Towards the Standardization of Disease Registry Form Structure

Fatimetou Sidina, Hatem Bellaaj and Mohamed Jmaiel

ReDCAD Laboratory, University of Sfax, Sfax, Tunisia

Keywords: Disease Registry Form, Structure, Standardization.

Abstract: This paper presents a set of specifications for disease registry forms that vary from one registry to another,

emphasizing their standardization to ensure better interoperability and data analysis. After an in-depth review

of the state-of-the-art disease registry forms, we introduce a standardized structure adhering to the essential

data standards set by EPIRARE (Taruscio et al, 2014), a project funded by the European Union to improve

standardization and data comparability among patient registries, while respecting all question suggestions

provided by the Patient Registry Item Specifications and Metadata for Rare Disease PRISM project

(Richesson, Shereff and Andrews, 2012). This structure has been validated on several registries currently in

use, demonstrating a high level of accuracy.

1 INTRODUCTION

A disease registry (DR) includes information about

patients suffering from the same disease in order to

collect and track data related to their diagnoses,

treatments, outcomes, and demographics for research,

monitoring, and improving the understanding and

management of the condition. The information

collected by these registries becomes increasingly

meaningful depending on the protocols they follow.

It is necessary to establish standardized protocols for

diagnosis and treatment, which contributes to making

the collected data more reliable and comparable,

thereby enhancing the robustness of research

findings. Protocols can vary from one country to

another due to economic, demographic, and even

genetic differences.

Different national and international experiences

have been conducted. The latest report from Orphanet

(Orphanet Report Series, 2023) indicates a total of

827 registries, cohorts, and databases worldwide:

11% regional, 66.5% national, 11% European, and

11.5% global. Germany has the highest number (171

disease registries), followed by France (117 disease

registries).

There are many forms and structures which can be

included in disease registries. The main component is

the disease sheet (i.e. disease form), which is our

focus in this paper. Disease registry form includes

general data about the patient, circumstance of

discovery, clinical and analysis symptoms, treatment

and evolution. More information can be added

depending on physicians' needs.

The content of the disease registry form varies

across registries in terms of the data collected and the

structure, types, and presentation of that data.

There are several standardization efforts in the

creation of registries, defining essential data that

should be included in the registry form. (Aktaa et al,

2023) going further to specify the data type and how

it should be retrieved. There are also efforts to group

the questions/fields to be collected by registries

(Richesson, Shereff and Andrews, 2012), which can

be shared across various disease types. This is driven

by the fact that standardizing disease registry forms

will enhance the interoperability of health and

research data (Richesson, Shereff and Andrews,

2012). This, in turn, widens the scope of analyses and

research on diseases worldwide.

However, standardization efforts do not

encompass the standardization of the structures and

representation of registry forms, leading to multiple

implementation approaches for these registries. Each

registry has its unique way of implementing and

representing its forms. The Standardization of the

structure and representation of registries proposed in

this paper would not only reduce the design and

implementation efforts for registry forms but also

unify the structure of gathered data, even for registries

that do not adhere to a standard. For example, we

would no longer find registries with three levels of

organization alongside others with four levels, and we

would no longer encounter entire sections lacking

250

Sidina, F., Bellaaj, H. and Jmaiel, M.

Towards the Standardization of Disease Registry Form Structure.

DOI: 10.5220/0012708500003699

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 10th International Conference on Information and Communication Technologies for Ageing Well and e-Health (ICT4AWE 2024), pages 250-257

ISBN: 978-989-758-700-9; ISSN: 2184-4984

fields specifying the collected data. These structuring

issues, often overlooked, can impact the automation

of disease registry implementations that comply with

global standards.

In this paper, we base our work on established

standards to introduce a well-defined structure and

representation for the sections of disease registry

forms. This is a crucial step for all who aim to

generate registry forms that comply with global

standards in terms of organization and structure. The

structure we have established has been validated

against seven resources, including standards and

currently used registries, and has yielded an average

accuracy of 0.88 and an accuracy of 1 for the two

standards used.

This paper is organized as follows: Section 2

delves into related work regarding the standardization

of disease registry forms, highlighting our specific

contributions in this area. Section 3 outlines the

skeleton and structure of disease registry forms.

Moving to Section 4, we introduce a theoretical and

conceptual representation of disease registry forms,

discussing its validation. Finally, in Section 5, we

conclude our work.

2 RELATED WORK

Data collected from disease registries represent a

valuable source for clinical research. However,

despite the availability of a large amount of data from

registries worldwide, the utility of this data remains

limited due to the lack of interoperability and

consistency among these registries. For example, the

same question may be asked in multiple registries, but

it is formulated differently, and the types of responses

vary from one registry to another (Spisla and

Lundberg, 2012). Therefore, the standardization of

disease registries is a necessity to enhance the quality

of medical research and care globally (Computerized

Disease Registries | Digital Healthcare Research). This

underscores our commitment to this standardization

effort by establishing a uniform structure for all

registries, clearly defining the diverse components of

registry forms and their appropriate relationships.

The PRISM project, funded through an American

Recovery and Reinvestment Act (ARRA) grant

administered by the National Library of Medicine

(NIH), serves as a valuable resource for standardizing

questions in rare disease registries. It encompasses

over 2,200 questions (Richesson, Shereff and

Andrews, 2012). Each question is indexed by one or

more keywords that characterize its general content

category, such as demographic information,

medication details, medical history, and special

histories. Additionally, EPIRARE, funded by the

European Commission, presents a collection of

indicators and common data elements for the

European platform dedicated to the registration of

rare diseases (Taruscio et al, 2014). These are based

on the indicators identified by the EUROPLAN

project (Posada, Carroquino and Pérez, 2011) and the

EU Rare Disease Task Force (RDTF). The FHIR®

(Fast Healthcare Interoperability Resources)

standard, developed by HL7® (Health Level Seven),

facilitates easier and faster healthcare data exchange.

It defines a set of standardized formats, known as

resources, to represent various healthcare data types

such as medications, allergies, and diagnoses. These

standardized formats (FHIR® resources) enable

seamless exchange and sharing of data between

different healthcare systems and applications.

Similarly, SNOMED CT (Systematized

Nomenclature of Medicine Clinical Terms) provides

standardized terminology that can be utilized in

healthcare-related information systems, ensuring

consistency and interoperability across various

clinical specialties within healthcare systems.

However, despite this extensive collection, the

number of questions, indicators, and elements

provided by these initiatives remains limited when

compared to the diverse array of inquiries pertinent to

rare diseases. Consequently, disease registries

employing PRISM or common data elements for the

European platform, resources of FHIR®, or

standardized terminology of SNOMED CT may have

multiple sections lacking coverage by these

established questions, indicators, or elements,

resulting in an absence of complete standardization.

(Aktaa et al, 2023) undertook the standardization of

TAVI (Transcatheter Aortic Valve Implantation)

related data variables (i.e., data fields to be collected)

to address registry heterogeneity, facilitating

international comparative analyses and the

development of comprehensive valvular heart disease

registries, regardless of the treatment approach. These

variables were classified into two levels: Level 1 for

essential quality assessment data and Level 2 for

supplementary information useful in quality

evaluation and research but not universally required.

The selection of these variables was accomplished

through a modified Delphi method, with the Working

Group voting on a list of candidate variables

identified through a literature review. This effort

resulted in 93 Level 1 and 113 Level 2 variables

across ten TAVI care domains, including patient

characteristics, comorbidities, prior interventions,

and pre-procedural tests. This could be regarded as a

Towards the Standardization of Disease Registry Form Structure

251

general standardization of TAVI registries,

particularly as it takes into account demographic

differences. However, this applies only to a specific

procedure used for treating aortic valve disease,

Transcatheter Aortic Valve Implantation (TAVI).

Similarly, (Fulvio and Mantegazza, 2014) present the

European database for Myasthenia Gravis (EuroMG-

DB) as a model for an international disease registry.

The structure of EuroMG-DB follows a schematic

representation, including the Patient main page linked

to: (1) referring physicians and MG patients, (2)

diagnostic criteria, (3) thymus, (4) biological

samples, (5) other diseases, and (6) follow-up visits.

The RoPR project (Gliklich, Leavy and Dreyer,

2020) introduces an Outcome Measures Framework

that organizes disease registries into three

hierarchical levels: domains, subcategories of data

elements, and data elements. These domains

encompass: (1) Characteristics, which are further

divided into three main categories: Participants,

Diseases, and Providers. (2) Treatments, which can

be categorized into two main groups: Type and Intent.

(3) Outcomes, consisting of five main categories:

Survival, Clinical Response or Status, Events of

Interest, Patient-Reported, and Resource Utilization.

Finally, at the third level, you'll find subcategories of

data elements that are used to define an outcome

measure, including those that capture physical

findings and diagnoses. This project defines the

outcomes to be extracted from disease registries,

considering the variations among diseases and the

specific details they require. It goes further by

providing examples of these details for several

diseases, yet it does not specify the method or

structure for collecting this data.

All these standardization initiatives aim to establish

standardized disease registries. They do so by either

concentrating on specific aspects of disease registries,

emphasizing registry content and offering useful yet

limited collections, or by centering efforts on

standardizing the extracted outcomes. Nevertheless,

these approaches frequently result in an inability to

maintain a consistent standardized format for disease

registry forms. Alternatively, certain initiatives offer

a structured set of variables for collection, but this is

limited to a single disease.

The absence of standardization in the formats and

representation of disease registry forms renders

discussions on interoperability and comparability

between registries impossible and would necessitate

extensive reformatting of collected data (Fulvio and

Mantegazza, 2014)

. This drove our research efforts to

explore the standardized representation of disease

registry forms.

3 STRUCTURE AND CONTENT

OF A DISEASE REGISTRY

FORM

Disease registries can vary significantly based on

their intended purpose, context, and the overseeing

organization.The specific structure and content of a

registry depend on the registry's purpose (Gliklich,

Leavy and Dreyer, 2020). Regardless of the registry's

design, an electronic disease registry provides

healthcare professionals with a disease registry form

containing predefined options for its various sections.

The disease registry form acts as a key source of

input data, typically organized into sections or

domains (Item groups). The titles of these sections

may vary across registries. Each section consists of a

series of subsections (Item concepts), and within each

subsection, there is an array of fields (Questions) that

represent the data elements to be collected. These

fields may contain subfields (Content coding)

delineating the method or specifics of data collection,

as outlined in the Data Set for Rare Disease Patient

Registries Recommended for European Cooperation

(Version 3.0) (Berger et al, 2021). “Figure 1”

illustrates an example of this structure within a

portion of the "Malformation Syndrome" section of

the TFAR registry (Bellaaj et al., 2017).

Figure 1: Example of Registry Form Section Structuring.

Different content of disease registry form experiences

are presented in the literature. For instance, (Salenius

et al, 1992) includes the “Patient's History”, “Clinic

Statistics” and “Progress Note”. This last consists of

four sections: (1) vital signs, allergies, average

weekly glucose measurements, and any point-of-care

values (2) eye, foot, and psychological screening

information, (3) smoking history, medication

compliance, and activity (minutes per week) and (4)

ICT4AWE 2024 - 10th International Conference on Information and Communication Technologies for Ageing Well and e-Health

252

SOAP (subjective, objective, assessment, and plan)

note.

On the other hand, the Electronic Disease Form

(EDF) within the TFAR (Bellaaj et al, 2017)

encompasses 11 distinct sections: Register

Identification, Patient Identification, Family history,

Circumstances of discovery, Malformation

syndrome, Cytogenetic study, Hematological signs,

Molecular biology, Cell freezing, Clinical score and

Treatment. Each of these sections is further

subdivided into one or more subsections, totaling 37

subsections in the entirety of TFAR. For example, the

Malformation syndrome component comprises 11

subsections, many of which align with specific

medical specialties, such as Skin damage and

Urogenital malformation. Each subsection contains

various fields, including checkboxes and input fields.

Additionally, some fields are initially hidden and are

revealed only if the 'yes' option is selected.

The CASCADE FH Registry comprises four

domains (sections). 'Enrollment information'

encompasses the patient demographics section.

'Medical history' is divided into two subsections:

patient history and family history. 'Treatment,

laboratory, and examination' incorporates three

subsections: FH treatment, Examination/laboratory,

and Imaging/procedures (within 5 y). The 'Additional'

domain includes Patient-reported outcomes

subsection and an additional subsection for Clinical

trial participation and Provider contact information

(O’Brien et al, 2014).

The National Cardiovascular Data Registry

(NDCR)® ICD Registry ™ utilizes the ACC/AHA

Heart Failure Clinical Data Standards, clinical data

standards created by the American College of

Cardiology (ACC) for acute coronary syndromes,

heart failure, and atrial fibrillation (WRITING

COMMITTEE MEMBERS, Radford et al, 2005).

These standards categorize the collected data into 11

sections: Patient Demographics, Medical History,

Patient Assessment: Current Symptoms and Signs,

Patient Assessment: Summary Assessment,

Laboratory Tests, Diagnostic Procedures, Invasive

Therapeutic Procedures, Pharmacological Therapy,

End-of-Life Management, Patient Education:

Assessment of Status and Patient Education:

Intervention and Referral. For each of these sections,

the standards specify subsections and the requisite

data to be collected.

The structure of disease registry forms simplifies

data collection and analysis. The level of detail and

specialization, however, varies from one disease and

organization to another. For instance, in the case of a

Rare Disease Registry, more extensive and detailed

data may be necessary due to the rarity of the

conditions being studied. Conversely, for more

common diseases, less extensive data may suffice, as

a wealth of information about these conditions is

already available.

4 REPRESENTATION OF

DISEASE REGISTRIES FORMS

4.1 Definition

In our study, we investigated the contents of registry

forms, encompassing their individual sections, the

scope of data they cover, and the diversity in form

representation across each registry. However, upon

examining this structural representation, we observed

its near uniformity across the majority of international

and standardized registries. Nevertheless, not all

registries adhere to or adopt this common structure,

rendering the task of standardizing disease registries

increasingly challenging. This is why the definition

of formal representation is essential to guide the new

work of creating registries, especially for small

regional registry initiatives that generally do not

adhere to a well-defined standard. This

standardization of form format representation will

enable them to adopt a format that aligns with

international registries following standardization

guidelines. Hence, our aim is to introduce a

standardized theoretical and conceptual model for

registry forms, offering a universal representation.

This model holds significant value as it furnishes an

all-encompassing framework, enabling automated

systems to consistently interpret the diverse

information types present within registries.

The disease registry form consists of multiple

sections, each containing one or more subsections.

Within each subsection, there exists a set of fields,

which can have some subfields if needed.

So we can represent a registry like 𝑆={𝑆



| 𝑖 ∈

1..𝑚} with 𝑆



is the section number i of registry

form, m is the number of sections in the form and each

section 𝑆



can be represented as shown in “Figure 2”.

with:

- Λ is the set of subsections of 𝑆



, Λ =

∪





𝜆



, n = |Λ|

- Γ is the set of titles of subsections, Γ =

∪





𝛾



, n = |Λ| = |Γ|

- Ω is the set of fields of 𝑆



,𝜴 =

{(𝝀



,𝛺



)| 𝑗𝜖1. . 𝑛} with: 𝑛 = |𝜦| =

Towards the Standardization of Disease Registry Form Structure

253

|𝜴| ; 𝛺



=∪







𝜔



set of fields of

subsection 𝜆



; 𝒑

𝒋

= |𝛺



| number of fields of

subsection 𝜆



; if 𝑝



= 0 , then 𝜆



is a blank

subsection

- Π is the set of subfields where Π=

∪

∗





𝛱



| 𝛱



=∪







𝞹



; kl= |𝛱



number of subfields of field 𝜔



; if 𝑘



= 0 , then

𝜔



is a blank field

- Π= 𝑰

𝟏

∪ 𝑰

𝟐

∪ 𝑰

𝟑

∪ 𝑰

𝟑



∪ 𝑰

𝟒

∪ 𝑰

𝟒

′ , with

- 𝑰

𝟏

={0,1} the set of checkboxes that

don’t require a condition

- 𝑰

𝟐

the set of input fields that don’t

require a condition

- 𝑰

𝟑

𝒂𝒏𝒅 𝑰

𝟑



set of checkboxes that

require verification of condition

respectively “if yes” and “if no”

- 𝑰

𝟒

𝒂𝒏𝒅 𝑰

𝟒



the set of input fields, that

require verification of condition

respectively “if yes” and “if no”

For each subsection 𝜆



∈ Λ, it is associated with a

title 𝛾



∈ Γ and a set of fields 𝛺



∈ Ω, where for

each field 𝜔



∈ 𝛺



, it is associated with a set of

subfields 𝛱



∈ Π.

Figure 2: Relations between groups.

4.2 Entity-Relationship Disease

Registries Form Pattern

For a deeper and more accessible understanding of

the structure and easy interpretation of the

connections and interdependencies between entities,

as defined in the "Definition" section, we provide a

visual representation using the entity-relationship

diagram. This representation provides a clear and

concise representation of various entities, their

distinct attributes, and the connections between them,

making it easier to understand the interactions within

a given registry system (see “Figure 3”).

The diagram in “Figure 3” illustrates the essential

attributes for various entities. We use the attribute

'Name' for Register, Field, and SubField, while

employing the attribute 'Title' for Section and

Subsection. Additionally, Field and SubField have

additional attributes defining their representation and

data collection methods, as represented in the

algorithm outlined in “Figure 4” and “Figure 5”.

Figure 3: Diagram entity-relationship.

Figure 4: Field Attribute Constraints.

Figure 5: SubField Attribute Constraints.

4.3 Sample Structure of a Standardized

Registry Form

To illustrate the structured representation of data,

entities, and their connections, we present a sample

format of a standardized registry form. This

structured format is instrumental in ensuring

consistency, efficiency, and uniformity in the capture,

storage, and retrieval of information across the

diverse domains of registry forms.

ICT4AWE 2024 - 10th International Conference on Information and Communication Technologies for Ageing Well and e-Health

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The example showcased in “Figure 6” delineates

two sections within the registry. For instance, the first

section comprises two subsections: the first

subsection encompasses two fields, while the second

subsection includes one field. Within the first

subsection of the initial section, the first field has an

attribute of "withCheck" set to false, and all its

associated subfields possess the attribute

"isCheckBox" set to true. Conversely, the second

field has "withCheck" set to true, hence presenting

options for 'Yes' and 'No'. Subsequently, by selecting

“Yes”, the two subfields that appear have their

“isCheckBox” attributes set to false.

This detailed example (in “Figure 6”) exemplifies

how attributes like "withCheck" and "isCheckBox"

impact the presentation and behavior of fields and

subfields within the standardized registry structure,

demonstrating various conditional display settings

and attribute configurations.

Figure 6: Example of a standardized structure.

4.4 Universal Use of Proposed

Standard Structure

For our validation process, we examined the

structures of some registries actually in use alongside

a set of standard questions and responses, comparing

them to the standard structure proposed by our model

representation. We chose to use the accuracy metric

for this evaluation (see “Table 1”). In this simple

binary classification scenario, our goal is to determine

whether a given structure matches our model

representation or not. The choice to use the accuracy

metric is well-suited for this context. The

classification task is straightforward, dividing

structures into two categories: those that match (the

positive class) and those that do not (the negative

class), making accuracy a suitable measure.

Furthermore, we observed that the consequences and

costs associated with both false positives and false

negatives are similar, which further supports the use

of accuracy. Indeed, the accuracy metric measures the

proportion of correctly classified instances among the

total instances evaluated. It provides a

straightforward measure of how well a model is

performing overall in terms of classification

accuracy. The formula for calculating accuracy is as

follows:

Accuracy = (Number of Valid Sections) /

(Total Number of Sections)

(1)

Table 1: Validation Results: Comparison with proposed Structure.

Sections

Total Number of

Sections

Accuracy

Tunisian registry GUELT 2013 36 44 0.82

Maghreb group for the evaluation of large B cell lymphomas

GEMLA

8 9 0.89

Tunisian registry of AMINOACIDOPATHIES 8 12 0.67

Tunisian registry of DIALYSIS 5 5 1

TFAR (Hadiji et al, 2012) 32 37 0.84

- (TARUSCIO ET AL, 2014) SET OF COMMON DATA

ELEMENTS FOR THE EUROPEAN RDR PLATFORM

5 5 1

- SAMPLE OF PRISM QUESTIONS AND SELECTED

METADATA (RICHESSON, SHEREFF AND ANDREWS,

2012). (224/2,200 QUESTIONS PRESENTED IN: [PDF FILE

(ADOBE PDF FILE), 318KB-MULTIMEDIA APPENDIX 1] )

22 22 1

Towards the Standardization of Disease Registry Form Structure

255

We've noted complete adherence to the standardized

structure model, reaching 100%, in the two standards

used. However, this varies between 100% and 67%

among other disease registries. Notably, even in cases

where sections deviate from our proposed

representation, there's potential to realign them with

our standardized model. Nevertheless, the lack of

standardization in section structures often leads

disease registry form developers to create sections

that diverge from the standardized form structure,

presenting the initial obstacle toward achieving

complete standardization of disease registries.

5 CONCLUSIONS

The paper proposes a standardized structuring of

disease registry forms, providing a clear definition of

various concepts and components within these forms,

as well as the relationships among these different

elements. Such structuring is crucial in progressing

towards the standardization of disease registries.

Adhering to this standard will result in a uniform

structural representation of disease registry forms, a

valuable uniformity for subsequent data analyses, and

a detailed guide for generating new disease registry

forms.

This work aims to simplify and unify the structure

of disease registry forms, establishing a standardized

representation that is universally applied. This

standardization represents the initial phase in a

broader effort to create a unified approach for data

collection and analysis across different disease

registries. By doing so, we not only enhance the

efficiency of this process but also facilitate the cross-

comparison of data and findings from various

sources.

The work represents the initial step towards

standardizing disease registries. A more generic

standardization will require further work on the

nature of different registry sections and their contents.

This will be our focus in future endeavors.

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