Tailored Patient Information: Some Issues and Questions 
Ehud Reiter 
Dept of Computing Science 
University of Aberdeen 
Aberdeen AB24 2TN 
BRITAIN 
ereiter@csd.abdn.ac.uk 
Liesl Osman 
Dept of Medicine and Therapeutics 
University of Aberdeen 
Aberdeen AB9 2ZD 
BRITAIN 
l.osman@abdn.ac.uk 
Abstract 
Tailored patient information (TPI) systems 
are computer programs which produce 
persoualised heath-information material for 
patients. TPI systems are of growing interest 
to the natural-language generation (NLG) 
community; many TPI systems have also 
been developed in the medical community, 
usually with mail-merge technology. No 
matter what technology is used, experience 
shows that it is not easy to field a TPI 
system, even ff it is shown to be effective in 
clinical trials. In this paper we discuss some 
of the difficulties in fielding TPI systems. 
This is based on our experiences with 2 TPI 
systems, one for generating asthma- 
information booklets and one for generating 
smoking-cessation letters. 
1 Introduction 
Tailored patient information systems are computer 
programs which generate personalised medical 
information or advice. There are a growing number 
of natural-language generation (NLG) projects 
which fall into this category, such as (Buchanan et 
al., 1995; Cawsey et al., 1995). There have also 
been several projects in the medical community 
which used mail-merge technology to produce 
personalised medical information, such as (Velicer 
et al., 1993; Campbell et al., 1994). We are also 
aware of one mail-merge TPI system that is used 
commercially (see 
http:Hwww.commitedquitters.com), although it is 
unclear whether this system is genuinely effective or 
merely a marketing gimmick. 
In this paper, we discuss some of the practical 
issues and trade-offs involved in building tailored- 
patient information systems (henceforth referred to 
as TPI). This discussion is partially based on our 
experiences in two projects: GRASSIC, a TPI 
system for asthma booklets which was proven 
clinically effective but nevertheless never deployed; 
and a newer project to generate personalised 
smoking-cessation letters. We believe that the 
points we raise apply to other TPI systems as well, 
and perhaps also to other NLG applications. 
The rest of this section gives some general 
background on TPI systems. Section 2 introduces 
the two specific systems mentioned above. Section 
3 discusses some specific issues which affect 
deployment, and is the heart of the paper. Section 4 
gives some concluding comments. 
1.1 Tailored patient information 
TPI systems generate personalised letters, booklets, 
hypertext, or other text-based documents for 
patients (or other health-care consumers). Tailoring 
is based on information about the patient, some of 
which may be extracted from a standard Patient 
Record System (PRS) database. TPI systems can be 
based on NLG or on simpler mail-merge 
technology; this is an engineering decision (Reiter, 
1995), based on what functionality is desired. 
TPI systems are usually intended to change either 
the behaviour or mental state of a patient. For 
example, TPI systems can be used to help people 
stop smoking (Velieer et al., 1993); to increase 
compliance with a treatment regime (Osman et al., 
1994); or to reduce anxiety in patients (Cawsey and 
Grasso, 1996). Usually these goals are stated in 
clinical terms, and the effectiveness of the TPI 
system is evaluated in a controlled clinical trial. 
29 
2 Our Projects 
2.1 GRASSIC 
The GRASSIC system (Osman et al., 1994) used 
mail-merge techniques to generate personalised 
asthma-information booklets. Personalisation 
mainly consisted of making local changes in the 
document; style and overall document structure 
were not affected. For example, whenever the 
booklets discussed medication, they only mentioned 
the medication prescribed for the target patient; 
whenever they discussed side-effects, they only 
mentioned side-effects associated with the 
prescribed medication; and so forth. An attempt 
was also made to avoid terminology and names 
unfamiliar to patients; for example, commercial 
names were used for medication, instead of 
scientific names. Although mail-merge technology 
was used, care was taken to avoid the usual "fill-in- 
the-blank" form-letter look. 
Despite its simple technology, a clinical 
evaluation showed that GRASSIC was successful in 
reducing hospital admissions among severe asthma 
patients. Indeed, severe asthma patients who 
received the GRASSIC material had half of the 
number of hospital admission as control patients 
who received non-personalised material. Thus, 
GRASSIC improved the health and quality of life of 
its patients; it also saved the health service 
approximately £500/patient/year by reducing 
hospital admissions. These figures imply that if 
GRASSIC was deployed throughout Scotland, it 
could save the health service perhaps £5,000,000 
per year; deployment throughout the UK might save 
an order of magnitude more money. 
Even though it was clinically effective, however, 
GRASSIC was never fielded. Instead, when the 
study was finished the non-personalised booldets 
were rewritten based on a better understanding of 
patient needs that was one result of GRASSIC. 
Also, a single page was added at the front of the 
booklet where a health professional could 
(manually) write in the details of a personal 
management plan for this patient; this required a 
few minutes at most, and was typically done during 
a Consultation with the patient. 
Why was GRASSIC not fielded? Partly this was 
due to classic technology-transfer issues. For 
instance, the team which developed GRASSIC was 
a research group, and did not have the skills and 
resources necessary to turn the prototype into a 
fieldable system; this would have required 
developing better user interfaces, making the code 
more robust, writing manuals and other supporting 
documentation, helping users install the system, and 
so forth. Furthermore, there was no existing 
development team whose remit covered GRASSIC's 
functionality, and hence which GRASSIC could 
naturally be transitioned to. 
Another problem was that the developers were 
concerned that doctors would be reluctant to use 
GRASSIC, because it was a new technology and did 
not deliver dramatic and visible benefits to 
individual medical professionals. That is, while 
fielding GRASSIC might provide significant benefit 
to the health service as a whole, from the 
perspective of an individual doctor, who dealt with 
many kinds of patients in addition to people 
suffering from severe asthma, the effect of using 
GRASSIC was a relatively small reduction in the 
total number of his or her patients admitted to 
hospital. Given the natural reluctance of many 
people to adopt new technology, the developers 
were worried that doctors would in practice be 
reluctant to learn about and use GRASSIC, even ff 
its use Was recommended by the health service. 
Because of these problems, the development team 
decided to go for the alternative approach of 
improved non-personalised material, plus a limited 
amount of manual personalisation. No clinical 
evaluation was done of the alternative approach, but 
studies elsewhere (such as (Lahdensuo et al., 1996)) 
have demonstrated the effectiveness of manually- 
written personal management plans. The manual 
approach was probably less effective at reducing 
hospital admissions than the tailored-material 
produced by GRASSIC, but it could be implemented 
with the skills and resources available to the 
development team, and furthermore fit much more 
naturally into the. current working practices of 
Scottish medical professionals.. 
It may seem odd, incidentally, to discuss a mail- 
merge system in a workshop devoted to NLP, but we 
believe that the fielding/deployment issues that 
arose with GRASSIC are likely to affect any TPI 
system, regardless of which technology it is based 
on. 
2.2 Smoking-cessation letters 
More recently, we have begun working on a TPI 
system which generates personalised smoking- 
cessation letters, and which uses some NLG 
technology (Reiter et al., 1997). Personalisafion is 
based on an "Attitudes Towards Smoking" 
questionnaire which patients fill out. This project is 30 
still at an early stage, but we want to be sure that it 
can be deployed if it proves effective in clinical 
trials. Hence we have been trying to develop a 
better understanding of deployment issues of TPI 
systems, in the hope that this will help us design the 
system in an appropriate fashion. 
3 Deployment Issues 
In the course of thinking about why GRASSIC was 
not fielded, and how to build the smoking-letters 
system so that it can be fielded, we have identified a 
number of specific issues. We believe these apply in 
some manner to all TPI systems, and perhaps to 
other types of NLG systems as well. 
3.1 Cost-Effectiveness 
Perhaps the most obvious real-world consideration 
for TPI systems is cost-effectiveness. No one is 
going to use a TPI system unless it is cheaper than 
having "a person manually write letters, explain 
patient records, etc, In the medical setting, money 
will not be spent on TPI unless it is seen as being 
effective in improving clinical outcomes for 
patients, and/or saving money for the health service. 
We will examine both GRASSIC and our 
smoking--cessation letters system by this criteria. 
Incidentally, a general rule of thumb in AI and 
other advanced computing applications is that such 
systems need to have a pay-back period of 2-3 years 
at most, with I year being preferable. 
If we look at GRASSIC first, there are three 
comparisons that can be made: 
• GRASSIC vs. non-personalised booklets: As 
pointed out above, GRASSIC has the potential 
to save the Scottish health service several 
million pounds per year (assuming that doctors 
are willing to use the system), which means that 
its development, fielding, and deployment costs 
would probably be paid back within a year. 
• Manually-tailored vs. non-personalised booklets: 
We have no data on the effectiveness of the 
manually-tailored booklets, but our best guess is 
that they capture most but not all of the benefits 
of GRASSIC. Since fielding and deployment 
costs for these booklets are minimal, the pay- 
back period for using the manually-tailored 
booklets is very short. 
• GRASSIC vs. manually-tailored booklets: With 
the above assumptions, the pay-back period for 
GRASSIC compared to the manually-tailored 
booklets could be more than 3 years. 
In short, when compared to the alternative of the 
manually-tailored letters, GRASSIC may not meet 
the "pay back within 2-3 years" criteria for cost- 
effectiveness. A big caveat here, though, is that this 
assumes that the manually personalised letters are 
effective at reducing hospital admission rates for 
severe asthmatics; if this is not the case, than 
GRASSIC does meet the cost-effectiveness rule. 
For the smoking-letters system, it is hard to 
estimate the monetary value of helping someone 
quit smoking, but since smoking a pack a day can 
cut life expectancy by 5 years (Austoker et al., 
1994), we would hope that society places a benefit 
of at least £10,000 on a successful cessation. We do 
not yet know if our smoking-cessation letters are 
effective, but if they are successful in convincing 
2% of smokers to quit, that will mean a benefit to 
Scottish society of several hundred million pounds, 
which exceeds likely deployment costs by almost 2 
orders of magnitude. The 2% goal, incidentally, is 
based on the observation that 5% of smokers will 
quit following a brief consultation with their GPs on 
smoking-cessation (Austoker et al., 1994). Hence, 
if our system can convince even a small number of 
smokers to quit, it should easily meet cost- 
effectiveness goals. 
3.2 Acceptability to Medical Professionals 
Most TPI systems are formally evaluated in terms of 
their impact on patients. However, no TPI system 
is going to be used in the real-world unless it is also 
acceptable to doctors and other health-care 
practitioners. 
In particular, one issue that comes up in both of 
our systems is whether individual doctors (or other 
medical practitioners) perceive enough benefit from 
the systems to make it worth their while to go 
through the effort of installing and learning how to 
use the system. An issue here is that although many 
younger doctors in Scotland enjoy using computers 
and are quite keen to try new computer tools, some 
older doctors are less enthusiastic about using 
computer-based systems, unless they provide very 
clear and tangible benefits. Of course, the 
percentage of "computer-friendly" doctors should 
increase over time, as the older generation of pre- 
computer doctors retire. 
As mentioned in Section 2.1, this was a major 
concern with GRASSIC; since using GRASSIC 
would only result in a small reduction in the 
number of patients each doctor sent to hospital, 
there were real doubts as to whether doctors would 
be willing to make the personal investment required 
31 
of their time and energy to use the system. 
Furthermore, using GRASSIC required a significant 
change in the way doctors interacted with severe 
asthma patients. The alternative approach of 
manually customising (improved) booklets, in 
contrast, did not require doctors to learn new 
computer skills, and fit much more naturally into 
the existing procedures for managing asthma 
patients. 
The attitude of doctors as again an issue in 
smoking-cessation. For instance, as mentioned 
above, research shows that brief consultations with 
GPs will help 5% of smokers quit; but yet few GPs 
regularly make such consultations. This is largely 
bocausc from a GP's perspective, it is hard to 
remain excited and motivated about a technique that 
has a 95% failure rate. This is one of the reasons 
why we believe it is sensible to try to automate this 
advice-giving in a computer letter-generator; 
computers, unlike people, do not get discouraged if 
they fail in 95% or cvcn 99.99% of cases. 
Of court, there is a real possibility that doctors 
will be reluctant to make the effort to install our 
letter-generation system. After all, even if it is 
successful in achieving a 2% cessation rate, from 
the point of view of an individual medical 
practitioner, this translates into a very small 
reduction in the number of his or her patients who 
smoke. Partially for this reason, wc arc exploring a 
number of alternative fielding possibilities for our 
system, including through GP offices, via health 
promotion services (such as telephone hclplincs), 
inside hospital clinics, and as a service provided by 
employers. Again it is very early days in our 
project, but we hope that by exploring several 
fielding possibilities, we can find one where there is 
maximal willingness to use our system. 
Finally, a fairly obvious point is that individuals 
will be most willing to use a TPI system if the 
benefits of the system accrue to them as well as to 
the health service as a whole. For example, GPs 
will probably be more willing to use our smoking- 
letters system if the health service rewards them for 
lowenng smoking rates, or gives them smoking.~ 
cessation targets. From this perspective, 
incidentally, it may well be that most acceptable 
medical application of NLG is not TPI, but rather 
systems which help doctors author routine 
documents (discharge summaries, for example); in 
such cases the benefits to the individual using the 
system are much clearer. 
3.3 Amount of lnformation Needed 
Another important issue for TPI systems is the 
amount of information they need about patients in 
order to successfully tailor the documents, and 
whether this information can be extracted from 
existing data sources, such as Patient Record 
System (PRS) databases, or whether it needs to be 
entered just for the TPI system. A TPI system 
which requires no extra data will probably be more 
acceptable to users, since they do not have to spend 
any time entering information in order to use the 
system. Similar observations have been made in 
other NLG projects, e.g., (Reiter et al., 1995). 
The GRASSIC system obtained all its patient 
information from a PRS system; it did not nccd to 
acquire additional information for tailoring 
parposes. However, the PRS system used in the 
clinic where GRASSIC was developed was 
relatively advanced. It is not clear whether PRS 
systems in other Scottish clinics would also contain 
sufficient information to support GRASSIC's 
tailoring. Also, the fact that different sites use 
different PRS systems increases the complexity of 
installing GRASSIC in a site. 
The smoking-letters system, in contrast, requires 
extensive information to be entered for tailoring; 
patients must fill out a 4-page questionnaire about 
their attitudes towards smoking before the system 
can be used. We are trying to develop ways to make 
questionnaire entry as easy (and as error-proof) as 
possible, but the need to enter this information is a 
significant cost to using the system. On the other 
hand, because the smoking-letters system makes 
minimal use of PRS data, it does not need to be 
customised to the specifics of each site's PRS 
system, and hence will have a lower installation 
cost. 
The amount of patient-information available to 
TPI systems should increase over time, as PRS 
systems become both more comprehensive and more 
standardised. 
3.4 Risk and Impact of Mistakes 
It is probably inevitable that documents produced by 
a real-world TPI system will sometimes contain 
mistakes. This may be a consequence of problems 
in the tailoring data (for example, incorrect PRS 
entries or patient questionnaires); it may also be a 
consequence of bugs in the software. 
In some cases mistakes may not be important. 
For example, ff mistakes slightly roduce the 
effectiveness (via inappropriate tailoring) of a letter 
encouraging smoking cessation, this is acceptable as 32 
long as the TPI system still has sufficient overall 
effectiveness. If mistakes can lead to medically 
harmful advice, however, this is a serious problem. 
There are a number of solutions to this problem, 
none of them ideal. These include 
• Documents can be reviewed by doctor or nurse 
before being sent to patients; this was the 
procedure used in GRASSIC. This may 
significantly decrease the attractiveness of the 
system, if the amount of doctor-time required is 
non-trivial. Human review may not be possible 
for interactive hypertext systems, such as 
Migraine (Buchanan et al., 1995) or Piglet 
(Cawsey et al., 1995), which generate texts "on 
demand" when requested by patients. 
• The TPI system can include disclaimers and 
warnings in its texts. For instance, tailored 
nutritional advice which includes recipes 
(Campbell et al., 1994) could also include 
warnings such as do not use this recipe if you 
are allergic to dairy products. Such disclaimers 
will significantly reduce the "personalised" 
aspect of the generated texts, however, which is 
the whole purpose of TPI systems. 
• The TPI system may be designed so that 
documents do not contain specific advice or 
instructions. For example, the smoking-letters 
system could stress facts (e.g., have you realised 
that you are spending £100 per month on 
smoking) or motivational stories (e.g., Many 
other single mothers have managed to quit. For 
example Jane Doe...) instead of advice (e.g., 
Start jogging to lose weight). Of course, if the 
TPI system is communicating a treatment 
regime (medication, diet change, etc.), then this 
approach will not be possible. 
We have not yet decided which of the above 
approaches to use in our smoking-cessation system. 
Another possibility is to simply accept that 
mistakes will occur. Doctors, after all, occasionally 
make mistakes, and perhaps the right goal for 
computer systems is not "be perfect" but rather 
"make mistakes less often than doctors". However, 
in current medical contexts, computer systems are 
held to a much higher standard than doctors. If a 
doctor gives bad advice that causes a patient to 
become sick, this is regrettable but hardly news. 
However, if a computer system does the same, even 
on just one patient out of thousands, it may cause 
the system to be withdrawn from service. 
4 Conclusions 
TPI systems are likely to be of increasing interest to 
health care providers. They clearly work to some 
degree, and they should become more effective as 
they start using more advanced technology, such as 
NLG. However, it is not sufficient for a TPI system 
to be clinically effective in order to be fieldable; it 
also needs to be cost-effective, acceptable to 
individual users (patients as well as medical 
practitioners), have low data-entry costs, and 
incorporate a satisfactory solution to the mistakes 
issue. This is a daunting set of requirements, and 
may explain why although many TPI systems have 
been developed in the lab, few have been fielded. 
We hope that a better understanding of these 
issues will help TPI developers (including 
ourselves) produce systems that are more likely to 
be deployed and used in the real world. We believe 
that TPI technology has the potential to make a real 
impact on health, especially given the increasing 
importance of life-style and compliance issues; good 
health is mostly a function of actions and decisions 
taken by patients, not by health-care professionals. 
But this potential will only be realised if we can 
build systems that are not only technologically 
ingenious and clinically effective, but also are easy 
to deploy and use. 
We would like to conclude by saying that we 
believe that these fielding problems will decrease in 
the future. In particular, cost-effectiveness should 
increase as technology improves; acceptance among 
health-care professionals should become easier as 
more such people become computer literate and 
friendly; data-entry should become less of a problem 
as PRS systems become richer and more 
standardised; and people may become more tolerant 
of computer mistakes if they adopt the "make 
mistakes less often than a doctor" criteria. So, in 
ten years time it should be much easier to deploy a 
TPI system; all the more reason for researchers to 
work today on developing appropriate technology 
and identifying good applications. 

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