Identifying Associations Between Health Services Operational Factors and Health Experience for Patients With Type 2 Diabetes in Iran

doi:10.21203/rs.3.rs-389328/v1

Download PDF

Research Article

Identifying Associations Between Health Services Operational Factors and Health Experience for Patients With Type 2 Diabetes in Iran

https://doi.org/10.21203/rs.3.rs-389328/v1

This work is licensed under a CC BY 4.0 License

You are reading this latest preprint version

Background. Facing limited health resources, healthcare providers need to rely on health service delivery models that produce the best clinical outcomes and patient experience. We aimed to contribute to developing a patient experience-based type 2 diabetes service delivery model by identifying operational structures and processes of care that were associated with clinical outcome, health experience, and service experience.

Methods. We conducted a cross-sectional survey of type 2 diabetes patients between Jan 2019 to Feb 2020. Having adjusted for demand variables, we examined relationships between independent variables (behaviors, services/processes, and structures) and three categories of dependent variables; clinical outcomes (HbA1c and fasting blood glucose), health experience (EuroQol quality of life (EQ-5D), evaluation of quality of life (visual analgene scale of EQ-5D), and satisfaction with overall health status), and service experience (evaluation of diabetes services in comparison with worst and best imaginable diabetes services and satisfaction with diabetes services). We analysed data using multivariate linear regression models.

Results. After adjusting for demand variables; structures, diabetes-specific health behaviours, and processes explained up to 22%, 12%, and 9% of variance in the outcomes, respectively. Based on significant associations between the diabetes service operations and outcomes, the components of an experience-based service delivery model included the structural elements (continuity of care, redistribution of task to low-cost resources, and improved access to provider), behaviours (improved patient awareness and adherence), and process elements (reduced variation in service utilization, increased responsiveness, caring, comprehensiveness of care, and shared decision-making).

Conclusions. Based on the extent of explained variance and identified significant variables, health services operational factors that determine patient reported outcomes for patients with type 2 diabetes in Iran were identified, which focus on improving continuity of care and access to providers at the first place, improving adherence to care at the second, and various operational process variables at the third place.

Health Economics & Outcomes Research

Health Policy

Type 2 diabetes

quality of life

satisfaction with healthcare

satisfaction with health status

patient experience

healthcare structure

healthcare process

health services operations

Diabetes is amongst the leading causes of morbidities and mortality around the world. The prevalence of diabetes amounted to 451 million in 2017 globally, of which 90% were Type 2 Diabetes (T2D). The number of adults aged 18 years and older living with diabetes is expected to increase to 693 million by 2045 [1]. The proportion of Iranian adults aged 25–70 years who were living with diabetes mellitus (DM) was 11.4% between 2007 and 2011 [2]. It is estimated that 85.5% of patients with DM have T2D in Iran [3].

A review study showed that the healthcare delivery system is struggling to improve the operations management of health services for patients with T2D in Iran [4]. Two major shortcomings hinder improving operations management of T2D services. Firstly, most evaluation studies examine single dimensions of T2D services in Iran. Through this type of studies, defect operations management of health services for patients with T2D deficits was recognized in terms of access to care, inefficient use of human resources, disorganized care processes, lack of continuity of care, and so forth [5, 6]. However, no study evaluates an operational model of T2D services. We define an operational model as health services activities and services that use health structures and resources to meet patient demands for better health outcomes [7, 8]. Therefore, there is an urgent need for research that examines multiple dimensions of services as included in an operational models of health services for patient with type 2 diabetes. This will give insight into various dimensions of care at the same.

Secondly, since there is no permanent cure for patients with T2D, it is of prominent importance that health services for this group of patients effectively maintain clinical states and quality of life and pay attentions to patient experience with services [9]. In this vein, health authorities call for patient-centred care (PCC) models [10] that aspire to build healthcare operations around patient needs, preferences, and expectations [11]. Yet, as a widely known phenomenon, service delivery models are driven by administrators and medical professionals in a top-down fashion without engaging patients in designing care processes. A review study showed PCC mission is not fully accomplished even in developed countries [12]. In Iran as a developing country, due to the lack of holistic understanding of patient needs and preferences, patient experience with care remains completely untouched.

We aimed to examine relationships between operational factors and health experience and service experience of the patients with T2D in Iran. This research identifies the elements of healthcare operational structures and processes that are associated with patient report outcomes in terms of clinical outcomes (HbA1c and fasting blood glucose), health states (EuroQol quality of life (EQ-5D), evaluation of quality of life (visual analgene scale of EQ-5D), satisfaction with health status, and service experience (evaluation of diabetes services and satisfaction with diabetes services). Our study allows an experience-based type 2 diabetes service delivery model to emerge from the empirical analysis of clinical outcomes and patient experience.

Health services operational models provide methods to identify the elements of services activities that are associated with patient experience [13]. Health services operational model is defined as a simplified description of healthcare processes that use structures to improve the patient outcomes and respond to patient demands [7]. The main elements of operational model comprise demands, processes (service activities, service use, costs, and quality), structures (human resources, access, and equipment), and patient behaviours [8, 14] to improve outcomes and patient experience (Fig. 1).

Study design, setting, and participants

We conducted a cross-sectional survey of health service operations and seven outcome variables among patients with type 2 diabetes. We conducted the survey in four healthcare facilities that are affiliated with the Iranian Ministry of Health and Medical Education (MoHME), Social Security Organization, and private physician office. Sampling units consisted of healthcare facilities that provide outpatient services to patients with type 2 diabetes. We only included patients with T2D that were not complicated. Therefore, patients who regularly need to be hospitalized were excluded. Our methods were carried out in accordance with guidelines for reporting observational studies (Strengthening the reporting of observational studies in epidemiology (STROBE) statement) [15].

Variables

Outcomes

Outcomes measures consisted out of clinical outcomes, health experience, and service experience (Table 1). Clinical outcomes were measured through HbA1c (mmol/mol) and fasting blood glucose (FBS) level (mg/dl) [16]. In one of studied clinics these were taken from patient records. In three clinics, both outcomes were self-reported by patients.

Table 1

Components, subcomponents, and variables of a service delivery model
Main components	Subcomponents	Variables	Definition
Outcomes	Clinical outcomes	Glycated haemoglobin (HbA1c)	HbA1c is a biomarker for diagnosis and prognosis of diabetes. It is also correlated with development of diabetes-related comorbidities such as cardiovascular problems.
		Fasting blood glucose (FBS) level	Fasting blood glucose (FBS) level is used for diagnosis and prognosis of diabetes. It is also a reliable indicator for assessing self-care status of patients with diabetes.
	Health experience	Perceived quality of life	Perception of quality of life was measured through EuroQol EQ-5D-5L in terms of five dimensions: mobility, self-care, usual activity, pain/discomfort, and anxiety/depression.
		Evaluation of health status	Health status evaluation refers to comparing health status to the best and worst imaginable health status.
		Satisfaction with health status	Satisfaction with health status refers to judgement made about overall health status based on patient’s interval values.
	Service experience	Evaluation of health services	The evaluation of diabetes services refers to judgment made about the overall quality of diabetes services by comparing the services with worst and best imaginable diabetes services.
		Satisfaction with type 2 diabetes services	Satisfaction with services refers to judgment made about overall diabetes service quality based on patient’s interval values.
Operational structures		Human resource models	Human resource model refers to the main type of healthcare providers that provide diabetes services. Human resource models are categorized based on the type of medical professionals e.g. nurse, GP, and specialized medical doctor. Resource models are developed by quantifying number of visits and time spent by medical professionals for patient care.
		Equipment	This refers to if equipment used to treat the patients are up to date and modern.
		Continuity of care	This refers to whether patients have a regular medical professional such as GP or specialist (continuity) or patients are seen by different provider for every visit (no continuity).
		Access	This refers to perceived overall access to care provider.
Operational processes		Comprehensiveness of consultation	Comprehensiveness of consultation determines if all diabetes-related questions of the patient were answered during the consultation visit.
		Shared decision making	Shared decision making refers to involving patients in making decision for their care.
		Consistency of treatment plans	Consistency of treatment plans refers to the situation that if providers involved in care of the patient provided similar advices and recommendations.
		Perceived service quality	Perceived service quality refers to responsiveness of providers, timeliness, caring providers, politeness of providers, and communication between the patients and providers [17]. Responsiveness of providers referred to process quality and measured whether providers promptly respond to patient demands. Timeliness measured a degree to which provider delivered services in the planned time.

Experience is defined as mental reactions to health status and health service that can be laid down by service users through perception or evaluation. Perception refers to representation of health status or health service, and evaluation refers to judgment made about health status or health service. Evaluation comprised of two classes; firstly, judging health status or health services to reference objects and secondly, directly assigning internal values or feeling to health status and healthcare [18, 19]. A reference object could be a best and worst conceivable state or quality of health services.

Health experience comprised of the perception of quality of life and two classes of health status evaluation (visual analgene scale (VAS) of quality of life and satisfaction with health status). Perception of quality of life was measured through EuroQol EQ-5D-5L in terms of five dimensions: mobility, self-care, usual activity, pain/discomfort, and anxiety/depression. On each dimension, valid responses had five options from no problem to severe problem. The individual’s utility score of EQ-5D was calculated from all five dimensions using the index developed for the Iranian population [20]. The VAS of quality of life, as the first class of health status evaluation was examined by comparing health status to the best and worst imaginable health status on a scale from 0 to 100 for death and full health, respectively. Satisfaction with health status, as the second class of health status evaluation, was measured through a single question in a Likert scale from completely dissatisfied to completely satisfied. It was then standardized between 0 and 100.

Service experience comprised of evaluation of diabetes services and satisfaction with diabetes services. The evaluation of diabetes services was conducted in comparison to worst and best imaginable diabetes services ranging between 0 as the worst and 100 as the best diabetes services, respectively. Satisfaction with services refers to satisfaction with whole diabetes services that patient receives from providers. It was measured through a single question in a Likert scale from completely dissatisfied to completely satisfied. It was also standardized between 0 and 100.

Demand variables

We described demands according to the demographic and socioeconomic indicators and diabetes stage. Demographic and socioeconomic indicators included age, gender, and education. Diabetes stages refer to two variables; whether the patient is dependent on medications or medications and/or insulin and whether patient has at least one of these diabetes comorbidities; problem with heart, eyes, kidney, feet, and hypertension.

Patient behaviours

Behaviour was defined as generic health behaviour and the disease-specific behaviour. The generic health behaviour considers physical activity and smoking. Physical activity was defined in terms of metabolic equivalents (METs) and was sufficient if participant had physical activity more than 500 METs per week (which equates to 150 minutes of moderate or vigorous activity per week), otherwise it was insufficient [21]. In terms of smoking, we categorized participants into these three groups: current smokers, former smokers, or non-smokers.

Diabetes-specific behaviours refer to patient adherence to diabetes-specific treatment and the use of glucometer by the diabetes patients. Diabetes-specific treatment regards treatment recommendations in terms of adherence to diet, taking medication, and insulin injection.

Structure of diabetes care

Structure contained four factors; human resources, access to provider, continuity of care, and the status of equipment used to treat diabetes patients [14]. Resources refer to types of human resources that were employed to provide health services for the diabetes patients. Access to care measured the perceived access to care for patients in the Likert scale. Continuity of care was measured through a question ‘are you visited by a new doctor in every visit?’. If patient answered ‘No’ to this question, diabetes care had continuity, otherwise there was no continuity of care. Status of equipment was measured in terms of being up-to-date and modern.

Processes of diabetes care

Diabetes care processes were measured through the comprehensiveness of consultation, shared decision making, the consistency of treatment plans [14], responsiveness of providers, timeliness, caring providers, politeness of providers, and communication between the patients and providers [17]. Diabetes service was considered comprehensive if all diabetes-related questions of the patient were answered during a consultation visit. Shared decision making refers to involving patients in making decision for their care plan. Consistency of treatment plans referred to an extent that providers involved in care of the patient, provided similar advices and recommendations. Responsiveness of providers referred to process quality and measured whether providers promptly respond to patient demands. Timeliness measured a degree to which provider delivered services in the planned time.

Instrument and data collection

Instrument used in this research was previously applied to study T2D services in regional healthcare provider network [14, 18, 19]. After translation, the questionnaire was check for validity by a group of experts specialized in internal medicine and diabetes treatment. The questionnaire was significantly adapted to the local condition of study settings. We conducted surveys between January 2019 to February 2020. Data collection was performed in person by trained interviewers.

Analysis methods

Developing service delivery model for patients with type 2 diabetes relied on analysis of association between the outcomes and operational variables. We examined associations using multivariate linear regression models for continuous outcomes. We did not build a single construct for outcome by combining all outcomes. Per outcome, we developed six regression models to determine the variables that explain the outcome. We assume that associations and variables that contributes to the outcomes, altogether, form our service delivery model. All analyses of outcomes were integrated in the Results and Discussion sections to allow developing an experience-based service delivery model.

The demographic factors were controlled in the first model. The second model controlled the main effect of the variables for demographic factors and diabetes stages. Third model included demographic factors, diabetes stages, and the general health behaviours. The fourth model had the variables of the third model and added the diabetes-specific behaviours to the regression model. The fifth had the variables of the fourth model and added variables for the structure of diabetes care to the regression model. And sixth model contained variables of the fifth model and added the variables of care processes. The value of R² was used to determine the contribution of each component of the operational model to clinical outcomes and the patient experience measures. Difference between R² of the statistical models in ordinal order shows the contribution of each component to the outcomes. To identify variables of the subcomponents that constitute our service delivery model, we reported the sixth regression model of all seven outcomes. We reported β and P values in the manuscript and reported more details of regression analyses including confidence interval of β in a supplementary file.

Data were collected from healthcare settings that are located in the urban areas and treat type 2 diabetes patients. Overall, 521 questionnaires were administered. With 94% response rate, diabetes patients returned 492 questionnaires of which 486 questionnaires were used in the analysis.

The mean age of participants was 61 years (Table 2). A large percentage of participants were females. Only a small percentage of participants (11.3%) had university education. Most participants were dependent on medications (64.6%). Regarding comorbidities, 42.6% of participants reported no comorbidity.

Table 2

Descriptive characteristics of study participants
Variable		Mean	n
Age		61 (51–71)	478
		Proportion
Gender	Male	70.7 %	340
	Female	29.3%	141
Education	Some high school	62.8%	300
	Completed high school/diploma	25.9%	124
	University education	11.3%	54
Stage of diabetes	Stage of type 2 diabetes treated by diet & medication	64.6%	306
	Stage of type 2 diabetes treated by diet & medication & insulin injection	35.4%	168
Comorbidity	No comorbidity	42.6%	206
	At least one comorbidity	57.4%	278

Model predictions

The contributions of model components to the seven outcomes are given in Table 3. Age, gender, and education altogether explained the largest percentage of variation in quality of life. The diabetes stage, with two variables dependency on medication or insulin injection and comorbidity, had the largest contribution to clinical outcomes HbA1c level. General health behaviours comprise much of explained variance in HbA1c level. Diabetes-specific health behaviour explained up to 12% of variance in the outcomes with the largest share for satisfaction with health services. Structure of diabetes care explained 3–22% of variance in the outcomes. It has the largest contribution to explaining service experience. The process of diabetes care explained up to 9% of variance in the outcomes with the largest contribution to the evaluation of diabetes services.

Table 3

Extent of explained variance in outcomes of type 2 diabetes services
	Statistical	Demographic factors (1)	1 + Diabetes stages (2)	2 + General health behaviours (3)	3 + Diabetes-specific health behaviours (4)	4 + Diabetes care structure (5)	5 + Diabetes care processes
HbA1c level	R square	2%	12%	18%	20%	25%	30%
	p value	0.17	p < .001	p < .001	p < .001	p < .001	p < .001
	frequency	254	249	247	232	221	212
Fasting Blood Sugar	R square	0	13%	18%	21%	25%	23%
	p value of F test	0.32	p < .001	p < .001	p < .001	p < .001	p < .001
	number	439	249	247	232	221	352
EuroQol EQ-5D quality of life	R square	12%	18%	21%	23%	28%	29%
	p value of f test	p < .001	p < .001	p < .001	p < .001	p < .001	p < .001
	number	466	455	449	410	386	371
Visual analogue scale of EQ-5D	R square	6%	8%	13%	18%	21%	26%
	p value of f test	p < .001	p < .001	p < .001	p < .001	p < .001	p < .001
	number	459	448	443	404	382	369
Satisfaction with health status	R square	4%	7%	8%	13%	17%	18%
	p value of f test	0.002	p < .001	p < .001	p < .001	p < .001	p < .001
	number	466	455	449	409	384	384
Satisfaction with health services	R square	1%	2%	4%	16%	38%	44%
	p value of F test	0.51	0.27	0.06	p < .001	p < .001	p < .001
	number	457	449	443	403	380	365
Evaluation of health services in comparison with best imaginable services	R square	1%	3%	5%	14%	36%	45%
	p value of F test	0.16	0.02	0.003	p < .001	p < .001	p < .001
	number	455	447	442	402	380	365

The final regression models, which include all components demand, behaviour, structure, and process and their elements, explained 18–45% of variance in the outcomes; the lowest extent of explained outcome with 18% belongs to satisfaction with health status and the largest with 45% belongs to the evaluation of diabetes services in comparison with best and worst imaginable services. Satisfaction with diabetes services with 44% was the second outcome with the highest extent of explained variance.

Diabetes stage

The analyses of relationships between the components of operational model and the outcomes are given in Table 4. Diabetes stage variables were associated with the clinical outcomes. Being dependent on medication and/or insulin was associated with increased HbA1c (β = 1.33, p < .05) as well as increased FBS level (β = 30.67, p < .05). Another variable of diabetes state, comorbidity was associated with worsening outcomes; it was significantly associated with increased FBS level (β = 1.33, p < .05) and decreases in three other health-related outcomes; quality of life (β=-0.05, p < .05), the evaluation of quality of life (β=-5.25, p < .05), and satisfaction with health status (β=-0.34, p < .05).

Table 4

Regression analysis of relationships between the outcomes and demographic, socioeconomic, health behavior, and operational factors among patients with type 2 diabetes ^a
	Variable	HbA1c level	Fasting Blood Sugar	EuroQol EQ-5D quality of life	Visual analogue scale of EQ-5D	Satisfaction with health status	Satisfaction with health services	Evaluation of health services
Demographic and socio-economic factors	Age	0.01	-0.24	-0.00	-0.13	0.23	-0.19	0.06
	Sex
	Female (reference)	1		1		1	1
	Male	-0.26	-10.61	0.08*	5.53*	5.64	1.06	1.64
	Education
	Some years of schooling	1				1	1
	High school diploma	0.46	-6.13	-0.01	4.11	4.78	-3.07	-0.50
	University education	0.42	8.19	0	6.50	1.86	-3.24	-1.66
Diabetes stages	Disease state
	Dependent on medication (ref)	1	1	1	1	1	1	1
	Medication and/or insulin-dependent	1.33*	30.67*	-0.03	-2.67	-3.01	-1.83	-0.25
	Chronic comorbidity
	Having no other chronic comorbidity (ref)	1	1	1	1	1	1	1
	Having at least one other chronic comorbidity	-0.23	13.87*	-0.05*	-5.25*	-8.42*	0.37	-1.06
General health behaviours	Physical exercises
	Physical exercises < 500 Metabolic Equivalents (METs) per week	1	1	1	1	1	1	1
	Physical exercises > = 500 Metabolic Equivalents (METs) per week	-0.07	-17.71*	0.02	11.33*	7.23*	-0.04	-1.38
	Smoking
	Non-smoker	1				1	1	1
	Former smoker	0.23	-0.29	-0.09*	-0.33	2.35	-0.61	5.31
	Current smoker	1.58*	13.14	-0.05	-4.28	-3.08*	-8.98	-6.04
Diabetes-specific health behaviours	Adherence to treatment (diet, medication, and/or insulin injection)	-0.33*	-10.03*	0.02*	3.64*	3.63*	3.31*	2.81*
	Use of glucometer
	Several times per day	1	1	1	1	1	1	1
	Once per day	1.23	-7.29	0.02	-4.10	-8.58	1.78	5.25
	Once per some days	1.46*	16.34	0.03	-7.93	-8.46	4.34	4.34
	Once per some weeks	1.12*	9.94	0.02	-4.18	-3.28	3.92	6.10
	No use of glucometer	1.2	7.40	0.03	-10.72*	-10.87	3.33	4.20
Diabetes care structures	Human resource model
	Only family physician or general practitioner	1	1	1	1	1	1	1
	Family physician or general practitioner & specialist physician	0.92	12.21	-0.01	-0.23	-8.05	5.59	2.62
	Only specialist physician	0.5	3.18	-0.02	-2.76	-9.03	3.54	3.28
	Access to diabetes services	-0.09	-5.40	0.02*	0.64	1.42	5.50*	1.65
	Continuity of care
	Being visited by a same doctor in every visit	1	1	1	1	1	1	1
	Being visited by new doctor in every visit	0.87*	28.21*	-0.01	-1.00	0.14	0.19	-3.73
	Up to date equipment for diabetes care	0.60*	16.18*	-0.02	-3.45	-2.72	3.18	3.02
Diabetes care processes	Comprehensiveness of medical consultation	-0.51	-4.28	0.01	2.58	1.88	4.83*	1.95
	Patient involvement in care decision	0.77*	1.53	0	0.38	-1.62	-2.13	2.27
	Consistency of treatment medical plans and advices	-0.2	2.25	0.01	3.14*	1.90	-1.08	1.17
	Responsiveness of providers	-0.06	-4.71	0	2.75	3.89	3.96*	3.94*
	Timeliness of provider	0.08	7.59	-0.01	-1.45	-0.56	-1.38	1.53
	Caring provider	0.27	-16.67*	0	-1.57	0.28	6.61*	6.59*
	Politeness of care provider	-0.15	1.63	0.02	6.59*	0.43	1.00	0.44
	Communication between patient and provider	-0.27	4.82	-0.01	-4.90*	-1.01	-2.72	-3.80
Notes: ^a Unstandardized coefficients (R2) of variables in relationship with type 2 diabetes care outcomes
* p-value < .05

Health behaviours

Health behaviours, both general and diabetes-specific behaviours, were associated with the outcomes. Physical activity was associated with decreased FBS level (β=-17.71, p < .05) and improved evaluation of health status (β = 11.33, p < .05), and satisfaction with health (β = 0.29, p < .05). Smoking was also associated with HbA1c level, quality of life, and satisfaction with health services. Current smoking was associated with increased level of HbA1c (β = 1.58, p < .05).

Adherence to treatment was significantly associated with all outcomes; it was associated with decreased level of HbA1c (β=-0.33, p < .05) and FBS level (β=-10.03, p < .05). On the other hand, it was significantly and directly associated with quality of life (β = 0.02, p < .05), evaluation of quality of life (β = 3.64, p < .05), satisfaction with health, satisfaction with health services, and evaluation of health services.

Decrease in number of uses of glucometer was associated with increased level of HbA1c and decreased level of evaluation of health status after controlling for dependency on insulin. The use of glucometer as ‘once per couple of days’ and ‘once per couple of weeks’ were associated with increased level of HbA1c (β = 1.46 and β = 1.12, p < .05). Patients with no use of glucometer had 11% lower level of the evaluation of health status (β= -10.72, p < .05) compared with patients who use glucometer for a number of times per day.

Structures of care

The variables of care structure included human resource models, access to provider, continuity of care, and state of equipment. Three models of human resource were emerged from the analysis: ‘diabetes care by family physician or general practitioner’, ‘diabetes care by family physician or general practitioner & specialist physician’, and ‘diabetes care by specialist physician only’. No difference was observed between three models of human resources in analysis of all outcomes. Improved perceived access to services was associated with EQ-5D quality of life (β = 0.02, p < .05) and satisfaction with health services (β = 5.52, p < .05). Continuity of care increased HbA1c level (β = 0.87, p < .05) and FBS level (β = 28.2, p < .05). More up to date equipment for diabetes care was associated with increased HbA1c and FBS levels.

Diabetes care processes

Regression analyses showed insignificant relationship between the utilization of routine GP visits, consultation visit by specialized internal medicine doctors, and endocrinologists and health outcomes. The only exception was the relationships between EQ-5D utility score and utilization of endocrinology visit, where by increasing in the number of visits, the utility score decreases (β=-0.02, p < .01). Furthermore, total hours of care (sum of service time spent in all diabetes services) per patient per year was negatively associated with EQ-5D utility score.

With regard to adherence to diabetes care standards, 37% of patients with GPs as their main care giver, reported less than four visits per year, only 8% reported four visits per year, and 55% reported more than four visits per year. Patients who reported medical specialist as their main care giver, 42% reported less than four visits per year, 30% of patients reported just four visits per year, and 28% had more than four visits per year.

Two dimensions of short SERVQUAL instrument ‘responsiveness’ and ‘caring’ were significantly associated with both satisfaction with health services and evaluation of diabetes services. One unit increase in responsiveness was associated with 4% increase in service satisfaction (β = 3.96, p < .05) and evaluation of diabetes services (β = 3.94, p < .05). One unit of caring, measured alike responsiveness, increased service satisfaction and evaluation of diabetes services by 7% (β = 6.61 and β = 6.59 respectively, p < .05). Furthermore, caring was associated with FBS level.

Our empirical analyses identified variables of behaviors, operational structure, and operational processes that were significantly correlated with clinical outcome, health experience, and service experience. These variables could essentially be the ingredients of an experience-based type 2 diabetes service delivery model. The empirical analyses showed total contribution of main components to the outcomes. In comparison between the main components, the structure of care explained a larger extent of variance in the outcomes than other components. In the next rank was the diabetes-specific health behaviours with up to 12% of explained variation in the outcomes. The third rank belongs to process of care with maximum 9% of explained variation in the outcomes.

Given the larger contribution of operational structures to the outcomes we firstly discuss structure variables. Despite the variation in the level of expertise and the tariff schedule for medical professionals, no health outcome varied between three models of human resources. Insignificant correlations between human resource models for type 2 diabetes services and the outcomes support the use of less costly resources i.e., GPs and family doctors instead of more expensive specialized doctors. This insignificant differences between human resources models also shed lights on the efficiency of task shifting from specialized doctors towards GPs or family doctors [22–24]. Previous studies confirm this finding that patient-reported outcomes were similar between T2D services that were provided by GPs compared with services provided by specialists [25]. At regional level, research showed that with a larger role for diabetes GPs, the percentage of good-control diabetes patients increases [14].

Perceived access to provider was also associated with the quality of life and service satisfaction, which confirms already established evidence on the importance of access to provider for patient-centred care models [26]. Our finding regarding the negative association between being treated by a new doctor in every visit and HbA1c and FBS level supports evidence that continuity of care positively affects health outcomes [27].

As far as the efficiency of services is concerned, the number of visits needs to be standardized which is also recommended by diabetes guidelines. We found a large variation in number of routine visits per patient/year, showing overuse and underuse of routine GP or medical specialist visits at the same time. This indicates that care processes are not under control in the studied settings [28]. The average number of routine visits that patient received was higher than 4 visits recommended by clinical guidelines [29]. The inefficiency of diabetes care is evident given the presence of 28–55% overuse of routine visits by the patients. There was a negative relationship between number of visits and quality of life, which essentially implies reverse causality that by decreased quality of life, the use of services increases [14].

The elements of diabetes care processes were mostly associated with service satisfaction and evaluation of diabetes services satisfaction. Comprehensiveness of care increases service satisfaction. Two dimensions of short SERVQUAL instrument ‘responsiveness’ and ‘caring’ were associated with service satisfaction and evaluation of diabetes services. Other dimensions of diabetes service quality were not associated with the outcomes. Caring was also associated with FBS level. Other studies support the association between these dimensions of short SERVQUAL instrument and satisfaction with services [14].

As predicting factors per outcome were identified in this research, specific interventions to change outcomes can be drawn based on our empirical analyses. Structural components that significantly associate with the outcomes comprise increased continuity of care, redistribution of task from internal medicine doctors to GPs or family doctors, and improved access, in terms of facilitated appointment scheduling and shorter travelling time and waiting time in provider offices. Furthermore, facilitating access to self-test at home, such as test kits is a structure component. Process components comprise reduction in variance of service utilization through the standardization of routine visits [30], more specifically tackling unnecessary overuse of services. Process indicators also include improving responsiveness and caring behaviour of providers. In the next rank, we regard improving comprehensiveness of care and patient involvement in care decision making as the subcomponents of care process. Our findings support certain elements of PCC models such as engagement, shared decision-making, holistic focus [11], emotional support, access to care, and continuity of care [31–33].

We controlled demographic and health state variables in our multivariate analysis. Age, sex, and education level were associated with quality of life. We found that two variables stage of diabetes and comorbidity of chronic diseases were associated with health outcomes. Other studies also confirmed that comorbidities and stage of diabetes strongly influence patient outcomes [34]. Our findings showed that comorbidity of other chronic diseases had no association with HbA1c level but had significant association with the quality of life. This imply that even with comorbidities of diabetes, patients can be in good-control diabetes stage.

This study faced several limitations. The study design relied on cross-sectional surveys to measure outcomes and operational variables. The survey design, as being non-experimental design, does not allow discovering causal relationship. We used a non-probabilistic sampling framework, given the limitation of research resources. This threatens external validity of our findings. Given this, study findings may not be generalized to larger populations of diabetes patients or provider settings. However, we may argue that we investigated typical cases with predefined inclusion criteria that preclude major biases. The measurement of clinical outcomes was a concern in this research. We therefore used two clinical outcomes namely the level of HbA1c and FBS. The latter outcome as a clinical outcome is rather unstable outcome measure. To have accurate measure of clinical health state we need to have average values of HbA1c over at least three months. However, we had no access to such data from medical information systems. As a strategy to make our measurement as complete, we used other clinical measures such as comorbidity and stage of diabetes.

Considering several types of outcomes that measure the perception and evaluation of health services, we examined various aspects of patient experience with type 2 diabetes service delivery. By considering several operational factors we strived to guide interventions for improving health services delivery towards a patient-centred service delivery model. The operational factors of a patient-centred care model that significantly support the clinical outcomes and patient experience are as follows. Structural components comprise continuity, redistribution of task to low-cost resources, and improved access. Process components comprise reduction in service utilization variation, responsiveness, caring providers, comprehensiveness of care, and shared decision-making. Overall, the structure variables had a greater contribution to the outcomes. Based on these findings, interventions to improve outcomes can be defined, which firstly embark on variables of structure. In the next place, interventions may focus on disease-specific health behaviors, notably adherence to treatment recommendation.

Type 2 Diabetes (T2D); patient-centred care (PCC); Iranian Ministry of Health and Medical Education (MoHME); Fasting blood glucose (FBS); Glycated haemoglobin (HbA1c).

Ethics approval and consent to participate

This study was granted ethical approval by Iran National Committee for Ethics in Biomedical Research (identification code: IR.BMSU.REC.1397.375). We obtained a written informed consent from all participants of the study.

Consent for publication: Not applicable.

Availability of data and materials

Data that underlie our findings might be made available upon reasonable request. The lead investigator of this research will make data for this manuscript available upon request as possible in compliance with local research ethics board requirements and data sharing agreements.

Competing interests

The authors declare that they have no competing interests.

Funding

This work was supported by Iranian Social Security Research Institute (No. 20961049). The funding agency had no role in the design, conduct, analysis, or interpretation of this study. The viewpoints presented in this study only reflect the views of the investigators.

Authors' contributions

MM is the lead investigator. MM supervised data collection from study sites. MM and MP conducted statistical analysis of data. MM wrote the first draft of the manuscript. ShB and RM critically reviewed the manuscript and contributed to the interpretation of findings. All authors approved the final version of the manuscript.

Acknowledgements

We are grateful to all who helped to conduct this research. We are particularly thankful to Dr. Shahram Ghaffari, Dr. Reza Toyserkanmanesh, and Mr. Abdolvahed Khodamoradi for their support at different stages of this study.

Cho N, Shaw J, Karuranga S, Huang Y, da Rocha Fernandes J, Ohlrogge A, Malanda B: IDF Diabetes Atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045. Diabetes research and clinical practice 2018, 138:271-281.
Esteghamati A, Etemad K, Koohpayehzadeh J, Abbasi M, Meysamie A, Noshad S, Asgari F, Mousavizadeh M, Rafei A, Khajeh E et al: Trends in the prevalence of diabetes and impaired fasting glucose in association with obesity in Iran: 2005–2011. Diabetes Research and Clinical Practice 2014, 103(2):319-327.
Esteghamati A, Larijani B, Aghajani MH, Ghaemi F, Kermanchi J, Shahrami A, Saadat M, Esfahani EN, Ganji M, Noshad S et al: Diabetes in Iran: Prospective Analysis from First Nationwide Diabetes Report of National Program for Prevention and Control of Diabetes (NPPCD-2016). Scientific Reports 2017, 7(1):13461.
Mohseni M, Ghoreishi TS, Houshmandi S, Moosavi A, Azami-Aghdash S, Asgarlou Z: Challenges of managing diabetes in Iran: meta-synthesis of qualitative studies. BMC health services research 2020, 20(1):1-12.
Molayaghobi NS, Abazari P, Taleghani F, Iraj B, Etesampour A, Zarei A, Hashemi H, Abasi F: Overcoming challenges of implementing chronic care model in diabetes management: an action research approach. International journal of preventive medicine 2019, 10.
Valizadeh R, Vali L, Bahaadinbeigy K, Amiresmaili M: The challenges of Iran’s type 2 diabetes prevention and control program. Int J Prev Med 2017, 8:175.
Mahdavi M, Malmström T, van de Klundert J, Elkhuizen S, Vissers J: Generic operational models in health service operations management: A systematic review. Socio-Economic Planning Sciences 2013, 47(4):271-280.
Elkhuizen SG, Vissers JMH, Mahdavi M, van de Klundert JJ: Modeling Patient Journeys for Demand Segments in Chronic Care, With an Illustration to Type 2 Diabetes. Frontiers in Public Health 2020, 8(428).
Rubin RR, Peyrot M: Quality of life and diabetes. Diabetes/metabolism research and reviews 1999, 15(3):205-218.
Grob R: The heart of patient-centered care. Journal of health politics, policy and law 2013, 38(2):457-465.
Håkansson EJ, Holmström IK, Kumlin T, Kaminsky E, Skoglund K, Höglander J, Sundler AJ, Condén E, Summer MM: " Same same or different?" A review of reviews of person-centered and patient-centered care. Patient education and counseling 2019, 102(1):3.
Rathert C, Wyrwich MD, Boren SA: Patient-centered care and outcomes: a systematic review of the literature. Medical Care Research and Review 2013, 70(4):351-379.
Vissers J, Beech R: Health operations management: patient flow logistics in health care: Psychology Press; 2005.
Mahdavi M, Vissers J, Elkhuizen S, Van Dijk M, Vanhala A, Karampli E, Faubel R, Forte P, Coroian E, Van De Klundert J: The relationship between context, structure, and processes with outcomes of 6 regional diabetes networks in Europe. PloS one 2018, 13(2).
Cuschieri S: The STROBE guidelines. Saudi J Anaesth 2019, 13(Suppl 1):S31-S34.
Sherwani SI, Khan HA, Ekhzaimy A, Masood A, Sakharkar MK: Significance of HbA1c test in diagnosis and prognosis of diabetic patients. Biomarker insights 2016, 11:BMI. S38440.
Parasuraman A, Zeithaml VA, Berry LL: Servqual: A multiple-item scale for measuring consumer perc. Journal of retailing 1988, 64(1):12.
Konerding U, Bowen T, Elkhuizen SG, Faubel R, Forte P, Karampli E, Mahdavi M, Malmström T, Pavi E, Torkki P: The impact of travel distance, travel time and waiting time on health-related quality of life of diabetes patients: An investigation in six European countries. Diabetes research and clinical practice 2017, 126:16-24.
Konerding U, Bowen T, Elkhuizen SG, Faubel R, Forte P, Karampli E, Malmström T, Pavi E, Torkki P: Development of a universal short patient satisfaction questionnaire on the basis of SERVQUAL: Psychometric analyses with data of diabetes and stroke patients from six different European countries. PloS one 2019, 14(10).
Ameri H, Safari H, Yousefi M, Goudarzi R, Sofi M: Interim value set for the EQ-5D-5L in Iran using the Crosswalk method. Medical Journal of The Islamic Republic of Iran (MJIRI) 2020, 34(1):828-832.
Colberg SR, Sigal RJ, Yardley JE, Riddell MC, Dunstan DW, Dempsey PC, Horton ES, Castorino K, Tate DF: Physical activity/exercise and diabetes: a position statement of the American Diabetes Association. Diabetes care 2016, 39(11):2065-2079.
Fulton BD, Scheffler RM, Sparkes SP, Auh EY, Vujicic M, Soucat A: Health workforce skill mix and task shifting in low income countries: a review of recent evidence. Human resources for health 2011, 9(1):1-11.
Joshi R, Alim M, Kengne AP, Jan S, Maulik PK, Peiris D, Patel AA: Task shifting for non-communicable disease management in low and middle income countries–a systematic review. PloS one 2014, 9(8):e103754.
McPake B, Mensah K: Task shifting in health care in resource-poor countries. Lancet 2008.
Vrijhoef HJM, Diederiks JPM, Spreeuwenberg C, Wolffenbuttel BHR: Substitution model with central role for nurse specialist is justified in the care for stable type 2 diabetic outpatients. Journal of advanced nursing 2001, 36(4):546-555.
Bergeson SC, Dean JD: A systems approach to patient-centered care. Jama 2006, 296(23):2848-2851.
Wei X, Barnsley J, Zakus D, Cockerill R, Glazier R, Su X: Evaluation of a diabetes management program in China demonstrated association of improved continuity of care with clinical outcomes. Journal of clinical epidemiology 2008, 61(9):932-939.
Thor J, Lundberg J, Ask J, Olsson J, Carli C, Härenstam KP, Brommels M: Application of statistical process control in healthcare improvement: systematic review. Quality and Safety in Health Care 2007, 16(5):387-399.
Rutten G, De Grauw W, Nijpels G, Houweling S, Van de Laar F, Bilo H, Holleman F, Burgers J, Wiersma T, Janssen P: NHG-Standaard Diabetes mellitus type 2 (derde herziening). Huisarts Wet 2013, 56(10):512-525.
Borgermans LA, Goderis G, Ouwens M, Wens J, Heyrman J, Grol RP: Diversity in diabetes care programmes and views on high quality diabetes care: are we in need of a standardized framework?International Journal of Integrated Care 2008, 8.
Kuipers SJ, Cramm JM, Nieboer AP: The importance of patient-centered care and co-creation of care for satisfaction with care and physical and social well-being of patients with multi-morbidity in the primary care setting. BMC health services research 2019, 19(1):13.
Berghout M, van Exel J, Leensvaart L, Cramm JM: Healthcare professionals’ views on patient-centered care in hospitals. BMC health services research 2015, 15(1):385.
Cramm JM, Nieboer AP: Validation of an instrument to assess the delivery of patient-centred care to people with intellectual disabilities as perceived by professionals. BMC health services research 2017, 17(1):472.
Shahraz S, Pittas AG, Lundquist CM, Danaei G, Kent DM: Do patient characteristics impact decisions by clinicians on hemoglobin A1c targets?Diabetes care 2016, 39(9):e145-e146.

No competing interests reported.

Download PDF

Editorial decision: Major revision
06 Jul, 2021
Reviews received at journal
29 Jun, 2021
Reviews received at journal
15 Jun, 2021
Reviewers agreed at journal
05 Jun, 2021
Reviewers agreed at journal
06 May, 2021
Reviewers agreed at journal
06 May, 2021
Reviewers invited by journal
19 Apr, 2021
Editor assigned by journal
19 Apr, 2021
Editor invited by journal
12 Apr, 2021
Submission checks completed at journal
12 Apr, 2021
First submitted to journal
03 Apr, 2021

You are reading this latest preprint version

Identifying Associations Between Health Services Operational Factors and Health Experience for Patients With Type 2 Diabetes in Iran

Status:

Version 1

Abstract

Figures

Introduction

Materials And Methods

Study design, setting, and participants

Variables

Instrument and data collection

Analysis methods

Results

Model predictions

Diabetes stage

Health behaviours

Structures of care

Diabetes care processes

Discussion

Conclusions

Abbreviations

Declarations

References

Additional Declarations

Status:

Version 1