UNDERSTANDING NEEDS AND REQUIREMENTS IN

APPLICATIONS FOR IDENTIFYING CLINICALLY RELEVANT

SIMILARITIES BETWEEN PATIENTS WITH

LIVER RELATED DISEASES

Christina Engström

and Kristina Groth

1,2

CLINTEC, Karolinska Institute, 100 44 Stockholm, Sweden

HCI, CSC, Royal Institute of Technology, 100 44 Stockholm, Sweden

Keywords: Clinical decision support system, similar patient cases, terminology, structure of documentation.

Abstract: We present a field study at a surgical clinic of what data that is relevant in order to identify clinically

relevant similarities. We have observed discussion meetings in which different medical specialities decide

how to treat patients with severe diseases in the liver. Our study also includes interviews with medical

personnel, and examination of two data sources, the electronic patient records and the local quality liver

registry used within the clinic. Our findings include a model of data that can be useful when searching for

clinically relevant similarities between patient cases, as well as requirements on the functionality of an

application that can identify clinically relevant similarities.

1 INTRODUCTION

Within a highly specialised medical care in the upper

abdominal tract at a hospital in Sweden, several

disciplines are involved in the treatment decision of a

patient. During such decision meetings, held once a

week within the area of liver, it often happens that a

doctor refers to a case experienced earlier. If no

doctor with adequate experience is present during a

meeting, or if a doctor referring to a similar case does

not remember everything, information relevant for

the diagnosis and treatment of the patient may be

missed. This implies a need for an application where

the medical information about a patient being

discussed can be compared with information from

earlier cases, information that could be useful.

Several studies have shown the need to uniform

and structure the content of medical records.

Häyrinen et al. (2008) point out that it is a challenge

to standardise content and structure of medical

journals, and that a clearly defined terminology and

uniform information structure are essential to

facilitate communication and the ability to compare

data. They argue that clearly defined terms decrease

the risk of misunderstandings, and that work-around

terminology is a requirement of applications that can

support decision making and follow-ups.

The lack of a uniform terminology makes it

difficult to reuse and communicate information since

one single term can have different meanings and

people who use the term can mean different things

(Lenz et al. 2007), which can make it difficult for

health care personnel to interpret the documentation.

This may increase the risk of wrong treatments.

Medical data stored in a structured way increases

the possibilities for processing. Considering this, an

electronic health record system (EHR) could be used

for several purposes, giving secondary advantages

(Britt 1995). The possibility to integrate a clinical

decision support system (CDSS) with an EHR

system is proposed by Porcelli & Lobach (1999).

Even though a large amount of effort has been

focusing on CDSS, it appears that only a few are in

use today. For a CDSS to be useful it is necessary to

use an existing data source where the user does not

have to manually register data for the CDSS (cf.

Grudin 1988). Also, the CDSS should not replace

manual decision making, but amplify and support the

decision process (see, e.g., Coiera 2003).

Another important issue is to make the CDSS

available at the time of the decision. O’Sullivan et al.

(2007) and Johnston et al. (1994) show that by

presenting the right information at the time of the

decision making, doctors can be supported to base

their decisions on solid grounds. They show that such

real time CDSS can enhance medical care and

187

Engström C. and Groth K. (2009).

UNDERSTANDING NEEDS AND REQUIREMENTS IN APPLICATIONS FOR IDENTIFYING CLINICALLY RELEVANT SIMILARITIES BETWEEN

PATIENTS WITH LIVER RELATED DISEASES.

In Proceedings of the International Conference on Health Informatics, pages 187-192

DOI: 10.5220/0001536901870192

 SciTePress

support efficient exchange of knowledge between

different groups of medical staff.

In our work, we have focused on what data that

would be relevant in an application that can compare

different patient cases and present information

showing similarities. We have conducted a field

study of what data that are relevant in order to

identify clinically relevant similarities between

patients with severe liver diseases. Our study also

investigates requirements on the data sources

necessary in order to accomplish such comparisons.

2 BACKGROUND

Medical experts base their decisions on, e.g., existing

guidelines, case studies and experiences from

previous patients that are clinically similar to the

current case. By developing applications that use

individual patient cases as an information source

including experiences from treating these patients,

decision making in new cases can be supported

(Rossille et al. 2005, Frize et al. 2005).

A comparison of patient cases can be based on

specific data from, e.g., the electronic patient records,

or on context, e.g., by indexing documented data or

by rating different factors, e.g., earlier diagnosis

(O’Sullivan et al. 2007). Information stored as free-

text are difficult for an application to understand and

interpret, but there have been attempts to use free-

text index based on certain nouns and their definition

in terminologies like Snomed CT (Huang et al.

2003). This requires a uniform terminology since the

meaning of different terms and relations between the

terms must be clearly defined. On the other hand, it

can be a challenge to structure complex clinical

observations and store them in forms (Hogan &

Wagner 1996, Bleeker et al. 2006).

In a recent study of identifying similarities

between patient cases, Melton et al. (2006) used five

metrics to assess the degree of closeness between

cases and to discover analogous cases. They defined

similarities between patient cases by counting the

differences in characteristics. They conclude that

their models have the potential to be useful in the

area of data mining, but that they are not yet as good

as clinical experts.

In a similar study within the domain of breast

cancer, Rossille et al. (2005) propose a future CDSS

based on data warehouse and automatically getting

similar patient cases from the EHR system. They

point out that patient data today are not stored in a

format proper for automatic analysis, and that the

system architecture, therefore, must be decided first.

In a case based reasoning approach, Frize et al.

(1996) compared and identified patient cases that are

“as similar as possible”. Documented parameters

were rated, in collaboration with doctors, using

special software. They conclude that this way of

using experiences from earlier treated patients can

enhance patient handling in intensive care units.

These studies imply that an application that can

identify similarities between patient cases has the

potential to be helpful in medical care. Also, these

earlier studies point out the importance of using

uniform and structured data and terminology.

In order to understand the conditions for such an

application, we have conducted a field study at a

surgical clinic at a hospital in Sweden (Gastro). We

have identified what data that are relevant to

compare, and examined the possibilities to use

documented information as data sources.

3 METHODS AND THE SETTING

Our research is based on close interdisciplinary

collaboration between researchers within the fields of

human-computer interaction, medical informatics,

and surgery. We have used qualitative methods and

based our work on grounded theory. The data

collection has been made through interviews,

observations, samples of medical records, and

examination of a local quality liver registry. In total

seven interviews have been conducted with liver

surgeons, a radiologist, a terminologist and a

medically trained doctor working with IT solutions.

Observations have been conducted of eight multi-

disciplinary video-mediated liver meetings, in which

decisions about how to treat the patients are made.

All interviews and observations have been recorded

and transcribed or documented with field notes (two

observations). Twelve samples of anonymised

electronic patient records from the liver decision

meeting have been analysed.

Multi-disciplinary video-mediated meetings are

held at Gastro every week within the area of liver.

The discussion during these meetings may identify

unclear issues that need to be further investigated,

focus on results from samples made and so forth, all

in order to come to a consensus about the best

possible treatment. Each patient case discussion

follows the same structure with a clinical

presentation of the patient, a walkthrough of the

radiological examinations, and a discussion of how

the patient can and should be treated.

Gastro uses an EHR system (hereafter named

TC), that supports the whole care process, and to

document different care related activities there are

different kinds of notes. These notes can, to a certain

degree, be structured by using common headlines.

The headlines have to be defined in a catalogue of

terms associated with the system, to make it possible

to search for the terms in free-text. By structuring

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notes based on already defined terms the uniformity

between patient records increases.

The decision made at each liver meeting is

documented using specific notes in TC, summarising

the patient’s condition, results and interpretations of

radiology pictures, the decision made and the plan of

how the decision should be fulfilled.

Gastro also uses a local liver registry, which

includes information about all patients with tumours

in the liver, gall bladder and biliary passages, and

who has been treated with surgery. Data in the liver

registry is registered using a form based input and

consists of parts of the information that is stored in

TC, but in a more structured way. There is also

information in the liver registry that has not been

documented in TC.

4 RESULTS

4.1 Description of Patient Cases

Our observations of the liver meetings show that the

same categories of data, to a large extent, are in focus

during the discussions:

 patient status, e.g., age, motivation, weight,

general health, and strength,

 subjective symptoms, e.g., gastrointestinal

symptoms and tumour symptoms,

 present disease, e.g., diagnosis, judgements,

treatments, progress,

 present and earlier diseases and treatments,

 status of the liver, e.g., the function,

 examinations made and their results, e.g.,

radiological examinations, laboratory tests, and

function tests,

 implemented and suggested actions, e.g.,

treatments and planned examinations.

When examining the documented notes in TC

from the decision meetings, we found that they

mainly included the categories subjective symptoms,

examinations made and their results, and

implemented and suggested actions. The most

significant difference found was that the information

in the medical records is not as detailed as in the

discussion during the liver meetings.

Examination of the liver registry indicates a

similar content, but structured using some other

categories:

 patient status, e.g., sex, length, weight, BMI,

 subjective symptoms, e.g., tumour symptoms,

 status of the liver, e.g., the function,

 examinations made and their results,

 tumour information, e.g., type and size,

 treatments.

The category “treatments”, contains data that can be

found in the category “implemented and suggested

actions” from the liver discussions. Also, “tumour

information” is a more specific category, but is

included in the category “present disease” from the

liver discussions.

Interviews with clinically working doctors gave

an understanding of what data about a patient they

find relevant to compare to identify clinically

relevant similarities. Two of the surgeons said:

Unni: It is how old the patient is, the condition,

earlier illness, liver function, if the patient have

had earlier liver diseases, present values of the

liver function /…/ other kinds of diseases,

diagnosis and how long the disease has

proceeded.

Bill: It can be plenty, anatomic variations,

tumour growth in different ways, how often we

manage to do a resection, how often we can

accomplish an R0, a radical situation. It can be a

case where we have ten liver metastases that are

located a couple of centimetres deep, and we

want to make local resections. Then you get a

feeling that this is not good, we will not

accomplish an R0-situation because we seldom

do. Today, we do not have that kind of structured

documentation that can confirm these suspicions.

What is interesting is that the two surgeons stress

the level of detail that often is missing from the

medical records, e.g., how the tumour is growing,

how long the disease has proceeded, and how the

tumours are located. They also mention other

diseases as relevant, i.e, comorbidity.

During the interviews we also asked what data in

general that are needed at the liver meetings to make

a good decision about the treatment. From the

answers it is obvious that there are specific kinds of

data that are relevant for the decision, but that the

surgeons do not find relevant when searching for

similarities different patient cases:

Bill: It is data about how ill the patient is, how

he or she can manage the treatment, how

motivated the patient is to different kinds of

treatment. Then it is all the details about the

oncology treatment, how the patient has

responded to that treatment. That [kind of

information] is sometimes poorly documented.

Hence, from the interviews the following categories

have been identified as relevant content:

 patient status, e.g. age, motivation, general

health, and strength,

 present disease, e.g. diagnosis and how long

the disease has proceeded,

 status of the liver, e.g., the function,

UNDERSTANDING NEEDS AND REQUIREMENTS IN APPLICATIONS FOR IDENTIFYING CLINICALLY

RELEVANT SIMILARITIES BETWEEN PATIENTS WITH LIVER RELATED DISEASES

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 examination results, e.g. describing attributes

of the present disease such as anatomic

variation and localization,

 implemented treatments and results,

 comorbidity and earlier diseases.

The examination results appear to be an important

source of information when identifying clinically

relevant similarities. Therefore, we have focused on

relevant examinations including treatments and

diseases (e.g., comorbidity and primary disease), e.g.,

radiological examinations, laboratory test, biopsies,

endoscopic examinations, functional tests of organs,

and clinical judgements. These examinations appear

to generate the same kind of information expressed in

different ways. One such example is the function of

the liver, where there are laboratory tests that can

measure levels of certain elements in the blood, e.g.,

ASAT, ALAT, ALP, albumin, PK, bilirubin. Also,

radiological examinations can, to some extent, show

the liver function, e.g., signs of cirrhosis. There are

also functional tests that can be used to describe the

function of the liver, e.g., ICG-clearance values and

elastographic imaging.

4.2 User Needs and Requirements

Another focus in our work has been the doctors’

needs and requirements of an application that can

identify clinically relevant similarities between

patient cases. During the interviews, the clinically

working doctors had, mainly, a positive attitude

towards such an application. To exemplify what is

expected, let us take an example from one of the

interviews, in which Joe, a senior surgeon and

manager, said:

All these [radiological examinations and data

about the patient’s medical history], the

discussion and the conclusions made, that they in

the specific moment, at the same time as they are

generated, can be collected in a database. That

the database thereafter can recognise, based on

a pattern, /…/ that five patient cases look similar

and are presented.

Responses from the doctors were that such an

application should support and facilitate experienced

based care and decision making, needs to be carefully

designed, is useful only if the similarity measures are

specific enough and clinically relevant, and can be

useful during the liver meetings when there is an

uncertain patient case. Bill even said that he thinks it

could be worthwhile an extra effort of entering

information into such a system if it could prove to be

useful (cf. Grudin 1988).

However, all surgeons were not equally positive.

Unni thought that this kind of system could mainly

be useful for doctors with less experience:

It is not obvious [how such a system can be

useful] because of the way we work, with the

contacts we have, attending congresses and so

forth. We keep ourselves updated and we have a

large volume [with patients]. I think we work

pretty much like such a system without having to

use it. Such a system feels like a cookbook for

people that are not as experienced, and that can

be interesting, but I don’t think it would be useful

for me. It takes a lot of effort to build it [to fill it

with data and keep it updated].

We have also observed several liver meetings in

which associations to similar patient cases were made

in the discussions. The surgeons interviewed

responded that similar patient cases that today come

up in a discussion as a reference case are helpful for a

decision in the present case. However, such

references are dependant on the medical doctors

present during the meeting, and they are usually quite

weak since the doctors are not always able to recall

all details. Sam, a meeting participant said:

We have had a similar case earlier, exactly the

same CT. /…/ We have the answer but I cannot

remember exactly.

Nora, a radiologist said in the interview that she

had worked with a similar application, in which

similarities were based on registration of ten codes

per patient, but that the similarities were not specific

enough. She found such an application useful, if the

similarities are more specific and detailed.

When asking Bill in what situations such an

application could be useful he said:

It is in those cases that are uncertain, for

example if we should do an operation or not. In

many cases it can be questioned if it is

meaningful to do an operation from a tumour

biological approach. If we had a fine grained

database that could show the results of these

kinds of patients.

It appears that the granularity of the data that are

compared and that are presented from similar cases is

an important aspect for success.

4.3 Documentation Routines

One important part in this kind of application is the

existing routines and the doctors’ attitude towards

documentation and changes of documentation

routines, including the terminology used.

All doctors interviewed agreed that the

terminology is important in the medical work. Some

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said that they are careful how they express

themselves, to avoid misunderstandings. They are

aware of a certain degree of shifting in the

terminology used, but think that misunderstandings

are rare since the situation provides a context for the

understanding. They said that each doctor has her

own way of expressing herself, but the use of terms

should not be restricted. Jim, a senior surgeon, said

that what terms that are used and how they are used

is a negotiation based on different aspects:

It is not that you try to talk using the terms

defined in TC. Instead the terms in TC needs to

be adapted to how people express themselves.

Then, of course, when you document you still

need to adapt [to the term catalogue in TC],

everybody can’t express themselves as they want

and all terms can’t exist. The goal with the terms

is that you can use them to search.

In TC there is a term catalogue that can be used to

increase the ability to search and unify the

documentation. However, the interviewed surgeons

found this work somewhat complicated because of a

lack of administrative routines for defining new

terms, both on a clinic and hospital level. Earlier

attempts to apply for new terms to be added to the

term catalogue had no result. The surgeons are also

unaware of new centralised efforts of a terminologist,

i.e., their attempts to work with terminology issues

have been down prioritised.

After the liver meetings each decision is

documented in TC. The decision note is not a

formalised structure in TC, but it follows a template

with headings such as decision and activity, how to

carry out the activity, major diagnosis, and whether

the decision involves operation or not. The

formalised way of doing this would be to use the

term catalogue to build the structure inside TC. Bill

said that in the beginning he found the template for

the decision documentation too structured, but now

he would like it to be more structured. He would like

the decision itself to be documented in more detail,

not only suggesting one alternative, but also why

other alternatives have been rejected. Bill also said

that one reason why this often is missing could be

that the surgeon documenting the decision is usually

not so experienced and, therefore, may not have

followed the whole discussion.

The local liver registry and TC consist, to a large

extent, on the same kind of information. The main

difference is that the documentation in TC is more

specific and detailed, but the liver registry contains

some specific parameters for statistical analysis, e.g.,

the amount of bleeding during the operation, if the

circulation was turned off to the liver during the

operation, how the liver was cut, how much of the

liver that was removed, and what kind of operation

that was made. About the liver registry, Bill said:

It is that kind of data that you want to use to

learn for the future, what patients that can

manage a turndown of the vessels or not. These

kinds of things are never documented in TC. /.../

There is usually an operation documentation, but

in the best case the operation code coincides

with what was actually done.

5 DISCUSSION

Based on our observations, interviews and

investigations of documentation in TC and the liver

registry, we identified five categories of information

that can be relevant when identifying clinically

relevant similarities between liver patients:

 General Data about the Patient, including

general health and strength, age, function of

other organs, and clinical assessment.

 Data about the Liver Function, including

laboratory tests, examinations of the liver

function, and comorbidity concerning the liver.

 Data about the Present Disease, including

radiological examinations describing the type of

examination, contrast load, position and size of

affected tissue, and relationships to large blood

vessels and bilary passages, laboratory tests,

biopsies, and diagnosis.

 Data about the History of Diseases, including a

documented history of diseases with diagnose,

spread of disease and point of time.

 Data about Treatments, including point of time,

kind of treatment, and effect.

The above presented data model is based on

information that is documented in TC and the liver

registry, but origins from mainly three sources:

examinations, documented medical history including

earlier examinations, treatments and diseases, and the

patient’s subjective description of the symptoms,

motivation, disease history and so forth.

One consideration regarding the data model

concerns the validity of different data, e.g., the

documented symptoms, which are dependent on the

patient’s communication ability. This results in a

subjectivity, which makes comparisons of symptoms

unreliable. Symptoms were not mentioned by the

doctors during the interviews, but were frequently

discussed during the observed liver meetings.

Although symptoms can give important information

about the disease and possible treatments, we have

chosen not to include such data in the proposed data

model because of this uncertainty and subjectivity.

One interesting question concerns how the data of

UNDERSTANDING NEEDS AND REQUIREMENTS IN APPLICATIONS FOR IDENTIFYING CLINICALLY

RELEVANT SIMILARITIES BETWEEN PATIENTS WITH LIVER RELATED DISEASES

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interest can be found and should be described.

We have shown that there are several alternatives

used to describe the same kind of information. To

make it possible to compare data, it must either be

documented in the same format and/or using the

same terms, or it must be possible to use a translator

between the different formats and/or terms.

During the interviews the radiologists expressed

that they are careful in how they express themselves,

both during the meetings and when documenting the

examination. If they say that they are certain that the

dark change on the CT is a tumour, then they are.

Otherwise, they say that it appears to be or behaves

like a tumour. It is a relevant difference between a

certain tumour and a likely tumour and an application

should be able to understand such difference. The

question is how the level of uncertainty can be used

when searching for similarities. One solution could

be to use same kind of rating variables, but needs to

be further examined.

Also, the level of detail in content is important to

make the application useful for experienced doctors.

If the data is not detailed enough, it will not identify

similarities on the right level.

6 CONCLUSIONS

We have focused on what data that would be relevant

in an application that can compare different patient

cases and present information showing similarities.

This is only part of a broader perspective, including

not only data but also information from different

kinds of media.

The aim of such an application would be to create

a kind of “clinical binocular” that can focus on the

right information at specific moments. It should not

only cover for situations when the right experience is

missing, but also for situations when a doctor may

not fully remember the previous case or when

individual interpretations previous cases influences

what is remembered. In some sense it should

strengthen the “clinical eye”.

It is also of interest to keep in mind that the

functionality of such an application affects the

content of the data sources, i.e., it needs to be

detailed, searchable, and structured. This, in turn,

affects how the documentation is made, something

that may require changes in documentation activities

and routines. Two important issues have been

pointed out during the interviews: the effort to make

changes must be rewarded and give a clear surplus

value, and the changes must be easy to implement

with helpful assistance and aid that reduce the

doctors’ efforts.

ACKNOWLEDGEMENTS

We are grateful to all medical personnel who have

been there for us when conducting our studies.

REFERENCES

Bleeker S., Derksen-Lubsen G., van Ginneken A., van der

Lei J., & Moll, H. (2006). Structured data entry for

narrative data in a broad speciality: Patient history and

physical examination in pediatrics. BMC Medical

Informatics Decision Making, 6 (29).

Britt H., Beaton N., & Miller G. (1995). General practice

medical records: Why code? Why classify? Australian

Family Physician, 24 (4), 612–615.

Coiera E. (2003). Guide to health informatics. second

edition, Arnold.

Frize M., Solven F.G., Stevenson, M., & Nickerson, B.G.

(1996). Computer-assisted decision support systems for

various medical applications. Proc. of 10th Int. Conf. of

Women Engineers and Scientists, 91–93.

Grudin J. (1988). Why CSCW applications fail.

Proceedings of CSCW’88, ACM.

Hogan W. R., & Wagner M. M. (1996). Free-text fields

change the meaning of coded data. Proceedings of

AMIA Symposium, 517–21.

Huang Y., Lowe H. J., & Hersh W. R. (2003). A pilot study

of contextual UMLS indexing to improve the precision

of concept-based representation in XML-structured

clinical radiology reports. JAMIA, 10 (6), 580–587.

Häyrinen K., Saranto K., & Nykänen P. (2008). Definition,

structure, content, use and impacts of electronic health

records: A review of research literature. Int J Medical

Informatics, 77 (5), 291–304.

Johnston M. E., Langton K. B., Haynes R. B., & Mathieu A.

(1994). Effects of computer-based clinical decision

support systems on clinician performance and patient

outcome: A critical appraisal of research. Ann. Intern

Med., 120, (2), 135–42.

Lenz R., Beyer M., & Kuhn K. A. (2007). Semantic

integration in healthcare networks. Int J Medical

Informatics, 76 (2- 3), 201–207.

Melton G. B., Parsons S., Morrison F. P., Rothschild A. S.,

Markatou M., & Hripcsak G. (2006). Inter-patient

distance metrics using SNOMED CT defining

relationships. J Biomedical Informatics, 39 (6), 697–705.

O'Sullivan D., McLoughlin E., Bertolotto M., & Wilson D.

C. (2007). Mobile case-based decision support for

intelligent patient knowledge management. Health

Informatics J., 13 (3), 179–193

Porcelli P. J., & Lobach D. F. (1999). Integration of clinical

decision support with on-line encounter documentation

for well child care at the point of care. Proceedings of

AMIA Symposium, 599–603.

Rossille D., Laurent J. F., & Burgun A. (2005). Modelling a

decision-support system for oncology using rule-based

and case-based reasoning methodologies. Int J Medical

Informatics, 74 (2-4), 299–306.

HEALTHINF 2009 - International Conference on Health Informatics

192