Integrating Clinical Expertise into Software Development: Evaluating

the Use of openEHR Archetypes for Requirements Elicitation in

Healthcare Applications

Jos

e Silva

and Andr

e Ara

ujo

Computing Institute, Federal University of Alagoas, Av. Lourival Melo Mota, S/N - Cidade Universit

aria, Macei

o, Brazil

Keywords:

OpenEHR Archetypes, Software Requirements Elicitation, Healthcare Professionals.

Abstract:

OpenEHR archetypes are standardized frameworks designed to model clinical information in healthcare sys-

tems, enabling a consistent and integrated representation of complex data. These models encompass common

clinical elements such as symptoms, diagnoses, and treatments, ensuring that information is understood and

applied uniformly across different contexts. This paper investigates the use of openEHR archetypes in the

requirements elicitation and speciﬁcation process for clinical systems, highlighting their potential to integrate

healthcare professionals’ knowledge into software development. The literature review reveals a signiﬁcant

gap in the participation of these professionals during the requirements elicitation phase, especially in studies

that apply archetypes. Quantitative and qualitative results positively perceive the methodology used, high-

lighting clarity, collaboration, and alignment with end-user needs. Statistical analysis using the Wilcoxon test

presented signiﬁcant p-values, indicating that professionals considered the method straightforward, intuitive,

and conducive to engagement, with real opportunities for contribution to the validation of requirements. The

qualitative data reinforce the importance of a collaborative environment and suggest the need for deeper in-

volvement of healthcare professionals at all process stages. In conclusion, this research indicates that applying

openEHR archetypes, combined with more signiﬁcant interaction with healthcare professionals, is promising

for integrating clinical expertise effectively and directly into developing clinical systems.

1 INTRODUCTION

The health sector is vital to society, inﬂuencing indi-

viduals’ quality of life and well-being. With grow-

ing population demands and complex care needs, de-

veloping efﬁcient, accessible healthcare applications

is a priority (Bitkina et al., 2020). Active participa-

tion from healthcare professionals throughout devel-

opment ensures systems address speciﬁc needs, prior-

itizing usability, security, and accuracy while reﬂect-

ing clinical practices (Barbosa et al., 2021; Maramba

et al., 2019).

The openEHR archetype standard supports inter-

operability and standardization of healthcare data,

structuring clinical information consistently across

platforms (Badr, 2019; de Ara

ujo et al., 2016). Each

archetype represents a clinical concept, such as di-

agnosis or medication, organizing data in a reusable

and extensible way. Academically, openEHR

https://orcid.org/0009-0001-0225-2696

https://orcid.org/0000-0001-8321-2268

archetypes are studied for improving health data qual-

ity and modeling requirements in complex systems

(de Ara

ujo et al., 2019; Chen et al., 2009). In indus-

try, they are used to build interoperable applications

that integrate data and support patient care continuity

(Ding et al., 2023). Their adoption highlights their po-

tential as a robust standard for safe, effective clinical

applications (Ferreira and de Souza, 2023).

End-user involvement is critical in software de-

velopment to ensure requirements reﬂect the prob-

lem domain, particularly in healthcare, where clini-

cal knowledge impacts patient safety (Govella, 2019).

Engaging healthcare professionals during require-

ments elicitation ensures solutions align with clini-

cal realities (Leslie, 2020; Tian et al., 2021; Hak

et al., 2020; Silva et al., 2024). While openEHR

archetypes are traditionally used for EHR standard-

ization and interoperability during development (Li

et al., 2021; Wulff et al., 2021; Arevshatyan et al.,

2020), their application in elicitation could formalize

specialist knowledge earlier, optimizing development.

This research explores whether using openEHR

672

Silva, J. and Araújo, A.

Integrating Clinical Expertise into Software Development: Evaluating the Use of openEHR Archetypes for Requirements Elicitation in Healthcare Applications.

DOI: 10.5220/0013226900003911

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

In Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2025) - Volume 2: HEALTHINF, pages 672-679

ISBN: 978-989-758-731-3; ISSN: 2184-4305

archetypes in requirements elicitation aids in identify-

ing and expressing domain expert knowledge during

healthcare application development. We examine if

archetypes can effectively capture clinical needs, en-

hancing communication between developers and spe-

cialists while ensuring that the solutions align with

healthcare professionals’ requirements.

We conducted interviews and practical activities

with openEHR archetypes involving healthcare pro-

fessionals to evaluate their role in bridging commu-

nication gaps and accurately representing clinical re-

quirements.

The article is organized as follows: Section 2 in-

troduces openEHR archetypes, their structure, and

applications. Section 3 details the study methodol-

ogy. Section 4 analyzes related work on archetypes in

health systems. Section 5 presents our study, method-

ology, and insights. Section 6 concludes with remarks

and future research directions on archetypes in health-

care software development.

2 OpenEHR ARCHETYPE

An archetype is a reusable, structured model that de-

ﬁnes clinical concepts and data structures necessary

for capturing healthcare information (Palojoki et al.,

2024; da Silva et al., 2019). These archetypes rely on

two core models: the reference and archetype mod-

els (openEHR Foundation, 2024b). The reference

model provides foundational structure and data types

for interoperability, representing entities, attributes,

and relationships consistently in health systems. The

archetype model outlines speciﬁc clinical content

within the reference model, such as diagnoses, treat-

ments, and measurements. This dual modeling ap-

proach separates clinical knowledge from techni-

cal constraints, facilitating adaptation and mainte-

nance of clinical information across health applica-

tions (openEHR Foundation, 2024a).

Archetypes include key elements that enhance

adaptability for clinical scenarios (Ferreira and

de Souza, 2023). Primary components are data at-

tributes, terminologies, and constraints. Data at-

tributes capture speciﬁc clinical data, such as patient

identiﬁers or observations. Terminologies ensure con-

sistent use of clinical vocabularies, linking data to

standardized medical coding systems. Constraints

limit values or structures, maintaining data quality

and consistency across implementations.

Templates combine multiple archetypes into

structured forms tailored to clinical workﬂows (Badr,

2019). These templates customize archetypes for

speciﬁc use cases, offering ﬂexibility in their appli-

cation. The Clinical Knowledge Manager (CKM)

serves as a repository for openEHR archetypes and

templates, enabling access, sharing, and collabora-

tion among healthcare professionals and developers

(Foundation, 2023). CKM promotes reuse of stan-

dardized archetypes, ensuring consistency and inter-

operability in healthcare data models.

The Archetype Editor is a tool for creating and

specifying openEHR archetypes (Moner et al., 2018).

It enables users to deﬁne, edit, and validate archetypes

by structuring clinical concepts with data attributes,

terminologies, and constraints. This tool facilitates

precise modeling of clinical concepts, improving in-

teroperability and data accuracy. Users can specify

elements such as data attributes, permissible values,

terminology bindings, and constraints, ensuring struc-

tured clinical models align with the dual-model ap-

proach.

The tool supports collaborative development, in-

corporating clinician feedback to ensure relevance. It

plays a key role in the openEHR ecosystem, enabling

standardized archetypes for integration into templates

and repositories like CKM. Figure 1 illustrates an

archetype speciﬁed in the Archetype Editor, show-

ing organized sections for data attributes, terminology

bindings, and constraints, which ensure data consis-

tency and quality.

3 METHODOLOGY

This section describes the ethical considerations

and methodological approach that guided this study.

Firstly, institutional and legal regulations describe the

ethical aspects guaranteeing participants’ protection

and privacy. Finally, the methodological path is pre-

sented, explaining the stages of reviewing literature

and conducting the practical study with health profes-

sionals using archetypes.

3.1 Ethical Considerations

The research followed the ethical principles estab-

lished by the Brazilian resolution, considering the es-

sential pillars of bioethics and human rights. It was

ensured that all participants received precise informa-

tion about the objectives and methods of the research,

with emphasis on the voluntary nature of participation

and the protection of anonymity and conﬁdentiality of

data.

Interactions with the participants only took place

after they had signed the Informed Consent Form

(ICF). The data processing and conﬁdentiality strate-

gies were detailed, guaranteeing the security of per-

Integrating Clinical Expertise into Software Development: Evaluating the Use of openEHR Archetypes for Requirements Elicitation in

Healthcare Applications

673

Figure 1: Representation of the elements of an archetype.

sonal data and sensitive information that might arise

during the interviews and questionnaires.

3.2 Methodological Approach

The research methodology consisted of a literature re-

view and a practical study using archetypes, as shown

in Figure 2. The literature review aimed to analyze

studies using archetypes in healthcare software devel-

opment. This involved reviewing ten studies, synthe-

sizing ﬁndings, and discussing them to contextualize

the research proposal. Based on this synthesis, the

research question for the practical study was formu-

lated.

In the practical study, archetypes were used to

simulate a patient anamnesis and follow-up scenario

with fourteen healthcare professionals. This aimed

to demonstrate and specify requirements, collecting

both qualitative and quantitative data. The study

explored professionals’ perceptions of archetypes in

software requirements elicitation for healthcare. Af-

ter the interviews, a structured form with three Likert-

scale questions was administered to capture partic-

ipants’ perceptions. The collected data underwent

qualitative and statistical analyses, and the results

were discussed in the ﬁnal stage of the study.

4 RELATED WORK

The related work section is divided into two main

parts: a state-of-the-art analysis and a discussion.

In the state-of-the-art analysis, we review existing

studies on using openEHR archetypes, focusing on

their objectives, methodologies, and ﬁndings in de-

tail. This analysis thoroughly overviews current ap-

proaches and trends in applying openEHR archetypes

in healthcare. Following this, the discussion synthe-

sizes the insights gained from the study, highlighting

gaps, limitations, and potential areas for further re-

search.

4.1 State of the Art Analysis

In healthcare, clinical data modeling has advanced to

better meet the needs and expertise of healthcare pro-

fessionals. In this context, openEHR archetypes stan-

dardize health data and incorporate speciﬁc insights

and knowledge provided by professionals at various

stages of application development. Thus, this section

analyzes related works to investigate how each study

employs openEHR archetypes, including the level of

involvement of healthcare professionals, based on the

observations in Table 1.

In the context of clinical standardization, the study

(Tian et al., 2021) focused on automating the cre-

ation of data quality rules in a Chinese hospital, using

archetypes to ensure data consistency and complete-

ness. Meanwhile, the study by (Oliveira et al., 2022)

explored the use of archetypes to standardize requests

for complementary diagnostics, enabling the creation

of indicators to monitor waiting times and test execu-

tion. However, neither of the studies speciﬁed when

the archetypes were modeled, at which phase they

were applied nor did they involve end users directly.

The study by (Wulff et al., 2021) aimed to stan-

dardize microbiological data for infection control, us-

ing archetypes to create a reusable model for different

institutions. In contrast to the studies in (Tian et al.,

2021) and (Oliveira et al., 2022), (Wulff et al., 2021)

actively involved healthcare professionals during the

development and validation phases, which was essen-

tial to ensure the proposed model’s applicability in

real-world scenarios.

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674

Figure 2: Methodological Approach.

Regarding screening and monitoring, the study (Li

et al., 2020) developed a shared model for COVID-19

based on Chinese guidelines, applying archetypes to

model clinical data, but without specifying at which

stage of the development cycle this was used. This

work did not involve direct interaction with healthcare

professionals. Similarly, the study (Li et al., 2022)

proposed a methodology to measure clinical quality,

integrating openEHR with Clinical Quality Language

(CQL) in the application development phases, involv-

ing doctors directly to provide feedback and adjust

quality indicators, improving the system’s usability.

In the context of clinical decision support, the

study (Silva et al., 2023) focused on rehabilitation

medicine and sought to develop a platform for collect-

ing and consolidating clinical data using archetypes

during the elicitation and development phases. In

this study, healthcare professionals did not partici-

pate in archetype modeling but were involved in re-

quirements gathering, which was critical for structur-

ing the platform’s standardized data. The study (Hak

et al., 2020) discusses the adoption of the openEHR

standard with a focus on health information manage-

ment. In this case, archetypes were applied during

validation phases, with engineers and clinical pro-

fessionals working together to ensure the platform’s

usability and its impact on clinical practice. The

study (Li et al., 2021) developed an openEHR-based

screening tool for strokes, applying archetypes during

both development and validation phases for eligibility

queries in electronic health records (EHRs). The tool

was reﬁned with input from healthcare professionals

to enhance its accuracy and clinical applicability in

medical consultations.

Finally, in the category of clinical use of EHRs,

the study (Leslie, 2020) explored the reuse of

openEHR archetypes in multilingual COVID-19 clin-

ical data, focusing on symptom tracking. Archetypes

were applied without the direct participation of end

users, as the development followed international clin-

ical guidelines. Similarly, (Arevshatyan et al., 2020)

focused on the integration of clinical and genomic

data for cancer treatment in two hospitals in Paraguay,

applying archetypes during the development and val-

idation phases, involving oncologists in the process,

and ensuring the system met the needs of oncological

data analysis.

4.2 Discussion

The analysis of the ten papers listed in Table 1 reveals

that all the articles utilize openEHR archetypes in de-

veloping their healthcare applications. This approach

aims to create standardized, interoperable solutions

suitable for clinical contexts. Healthcare profession-

als are recognized as the end users for all the appli-

cations, highlighting that these tools are designed to

support their practical activities. However, only six

of the ten papers involve healthcare professionals at

some point during the development process, primar-

ily for validation purposes, and they do not participate

directly in the initial stages.

A standard limitation in the studies is the lack of

involvement from healthcare professionals during the

elicitation and prototyping phases of archetype mod-

eling. This suggests that clinical requirements are

often deﬁned based on external guidelines or tech-

nical knowledge rather than through active collabo-

ration with the professionals who will use the appli-

cations. Additionally, ﬁve studies do not specify at

which stage of development archetype modeling oc-

curs, making it challenging to understand how the

needs of end users have been translated into techno-

logical solutions.

In addition, the studies analyzed are distributed in

four focus areas: clinical standardization, screening

and monitoring, clinical decision support, and clini-

cal use of EHRs. Each of these areas addresses spe-

ciﬁc aspects of the needs of healthcare professionals;

however, the lack of interaction in the early stages

Integrating Clinical Expertise into Software Development: Evaluating the Use of openEHR Archetypes for Requirements Elicitation in

Healthcare Applications

675

Table 1: Comparative Analysis of Related Works.

Related Work [1] [2] [3] [4] [5] [6] [7] [8] [9] [10]

Use of openEHR Archetypes • • • • • • • • • •

Health

Professionals

Participation ◦ ◦ ◦ • ◦ • • • • •

Considered End-Users • • • • • • • • • •

Modeling of

Archetypes with

Healthcare

Professionals

Requirements Analysis ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦

Prototyping ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦

Development ◦ ◦ ◦ ◦ ◦ • ◦ • • •

Validation ◦ ◦ ◦ ◦ ◦ ◦ • • • •

Not Speciﬁed • • • • • ◦ ◦ ◦ ◦ ◦

Focus Area

Clinical Standardization ◦ • ◦ ◦ • ◦ ◦ ◦ • ◦

Screening and Monitoring ◦ ◦ • ◦ ◦ • ◦ ◦ ◦ ◦

Decision Support ◦ ◦ ◦ • ◦ ◦ • • ◦ ◦

Clinical Use of EHR • ◦ ◦ ◦ ◦ ◦ ◦ ◦ ◦ •

Note: [1] = (Leslie, 2020); [2] = (Tian et al., 2021); [3] = (Li et al., 2020); [4] = (Silva et al., 2023); [5] = (Oliveira

et al., 2022); [6] = (Li et al., 2022); [7] = (Hak et al., 2020); [8] = (Li et al., 2021); [9] = (Wulff et al., 2021); and [10] =

(Arevshatyan et al., 2020).

of development can limit the adaptation of tools to

the reality of end users. This panorama, evidenced

by the analysis of the state of the art, indicates that

although openEHR archetypes offer a robust frame-

work for standardization, their effectiveness in repre-

senting the specialized knowledge of healthcare pro-

fessionals is compromised in the absence of a collab-

orative requirements elicitation process. Thus, the re-

search question highlights the need for more effec-

tive strategies for involving domain experts as early

as the elicitation phase, ensuring that the knowledge

of these professionals is reﬂected in the applications

developed.

5 USE OF ARCHETYPES BY

HEALTH PROFESSIONALS

This section explores the use of archetypes by health

professionals. It is divided into three stages. The

ﬁrst stage outlines the evaluation scenario, detailing

the context and procedures followed during the ex-

periment. The second stage presents a quantitative

analysis of the statistical test results, focusing on the

responses. The ﬁnal stage offers a qualitative study,

examining key points from the interviews and high-

lighting professionals’ perceptions, challenges, and

suggestions for improvement.

5.1 Evaluation Scenario

Our study involved 14 healthcare professionals di-

rectly engaged in patient care. We presented a sce-

nario focused on patient anamnesis and follow-up

processes. During anamnesis, essential patient in-

formation such as medical history, symptoms, and

lifestyle is gathered, while the follow-up phase moni-

tors progress and responses to treatment, aiding clini-

cal decisions.

The scenario was introduced to participants, and

we discussed the workﬂow to understand the de-

tails. Two archetypes representing anamnesis and

follow-up were presented, explaining data attributes,

terminologies, and constraints. Each professional

was encouraged to share opinions and ask questions,

leading to collaborative adjustments to ensure the

archetypes reﬂected the patient care scenario accu-

rately. Afterward, the graphical interface generated

from the archetypes was reviewed, and the impor-

tance of healthcare professionals’ involvement in re-

quirements elicitation was discussed. The profession-

als also completed a questionnaire, and the results are

presented in the next section.

5.2 Analysis of Quantitative Results

After applying the form, the data was analyzed using

the Wilcoxon test, which assesses whether a reference

value’s median differs signiﬁcantly from zero. Imple-

mented in Python, the responses were compared to

the reference point 4, with results shown in the sec-

ond column of Table 2.

The ﬁrst question, on method clarity, had a p-value

of 0.0005, indicating professionals found the method

straightforward. The second, on collaboration, had a

p-value of 0.0008, showing participants felt engaged

and able to contribute actively. The third, on the

archetype reﬂecting workﬂow understanding, had a

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676

Table 2: Wilcoxon Test Results for Healthcare Profession-

als.

Question P-value

1. I believe that the method used to

identify the requirements was clear

and easy to follow.

0.0005

2. The method used to identify and

validate the requirements allowed me

to actively collaborate during the

requirements elicitation process.

0,0008

3. The data modeling represented by

the archetype facilitates my

understanding of the workﬂow in

which I am involved.

0,0013

p-value of 0.0013, demonstrating signiﬁcant contri-

butions to understanding activity contexts.

These results highlight a positive perception of the

method, which is considered transparent, collabora-

tive, and effective, with high satisfaction among pro-

fessionals, meeting their expectations.

5.3 Analysis of Qualitative Results

During the experiment, various aspects were identi-

ﬁed through content analysis to specify functionali-

ties in patient anamnesis and evolution systems using

archetypes. Participants’ perceptions and researchers’

observations provided insights into knowledge gaps

and challenges in enhancing understanding and en-

gagement. The analysis was organized into four key

moments. The ﬁrst revealed participants’ limited ini-

tial knowledge of archetypes and requirements elic-

itation, leading to the introduction of fundamental

concepts. The second emphasized the importance

of healthcare professionals’ active participation in re-

quirements elicitation, highlighting beneﬁts such as

improved clinical needs, efﬁciency, and risk reduc-

tion.

The third addressed limitations in existing sys-

tems and professionals’ need for tailored solutions,

demonstrating how well-designed systems improve

efﬁciency, precision, and satisfaction. Finally, the

fourth presented feedback and suggestions for im-

provement, as detailed in Figure 3.

• Familiarity with Archetypes and Require-

ments Elicitation

A key issue identiﬁed was the preconception that

healthcare professionals lacked familiarity with soft-

ware archetypes and requirements elicitation. This

was conﬁrmed during interactions, highlighting the

need for clearer initial contextualization to explain

these concepts and better prepare participants.

Figure 3: Points of Perceptions and Contributions.

The tool was introduced, and doubts about pro-

fessionals’ involvement were clariﬁed, emphasizing

their signiﬁcant contributions. The fundamentals of

openEHR archetypes and the importance of active

participation to capture clinical needs were explained,

ensuring the elicitation process aligned with actual

demands. Professionals must understand their role

and development methodologies to avoid gaps in the

process.

Limited knowledge also created difﬁculties in

envisioning their role in requirements speciﬁcation.

Without early intervention, this barrier can reduce

engagement, as technical terminology often makes

participants feel out of place. Addressing this re-

quires accessible language, explanations of funda-

mental concepts, and practical examples showing how

archetypes and the speciﬁcation process beneﬁt their

work.

• Importance of Participation in Requirements

Elicitation

After participating in the experiment, healthcare pro-

fessionals emphasized their value in the requirements

elicitation process, noting that their input is essential

for creating systems that meet clinical demands. One

participant stated, “Our presence is essential because

although you know very well how to develop the sys-

tem, you are not in practice to understand what our

real needs are,” underscoring that developers cannot

fully grasp clinical demands without ﬁrsthand experi-

ence.

The effectiveness of health applications depends

on both developers’ technical skills and direct feed-

back from end users. Professionals expressed concern

about ensuring the system supports speciﬁc tasks,

such as documenting essential information, customiz-

ing ﬁelds for specialties, and recording details like

allergies. During prototype analysis, one participant

suggested adding an ”allergies” ﬁeld, emphasizing the

need for functionalities critical to clinical practice but

Integrating Clinical Expertise into Software Development: Evaluating the Use of openEHR Archetypes for Requirements Elicitation in

Healthcare Applications

677

easily overlooked in development.

Professionals also noted they often resort to paper

or manual processes due to the inﬂexibility of current

tools, which frequently fail to adapt to their needs.

• Limitations and Problems

Participants frequently reported challenges with cur-

rent software systems, which, despite being designed

for multi-professional use, rarely meet the speciﬁc

needs of all specialties. Healthcare professionals

highlighted limitations in electronic medical record

systems, which often fail to address their unique de-

mands. One participant remarked, “In the public ser-

vice, we get everything ready-made. [...] There are no

tools to open the system and ﬁnd speciﬁc resources

for each profession.”

The inability to support multiple specialties was a

recurring frustration. In hospitals and public health

centers, a system equally effective for psychologists,

nutritionists, and other professionals is crucial. How-

ever, current systems often lack this ﬂexibility, forc-

ing informal adaptations and note summarizations

that compromise care quality and patient records.

Ethical and data security issues were also noted, par-

ticularly regarding privacy and the risk of inappropri-

ate sharing between specialties. Participants stressed

the need for systems with controls to limit access to

sensitive information based on professional bound-

aries.

Professionals highlighted that their inclusion in

system development could address these issues, en-

suring each specialty’s needs are integrated from the

start. By participating in the requirements elicitation

process, they can identify essential functionalities for

their practices, creating ﬂexible and practical systems.

Additionally, they pointed out that mere training in

tool usage is insufﬁcient, as the challenges stem from

structural and functional limitations beyond usability.

• Feedback and Suggestions for Improvements

After completing the survey, participants shared im-

pressions of the speciﬁcation process and tool, ex-

pressing positive experiences while suggesting im-

provements. A key observation was the need for more

time, ideally available in real environments, as the ex-

periment’s simulated context limited time.

A prominent suggestion was improving the

graphic interface for a more intuitive and visually or-

ganized design. Simpler navigation for locating and

ﬁlling in ﬁelds could enhance efﬁciency and reduce

cognitive load, making the speciﬁcation process more

agile. One participant remarked, “With a prettier in-

terface, it would be much easier to use and even more

intuitive.”

This is particularly relevant given the technical

complexity of openEHR archetypes. While designed

to represent clinical data accurately, the current in-

terface can be challenging for users unfamiliar with

speciﬁc health concepts. A more intuitive design

could make openEHR more accessible, enabling pro-

fessionals to focus on specifying functionalities with-

out unnecessary distractions or difﬁculties.

6 FINAL CONSIDERATIONS

This article demonstrates that using openEHR

archetypes in requirements elicitation and speciﬁca-

tion effectively integrates healthcare professionals’

specialized knowledge into clinical systems develop-

ment. The literature review revealed a signiﬁcant

gap in involving these professionals during require-

ments elicitation, particularly in studies incorporating

archetypes.

The results indicate a positive perception among

health professionals, highlighting clarity, collabora-

tion, and effectiveness. Participants considered the

process transparent and engaging, with opportunities

to deﬁne and validate requirements. Modeling with

archetypes facilitated understanding activities, con-

ﬁrming a collaborative approach aligned with profes-

sional expectations. These ﬁndings show the method-

ology effectively integrates healthcare professionals’

knowledge. However, limitations included healthcare

professionals’ unfamiliarity with the technology, em-

phasizing the need for robust educational support on

openEHR archetypes and their role. Limited time for

demonstrating and specifying functionalities in the

simulated environment may have impacted contribu-

tions.

Future recommendations include expanding ex-

periments to a diverse healthcare professional sam-

ple across specialties and experience levels, en-

abling comprehensive analyses of challenges and

needs. Including varied proﬁles—doctors, nurses,

managers, and technicians—could reveal differences

in archetype use, aiding in versatile system develop-

ment. Formalizing good practices for archetype use

through guides, checklists, and manuals is also essen-

tial, alongside applying these practices in real devel-

opment environments to assess effectiveness, observe

user-developer interactions, and adjust as needed.

This study’s limitations include a small sample

size, controlled environment, professionals’ unfamil-

iarity with archetypes, and limited time for speciﬁca-

tion, which restrict generalization to all clinical con-

texts or proﬁles.

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