Reducing Waiting Times in Diabetes Care: A Value Stream Mapping and OPA-Fuzzy-TOPSIS Analysis

Abstract

Healthcare systems are inherently complex, and service delivery requires patient customization. Chronic care requires patients to spend significant time in the clinic for proper care. Long waiting times in healthcare facilities contribute to patient dissatisfaction and poor adherence to medical regimes. This study presents a method for analyzing non-value-added activities in the service supply chain and offers a prescriptive framework to minimize them. The method used to analyze the current and future state is value stream mapping, while stakeholder analysis was performed to understand the power and interest of stakeholders. Finally, this study uses the ordinal priority approach (OPA) and Fuzzy-TOPSIS to prioritize the action points for achieving the future state in the case organization. To illustrate the approach suggested in this study, a case of a diabetes speciality clinic was used. This study’s findings suggest hiring paramedical staff and clubbing diabetes education in medical nutrition therapy can reduce waiting time without affecting quality. The study further suggested that using multimedia in diabetes education is an effective strategy for reducing waiting times. Thus, this study provides a descriptive and prescriptive approach for analysis and improving healthcare service delivery. The findings of this study are useful for healthcare administrators and health policymakers.

Keywords

Healthcare systems value stream mapping value unlocking ordinal priority approach Fuzzy-TOPSIS

Introduction

Healthcare is a complex service delivery and needs customization as each customer’s requirements differ (Myers et al., 2020). Inherent complexity results in inefficient use of resources and high cost of care. On the other hand, high-cost patients make up the sickest and most complex populations, and their high utilization is primarily explained by high levels of chronic conditions (Wammes et al., 2018). Healthcare value refers to the quantifiable enhancement in an individual’s health results relative to the expenses incurred to attain that enhancement. Although certain explanations may blend value-centric healthcare with expense reduction, betterment of quality or patient satisfaction, these significant endeavours are distinct from value (Prinja et al., 2020). Value predominantly enhances patient health outcomes (Teisberg et al., 2020). Thus, value in healthcare is defined as:

V a l u e D e l i v e r e d (Q, C) = \frac{Q}{C}

(1)

‘Q’ is quality delivered, and ‘C’ is the associate cost. If this delivered value is greater than the expected value of the patient, it results in customer satisfaction (Nguyen et al., 2021). This customer satisfaction results in less doctor hopping and higher adherence to medication regimes (Young et al., 2019). ‘Doctor hopping’ refers to regularly switching or changing healthcare providers, such as doctors or specialists, for various reasons. Studies have reported a positive association of personal and cultural beliefs on patients’ medication adherence with chronic illnesses such as diabetes (Shahin et al., 2019).

Diabetes is rising like an epidemic in various parts of the world. Based on data from the International Diabetes Federation’s Diabetes Atlas, 2021 witnessed 537 million individuals living with diabetes, and projections indicate an escalation to 643 million by 2030 and a staggering 783 million by 2045. Moreover, an estimated 541 million people exhibited impaired glucose tolerance in 2021. The statistics also foresee over 6.7 million deaths among individuals aged 20–79 attributable to diabetes-related factors in 2021 (Magliano & Boyko, 2022). Diabetes is a gateway to many micro- and macrovascular complications such as retinopathy, neuropathy, nephropathy, strokes and diabetic foot (Rangel et al., 2019). This situation results in several lab investigations and consultations to decide the medication regime. Historically, medications that targeted multiple factors were considered less favourable, as the focus was on single-target drugs. Despite the achievements of such single-target drugs, there is now unquestionable evidence that they possess restricted effectiveness against intricate diseases where the disease’s development depends on a series of biochemical occurrences and the simultaneous operation of numerous bioreceptors (Makhoba et al., 2020). Diabetes medicine prescription is complex and needs to be customized for each patient depending on their family history, lifestyle and stage of the disease progression. Thus, a diabetes management team generally involves a physician, diabetes educator, nutritionist and pharmacist (Knezevich et al., 2022; Prahalad et al., 2020). The complex nature of care delivery results in long waiting times for people with diabetes in traditional clinical practice (Al-Badri & Hamdy, 2021). Non-value-added activities in diabetes care include redundant paperwork, unnecessary tests, excessive patient wait times, inefficient scheduling, repeated data entry and prolonged administrative processes. The non-value-added activities in diabetes care delivery result in lower patient satisfaction while increasing the cost of care. There is a need to identify and eliminate the non-value-added activities and devise strategies to reduce them.

Lean and Six Sigma methodologies are crucial in healthcare process improvement, aiming to enhance patient care quality and operational efficiency. The integration of Lean and Six Sigma tools such as Defile, Measure, Analyze, Improve, Control (DMAIC), value stream mapping (VSM), Suppliers, Inputs, Process, Outputs and Customers (SIPOC) analysis, Ishikawa Diagram and 5S has been widely recognized in healthcare settings for achieving continuous improvement by reducing waste, improving processes and enhancing patient satisfaction (de Barros et al., 2021; Thakur et al., 2023). However, it is essential to consider the potential challenges and limitations associated with Lean Six Sigma implementations, such as the need for flexibility and buffering to address unexpected demand shocks, as highlighted in the context of the COVID-19 pandemic (Kuiper et al., 2022).

VSM is a Lean management tool used to visualize and analyze the flow of materials and information required to bring a product or service to a consumer. The construction of a VSM begins with identifying the product or service to be mapped and the specific process steps involved from start to finish (Abdulmalek & Rajgopal, 2007). Core components of a VSM include process steps, which are the individual tasks or activities that transform input into output. Cycle time refers to the total time to complete a process step, including value-added and non-value-added activities. Value-added cycle time is the portion of the cycle time where actual value is added to the product or service. In contrast, non-value-added cycle time represents activities that do not add direct value and could be eliminated to improve efficiency. By mapping these components, VSM helps identify bottlenecks, waste and opportunities for improvement within the process. The VSM helped us visualize the process and identify the waste in the value stream (Setiawan et al., 2021).

Although there is literature on value analysis and maximization in service supply chains using the Lean implementation, there is a lack of extant literature on complex delivery systems such as diabetes care (de Barros et al., 2021; Thakur et al., 2023). This study attempts to fill this knowledge gap. Lean implementation, best practices such as continuous incremental improvement, known as Kaizen, can be utilized to analyze and improve the service supply chain (Bhat et al., 2020). With this background, the objective of this study is two prongs. First, this study performs VSM for the case organization to identify the present state and proposes an improved one using Kaizen. Second, the study illustrates an approach to achieving the proposed state. The specific research objectives (RO) of this study are as follows:

RO1: VSM of diabetes clinic for situation analysis and identifying improvement.

RO2: Stakeholder analysis for the Kaizen project implementation in the diabetes clinic.

RO3: Prioritization of Kaizen action plan for the diabetes clinic.

The remaining article is arranged as follows. The next section discusses the methods used in this study and the rationale behind that, followed by results and related discussions. The study concludes with implications of this study for theory and practice and future research directions.

Methodology

This study uses VSM to identify the opportunity for value unlocking, followed by stakeholder analysis for the Kaizen action. The ordinal priority approach (OPA)-Fuzzy TOPSIS approach prioritized these action points to implement improvements effectively.

Value Stream Mapping

VSM is a visual and analytical technique used to analyze, understand and improve the flow of materials, information and processes within a healthcare system (Antony et al., 2019). VSM excels by providing a holistic view of processes, emphasizing Lean principles to eliminate waste, focusing on material and information flow and prioritizing customer value. Its straightforward nature offers actionable insights, making it more effective for continuous improvement compared to Business Process Model and Notation (BPMN), SIPOC, Integration Definition for Function (IDEF) modelling, Unified Modelling Language (UML) or Design and Engineering Methodology for Organizations (DEMO) (Setiawan et al., 2021). In their seminal work, Cerfolio et al. (2019) report improved turnover time in an academic hospital after implementing Lean practices. In their paper, Halawa et al. (2020) discuss the utility of VSM in a healthcare facility design. The usefulness of VSM in achieving resource utilization, such as equipment, is reported in the studies (Dadashnejad & Valmohammadi, 2019). Borges et al. (2019), in their scoping review of Lean implementation in healthcare, highlight the importance of implementing Lean practices in healthcare; at the same time, they highlight that implementation is restricted to specific units within a hospital. Kaizen is a Japanese term that translates to ‘continuous improvement’ or ‘change for the better’ (Goyal & Law, 2019) (Figure 1).

Figure 1.

Note: VSM, value stream mapping.

It is a widely used approach for implementation in improvement in healthcare organizations (Ishijima et al., 2019; Shatrov et al., 2019). While VSM was used to identify the improvement areas, the DMAIC approach was used to implement improvements. DMAIC is a structured problem-solving and process improvement methodology widely used in Six Sigma (Antony et al., 2023).

Stakeholder Analysis

Stakeholder analysis is crucial when implementing any change or improvement initiative like Kaizen (de Oliveira & Rabechini, 2019). Stakeholder analysis is widely used in analyzing improvement projects (Aaltonen, 2011; Jepsen & Eskerod, 2009). Based on the review of the extant literature, steps for stakeholder analysis were ascertained. The 10 steps used for stakeholder analysis in this study are: (a) identify stakeholders, (b) prioritize stakeholders, (c) analyze stakeholder interests, (d) understand stakeholder influence, (e) assess stakeholders’ support, (f) plan engagement strategies, (g) address resistance and concerns, (h) monitor and update, (i) communicate progress and (j) repeat steps 1–9. A focus group of seven decision-makers was utilized to perform stakeholder analysis. The focus group size was chosen to balance manageability with stakeholder representation. The literature recommends a sample size of six to eight participants (Mishra, 2016). To ensure the representation of every type of stakeholder while keeping the group size below eight, only one stakeholder from each category was selected for the focus group. The characteristics of the focus group are listed in Table 1. A researcher from this study, VM, acted as a moderator. Further judgmental sampling was used to make the focus group representative of the population (Hennink & Kaiser, 2022).

Table 1.

Characteristics of the Focus Group for the Study.

SN	Responsibility	Decision Makers	No
1	Management	Director	1
2	Supervisor	Operations manager	1
3	Supervisor	Human resource manager	1
4	Patient	Patients with diabetes	1
5	Pharmacy	Pharmacy manager	1
6	Supplier	Medical representative	1
7	Other	Social worker	1

Delphi’s approach was used to build consensus for stakeholder analysis. With three iterations, the Delphi method allowed respondents to provide input, review and respond to others’ perspectives, and revise their opinions based on the group’s collective insights. This process helped comprehensively understand stakeholders’ views and priorities (Nasa et al., 2021). The approach used for the Delphi Method is depicted in Figure 2.

Figure 2.

Approach for Delphi Method Used in the Study.

Prioritization of Action Plan

Prioritization of an action plan helps to implement it effectively in resource-constrained settings. In resource-constrained settings, where there are limitations regarding available time, funds, personnel and other resources, prioritizing an action plan becomes crucial to ensure its successful implementation. The prioritization process involves identifying and ranking tasks or actions based on the 3E criteria of ease, effect and economic cost (Mishra & Singh, 2022). This study uses the OPA to rank the criteria, while the Fuzzy-TOPSIS approach was used to rank the identified tasks.

The seven experts used earlier in the study were surveyed to solve the prioritization problem. First, the Delphi approach was used to rank the criteria. The linear programming formulation of the OPA can be written as follows:

Max Z

Such that

Z \leq r_{i} (r_{j} (r_{k} (w_{i j k}^{r_{k}} - w_{i} j k^{r_{k + 1}}))) \forall i, j and r_{k}

(2)

Z \leq r_{i} r_{j} r_{m} w_{i j k}^{r_{m}} \forall i, j and r_{m}

(3)

\sum_{i = 1}^{p} \sum_{j = 1}^{n} \sum_{k = 1}^{m} w_{i j k} = 1

(4)

w_{i j k} \geq 0 \forall i, j and k

Z : Unrestricted in sign

In the model discussed above, $r_{i} (i = 1, 2, \dots, p)$ represents the rank of the expert ‘i’, $r_{j} (i = 1, 2, \dots, n)$ represents the rank of the criteria ‘j’ and $r_{k} (i = 1, 2, \dots, m)$ represents the rank of the factor ‘k’. Furthermore, $w_{i j k}$ represents the weight of the factor ‘k’ on criterion ‘j’ by expert ‘i’. After solving the linear programming formulation of OPA, the weight of each factor is given by the following equation:

w_{k =} \sum_{i = 1}^{p} \sum_{j = 1}^{n} w_{i j k} \forall k

(5)

Similarly, the weight of each criterion is given by the following equation:

w_{j =} \sum_{i = 1}^{p} \sum_{k = 1}^{m} w_{i j k} \forall j

(6)

Finally, the weight of each expert is given by the following equation:

w_{i =} \sum_{j = 1}^{n} \sum_{k = 1}^{m} w_{i j k} \forall i

(7)

The mathematical formulation’s specifics are available on the website established by the creators of OPA, as documented by Ataei et al. in 2020. These researchers have also made open-source software available to solve the OPA formulation and evaluate result reliability. The present study employs the web-based open-source software version 1.4 of OPA Solver. Kendall’s coefficient of concordance was used to assess the agreement between decision-makers. Kendall’s W is a non-parametric rank correlation statistic used to assess agreement among raters and inter-rater reliability. Kendall’s W ranges from 0 (no agreement) to 1 (complete agreement).

Fuzzy Logic

A fuzzy set $\tilde{A}$ in a universe of discourse $X$ is characterized by a membership function $μ_{\tilde{A}} (x)$ that associates each element $X$ with a real number in the interval $[0, 1]$ . The function value $μ_{\tilde{A}} (x)$ is termed as the grade of membership of $x$ in $\tilde{A}$ . This study uses linguistic variables to rate alternatives based on different criteria. The linguistic variables are then converted into triangular fuzzy numbers for further processing. Linguistic variables help deal with situations which are complex or ill-defined. A triangular fuzzy number can be defined by a triplet $(a, b, c)$ , as shown in Figure 3. The membership function $μ_{\tilde{A}} (x)$ is defined as:

μ_{\tilde{A}} (x) = \{\begin{matrix} 0 & x < a, \\ \frac{x - a}{b - a} & a \leq x \leq b, \\ \frac{x - c}{b - c} & b \leq x \leq c, \\ 0 & x > c, \end{matrix}

(8)

Figure 3.

A Triangular Fuzzy Number $n$ n.

Fuzzy-TOPSIS

The steps for performing Fuzzy-TOPSIS are described as follows (Mishra, 2022):

Step 1: Seven decision-makers were tasked with evaluating three different alternatives based on four specific criteria. This process resulted in the creation of three decision matrices. Within these matrices, the linguistic variables were substituted with triangular fuzzy numbers, following the guidelines outlined in Table 2.

Table 2.

Linguistic Variables for the Rating.

Linguistic Variable	Fuzzy Number
Very low (VL)	(1,1,3)
Low (L)	(1,3,5)
Average (A)	(3,5,7)
High (H)	(5,7,9)
Very high (VH)	(7,9,9)

Step 2: The subsequent stage involves computing the combined decision matrix for subsequent analyses. The constituents of the combined decision matrix are determined by an element denoted as ${\tilde{x}}_{i j}$ , where its value is defined as follows:

{\tilde{x}}_{i j} = (a_{i j}, b_{i j}, c_{i j})

(9)

a_{i j} = \min_{k} {a_{i j}^{K}}, b_{i j} = \frac{1}{k} \sum_{k = 1}^{K} b_{i j}^{K}, c_{i j} = \max_{k} {c_{i j}^{K}}

(10)

Step 3: The weight of the criteria was calculated using the OPA discussed earlier.

Step 4: The next step is to categorize the criteria as benefit and cost criteria. The benefit criteria are maximized, whereas the cost criteria are minimized. Table 3 categorizes various criteria used in this study into two categories.

Table 3.

Criteria Used and Categories.

Criteria	Description	Category
C1	Ease	Benefit
C2	Effect	Benefit
C3	Economic cost	Cost

Step 5: In this step, the combined decision matrix was normalized to get a normalized fuzzy decision matrix using the following rule:

\begin{array}{l} {\tilde{r}}_{i j} = (\frac{a_{i j}}{c_{j}^{*}}, \frac{b_{i j}}{c_{j}^{*}}, \frac{c_{i j}}{c_{j}^{*}}), c_{j}^{*} = \max_{i} {c_{i j}} (benefit criteria) \\ {\tilde{r}}_{i j} = (\frac{{a^{-}}_{j}}{c_{i j}}, \frac{{a^{-}}_{j}}{b_{i j}}, \frac{{a^{-}}_{j}}{a_{i j}}), a_{j}^{-} = \min_{i} {a_{i j}} (cost criteria) \end{array}

(11)

Step 6: The weight calculated using the OPA method was used to calculate the weighted normalized fuzzy decision matrix from the normalized fuzzy decision matrix.

{\tilde{v}}_{i j} = {\tilde{r}}_{i j} \otimes w_{j}

(12)

Step 7: The next step is to calculate the Fuzzy Positive Ideal Solution (FPIS) and Fuzzy Negative Ideal Solution (FNIS) using the following rule:

\begin{array}{l} A^{*} = ({\tilde{v}}_{1}^{*}, {\tilde{v}}_{2}^{*}, ....., {\tilde{v}}_{n}^{*}) where {\tilde{v}}_{j}^{*} = \max_{i} {v_{i j 3}} \\ A^{-} = ({\tilde{v}}_{1}^{-}, {\tilde{v}}_{2}^{-}, ....., {\tilde{v}}_{n}^{-}) where {\tilde{v}}_{j}^{-} = \min_{i} {v_{i j 1}} \end{array}

(13)

Step 8: Once FPIS and FNIS are identified, each alternative’s distance to these points is calculated. The study uses the vertex method to calculate the distance between two fuzzy numbers.

d (\tilde{x}, \tilde{y}) = \sqrt{\frac{1}{3} [{(a_{1} - a_{2})}^{2} + {(b_{1} - b_{2})}^{2} + {(c_{1} - c_{2})}^{2}}

(14)

This exercise gave us two matrices with distance, one each for FPIS and FNIS.

Step 9: Now, the distance from the best solution $d_{i}^{*}$ was calculated by summing the distance of each criterion for the given alternative. Similarly, the distance from the worst solution $d_{i}^{-}$ is calculated. These values were used to calculate the closeness coefficient $C C_{i}$ using the following formula:

C C_{i} = \frac{d_{i}^{-}}{d_{i}^{-} + d_{i}^{*}}

(15)

Step 10: The alternatives were arranged in decreasing order of closeness coefficient. The alternative with the highest value $C C_{i}$ was selected.

Results and Discussion

This section first provides background information about the case organization, an entity based in India, a developing country where long waiting times pose a significant challenge in healthcare delivery. It then illustrates the discussed approach using this case organization.

Case: Diabetes Speciality Clinic

The case organization used in this study is a diabetes speciality clinic from India. The clinic witnesses over a 100 footfalls daily from the city and the extended catchment area. As customers travel from distant locations, a high waiting time adversely affects their cost and patient satisfaction. An unstructured survey of 162 patients and their attendees was done to identify the factors affecting customer satisfaction. The Pareto chart from the responses was created to identify the most critical factor for customer satisfaction (Figure 4). As the chart suggests, waiting time is the most critical to the quality of service being delivered.

Figure 4.

Pareto Chart for the Factors Affecting Customer Satisfaction.

Further discussion with management helps us conclude the following gap in the targeted and actual waiting time for different categories of patients with diabetes. Further analysis found the highest gap in the targeted and actual time for the category of new diabetic patients (Figure 5).

Figure 5.

Gap Analysis for Different Service Categories in Case Organization.

Next, we created a SIPOC diagram for the diabetes speciality clinic to gain more visibility on value creation at the case organization (Figure 6). Registration, Consultation 1, Consultation 2, Investigation, Education and Medical Nutrition Therapy (MNT) are the five sub-processes.

Figure 6.

Note: MNT, Medical Nutrition Therapy.

Before deciding the action point for reducing waiting time, the study performed VSM of the present state. VSM of the current state is depicted in Figure 7.

Figure 7.

Note: MNT, Medical Nutrition Therapy; CT, cycle time; NVA, non-value-added; VA, value-added.

The focus group was presented with the value stream map of the current state and asked to identify waste for the same. The focus group was also asked to suggest an action point for improvement. These action plans were also ranked on three criteria: ease, effect and economic cost on the Likert scale, where very low (VRL), low (LOW), average (AVG), high (HGH) and very high (VHG) are different levels of ordinal scale. The results of the focus group discussion are tabulated in Table 4.

Table 4.

Initial Response Matrix for Fuzzy-TOPSIS.

SN	Process	Observation	Action Point	Ease	Effect	Cost
1	Registration	Nurse doing appointment registration and vital taking	Addition of a receptionist for appointment and registration (A1)	HGH	AVG	HGH
2	Consultation (1)	Doctor taking too much time	Putting a stopwatch with a consultant (A2)	VGH	AVG	VGH
3	Lab Investigation	Less scope for improvement unless advanced equipment is procured	Use of software with a populated template for a diabetes test (A3)	AVG	HGH	AVG
4	Diabetes Educator	Uses pen and paper for explanation	Clubbing with nutrition therapy and general diabetes education is provided in the waiting area using multimedia (A4)	HGH	HGH	HGH
5	Consultation (2)	Patients need to visit the doctor again	Remove the need for a second consultation. Doctors write prescriptions based on lab tests (A5)	HGH	HGH	LOW
6	Medical Nutrition Therapy	The patient is fed up and tired till this stage	Clubbing nutrition therapy with diabetes education (A6)	HGH	HGH	VHG
7	Medicine Dispensing	Crowded and long queues	Creating separate counters for medicine return and free home delivery for patients within a 5 km radius (A7)	AVG	HGH	AVG

After implementing the seven action points recommended by the focus group, the process was re-evaluated, and the differences from the initial state were analyzed. In resource constraints, the higher-ranked action points can be prioritized for implementation. In this instance, the hospital implemented all seven action points. Based on observation, the difference between the mean values was calculated and reported in Table 5.

Table 5.

Summary of the Improvement Post Implementation of Action Points.

Metrics	Current State	Future State	Improvement (%)
Total CT	5.08 hours	3.50 hours	29.53
Total VA CT	2.25 hours	1.42 hours	36.89
Total NVA CT	2.83 hours	2.08 hours	26.50
Lead time	0.73 days	0.51 days	30.14

Notes: CT, cycle time; NVA, non-value-added; VA, value-added. One day is equivalent to 10 hours minus 30 minutes of lunch break.

The VSM after the implementation of action points is given in Figure 8.

Figure 8.

Value Stream Mapping Post Implementation of Action Points.

A stakeholder analysis was performed to successfully implement the action plan (project). It is an ongoing process that helps the case organizations maintain a clear understanding of the environment in which they operate, ensuring that they can adapt and respond effectively to stakeholder expectations and dynamic changes. The results of the stakeholder analysis are depicted in Table 6.

Table 6.

Results of the Stakeholder Analysis for the Implementation Project.

Stakeholder	Stake	Potential Role	Empowerment Level
Management	Cost, revenue, profitability	Management, stewardship	High
Supervisors	Job performance	Management	Medium
Doctor	Job performance	Consultation, prescription	High
Lab technician	Job performance	Investigation, reporting	Low
Diabetes educator	Job performance	Consultation, prescription (diet and exercise)	Low
Pharmacist	Job performance	Medicine dispensing, billing	Low
Patients	Health, cost, time	Medical adherence, payment	Low
Trade union	Employee welfare	Advocacy for employee welfare	Absent
Government	Patient and employee welfare	Medical law formation and enforcement	High
Society	Health, social welfare	Health promotion, amenities	Low
Suppliers and vendors	Cost, time and business	Supply of goods and services for care delivery	Medium

Next, we created a Power Interest Matrix to categorize stakeholders based on their power level and interest in the project. This matrix helps project managers and stakeholders understand the dynamics between different stakeholder groups and tailor their engagement strategies accordingly. The matrix and recommendations for the project are depicted in Figure 9.

Now, the result of the OPA is discussed. The weight of the decision of all seven decision-makers was taken equal (0.14), while the weight of three criteria was found as ease (0.16), effect (0.52) and economic cost (0.33). Kendall’s W for the study was found to be 0.510204. A global confidence level of 0.98 suggests that this level is suitable for very sensitive problems.

Figure 9.

Power Interest Matrix for Implementation Project.

Once the weight of the criteria was ascertained, we calculated the rank of the seven action plans identified. Based on the responses of seven decision-makers, a combined fuzzy decision matrix was created. Next, this matrix was normalized to get a normalized fuzzy decision matrix. Finally, this matrix was multiplied with the weight vector of the criteria to get a weighted normalized fuzzy decision matrix given in Table 7.

Table 7.

Final Fuzzy Decision Matrix.

Symbol	Ease	Effect	Cost
A1	0.1,0.2,0.2)	(0.1,0.1,0.2)	(0.2,0.2,0.3)
A2	(0.1,0.1,0.2)	(0.1,0.1,0.2)	(0.2,0.3,0.3)
A3	(0.1,0.1,0.2)	0.1,0.1,0.2)	(0.1,0.2,0.3)
A4	(0.1,0.2,0.2)	(0.2,0.4,0.5)	(0.2.0.2,0.3)
A5	(0.1,0.1,0.2)	(0.2,0.4,0.5)	(0.1,0.2,0.3)
A6	(0.1,0.2,0.2)	(0.3,0.4,0.5)	(0.2,0.3,0.3)
A7	(0.1,0.1,0.2)	(0.3,0.4,0.5)	(0.1,0.2,0.3)

From the final fuzzy decision matrix, the FPIS and FNIS vectors were calculated, and the distance from these vectors was ascertained. The results of the Fuzzy-TOPSIS are listed in Table 8, where d*, d^– and CC are the distance from FPIS, distance from FNIS and closeness coefficient, respectively. The action point’s rank was determined based on the closeness coefficient. The result and ranking on the Fuzzy-TOPSIS approach are given in Table 8.

Table 8.

Calculation of Rank for Action Point.

Action Point	d*	d ^–	CC	Rank
A1	0.35	0.12	0.25	5
A2	0.35	0.06	0.14	6
A3	0.41	0.00	0.00	7
A4	0.12	0.35	0.75	2
A5	0.18	0.23	0.57	4
A6	0.00	0.40	1.00	1
A7	0.12	0.29	0.71	3

The result suggests that action points combining nutrition therapy with diabetes education should be implemented first. The next action point to be implemented is multimedia for diabetes education in the waiting area. Of all the points, buying new equipment for the lab to improve the cycle time was the least important.

Conclusion

The study performs a situation analysis using VSM of a diabetes speciality clinic. The study uses tools such as the Pareto chart and SIPOC to understand the cause of customer dissatisfaction and associated processes. Pareto’s analysis suggested that waiting time is a major factor affecting customer satisfaction. The customer categorization suggests a maximum scope of improvement in waiting time for new patients. The study further proposed seven action points to improve waiting time for new patients with diabetes and pre-diabetes. After implementing these action points, VSM was done to find significant changes (approx. 30 per cent) in the cycle time. Similarity value-added cycle time was improved, and non-value-added cycle time was decreased significantly. Stakeholder analysis was performed next, and a strategy for managing major stakeholders was suggested. Finally, the prioritization of action points was done using a two-stage multi-criteria decision analysis approach. This study found that combining nutrition therapy with diabetes education was the most important, while buying new equipment for the lab to improve the cycle time was the least important. This study has one implication each for theory and practice. For theory, it identifies various action points for improving the waiting time in complex healthcare delivery such as diabetes care. Second, it identifies criteria for evaluating these action points. For practice, this study provides a comprehensive approach to value mapping and value unlocking in healthcare delivery. The findings of this study are useful for healthcare administrators and health policymakers in developing countries such as India. The findings are especially helpful for healthcare administrators and health policymakers facing the problem of high waiting times in chronic care delivery such as diabetes.

Limitations and Future Directions

Although this study provides a framework for value maximization in diabetes care, its findings should be generalized to other healthcare organizations with caution. Healthcare organizations, particularly specialized clinics, differ significantly in their procedures, patient populations and care delivery models. These variations mean that the strategies effective in one setting may not be directly applicable or as effective in another without adaptation. For instance, a diabetes care model optimized for a large urban hospital may not yield the same results in a rural clinic with fewer resources and different patient demographics.

Future studies can employ simulation techniques to analyze the potential effects of implementing action points before execution. Organizations can develop digital twins for policy simulation using system dynamics modelling or agent-based simulation techniques.

Footnotes

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The author received no financial support for the research, authorship and/or publication of this article.

ORCID iD

Vinaytosh Mishra

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Reducing Waiting Times in Diabetes Care: A Value Stream Mapping and OPA-Fuzzy-TOPSIS Analysis

Abstract

Keywords

Introduction

Methodology

Value Stream Mapping

Stakeholder Analysis

Characteristics of the Focus Group for the Study.

Approach for Delphi Method Used in the Study.

Prioritization of Action Plan

Fuzzy Logic

A Triangular Fuzzy Number n n.

Fuzzy-TOPSIS

Linguistic Variables for the Rating.

Criteria Used and Categories.

Results and Discussion

Case: Diabetes Speciality Clinic

Pareto Chart for the Factors Affecting Customer Satisfaction.

Gap Analysis for Different Service Categories in Case Organization.

Initial Response Matrix for Fuzzy-TOPSIS.

Summary of the Improvement Post Implementation of Action Points.

Value Stream Mapping Post Implementation of Action Points.

Results of the Stakeholder Analysis for the Implementation Project.

Power Interest Matrix for Implementation Project.

Final Fuzzy Decision Matrix.

Calculation of Rank for Action Point.

Conclusion

Limitations and Future Directions

Footnotes

Declaration of Conflicting Interests

Funding

ORCID iD

References

A Triangular Fuzzy Number $n$ n.