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Determining the minimum data set of geriatric assessment at the Iran primary health care referral system: shifting from fragmentation to integration care for older people

BMC Health Services Research volume 24, Article number: 1039 (2024) Cite this article

Abstract

Background

Geriatric assessment (GA) is a multidimensional process that disrupts the primary health care (PHC) referral system. Accessing consistent data is central to the provision of integrated geriatric care across multiple healthcare settings. However, due to poor-quality data and documentation of GA, developing an agreed minimum data set (MDS) is required. Therefore, this study aimed to develop a GA-MDS in the PHC referral system to improve data quality, data exchange, and continuum of care to address the multifaceted necessities of older people.

Methods

In our study, the items to be included within GA-MDS were determined in a three-stepwise process. First, an exploratory literature search was done to determine the related items. Then, we used a two-round Delphi survey to obtain an agreement view on items to be contained within GA-MDS. Finally, the validity of the GA-MDS content was evaluated.

Results

Sixty specialists from different health geriatric care disciplines scored data items. After, the Delphi phase from the 230 selected items, 35 items were removed by calculating the content validity index (CVI), content validity ratio (CVR), and other statistical measures. Finally, GA-MDS was prepared with 195 items and four sections including administrative data, clinical, physiological, and psychological assessments.

Conclusions

The development of GA-MDS can serve as a platform to inform the geriatric referral system, standardize the GA process, and streamline their referral to specialized levels of care. We hope GA-MDS supports clinicians, researchers, and policymakers by providing aggregated data to inform medical practice and enhance patient-centered outcomes.

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Background

The aged population is growing faster than any other age group worldwide [1]. It’s estimated that by 2050, people aged 60 years or older will make up over 21.4% of the global population. This percentage is predicted to triple to more than 27.7% by the year 2100 [2]. This demographic shift is having a significant impact on developing countries, which are home to two-thirds of the world’s aging population. Iran is one such country where the population is shifting from youth to old age. Currently, 10% of Iran’s population is aged 60 or older, and projections indicate that by 2050, the number of people aged 65 and over will make up 31% of the country’s total population [3, 4]. Population aging has negative impacts on the economy, society, and health fields [5]. This period of life closely contributes to a similar rise in the wide variety of chronic and costly diseases. Older people are hospitalized more frequently than other age groups, exposing them to iatrogenic diseases and leading to physical and psychological complications [6, 7]. Declining health in older adults has been linked to functional disabilities, dependencies, and comorbidities. Additionally, psychosocial stressors such as loneliness, the loss of loved ones, loss of individuality, and changes in socioeconomic status can increase the risk of mental health issues in older people [8]. Therefore, it is crucial to address the health needs of this vulnerable group by considering their economic and social status and prioritizing the affordability of their healthcare should be the primary focus of health policymakers [9, 10]. Given the high incidence of undiagnosed and untreated health conditions in older adults, it is necessary to establish an effective geriatric assessment (GA) plan [11]. GA involves identifying a list of health problems related to older people’s general health status, including comorbidities, functional, cognitive, social, nutritional, and psychological aspects [12, 13].

Despite the broadly encouraged worth of preventive and promotive care, most healthcare systems still mainly focus on treating single diseases. This approach leads to inefficient, wasteful, and fragmented care for older adults, causing patient confusion, low participation in treatment, and even treatment faults [14]. Even within healing medicine, care for older people is facing a crisis, remarkable fragmentation, and aggressiveness of emergency care rather than thoughtful planning and effective care management. This fragmentation in service delivery impedes proactive, person-centered, and integrated care for older adults [15, 16]. To overcome these problems, effective communication between primary health care (PHC) and specialist care is crucial [17].

PHC level serves as a gateway to providing healthcare services and has a determining effect on the economic and social progress of nations. Providing longitudinal, comprehensive, and coordinated care at the PHC level has a direct influence on public health and reduces the number of non-essential visits to healthcare settings [18, 19]. However, PHC providers report a high volume of outpatient visits daily, making it impossible to allocate appropriate time for examining and performing a comprehensive assessment of older people’s health status [20, 21]. Additionally, PHC providers are less equipped to manage complex clinical situations, so they need a system that simplifies the process of seeking help from experts or using higher-level resources for direction in the management of clinical events without shifting accountability [22].

In this regard, a referral system is a fundamental necessity for connecting primary care to specialty care [23]. The traditional referral system has several challenges that include the accumulation of errors in the next stages, the absence of a comprehensive and uniform referral system, the direct referral to medical centers by bypassing lower levels, which causes an increase in the burden of hospitals and disrupts service delivery, lack of expert and motivated workforce, failure to comply with the guidelines intended for an effective referral system, inadequate responsibility to control needless referrals at each level, and insufficient back referral system of trivial cases that come straight to the higher level [24,25,26].

These problems are partly due to the lack of a nationally agreed framework to guide the workforce to collect required data and perform documentation processes efficiently [27, 28]. However, the current referral processes in Iran PHC are often paper-based and poorly documented. The data relating to older people has not been received by the next health settings [29, 30]. Paper-based referrals are particularly difficult to track, information is regularly inadequate and does not encompass all important data required for the delivery of quality care. These referral methods are susceptible in the fragmented ambulatory environment where information is transferred between health centers physically [31]. To address these challenges, we developed a minimum data set (MDS) to standardize and improve the quality of GA data [32,33,34]. Having an agreed MDS enables the generation of nationally comparable and reliable data, regardless of how the data is gathered. It allows for consistent assessment across various authorities, organizations, and subdivisions, and encourages more efficient data gathering by reducing duplication of effort [35]. Hence, the objective of this study is to establish an MDS for this purpose. This MDS can serve as a foundation for developing a consistent referral system for older people throughout the primary healthcare system.

Methods

Study design

This study is a mixed-method investigation performed in 2023 in Iran. It utilized both quantitative and qualitative methods to design GA-MDS. First, a review of scientific and grey literature was conducted to extract potential data items related to GA. Then, a two-round Delphi survey was used to obtain the perspective of experts. Finally, two additional supplementary surveys were conducted to calculate the content validity ratio (CVR) and content validity index (CVI) of the final GA-MDS.

Literature review

As part of the literature review, scientific databases, including Web of Science, PubMed, Scopus, Google Scholar, Society for Information Display (SID), and MagIran, were searched to retrieve relevant data sources and data collection projects related to GA. The search utilized keywords such as “referral system”, “information system”, “primary care”, “registry system”, “data management”, “MDS”, “minimum data set”, “minimum dataset”, “required data set”, “core data items”, along with elderly-related terms (elderly, geriatric, aged, aging, senior, older age, older adults, older person, older people, older population, older individual, older patient) in individual or combined form. The search was conducted in English and Persian languages up until 2023, with no restrictions applied regarding the publication date. A systematic literature review was not conducted, and instead, a formative review was performed to extract possible data items. Additionally, grey literature, such as geriatric health websites and records, was reviewed until data saturation was achieved, where no new piece of data item emerged from the sources. Finally, relevant data points were extracted and compiled into a primary checklist, which was organized into four distinct sections: administrative, clinical assessment, body systems assessment, and psychology assessment items.

Delphi study

Following the preparation of the initial checklist in the previous step, a Delphi survey consisting of two rounds was conducted to identify the most significant data items among the primary extracted ones. The first and second rounds were held two months apart. During this period, the data collection tool was refined based on the feedback received from the experts. The same panel members participated in both rounds, and a 5-point Likert scale was used to evaluate responses. There is no standard agreement level for Delphi studies, but some suggest a threshold of ≥ 70% for each round [36,37,38]. In this study, an 80% agreement level was set for each item to be included in the GA-MDS. This means that items with an agreement level of less than 50% and a mean score of < 3.5 were excluded. Items with an agreement level of 50–79% and a mean score ranging from 3.5 to < 4, as well as any additional data items suggested by the expert panel, were evaluated in the second round. Any items with an agreement threshold of 80% or more were accepted in the first round [39, 40]. In the second round of Delphi, the experts’ feedback and comments on the initial items from the first round were taken into consideration. The acceptance criteria for the data items remained the same as in the first round. Finally, the collected data was analyzed using SPSS 22 (SPSS Inc., Chicago, IL), and a statistical significance level of p-value < 0.05 was set.

Panel of experts

In Delphi surveys, a panel of 15–20 experts is typically used. However, we selected a sample of 60 experts to reduce errors [41]. The participants were selected using a purposive/non-random sampling method. The criteria for participation were as follows: (1) have sufficient expertise in the care and treatment of older people, (2) have more than three years of practical skill, and, if possible, have related scientific publications. The panel of experts comprised 60 participants, including psychiatrists, general physicians, gerontologists, geriatrics specialists, nursing geriatrics, community health nursing, cardiologists, urologists, neurologists, respiratory specialists, and epidemiologists was formed. The panel of experts used to develop and evaluate the MDS remained consistent across the Delphi phase and content evaluation. Specifically, the same group of experts participated in both stages of the process.

Evaluating the content validity of GA-MDS

To compute the ultimate multidimensional scaling MDS, we evaluated the temporal information of the initial MDS, utilizing the phases as the fundamental metric for analysis. During the Delphi phase of our study, we relied on the input of 60 panel experts to evaluate the MDS content. The data collection period for this phase lasted three months. Following the Delphi stage, a group of experts was tasked with assessing the content of the MDS using an initial checklist. To facilitate the evaluation process, the team enlisted the services of ten individuals, who were responsible for the collection of data in a blind manner. These individuals subsequently followed up on the return of the initial checklist to gather relevant information for further analysis. The MDS content validity was checked in the following steps:

CVI

The most commonly used way to measure an instrument’s content validity is the CVI calculation. The CVI can be calculated for each item on the instrument (known as item level-CVI or I-CVI) as well as for the instrument as a whole (known as the instrument level-CVI). To assess the CVI, experts rate each item based on its relevance or representativeness on a 4-point Likert scale ranging from 1 (not relevant or not representative) to 4 (extremely relevant or representative). The I-CVI is calculated by determining the proportion of experts who rated an item as 3 or 4, divided by the total number of experts. It is important to note that using the CVI as a measure of inter-rater agreement can lead to an inflation of agreement due to chance factors. To address this issue, Lynn has provided guidelines for the number of experts and the minimum number of experts who must agree with an item or instrument’s content to achieve an acceptable CVI, using the standard error of the proportion. So, the CVI evaluates the relevancy of the items to the main purpose of the instrument based on experts’ opinions. In the present study, the items selected from the Delphi survey were sent to the panel of experts, and they were asked to assign an importance value for the relevancy of items using a four-point Likert scale from not relevant to completely relevant. The CVI was calculated using Formula 1. The acceptable value for CVI was considered 0.78% [42,43,44,45]. To eliminate this chance, Scale-CVI (universal agreement) and (average) were calculated. It is suggested that adequate S-CVI should be considered 0.8 to indicate content validity. (see formula 1)

$$\begin{array}{l}\:\text{C}\text{V}\text{I}\\=\frac{\text{t}\text{h}\text{e}\:\text{n}\text{u}\text{m}\text{b}\text{e}\text{r}\:\text{o}\text{f}\:\text{s}\text{p}\text{e}\text{c}\text{i}\text{a}\text{l}\text{i}\text{s}\text{t}\text{s}\:\text{w}\text{h}\text{o}\:\text{a}\text{s}\text{s}\text{i}\text{g}\text{n}\:\text{a}\text{n}\:\text{i}\text{m}\text{p}\text{o}\text{r}\text{t}\text{a}\text{n}\text{c}\text{e}\:\text{v}\text{a}\text{l}\text{u}\text{e}\:\text{o}\text{f}\:3\:\text{o}\text{r}\:4\:\text{f}\text{o}\text{r}\:\text{e}\text{a}\text{c}\text{h}\:\text{i}\text{t}\text{e}\text{m}}{\text{t}\text{h}\text{e}\:\text{t}\text{o}\text{t}\text{a}\text{l}\:\text{n}\text{u}\text{m}\text{b}\text{e}\text{r}\:\text{o}\text{f}\:\text{s}\text{p}\text{e}\text{c}\text{i}\text{a}\text{l}\text{i}\text{s}\text{t}\text{s}.}\end{array}$$
(1)

Kappa Statistic coefficient

The Kappa statistic is a widely recognized measure for evaluating inter-rater agreement. Yet, it does have its limitations, and the proportion of the agreement index is often deemed basic. In response to these shortcomings, the CVI was developed, which assesses inter-rater agreement based on relevance and non-relevance. The CVI has an edge over kappa and other inter-rater agreement measures that solely focus on relevance. To determine the CVI, one must first calculate the probability of chance agreement using a specific formula. The I-CVI is then calculated for each item, and the Kappa modified (k*) can be derived from the values of both the probability of chance agreement (Pc) (see formula 2) and the I-CVI. Several different standards have been proposed for evaluating kappa, including the Landis and Koch standard, which deems a value above 0.60 to be substantial, and the Fleiss Cicchttie and Sparrow standards, which suggest that a value of 0.75 or higher is excellent. The Kappa coefficient is an index that eliminates the possibility of chance agreement between several raters [46]. In our study, first, the likelihood of chance agreement was computed according to Formula 2, where N indicates the number of experts and A is the number of experts who agreed that the item was relevant. Then, the Kappa coefficient was measured for each item according to Formula 3. The evaluation for K* is that values greater than 0.74 indicate that the item was excellent; values between 0.6 and 0.74 mean good, and the K* values between 0.4 and 0.59 indicate that the item is fair.

$${\rm{pc}}\,{\rm{ = }}\,{\rm{[N!/A!}}\,{\rm{(N}} - {\rm{A)!]}}\,{\rm{*}}\,{\rm{[0}}{\rm{.5]}}{\,^ \wedge }\,{\rm{N}}$$
(2)
$$\:\text{K}\text{*}\:=\frac{(\text{I}-\text{C}\text{V}\text{I}-\text{P}\text{C})}{\:(1-PC)}$$
(3)

CVR

One of the most reliable techniques for ensuring the content validity of an instrument is to gather a group of experts who can evaluate the relevance of each item. Among the many methods available for measuring content validity, the CVR developed by Lawshe in 1975 has gained widespread popularity. This approach involves asking experts to classify each item as “Essential,” “Useful, but not essential,” or “Not necessary.” The items that receive a critical mass of “Essential” ratings are retained in the final form, while those that fall short are eliminated. This process helps to ensure that the final instrument is both effective and reliable, providing accurate results. CVR shows the necessity of the items for operating a construct [47]. In our study, to measure the CVR, the experts were requested to score each item using a three-point Likert scale, where a score of 1 shows the non-necessity and a score of 3 shows the necessity of an individual item. CVR was calculated according to Formula 4. In this formula Ne is the number of panelists representing “essential” and N is the total number of panelists.

$$\:\text{C}\text{V}\text{R}\:=\frac{(\text{N}\text{e}\:-\:\text{N}\:/\:2)}{\:(\text{N}\:/\:2)}$$
(4)

Results

Extracting potential data items related to the GA

After conducting an extensive search, a list of primary items was compiled under the guidance of a geriatric nurse and two HIM experts. These items were organized into a checklist and forwarded for validation through the Delphi survey. All the possible data items related to the GA process were extracted and a maximum of probable data items was determined. The initial set of items were classified into four classes in a checklist.

Participant characteristics

The panel of experts comprised 60 participants, including psychiatrists, general physicians, gerontologists, geriatrics specialists, nursing geriatrics, community health nursing, cardiologists, urologists, neurologists, respiratory specialists, and epidemiologists was formed. Table 1 shows the characteristics of the expert panel.

Table 1 Characteristics of the expert panel
Full size table

The present study employed a Delphi survey in two rounds to identify the important primary components of an initial checklist. The checklist comprised 256 items grouped into four categories. Panel experts kept 205 items in during the first round of the survey, while they rejected 51 items. They considered for inclusion but ultimately omitted, in the second round, five items. The outcome was a primary checklist of 200 essential items, as determined by the panel of experts. Table 2 provides an illustrative example of the Delphi survey results. After the completion of the Delphi phase, the MDS was formulated and made available for assessment. In the Delphi phase, the Wilcoxon test and Bonferroni correction were performed to reduce type I error and ensure the accuracy of the answer.

Table 2 CVI and Delphi phase for the cardiovascular examination class
Full size table

Checking validity

After the Delphi survey, selected items were sent to a panel of experts to calculate the CVI and 200 items were identified as relevant items. Table 3 shows the CVI of the cardiovascular examination items as a sample of GA-MDS. The S-CVI was also computed to remove the chances of agreement (Table 3).

Table 3 S-CVI for the cardiovascular examination class as a sample of GA-MDS
Full size table

CVR and modified kappa

CVR and kappa were computed for 200 items. Of them, 195 items remained and five items were removed. Table 4 demonstrates the CVR and kappa of each item of the cardiovascular examination class.

Table 4 CVR, modified Kappa
Full size table

Final GA-MDS

After the literature review step, Delphi survey, and validation of content. The final platform of GA-MDS with 195 items was prepared. The MDS items in their final form can be referred to from Tables 5, 6, 7, and 8, which are presented in the appendix for reference.

Table 5 Administrative items
Full size table
Table 6 Clinical assessment
Full size table
Table 7 Physiological assessment
Full size table
Table 8 Psychological assessment
Full size table

Discussion

In this study, the GA-MDS was developed with 195 items classified into four domains, including administrative, clinical (past medical history and vital signs), physiological, and psychological assessments. The administrative section contains socio-demographic data such as age, gender, residence status, economic level, and legal data, which can be used as a valued source to inform policy-making decisions about healthcare and other services demanded by the older population [48, 49]. The clinical section of GA-MDS provides a comprehensive assessment of the older person’s body systems based on normal and abnormal changes. In the GA process, abnormal changes in the body are examined and the referral process of the older person to the next levels, i.e., medical specialists, is clarified. Studies have shown that chronic pain or discomfort, mobility impairments, and depression or anxiety significantly affect the well-being of older adults. Therefore, PHC providers must proactively address these concerns. To achieve this, PHC practitioners can conduct a comprehensive health assessment of older adults using the GA-MDS. With this thorough examination, PHC providers can accurately refer patients, administer appropriate treatments, and reduce the risk of disease-related complications in older adults [50]. The physical domain of GA-MDS contains data about overall physical assessment, nutrition status, independence status or activities of daily living (ADLs), malnutrition, and fall risk assessment. Frailty as an important syndrome of geriatrics is a state of clear vulnerability to stressful conditions such as diseases, etc., which leads to a gradual decrease in physiological functions during a lifetime. This syndrome increases the risk of mortality, morbidity, and falls. One of the most important ways to identify and reduce the amount of this syndrome in older people is GA [51]. In our developed MDS, it was tried to examine the degree of older adults’ dependence so that their referral can be done better. One of the most effective factors in the referral of older persons is cognitive problems and frail conditions [36]. Therefore, the developed MDS in our study contain such information, improve their referral, and on the other hand, help health policy making for better planning. Nutrition is another important factor that affects the aging process, the incidence of diseases related to old age, and the functional changes of the body in the aging process. Therefore, consistent collection and analysis of data related to the nutritional status of older adults can help improve and promote their health [52]. In the physiological section of GA-MDS, it is also possible to capture the data regarding nutritional status, the risk of falling, and dependency. In the referral process, older people depend on daily tasks that may disturb their correct referral [53]. Falling is the second cause of death and the first cause of trauma in the older adults and the primary cause of their hospitalizations. It causes irreparable complications such as mobility, mortality, and greatly increases costs [54, 55]. So, using the present MDS developed in our study can assess the complications of falls in older people in the primary referral system.

In developing countries, mental disorders are common. For example, the rate of people with depression is 79–93% and the rate of people with anxiety is 85–95%. Many of these clients do not have access to proper treatment facilities. Therefore, WHO found it necessary to have an integrated program to assess patients in the primary referral system. However, there were some barriers to the implementation of this program such as the lack of an integrated information system and the lack of implementation in many areas of these countries [56]. So, the MDS designed in our study can provide a uniform system that assesses the physical and mental condition of the older adults at the primary level of referral in different regions of the country. Thus, it can solve the barriers to the implementation of the WHO program in developing countries to some extent.

So far, no MDS has been developed specifically for GA at the PHC level. However, some studies have developed MDS for other purposes for the older population. For instance, Lutomski et al. [57] developed an MDS survey for older persons and informal caregivers (TOPICS-MDS). This MDS has the advantage of gathering uniform data on a large sample of older persons and caregivers, and it also promotes data sharing between institutions. Abellan Van Kan et al. [58] developed an MDS for geriatric clinical trials. This MDS offers an opportunity for research in older people with appropriate outcome measures and significantly facilitates meta-analysis of relevant clinical trials. Soleimani et al. [32] developed an MDS for the information management system of aged care centers in Iran, which could be used to standardize data in aged care centers and improve the quality of care and services related to the older population. Massirfufulay et al. [59] and Jennifer et al. [60] also developed two MDSs for older people living in homecare centers. Our developed MDS represents the current scientific agreement view on GA across the PHC. It is a tool for capturing data related to the assessment of individuals aged 60 years and above. This instrument is designed to identify the key factors that influence their overall well-being. We envision that further development and use of this data set will foster collaboration between three levels of the care referral system, organizations involved in geriatric care, as well as researchers, and academic institutions. Significantly, the use of GA-MDS will contribute to streamlining the overall older adults referral process. It can address data requirements and furthermore enable data reporting purposes of the older adults referral system by lessening repetition of effort and enhancing data quality. It is expected that our developed MDS will show that standardization of clinical data and documentation will, in turn, have a great influence on providing geriatric care, clinical outcomes, and decrease older adults’ treatment costs and healthcare burden.

Globally, health policymakers, gerontologists, and other health authorities have long recognized the value of integrating minimal data gathering as part of routine management in healthcare organizations and hospitals as well as an instrument to reach standardized outcome measurements in clinical research [57]. Our developed MDS would not only have the inherent benefit of collecting consistent information on a large sample of older people but also promote data exchange between involved organizations. This MDS proposed specific patient data can then be shared to enable meta-analysis as well as serve as a source for external users. In this context, the GA-MDS was developed as a tool that not only collects information on older adults but also informs the decisions of policymakers and healthcare planners.

Our study has some limitations that need to be addressed. First, the primary literature search did not take a systematic method. However, we tried to review all the articles and documents available in the field of geriatric medicine to reach data saturation and find all the possible data items to enter into the GA-MDS. In addition, respondents were asked to suggest new data items that they felt were important but not included in the initial list. Second, although a multidisciplinary team, including physicians, allied health specialists, and pharmacists were requested to contribute, respondents were mainly geriatric nurses. In this study, to design MDS, various statistical methods and measures were taken to reduce the chance of error. In our study, GA-MDS was designed and developed because a standard core dataset is required to develop a uniform older adults’ referral information system. Therefore, in future studies, an external evaluation is suggested to refine some data categories.

Conclusions

The establishment of the MDS is a crucial first step in the development of an electronic referral system for GA in Iran. The proposed MDS will greatly simplify and standardize data capture in PHC settings, leading to higher data quality and streamlined referral processes. Health management professionals and policymakers will have access to this data set, which they can use to make informed decisions. The GA-MDS has been developed through a modified Delphi survey, specifically for geriatric medicine research. In the future, this MDS will enable the exchange and assembly of data across various organizations for individual patient data meta-analyses and secondary research analyses.

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

MDS:

Minimum data set

GA:

Geriatric assessment

CVI:

Content validly index

PHC:

Primary health care

SID:

Society for information display

CVR:

Content validity ratio

S-CVI:

Scale-level content validity indices

S-CVI/UA:

Universal agreement among experts

PHC:

Primary Health Centers

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Acknowledgements

We thank the research deputy of the Abadan University of Medical Sciences for financially supporting this project. Also, we would like to thank all Experts who freely participated in this study.

Funding

There was no funding for this research project.

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Authors and Affiliations

  1. Department of Health Information Management, Shahrekord University of Medical Sciences, Shahrekord, Iran

    Razieh Mirzaeian

  2. Department of Modeling in Health Research Center, Shahrekord University of Medical Sciences, Shahrekord, Iran

    Razieh Mirzaeian

  3. Department of Nursing, Abadan University of Medical Sciences, Abadan, Iran

    Mohsen Shafiee

  4. Department of Artificial Intelligence, Smart University of Medical Sciences, Tehran, Iran

    Mohammad Reza Afrash

  5. Artificial Intelligence in Medical Sciences Research Center, Smart University of Medical Sciences, Tehran, Iran

    Mohammad Reza Afrash

  6. Department of Health Information Technology, Abadan University of Medical Sciences, Abadan, Iran

    Hadi Kazemi-Arpanahi

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  1. Razieh Mirzaeian
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  4. Hadi Kazemi-Arpanahi
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Contributions

HKA, MSH: Conceptualization; Data curation; Formal analysis; Investigation; Software; Roles/Writing - original draft. HKA, RM: Funding acquisition; Methodology; Project administration; Resources; Supervision; Writing – review & editing. HKA, MSH, MRA: Methodology; Validation; Writing – review & editing.

Corresponding author

Correspondence to Hadi Kazemi-Arpanahi.

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Ethics approval and consent to participate

The Research & Ethics Committee of the Abadan University of Medical Sciences approved the design and procedure of the study (ethic code: IR.ABADANUMS.REC.1401.120), and the implementation of all methods in this study complied with the Declaration of Helsinki. Written informed consents were required for all participants in this study in accordance with the institutional requirements. Participants were fully aware of the study’s objectives and were informed that their participation was voluntary, with the freedom to withdraw at any time. Participation in the study posed no risks to them, and they were assured that non-participation would not affect the services they received at the center.

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The authors declare no competing interests.

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Mirzaeian, R., Shafiee, M., Afrash, M.R. et al. Determining the minimum data set of geriatric assessment at the Iran primary health care referral system: shifting from fragmentation to integration care for older people. BMC Health Serv Res 24, 1039 (2024). https://doi.org/10.1186/s12913-024-11498-8

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BMC Health Services Research

ISSN: 1472-6963

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