Linking theory and practice to advance sustainable healthcare: the development of maturity model version 1.0 | BMC Health Services Research

There is currently no prevailing preferred approach for the development of maturity models. Typical methodologies involve utilizing established maturity models as a foundational framework and applying design-oriented approaches [15]. Testing the model is an essential step when developing a maturity model, to ascertain the practicality and advantages in real-life situations [15]. In the present study we apply the design-oriented methodology outlined by De Bruin et al. [16] to construct a maturity model for sustainable healthcare. The approach includes a test phase and is one of the few structured approaches for developing new maturity models available in scientific literature. The methodology consists of the following phases: scope, design, populate, test, deploy and maintain. The latter two phases can be accomplished when the model has been made available to a wider audience which is an objective of the current paper. Consequently, these phases are not included in this publication. The overall research design of the present study is shown in Fig. 1. The following sections provide further details on each phase.

Scope phase

The scope of the model is defined by the demand from healthcare professionals involved in reducing environmental impact of a healthcare organization. The focus was set to the specific domain of sustainability in terms of environmental impact of healthcare organizations, in the Dutch context. Since the research team that developed the model consisted of both scientists and healthcare professionals involved in sustainable healthcare in the Netherlands, the model to be developed was viewed as an evidence-based framework with a strong practical focus.

Design phase

In the design phase the model’s structure and content are developed. The design decisions agreed upon by the research team are presented in Appendix A, table A1. The overall architecture of the model was defined by the research team, following the design process as proposed by De Bruin et al. [16] with five progressive stages where higher stages are built upon the requirements of lower stages. Level 1 represents a low level of maturity, while level 5 signifies the highest level of maturity.

The content of the model was defined by using a mixed-method approach consisting of a literature review, a parallel design project by medical doctors, an expert panel review and a final synthesis by the research team. The expert panel consisted of two academia, three healthcare practitioners, an environmental expert and a policy maker all involved and experienced in environmental sustainability improvement in healthcare.

The literature review aimed to identify relevant concepts from the domain of sustainable healthcare and the relevant enabling and impeding factors to be included in the model. Because this domain was relatively new at the time of constructing the maturity model, a systematic literature review would not be an efficient method for identifying these concepts. Instead, a RAPID review was conducted based on the Cochrane Rapid reviews methods group [17]. A summary of the RAPID review protocol can be found in Appendix B. Records identified by running the search query were imported into Rayyan.ai web application to screen titles and abstracts by two researchers. In an independent selection process, papers were identified for inclusion and subsequently analyzed to extract key concepts and measurement criteria. Differences were discussed and resolved between the researchers.

To supplement the limited theoretical basis of the emerging field of sustainable healthcare maturity, a parallel subproject was conducted with four medical doctors with diverse professional backgrounds to design a maturity model for sustainable healthcare. They participated in an 8-week masterclass on sustainability in healthcare as part of a two-year MBA program at the University of Amsterdam. In parallel with the literature review, they designed their own version of a maturity model for sustainable healthcare by using their perspectives and experiences as medical doctors as well as any other materials they wanted to use such as educational materials from the masterclass or scientific literature.

After completing the literature review and the design by medical doctors, concepts with measurement criteria resulting from both research steps were combined and consolidated into a structured overview of unique concepts organized in groups (concept categories).

Subsequently, an expert panel reviewed the structure to determine the optimal set of concepts and concept categories to include in the maturity model. The expert panel consisted of two scientists, three healthcare practitioners, an environmental expert and a healthcare policy advisor, all experienced in environmental sustainability improvement in healthcare. In accordance with the design principle on minimizing the number of objects from De Bruin et al. [16], the expert panel was asked to select the most important concept categories and concepts to minimize complexity and ensure independence of model components (eg. to prevent overlap between definitions of concepts). The selection resulted in an overview of potential concepts with measurement criteria organized in categories to form the domains of the maturity model.

Populate phase

A preliminary maturity model was constructed, utilizing the conceptual categories selected by the expert panel. These concept categories formed the domains in which the concepts are categorized. Furthermore, a measurement instrument was developed to support the documentation of maturity levels for each concept (or: maturity model object) and to automatically provide maturity level averages per domain (concept category). This instrument was designated as the “Sustainable Healthcare Maturity Checklist” and is alternatively referred to as “the checklist.” It was generated through an iterative process conducted by the research team over several working sessions.

Test phase

The efficacy of the model and its checklist were evaluated by implementing them in case studies from different healthcare settings, while assessing their ease of understanding, ease of use, usefulness, and practicality with the individuals involved in implementing the model.

Six case studies were chosen based on availability and location (within or near Amsterdam, the Netherlands) to allow for actual attendance by junior researchers during the assessment in June 2023. After receiving a briefing on the model’s aim and structure, different researchers with no prior knowledge or experience with the maturity model applied the model to the case study assigned to them. Five case studies focused on a department (Gynecology, Surgery, Psychiatry, Pediatric surgery, and Neonatal care) within the Amsterdam University Medical Center and one in a geriatric care institute in the city of Purmerend. The researchers independently reviewed the maturity model checklist and collected data over a two- to five-hour site visit to determine each checklist item’s maturity level. Researchers rated each checklist item in conjunction with department staff, particularly nurses and doctor’s assistants. The ratings were determined by considering input from department staff, observing process workflows, analyzing reports and (electronic) documentation such as management dashboards and product ordering history. If there were any remaining inquiries, supervisors or facilitating staff members were contacted.

Maturity checklist scores were reviewed and agreed upon by the researchers with one or more staff members involved in daily practice at the respective organization/department. Strengths (higher maturity level scores) and improvement areas (lower maturity level scores) were identified, and improvement actions resulting from the model were discussed accordingly. To define these improvement actions, particular attention was given to the lower scores. A cause & effect analysis was used to ensure that actions were targeted and effective. This process involved two main steps: first, identifying the root causes of the low scores, and then brainstorming potential solutions. Researchers played a facilitative role by asking probing questions, such as “Why do you think this score was low?” or “What challenges might be contributing to this issue?” This helped the staff to dig deeper into the underlying problems. After identifying the root causes, the team collectively brainstormed various solutions, considering the feasibility and practicality of each option. The most viable solutions were selected for implementation, ensuring that the improvement actions were both realistic and directly addressed the root causes identified. This collaborative and systematic approach ensured that the improvement actions were well-grounded and had a higher likelihood of leading to meaningful changes.

A survey based on the form for evaluating maturity models from Salah et al. [18] was then used to assess the model’s understandability, ease of use, usefulness, practicality and ability to identify improvement actions for environmental sustainability in healthcare organizations. The maturity model evaluation survey is presented in Appendix C. The survey was completed by the researchers assigned to each case study after applying the maturity model. Survey findings were consolidated and aggregated to determine scores given to each evaluation criterion. Statistical analysis was applied to test on correlation between criterion and differences between departments in R (version 4.3.1.), using a parametric ANOVA test for categories with normal distribution and similar variances and a non-parametric Kruskal–Wallis test for the remaining categories. The answers to open questions were consolidated and analyzed via deductive coding using ATLAS.ti (version 23.3.4.) by two researchers independently and by using the themes of the questionnaire as the coding structure. Differences between coding were resolved by discussion between the researchers. Findings from the surveys were accordingly evaluated by the research team to identify improvements to be made to the maturity model and instrument.