Pielęgniarstwo Chirurgiczne i Angiologiczne

Systematic review, identification, and selection of quality indicators for the care of patients with cataract

Monica Garcia Anguas 1, 2
,
Ana Myriam Seva-Llor 1
,
Rubén Cabrera Beyrouti 3

  1. University of Murcia, Murcia, Spain

  2. Grup de Recerca en Cures en Salut (GreCS)

  3. Hospital Vega Baja, Orihuela, Spain

Pielęgniarstwo Chirurgiczne i Angiologiczne 2026; 20(2): –0:

DOI: https://doi.org/10.5114/pchia.2026.162922
Data publikacji online: 2026/07/14
Article file
00407 - Systematic review.pdf
Confronting perimenopausal women’s knowledge of coronary heart disease with their health behaviours. Controversial role of hormone replacement therapy in the protection of coronary heart disease

Introduction

Cataract in the crystalline lens is a leading cause of blindness worldwide and is considered one of the major ophthalmologic public health concerns. In 2020, nearly 94 million individuals globally were affected by moderate or severe visual impairment or blindness due to cataracts [1]. Multiple studies confirm that the prevalence of cataracts increases with age, from 3.9% in individuals aged 55–64 to 92.6% in those over 80 years old [2]. The increasing aging of the population and patients’ desire to remain active and maintain a good quality of life have led healthcare institutions to implement quality improvement measures aimed at enhancing the care provided by professionals, as well as ensuring patient safety [3, 4].

Quality of care is a fundamental principle in healthcare services, and its assessment and improvement have become central objectives for many hospitals, with growing interest at the global level [5]. Quality can be defined as the extent to which health services for individuals and populations increase the likelihood of achieving desired health outcomes and are consistent with current professional knowledge [6, 7]. The emphasis on quality initiatives in some healthcare systems has also led to the development and measurement of quality indicators (QIs) in eye care [8]. In 2019, the World Health Organization published a report containing essential recommendations for implementing person-centered eye care, monitoring trends, and evaluating progress toward such implementation [9].

From the standpoint of quality measurement and improvement, achieving these goals requires standardized and evidence-based tools to evaluate, monitor, and guide improvements in clinical practice. These tools include QIs that inform decision-making and measure the effectiveness of strategies and actions established by healthcare systems [10]. Scientific evidence has demonstrated that the use and monitoring of QIs in healthcare can improve patient outcomes and optimize resource utilization [11]. In ophthalmology, literature shows that the implementation of QIs in clinical practice is associated with better adherence to clinical practice guidelines, resulting in reduced healthcare costs and improved functional outcomes for patients [12].

Therefore, the aim of this systematic review was to identify, describe, and critically appraise the methodological quality of QIs for cataract care that can be used in ophthalmology units to assess and improve the quality of care, postoperative follow-up, and patient outcomes.

Material and methods

The set of QIs was developed in several phases:

• formation of a multidisciplinary team,

• a literature search across various databases to identify and select a list of potential QIs,

• methodological evaluation of the QIs and creation of indicator sheets,

• final selection of QIs through consensus using the Delphi method.

This study was conducted between March and May 2025.

Formation of the professional team

The team consisted of nurses (n = 2), ophthalmologists (n = 3), and researchers (n = 3). Of these, seven were professionals with expertise directly related to the field of study.

Literature search

Information sources

A systematic search was conducted in several databases, including PubMed, Web of Science (WoS), and CINAHL. Additionally, resources such as the Agency for Healthcare Research and Quality, key indicators from the National Health System, the National Institute for Health and Care Excellence, the Joint Commission on Accreditation of Healthcare Organizations (The Joint Commission), and gray literature were reviewed. The search strategy also encompassed the examination of the reference lists of the included articles; this allows the identification of relevant studies that did not emerge in the initial database search. The search was carried out in March 2025 using validated terms from health sciences descriptors/medical subject headings such as cataract, quality of care, perioperative care, health status indicators, and quality indicators.

The literature search was conducted using three predefined search strategies across PubMed, CINAHL, and WoS databases. The first strategy combined the terms cataract and quality of care, applying filters for English and Spanish language publications between 2017 and 2025. This search yielded 42 records in PubMed, 110 in CINAHL, and 39 in WoS. The second search strategy focused on the combination of cataract and perioperative care, using the same language and time filters. This strategy identified 46 records in PubMed, 77 in CINAHL, and 19 in WoS. The third search strategy combined cataract with health status indicators or quality indicators, again was limited to English and Spanish publications from 2017 to 2025. This search retrieved 17 records from PubMed, 41 from CINAHL, and 9 from WoS.


Eligibility criteria and study selection

Studies were included if they met the following criteria:

• published in Spanish and/or English,

• described the development process of QIs for managing patients with cataracts,

• involved adult populations diagnosed with cataracts (according to any recognized diagnostic criteria),

• published between 2017 and 2025.

Exclusion criteria were:

• incomplete texts,

• studies with QIs that had been updated in more recent versions (only the most recent publication was considered),

• abstracts, letters to the editor, conference proceedings, and duplicate publications,

• studies lacking sufficient detail on the development of QIs,

• studies involving patients under 18 years of age.

Regarding the selection process of the articles retrieved during the bibliographic search, duplicates were first removed. Two reviewers independently conducted a paired screening of titles and abstracts using Rayyan QCRI software [13]. A second screening phase involved full-text reading of eligible articles. In cases of disagreement, a third reviewer served as moderator to reach a consensus.

For studies that met the eligibility criteria, data extraction and methodological quality assessment were performed. In the case of systematic reviews, articles that did not follow a transparent methodology in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 checklist [14] were excluded. Additionally, the reference lists of the included studies were reviewed to identify any relevant studies that may have been missed during the initial search.


Data extraction process

A standardized data extraction table was designed in Microsoft Excel. The variables extracted included: document title, authors, year of publication, proposed indicators, as well as their descriptions and formulas (numerator and denominator), target population, and methodology employed.

Subsequently, the identified QIs were classified by thematic area according to the Donabedian /study, which categorizes indicators into structure, process, and outcome domains [15].


Methodological analysis

Two independent evaluators reviewed the initial list of potential indicators to reduce it to a practical and manageable set. To do so, the appraisal of indicators through research and evaluation (AIRE) instrument was used to perform a critical appraisal of the methodological quality of the QIs [16]. This instrument consists of 20 items grouped into four domains:

• purpose, relevance, and organizational context,

• stakeholder involvement,

• scientific methods,

• additional evidence, formulation, and use.

The appraisal of indicators through research and evaluation instrument has been specifically designed and validated to assess the quality of indicators across various healthcare areas [17]. Each item is scored on a Likert scale ranging from 1 (strongly disagree) to 4 (strongly agree). After scoring, a final standardized score is calculated and expressed as a percentage
(0–100%). In this study, a score of 50% or higher was considered acceptable, following scientific literature [18, 19]. Furthermore, the selection of indicators was justified not only by achieving high overall scores but also because specific key items – namely item 12 (complete formula), item 15 (validity), and item 16 (reliability) – received high inter-rater mean scores. This double validation supports the relevance of their selection, demonstrating both strong overall performance and solid consistency in critical methodological aspects. Table 1 describes the AIRE instrument. The quality indicators were categorized into structure, process, and outcome domains according to the model developed by Donabedian [15].


Development of indicator sheets

The identified QIs were analyzed to assess their level of supporting evidence, strength of recommendation, and degree of expert consensus. The indicators were classified by type, creating separate groups according to the stage of the care process in which they are applied:

• preoperative period,

• day of surgery,

• postoperative period.

Additionally, following Donabedian’s framework, the QIs were categorized as structure, process, or outcome indicators [15].

The working group also proposed additional QIs based on their clinical expertise. As a result, a comprehensive list of indicators was developed, each accompanied by a corresponding technical sheet.

Delphi consensus method

The Delphi method was employed as a consensus-building process to select the most relevant QIs related to the care of patients with cataracts. This method aims to achieve expert consensus through structured analysis and reflection on a specific topic [20]. It consists of an initial questionnaire distributed to a group of experts, followed by a second questionnaire based on the results of the first. The process continues with refinement of the questionnaires and the final definition of QIs to be measured.

In our study, three rounds of consensus were conducted to identify the final set of Qis – one via e-mail and two through in-person meetings. In March 2025, an initial meeting was held during which the indicators evaluated using the AIRE instrument were presented, and those eligible for voting were selected.

The first Delphi questionnaire was distributed via e-mail to all group members. Each participant was asked to anonymously rate the proposed indicators. The technical sheet for each indicator was provided, along with a 1–5 rating scale (where 1 indicated least agreement and 5 indicated highest agreement). Key aspects assessed for each indicator included:

• relevance to clinical practice,

• feasibility,

• availability of resources for measurement.

Participants were also encouraged to provide additional comments or relevant information. All responses from this first round were compiled into an Excel table, and the mean scores were calculated. In May 2025, a final consensus meeting was held to determine the QIs that would be included in a future pilot phase. The anonymized mean scores from the first round, along with participant comments, were shared. A new round of selection was conducted collectively to finalize the list of indicators.

Results

A total of 400 articles were identified as potentially relevant. After removing duplicates, 166 studies were screened by title and abstract. From these, 39 references were selected, of which 5 were excluded due to lack of access to the full text, despite attempts to contact the corresponding authors via e-mail. Subsequently, 34 full-text articles were assessed, and 30 were excluded for various reasons, including different target population, lack of identification of QIs, and use of unrelated study variables.

Ultimately, 4 studies met the eligibility criteria. At the end of the search process, a total of 62 QIs were identified: 7 derived from scientific articles retrieved from the databases, 25 were found on official websites of various scientific societies, and 30 were developed through expert consensus. The review process is summarized in Figure 1.

The identified indicators were analyzed, and 43 were excluded due to duplication, lack of methodological rigor in their development, or insufficient documentation regarding their construction. As a result, a final set of 19 indicators was retained for in-depth evaluation using the AIRE instrument.

The evaluation scores ranged from 9% to 100%. The domain with the lowest overall performance was stakeholder involvement, in which only one indicator scored 50% or higher. It was observed that most indicators had been developed by a single group of experts – specifically, ophthalmologists – without the involvement of other professionals with relevant expertise in the care process for patients with cataracts. In the first domain, Purpose, relevance, and organizational context, only three indicators scored above 50%, with a mean standardized score of 38.16% (range: 13–73%). In many cases, indicators did not clearly describe the specific stage of the clinical process to which they referred. By contrast, the highest-scoring domains were Scientific evidence and Additional evidence, formulation, and use, with average percentages of 55.25% (range: 33–100%) and 55.08% (range: 33–96%), respectively. Notably, many of the indicators in these domains were based on strong evidence from clinical guidelines or studies published in peer-reviewed scientific journals. Furthermore, both the numerator and denominator were clearly defined, as was the target population – an essential aspect for robust indicator development.

At the individual level, only 6 of the total indicators (no. 3, 4, 5, 9, 11, and 12) achieved a standardized score above 50% in at least one of the four AIRE domains. Specifically, three indicators (no. 9, 11, and 12) scored highly in domains I, III, and IV. The item related to the underlying evidence used in the development of the indicators, as well as their critical appraisal through systematic quality methods, received the highest scores.

However, one of these indicators (no. 9) showed certain limitations, obtaining the lowest possible score in one item due to the absence of a defined risk adjustment strategy. The complete AIRE evaluation results are presented in Table 2.

By consensus among the team members, three indicators were excluded due to their limited relevance for clinical practice. The selection process is illustrated in Figure 2. The subsequent Delphi round was completed by all team members. Eleven indicators were unanimously approved, while two were subjected to further discussion. All comments and suggestions from participants were incorporated. A second Delphi round was then conducted to address the two indicators pending from the first consensus. In this round, the final decision led to the acceptance of 12 indicators and the rejection of one. The primary reasons for exclusion were: duplication of indicators measuring the same outcome, lack of feasibility due to insufficient resources for measurement, and limited relevance for routine clinical practice in the service.

On the one hand, according to Donabedian’s model, 33.33% (n = 4) of the final indicators were classified as process indicators, which assess key procedures and services to ensure appropriate standards, such as cancellation and suspension rates, among others. The remaining 66.67% (n = 8) focused on outcome indicators, specifically measuring the impact on visual acuity achieved and potential consequences of the surgery, including infection, adverse events, postoperative pain, and others. No indicators were classified under the structure category.

On the other hand, considering the clinical period to which each indicator corresponds, the majority (58.33%, n = 7) focused on the postoperative period. Indicators related to the preoperative period accounted for 25% (n = 3), while a slightly smaller proportion (16.67%, n = 2) were associated with the intraoperative period. Table 3 provides a summary of each proposed indicator’s formula, classification (structure, process, outcome), and the clinical period in which it is measured.

Discussion

A systematic review of the literature was conducted across various databases to identify validated and useful QIs for assessing the quality of care provided to patients with cataracts in an ophthalmology unit. Our search yielded a total of 62 indicators, of which 19 were analyzed in depth for meeting methodological quality standards. Ultimately, a set of 12 indicators was selected by expert consensus. According to Donabedian’s model, the majority of the indicators in this study (nearly 67%) are outcome indicators, aimed at measuring the impact on visual improvement and potential complications resulting from surgery. Other systematic reviews [26] have also focused on identifying outcome indicators that assess the success of care, i.e., whether the intended results were achieved through the care process.

In contrast, this review did not identify any structure indicators, which are designed to assess how a healthcare system or department is organized. This finding primarily reflects characteristics of the existing literature rather than a deliberate exclusion. Most identified studies focused on process- and outcome-based indicators, while structure-related measures were infrequently defined as explicit, measurable QIs within the context of cataract care. As stated by Jiménez Paneque [27], the relationship between structure and quality is grounded in the idea that some deficiencies in processes may be explained, at least in part, by structural issues.

According to current literature, the combined use of scientific evidence and expert consensus is recommended for the development and validation of QIs [28]. This integration enhances both methodological rigor and practical applicability. In line with these recommendations, our study identified indicators supported by Level 4 evidence [29, 30], corresponding to expert consensus, demonstrating consistency with similar studies [31–33].

Furthermore, following the methodology used in previous research focused on methodological quality assessment [18, 19], a threshold of 50% or higher in AIRE domains was considered acceptable. The lowest-performing domain in our study was stakeholder involvement, a finding consistent with prior systematic reviews in other healthcare areas, which also reported limited engagement of users, patients, or other healthcare professionals in the development of indicators [34].

In this study, the indicator development process was led primarily by ophthalmologists, which may restrict the multidisciplinary and patient-centered perspective. The literature emphasizes the importance of involving all relevant stakeholders throughout the process to ensure greater relevance and applicability of the indicators [35, 36].

The relevance of this review is amplified by the fact that its findings are cited and reinforced in clinical practice guidelines, as other authors mention [37–39], which favors broader adoption of the resulting recommendations. The interaction between systematic reviews and clinical guidelines not only enhances the external validity of recommendations but also improves healthcare professionals’ confidence in their implementation. This process also promotes the standardization of care, increases quality of care, and can have a positive impact on clinical outcomes [40–42].

In this setting, digital health tools are becoming more available and can support better measurement, tracking, and use of QIs in surgical care. In the last few years, technological developments in the medical field have been rapid and are continuously evolving. One of the most revolutionizing breakthroughs was the introduction of the Internet of Things (IoT) concept within medical practice [43]. These technologies may help close gaps in current QI frameworks, such as limited stakeholder involvement, incomplete data, and weak support for ongoing quality improvement. Furthermore, IoT-based solutions such as telesurgery and surgical tele-mentoring may play an increasingly relevant role in optimizing surgical performance in cataract process and improving patient outcomes. Consequently, understanding the current evidence surrounding the application of IoT technologies in surgical practice is essential for contextualizing their potential impact on quality of care and for guiding future indicator development [44].

Limitations

A limitation identified in this study pertains to the low participation of multiple stakeholders. During the search and classification of indicators, we concluded that input from a multidisciplinary consensus (including nurses, ophthalmologists, opticians, etc.) is essential at all stages of development, particularly in the initial creation phase. Although our study aligns with the literature regarding the recommended methodological approaches for indicator development, it also highlights common challenges such as the limited involvement of professionals engaged in the care process.

However, the study uses a structured Delphi consensus process to ensure the selection of valid and applicable indicators, employs a rigorous methodological evaluation using the AIRE instrument, and offers a thorough systematic review of QIs for cataract care. These advantages provide a strong basis for putting quality improvement programs into clinical practice.

Conclusions

Although QIs are increasingly playing a crucial role in the improvement cycles of many healthcare services, there are few studies in our country that evaluate indicators with high methodological quality. Furthermore, the implementation of QIs in daily practice is likely limited due to the lack of supporting pilot testing.

The objective of this systematic review was to identify and describe QIs that can be used to assess quality in an ophthalmology unit and to evaluate the methodological quality of the identified set of QIs. In summary, the conclusions of this research provide a foundation for establishing quality control mechanisms in the field of ophthalmology, aimed at continuously improving service delivery and supporting decision-making based on concrete data.

The validity and relevance of the selected indicators will be tested through a pilot study to be conducted in our ophthalmology unit as a direct outcome of this work.

Additionally, QIs are an essential part of a continuous improvement cycle, meaning they should not remain static; ongoing measurement accompanied by improvement actions is necessary. Scientific evidence is dynamic, as is its clinical relevance; therefore, these factors should serve as criteria for eliminating existing indicators or adding new ones in future research.

Future studies addressing this topic should consider using the AIRE instrument for the development and evaluation of indicators, thereby ensuring the highest methodological quality. To evaluate the viability, relevance, and influence of the chosen indicators in actual ophthalmology settings, pilot studies are required. These studies could ascertain whether their use enhances overall care quality and postoperative results. In order to make sure that indicators accurately represent all facets of care, future research should methodically involve nurses, opticians, ophthalmologists, and patients in the creation and evaluation of indicators.

Disclosures

1. Institutional review board statement: The protocol for this systematic review was registered in PROSPERO
under the identification code CRD420250653982. This study was approved by the Clinical Research Ethics Committee (approval decision no. 2022/10500, dated: 14.09.2022).

2. Assistance with the article: None.

3. Financial support and sponsorship: None.

4. Conflicts of interest: None.

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