rote

ctiv

es1

1 (0

)

C

07: B

eta

blo

ckin

g ag

ents

108

55

1110

139

(3.8

)

C

08: C

alci

um c

hann

el b

lock

ers

486

510

69 (1

.9)

C

09: A

gent

s ac

ting

on t

he r

enin

-an

giot

ensi

n sy

stem

128

72

71

3417

9 (4

.9)

C

10: L

ipid

mod

ifyin

g ag

ents

532

17

871

(1.9

)

Con

tinue

d



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AT

C c

od

e 1.

Dru

g

sele

ctio

n2.

Dru

g

form

3. D

ose

se

lect

ion

4.

Trea

tmen

t d

urat

ion

5. D

rug

us

e p

roce

ss6.

Lo

gis

tics

7.

Mo

nito

ring

8.

Ad

vers

e d

rug

re

acti

on

9. O

ther

Tota

l, n

(%)

D: D

erm

atol

ogic

als

D

01: A

ntifu

ngal

s fo

r d

erm

atol

ogic

al u

se2

2 (0

.1)

D

02: E

mol

lient

s an

d p

r ote

ctiv

es3

3 (0

.1)

D

06: A

ntib

iotic

s an

d c

hem

othe

rap

eutic

s fo

r d

erm

atol

ogic

al u

se4

4 (0

.1)

D

07: C

ortic

oste

roid

s, d

erm

atol

ogic

al

pre

par

atio

ns4

15

(0.1

)

D

08: A

ntis

eptic

s an

d d

isin

fect

ants

11

(0)

D

10: A

nti-

acne

pre

par

atio

ns1

34

(0.1

)

D

11: O

ther

der

mat

olog

ical

pr e

par

atio

ns1

1 (0

)

G: G

enito

urin

ary

syst

em a

nd s

ex h

orm

ones

G

01: G

ynec

olog

ical

ant

iinfe

ctiv

es a

nd

antis

eptic

s4

12

18

(0.2

)

G

02: O

ther

gyn

ecol

ogic

als

22

(0.1

)

G

03: S

ex h

orm

ones

and

mod

ulat

ors

of

the

geni

tal s

yste

m29

98

46 (1

.3)

G

04: U

rolo

gica

ls16

52

23 (0

.6)

H: S

yste

mic

hor

mon

al p

rep

arat

ions

, exc

l. se

x ho

rmon

es a

nd in

sulin

s

H

01: P

ituita

ry a

nd h

ypot

hala

mic

ho

rmon

es a

nd a

nalo

gues

81

12

517

(0.5

)

H

02: C

ortic

oste

r oid

s fo

r sy

stem

ic u

se53

53

59

378

(2.1

)

H

03: T

hyro

id t

hera

py

22

25

11 (0

.3)

H

05: C

alci

um h

omeo

stas

is14

317

(0.5

)

J: A

ntiin

fect

ives

for

syst

emic

use

J0

1: A

ntib

acte

rials

for

syst

emic

use

176

79

1238

822

828

0 (7

.6)

J0

2: A

ntim

ycot

ics

for

syst

emic

use

21

3 (0

.1)

J0

4: A

ntim

ycob

acte

rials

21

3 (0

.1)

J0

5: A

ntiv

irals

for

syst

emic

use

122

26

43

29 (0

.8)

J0

7: V

acci

nes

9410

15

611

6 (3

.2)

L: A

ntin

eop

last

ic a

nd im

mun

omod

ulat

ing

agen

ts

L0

1: A

ntin

eop

last

ic a

gent

s94

35

353

453

1119

9 (5

.4)

Tab

le 2

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ued

Con

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d



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AT

C c

od

e 1.

Dru

g

sele

ctio

n2.

Dru

g

form

3. D

ose

se

lect

ion

4.

Trea

tmen

t d

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5. D

rug

us

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ss6.

Lo

gis

tics

7.

Mo

nito

ring

8.

Ad

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rug

re

acti

on

9. O

ther

Tota

l, n

(%)

L0

2: E

ndoc

rine

ther

apy

151

44

24 (0

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L0

3: Im

mun

ostim

ulan

ts4

26

(0.2

)

L0

4: Im

mun

osup

pre

ssan

ts23

41

62

1248

(1.3

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M: M

uscu

losk

elet

al s

yste

m

M

01: A

ntiin

flam

mat

ory

and

ant

irheu

mat

ic

pro

duc

ts53

56

141

182

110

0 (2

.7)

M

02:T

opic

al p

rod

ucts

for

join

t an

d

mus

cula

r p

ain,

455

513

118

21

90 (2

.5)

M

03: M

uscl

e r e

laxa

nts

201

13

25 (0

.7)

M

04: A

ntig

out

pr e

par

atio

ns10

33

16 (0

.4)

M

05: D

rugs

for

trea

tmen

t of

bon

e d

isea

ses

271

13

32 (0

.9)

N: N

ervo

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m

N

01: A

nest

hetic

s12

32

11

19 (0

.5)

N

02: A

nalg

esic

s82

19

124

524

33

152

(4.1

)

N

03: A

ntie

pile

ptic

s19

28

55

241

(1.1

)

N

04: a

nti-

Par

kins

on d

rugs

301

45

40 (1

.1)

N

05: P

sych

olep

tics

726

812

528

23

136

(3.7

)

N

06: P

sych

oana

lep

tics

738

139

229

213

6 (3

.7)

N

07: O

ther

ner

vous

sys

tem

dru

gs11

122

25 (0

.7)

P

: Ant

ipar

asiti

c p

rod

ucts

, ins

ectic

ides

an

d r

epel

lent

s (p

01: a

ntip

roto

zoal

s)

21

21

3

R: R

esp

irato

ry s

yste

m

R

01: N

asal

pre

par

atio

ns5

27

(0.2

)

R

03: D

rugs

for

obst

ruct

ive

airw

ay

dis

ease

s69

33

374

132

131

(3.6

)

R

05: C

ough

and

col

d p

rep

arat

ions

61

7(0

.2)

R

06: A

ntih

ista

min

es fo

r sy

stem

ic u

se31

23

36(1

)

S: S

enso

ry o

rgan

s

S

01: O

pht

halm

olog

ical

s23

53

233

(0.9

)

S

02: O

tolo

gica

ls2

2 (0

.1)

Tab

le 2

C

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Con

tinue

d



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ownloaded from



Open access

use of medicines and significant economic burden (eg, over use of antibiotics for upper respiratory tract infec-tion and overuse of opioid analgesics). Use of QIs in these areas may fill the evidencepractice gaps and minimise subsequent DRPs.165 166

QIs for antithrombotic agents (B01) accounted for the larger proportion of QIs targeting drug selection, dose selection, drug use process and monitoring in c-DRPs categories. This may be explained by the fact that the majorit

open access research quality indicators for responsible ... · three main terms: ‘quality...

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