Modelling changes in medication use following the
introduction of IT-based health policies
Rosemarie Sadsad
Centre for Health Informatics
University of New South Wales
Sydney NSW 2052
AUSTRALIA
Phone: +61 2 9385 9024
Fax: +61 2 9385 9006
E-mail r.sadsad@ student.unsw.edu
Dr Geoff McDonnell
Centre for Health Informatics
University of New South Wales
Sydney NSW 2052
AUSTRALIA
Phone: +61 419 016 116
Fax: +61 2 9386 0992
E-mail gmcdonne@ bigpond.com.au
Abstract
Medicines use involves the prescription, transcription, dispensing, administration and
monitoring of medicines. These processes are prone to error resulting in the misuse of
medicines. Medicines misuse associated with prescription errors and medication
administration by the elderly living at home is a growing concem. This problem will
worsen with the ageing population, development of new drugs and drug indications,
shortage in the general practitioner workforce, poor communication between patients
and healthcare providers and poor information management.
Electronic health records could improve the use of medicines by improving access and
management of patient and drug information, making work processes more efficient and
reducing the workload on general practitioners.
The use of medicines varies under different contexts. Current evaluation methods, such as
pilots and trials, fail to capture these differences and changes over time and are
inadequate in terms of time, cost, resources and transferability.
This paper will investigate multiscale and multi-method simulation as a tool to describe
the use of medicines under different contexts and evaluate altemative health record
systems.
Keywords
Multiscale, multi-method, hybrid modelling, system dynamics, agent based, aclverse drug
events, general practitioners, elderly, electronic health records, personal health records
Supported and funded by the HCF Health and Medical Research Foundation
Problem Description
The use of medicines is a system that involves the prescription of medicines, transcribing
this prescription, preparing and dispensing the prescribed medicines by a pharmacist,
administration of the medicine and monitoring of the patient for therapeutic or adverse
effects by a nurse, carer or self (Institute of Medicine 2006 p.60).
Multiple factors in the health system make these processes prone to enor and encourage
improper use of medicines. They subsequently cause adverse drug events, any injury
(preventable or non preventable) due to medication (Bates et al 1995). Factors include
errors in judgement, failure to recognise signs and symptoms, poor communication
between patients, multiple health providers and the prescriber, inadequate patient assessments
and poor patient follow up (Bhasale et al 1998). Incidents arising from inappropriate drugs,
prescribing and administration enors (Bhasale et al 1998) were most common.
Dispensing enors occurred in 10% of medication incidents (Bhasale et al 1998).
In asix month period, Miller et al (2006) estimate 10.4% of Australian general practice
patients experience an atlverse dng event. Of these encounters, 8% result in
hospitalisation (Miller et al 2006) and account for 2.4% - 3.6% of all hospital admissions
(Roughhead et al 1998).
The misuse of medicines is a growing concem particularly for the elderly living in the
community. The elderly are the main consumers of prescription drugs with 86% of
Australians aged 65 and over using prescribed medicines compared with 59% for the
general population (Australian Institute of Health and Welfare 2002). The elderly are thus
more exposed to the medicines use system and it’s flaws. The use of multiple drugs
(polypharmacy) and the effects of ageing on the mental and physical ability to manage
drugs further contribute to improper medicines use such as poor medication compliance,
and the occurrence of adverse drug events. The rate of adverse drug related presentations
to the general practitioner peaks in those aged 65 and over (Miller et al 2006) and account
for 30% of all unplanned hospital admissions for people aged 70 and over (Runciman et
al 2003).
It is estimated that 43% of adverse drug events are preventable (Department of Health
and Ageing 2002). This paper will describe how processes in medicines use could be
improved to help control the incidence of preventable adverse drug events.
patients with
poor
understanding
of medication
regimen
1,
medication
administration |
errors
e
workload general adverse
practitioner 4 drug events
¥ Visits (ade)
/ (m / @ / '
® + 4
prescribing g—__ communication 4 _hospitalisations prescription
information 5 breakdown 2 errors
gap between health
care providers
RI
Figure 1. Improper medicines use.
Figure 1 shows a causal diagram that describes how improper medicines use causes
adverse drug events and the management of these events. Administration and prescription
enors are shown as they are the leading causes of medicine incidents. For the elderly
living at home, preventable medication administration enors depend on their
understanding of their medication regimen. Non preventable causes for medication
administration enors such as the physical and mental inability to self medicate will not be
discussed in this paper.
Inappropriate medicines can be prescribed to patients due to a number of factors. These
include insufficient patient information, poor communication of patient history or
medication changes between the patient, other health service providers and the prescriber
or information for new drugs are incomplete. For example aclverse drug reactions to a
new drug, unintended and non preventable harm caused by a reaction to a drug (IOM
2006), are continually being discovered after the drug is available on the market.
Administration and prescription errors cause adverse drug events at home. Depending on
the severity of these events, treatment can be sought from the general practitioner or the
person can be admitted to the emergency department in hospital. Once the treatment is
completed, the health providers should exchange information about this encounter
(including changes to the medication regimen). Currently, this process is inadequate with
45% of general practitioners not providing medication information to hospitals and 63%
of general practitioners not receiving discharge information (including reasons for changing
medications) from hospitals (Mant et al 2002). Inadequate communication between health
providers form part of the reinforcing loops R1 and R2. Encounter information, especially
changes made to medication regimens, is essential for prescribing and thus preventing
prescription related adverse drug events and management of these events.
The misuse of medicines in the community setting will worsen due to a number of
contextual factors.
The shortage in the general practitioner workforce is projected to continue (Joyce et al 2006).
Past and projected FTE general practitioners
per capita
s 282
gs 140
mg dee
a 130
ge 125
o
uw «20
E115
nm er 2D aA mM H RP a a
BD a © © 5 6 S 6 4
® © © 6 6 6 6 S&S GB
ao a4 a NN AN AN AN AN
Year
Figure 2. Past and projected full time equivalent (FTE) general practitioners (GPs) per capita
(Joyce et al 2006).
This shortage will create a mismatch between the supply and demand for prescriptions. With
fewer general practitioners in the workforce, existing general practitioners will face heavier
workloads having to consult more patients.
patients with
poor
understanding
of medication
regimen
general | +
practitioner medication
workforce administration|
| errors
# | +
workload general adverse
practitioner 4 drug events
otf E/T
prescribing g——_ — communication —_— hospitalisation prescription
information 5 breakdown errors
gap between health +
care providers
RI
Figure 3. The impact of workload on prescription enors.
Figure 3 is a causal diagram describing the impact of workload on prescription errors. Shorter
consultation times can manage this increased workload but limits the opportunity to discuss
medications with the patient. Insufficient information increases the chance for prescribing
inappropriate medications. A rise in prescription errors will increase adverse drug event
related presentations to the general practitioner and further increase the patient workload for
the general practitioner. This creates the reinforcing loop R3.
In addition to the general practitioner shortage, the ageing population will contribute to
workload pressure.
The elderly aged 65 and over comprise 13% of the population. This proportion is
projected to increase to 26% - 28% by 2051 (Australian Bureau of Statistics, 2006a). The
elderly visit the general practitioner more often than the general population and account for
26% of all general practitioner visits (DOHA 2008a). The ageing population will create
workload pressure on general practitioners and subsequently increase prescription enors.
Older people consume more medicines as reflected by expenditure on the Pharmaceutical
Benefits Scheme. Expenditure per person for those aged 65 and over is 7 times higher than
those aged under 65 (Australian Govemment 2007, p. 102, table C2). The Pharmaceutical
Benefits Scheme is a list of drugs subsidised by the Australian govemment. If the proportion
of the elderly population with poor understanding of their medication regimens remains
constant, as the elderly population increases, so will the number of medication administration
enors at home.
ageing.» _ patients with
population + poor
understanding}
of medication
regimen
general | a
practitioner medication
workforce administration|
| errors
. a | +
workload general adverse
is practitioner 4 drug events
visits (ade)
R3 R2 | #
+ + +
prescribing g— communication 4 —hospitalisations prescription
information + breakdown ¢ errors
gap between health +
care providers
@
Figure 4. The impact of the ageing population on medicines use.
Finally, the development of new drugs and new uses for existing drugs (drug indications)
will influence the use of medicines.
Before a new dtug is available on the market, the drug or (new uses for an existing drug)
is evaluated for safety (approximately a year), cost and are considered for reimbursement
under the Pharmaceutical Benefits Scheme (Therapeutic Goods Administration 2008).
Govemment expenditure on the Pharmaceutical Benefits Scheme has grown significantly
at 145% over the past decade (Productivity Commission 2005) despite the small increase
in the number of new drugs listed the scheme (DoHA 2008b). The average price of newly
listed drugs exceeds that of older drugs (Productivity Commission 2005). This suggests
an increese in the prescription of new and more expensive drugs to substitute cheaper
drugs or meet medical needs not previously satisfied. This trend of expenditure on
pharmaceuticals will be difficult to sustain.
PBS Volume and Expenditure
180 7
160 7 6
— 140
B
6 120
= 100
2 80
5
3%
40
20 1
O i)
1 9 ¢ © © Rh ®@ 2oneA mM Tt HH
§ 8s 88 & & & 8s § 8s 8 8
$8888 8 &€&& & 8 8 § 8 8 8 8
2243 23 282 28 8 FR RRR
Year
[=== PBS Volume —PBS Expenditure |
Figure 5. Australian govemment expenditure on the Pharmaceutical Benefits Scheme and
the number of listed drugs dispensed annually (DoHA 2007).
Number of PBS Listed Drugs
800 3500
g 7 i 3000
“= a
2 600 = 2500 2
‘500 Su
3 2000 4 3
c
® 400 re) rs
a aor 1500
é 300 E a
5 200 1000
o
= 100 500 2
o> 0
non DMaAdSsHNMt NH OR ©
gq onaoaoeoceedsceceeeecse
aAaaannndcedscedececscerscnscoecse
Hada eA NNN NN ANNAN A
Year
= Drug Substances ——Item Forms and Strengths —— Brands
Figure 6. The number of drugs listed on the Australian Pharmaceutical Benefits Scheme
(DoHA 2008b) by drug substance (or new chemical entities), item forms, strengths and
brands.
Once a new dnug is available on the market additional adverse drug reactions are
reported. Detection of new adverse drug reactions often occurs after use by a larger
population with varying health conditions than those tested in the clinical trial. Delays in
receiving adverse drug reaction information could result in the prescription of a drug that
causes an adverse drug reaction. Delays in adopting the prescription of new drugs could
deny patients of more effective medication treatments. New uses for drugs expand
throughout the drug lifecycle (IOM 2006). General practitioners will need to keep abreast
with this new information. Figure 7 is a causal diagram that describes the influence new
drugs and new purposes for existing drugs have on information required for prescribing.
ageing patients with
population + poor
understanding}
of medication
regimen
general | e
practitioner medication
workforce administration
| errors
- + | +
new drugs workload general adverse
and drug Dy practitioner + drug events
indications visits (ade)
| R3 R2 | 3
kv + # +
prescribing g—— communication 4 —hospitalisations prescription
information + breakdown Dy errors
between health
care providers
gap
GC
Figure 7. The impact of new drugs and drug indications on medicines use.
Policy Intervention
Information technology, namely shared electronic health records and personal health
records is a systems intervention that can improve medicines use.
Electronic health records contain information such as patient details, medication histories,
general practitioner and hospital encounter summaries and are stored in an electronic
format. There are two main types of electronic health records. Shared electronic health
records (SEHR) and personal health records (PHR). Shared electronic health records are
stored and maintained by the health service provider for example general practitioners
and hospitals. The ‘shared’ feature of these records enables multiple health service
providers to access and manage the same records. Personal health records are stored and
managed by the patient and brought with the individual to consultations with health
Shared electronic health records can improve availability, acoess and exchange of
information required throughout the medicines use system. Their benefits for medicines
use extend further when integrated with systems such as prescribing support and
Pharmacy systems. Managing personal health records encourage patients to be more
active in their health management, including their medication regimen. Integrating
personal health records with health provider systems will enable health providers to have
acoess to patient managed information and could substitute some general practitioner
visits. A visit where a repeat prescription is requested could be managed electronically.
An integrated shared electronic and personal health record system is ideal for improving
medicines prescription, administration and monitoring tasks. This combined solution
encourages patients, carers and health providers to take a more active role and make
better decisions when using medicines.
Figure 8 is a causal diagram describing the benefits of an integrated shared electronic and
personal health record system. A more active role in personal health management can
improve understanding of medication regimens and reducing administration enors. This
system can improve communication between hospitals and general practitioners reducing
the gap in encounter information required for prescribing and thus reducing prescription
enors and finally, reduce the patient workload on general practitioners (and thus
prescribing errors) by substituting visits that could managed electronically.
ageing ————> patients with |
population + poor
understanding)
peeing of medication
personal health
records ~ Hegiaen
__ 1. |,
practitioner substituted medication
workforce visits administration |
| errors
4 |,
hew drugs workload general adverse
and drug : practitioner + drug events
indications visits (ade)
| R3 R2 |
bY + + +
prescribing ¢— communication 4 _hospitalisations prescription
information + breakdown : errors
gap between health +
care providers
| i RI
general practitioners
with shared electronic
ealth records
Figure 8. The impact of shared electronic health records and personal health records on.
The benefits of electronic health record systems for medicines use is currently evaluated
with pilots and longitudinal trials. These methods are time consuming, require sample
sizes large enough to provide statistical significance, disrupt current working practices,
and lack transferability across different settings (Eddy 2007). Policy experiments using
computer simulation saves on cost, resources and time and direct recommendations for
more appropriate health record systems to be piloted and trialled.
The Model
Systems models traditionally describe a problem at one scale, for example models using
aggregate values to generate pattems in system behaviour over a period of years or
models that describes interactions between individual objects occuring in seconds or
minutes. Medicines use operates over multiple time and population scales. For example,
the ageing population and shortage in the general practitioner workforce affect the supply
and demand for prescriptions. Ageing of the Australian population of 21 million (ABS
2008) occurs over decades, as does the training of new general practitioners to boost the
current general practitioner workforce of 25,000 (DoHA 2008c). Whereas the
administration of medicines by a single individual occurs in minutes as does the
prescription of medicines to a patient by a general practitioner.
The model presented in this paper extends traditional systems models by describing
medicines use across multiple scales in particular, how individuals use medicines under
different contexts.
System Dynamics is used to model the context of medicines use. System Dynamics
makes explicit key structures and aggregate pattems in behaviour that drive medicines
use. It identifies, reinforcing and balancing feedback loops and delayed responses
(Sterman 2000). System Dynamics is used to describe how the ageing population,
shortages in the general practitioner workforce, drug advances and health record system
deployment change over a period of decades.
Agent based modelling is used to model daily uses of medicines by the elderly and
general practitioners under these contexts and how these behaviours change as the
context changes. Agent based models comprise autonomous agents or individuals with
defined characteristics and mules for interacting with each other and the environment.
Emergent behaviour arises from repetitive and cooperative interactions between agents
and their environment (Bonabeau 2002; Axelrod and Tesfatsion 2008).
The integration of these models produces a multiscale model of medicines use. During
simulation of the model, the scale can be changed in real time (Bassingthwaighte 2006).
Tt can show the balance between the impact of context and collective individual
behaviour on medicines use and cross-scale effects (Villa 2001) such as the effect of
context on individual behaviour or how these behaviours collectively influence the
context. For example, the deployment schedule of shared electronic health record systems
detemmines the proportion of general practitioners and hospitals that can use these
records. With larger coverage, more information is available for a general practitioner to
use when prescribing medicines reducing the chance of prescribing enors. However,
even with health record technology implemented across the population adoption can limit
the improvements these systems have on medicines use.
This multiscale model is useful for understanding the extent to which large scale policies
affect the behaviours of individuals. In particular, how health record systems, along with
the ageing population, general practitioner workforce shortages and drug advances, affect
the way people take their medicines and how general practitioners prescribe medicines.
In addition to observing individual behaviour, interactions between individuals can be
analysed. For example, how the use of medicines differs between elderly patients who
use a personal health record and see general practitioners with shared electronic health
records versus those who do not have personal health records or see practitioners without
shared electronic health records.
Elderly Patient General Practitioner
No personal health record No shared electronic health record
No personal health record Shared electronic health record
Personal health record No shared electronic health record
Personal health record Shared electronic health record
Table 1. Possible combinations of elderly patient - general practitioner health records.
Collective behaviours of these individuals provide an overview of medication
administration and prescribing error rates and govemment expenditure on medicines use.
These expenditures include the Pharmaceutical Benefits Scheme and the Medicare
Benefits Schedule, a universal public health insurance scheme that subsidies for the cost
of bulk billed general practitioner consultations and public hospital admissions. This
analysis is useful for evaluating altemative health record systems and directing
appropriate health record pilot studies.
ageing —————> patients with |
population + poor |
pebertnih, yeaa
personal health Teale
records 9
general | + | +
practitioner substituted medication
workforce visits administration
| errors
hte |,
hew drugs workload general adverse
nd drug Pt practitioner 4. drug events
indications visits (ade)
| RS (Ro i
av + + +
rescribing communication 4 hospitalisation prescription
information 5 breakdown = errors
gap between health +
care providers
t 7 RI
general practitioners
with shared electronic
wealth records
Figure 9. The context and individual behaviours captured in the model.
Figure 9 highlights context components of medicines use modelled with System
Dynamics (blue) and individual behaviours modelled with the agent based method
(black). It also highlights factors that are calculated by integrating elements from the
System Dynamics model and the agent based model (green). Anylogic6 is the modelling
and simulation package used to develop this model. It can build and integrate System
Dynamics and agent based models. Anylogic6 can produce portable simulations that are
acoessible through a web browser (XJ Technologies 2008).
The components of the model will be discussed in more detail.
The Context
The ageing population is based on the Australian Bureau of Statistics 2006b population
projection model. In this model assumptions made about fubue levels of fertility,
mortality, overseas migration and intemal migration are applied to a base population
(split by sex and single year of age) to obtain a projected population for the following
year. This process is repeated to project subsequent years (ABS 2006b). These
projections span the period June 2004 to June 2024.
The general practitioner workforce model is based on Joyce’s 2006 medical workforce
model. In this model, new graduates, immigrants and re-entrants to the workforce are
added to the base general practitioner workforce (split by sex and age), and deaths,
retirements and attrition exits are subtracted for each year. Adjustments are made for
movement between occupations within the medical workforce and for ageing. This
obtains a projected number of general practitioners in the medical workforce for the next
year and repeated for subsequent years (Joyce 2006).
The model of the development of new drugs and availability on the Pharmaceutical
Benefits Scheme is based on the drug supply, listing and pricing components of
Heffeman et al 2004 National Medicines Use System Dynamics model. New drugs enter
the market. Some are listed on the Pharmaceutical Benefits Schedule. These drugs go
through the process of being listed as the first drug of its kind, a premium brand or a
generic brand before being removed off the list. The drug is priced accordingly with the
average drug price increasing with inflation. The proportions of drugs used amongst these
classes’ influences expenditure by the Australian govemment subsidising these drugs.
The model of the health record system is configured by the user through the user
interface of the simulation. The user, typically a policy analyst, sets the development,
deployment and adoption rates, start up and per-unit costs and effectiveness of shared
electronic health records and personal health records on prescription and medication
administration enors respectively. Factors such as time allocated to a health record
system project, budget and the health and cost benefits desired from the project will
influence the configuration settings.
The Individuals
Elderly patient “agents” and general practitioner agents are defined in the individual-
based model.
Senior GP
boolean
scrintionErrorRate; double
i sid
+pr rviptionError:
tadeVisit: boolean
an
boolean
icines(): void
id
t+administerb
+makeVisit
Figure 10. UML Class diagram of the Senior and GP agent.
Elderly Patient General Practitioner
adrrinister
medications
check ade or
regular gp visit
gp vistt}required?
check if
prescription repeat
require:
Check PHR/SEHR
can visit be|substituted?
no makeVisit()
> +( see patient
yes
has| SEHR or
patipnt has PHR?
yes
no
diagnose
use
information
t+ crescribe
request
medications
{prescrfption error}
Figure 11. UML Activity Diagram of a patient administering medicines, visiting a
general practitioner and a general practitioner seeing and treating patients.
Figure 10 shows the attributes and miles each elderly patient agent and general
practitioner use and follow when interacting with other. For example, the visit flags
(adeVisit, regularVisit) are used to determine whether the elderly patient will see the
general practitioner that day.
Figure 11 is a UML activity diagram that describes the core activity of medicines use,
administration of medicines by an elderly patient, prescribing of medicines by general
practitioners and the interaction between the patient and general practitioner in a
consultation.
Each elderly patient administers medicines daily. An adverse drug event can occur if
medicines are taken incorrectly or inappropriately prescribed medicines are consumed. If
the patient has a personal health record, the chance of administering medicines
incorrectly is reduced (by an amount provided by the simulation user through the user
interface). A patient will visit a general practitioner if they have experienced an adverse
drug event or are due for a regular visit (determined by the average general practitioner
visit rate). Serious adverse drug events result in hospitalisation (not shown in the
diagram). A general practitioner visit requesting a prescription repeat can be substituted.
with an electronic request provided the elderly patient has a personal health record that is
linked to his or her general practitioner's shared electronic health record system.
Otherwise, the patient will visit the general practitioner. The general practitioner will see
patients on his or her waiting list. If the general practitioner has access to shared
electronic health records or the patient’s personal health record, this information will be
used to help diagnose, treat and if required, prescribe medications. Inappropriate
medicines can be prescribed if the general practitioner has insufficient information or has
a heavy patient workload.
Ageing
Ponulation | _
General
Practiianer
Workforce [7
Drug
Development [
1
' 1
1 rate Saad it edninisteation
+ > OJ _ Share 1 understanding
' eoet Electronic it ---- Sent
1 Health Lo 4o1 i
1 Recaids : i par
20 Sonim. | 1 tr v
: adoption ‘
interface b - + Se ee A pet im feumimiount miniomen retin 1
1: 1 mm
1 Tnpect of 1 mt
1 chr on 1 mt
1 prescription 1 mt
1 error cate 1 mt
1 ! mt
1 *Oqaua it
1 rate mt
beeQue | Fersanal fan
i cast Health ta
! a FLECOTOS, [vee rceverurayumenrnanrsusred m4
1 1
1 1
i adopeion 1
H rote 1
On ge SSS
phe on
aduinistration
error rate
Figure 12. UML Component Diagram of the medicines use model
Figure 12 provides an overview of how the interface, context level of the model and
individual agents are linked. It shows information passed between components of the
medicines use model.
Conclusion
Improper medicines use results in many preventable adverse drug events, particularly
with the elderly living at home. This situation will worsen with the ageing population,
drugs advances, shortage in the general practitioner workforce and poor information
Management and exchange between the patient, health providers and the
Shared electronic health records and personal health records could bridge the information
gap and reduce work pressure on general practitioners. Current evaluation methods are in
inadequate in terms of time, cost, resources and transferability. Multiscale, multi-method
simulation is useful for describing how individuals use medicines under changing
contexts and altemative health record systems. This provides a tool to aid health record
system evaluation and direct better health information technology policies.
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