3 MATERIALS AND METHODS
5.3 Should health behaviours and experiences be included in EHRs in the
It is possible to obtain individual and area-level socioeconomic information from registers and link it with patient EHRs. A researcher can use GIS data and methods and acquire information about built environment and accessibility and link that material with patient EHRs. EHRs enable one to use a lot of data, but not everything is possible. What is currently lacking in registers are variables related to patient’s lifestyles, health behaviours and experiences with his or her health and health care.
Some information on health behaviours can be obtained from EHRs, such as smoking status and information on alcohol use based on Alcohol Use Disorders Identification Test (AUDIT). However, information on physical activity or dietary habits are not collected or recorded in a structural format. From a research point of view, it would be interesting to study the effects of health behaviours on type 2 diabetes care. If the health records are not in a structured format, it can be time consuming to further utilise them, at least from a research point of view. As stated earlier, EHRs include only patients who have been diagnosed with the disease or treated by the health care professional. Thus, even if a structured way of gathering health behaviour information exists, some patients would be missing the information.
In addition, a patient’s experience about the care is not included in EHRs. Notably, the patient experience is positively associated with clinical effectiveness (Doyle et al.
2013). Therefore, it would also be important to assess how the patient experiences the care and reports their well-being and state of health by subjective indicators. An example of these indicators are Patient Reported Outcome Measures (PROM) and Patient Reported Experience Measures (PREM).
To conclude, type 2 diabetes patients and the management of type 2 diabetes care might benefit if EHRs included structured information on the patient’s physical activity, dietary patterns and experiences with care. One way of gathering this kind of information would be using mobile applications designed for diabetes patients that would automatically send the information to the health care systems. Other ways of gathering data might be allowing patients to self-report data on a patient portal that links data to their EHRs (Casey et al. 2016) or GPs could record the information during the clinical visit.
6 CONCLUSIONS
Many studies in the past have concentrated on the risk and development of type 2 diabetes. Less is known about the progression, and management of the disease. It is important to assess the evidence-based quality of care because this factor can have a direct effect on the patient’s health and well-being. In the case of type 2 diabetes, balanced care of the disease prevents complications, comorbidities and reduces costs in health care (Rossi et al. 2011; Zoungas et al. 2012; Keng et al. 2019). In many countries, including Finland, type 2 diabetes care is based on clinical guidelines. Despite the guidelines, in Finland the real outcomes of care at the patient level and in different geographical contexts remain poorly explored. However, increasing deployment and better availability of EHRs have enabled a more straightforward assessment of quality of care of chronic diseases, such as type 2 diabetes.
This study has created a conceptual model that describes the relationship between individual characteristics, socioeconomic factors, built environment characteristics and access to care with the quality of type 2 diabetes care (Figure 3). The model helps to assess the relationships of the socioeconomic and environmental influences in the patient’s residential neighbourhood on type 2 diabetes care. The quality of care was assessed through indicators related to the process of care and treatment outcomes at the individual patient level. First, whether several register-based individual and register- or GIS-based small-area factors in the patient’s neighbourhood were associated with the quality of type 2 diabetes care was analysed empirically (articles I–IV). Second, how various geospatial scales or areal classifications can be utilised to demonstrate the spatial distribution of type 2 diabetes prevalence and quality of type 2 diabetes care was evaluated. Finally, this thesis discussed how the information about factors associated with the quality of care, and using different geospatial areal classifications, can be utilised in the management of type 2 diabetes care and health care service planning.
The process of care and treatment outcomes are two aspects of studying the quality of type 2 diabetes care. The third aspect is the cost-effectiveness of care (see Figure 3).
For example, the achievement of diabetes care targets leads to lower costs for the treatment of diabetes complications (Keng et al. 2019). What is the geospatial variability of the costs to achieving the quality of care according to treatment guidelines and targets? Is there variation and what sort of variation in healthcare costs across health care units? Do certain patients in certain areas cost more and why? These aspects were not covered in my thesis, and this research stream might be the natural way to proceed in the future.
The study design in this thesis is cross-sectional. In the future, a longitudinal study design should be considered in order to study how exposures throughout the life-course may influence health outcomes as well as the quality of care. Where did the type 2 diabetes patients live earlier in life? What was the SES of the individual, family or neighbourhood earlier in life? Is there a correlation between childhood SES and treatment outcomes in adulthood? Bilal and colleagues (2018a) suggest that future studies of neighbourhood characteristics and diabetes to measure and evaluate changes in neighbourhood characteristics. Previous research revealed that socioeconomic conditions in the place of residence during childhood are associated with health outcomes later in life (Curtis et al. 2004; Monden et al. 2006). Derks and
colleagues (2017) found that socioeconomic inequalities in early life are associated with diabetes-related outcomes in adulthood. The temporal dimension is one aspect in longitudinal analysis and the other one is residential mobility. The neighbourhood around an individual’s dwelling is not the only important spatial context for interactions (Kwan 2009; van Ham & Manley 2012). Focusing only on residential neighbourhoods at a certain time can introduce uncertainty into the research results (Park & Kwan 2017). Equally important might be the places for leisure, work and places people travel through during daily routines (van Ham & Manley 2012; Kwan 2018; Mennis
& Yoo 2018). However, the average age for type 2 diabetes patients is nearly 70; thus, places like worksites, would not be as relevant to study. Then again, this thesis is a register-based study, and it was not possible to obtain information about where people spend time. In addition, it was not possible to obtain a patient’s residential history from EHRs. The temporal dimension and residential mobility should be considered in future studies.
The findings of this thesis increase the understanding about the complex setting and various factors that can be related to the quality of type 2 diabetes care. The research gap between the evidence and treatment is narrowed by utilising and linking EHRs of all diagnosed type 2 diabetes patients with geospatial and other register-based data (see Figures 1 and 3) from the study region. First, area-level socioeconomic factors at the postal code level, and in the patient’s immediate neighbourhood, are associated with treatment outcomes among type 2 diabetes patients. The results indicate that valid postal code area based socioeconomic variables, such as education, provide a useful way to predict the treatment outcomes rather than using individual based socioeconomic factors. Second, when exploring urban-rural inequalities in the quality of care, a more refined settlement type classification better reveals the differences compared with a simple urban-rural dichotomy. The results also show that accessibility measured as travel distance from patient’s home to the health care centre is not a barrier to balanced type 2 diabetes care. Third, the patient’s individual characteristics, such as age and gender, are factors that relate to the quality of care. In addition to factors that are currently available in structural format in the EHRs, information related to the patient’s lifestyle and health behaviours would enhance the research on type 2 diabetes care. Fourth, EHRs of the type 2 diabetes patients—including spatial reference—have enabled the exploration of geospatial variation in the quality of care at various areal classifications (municipality, postal code area, urban-rural settlement type classification and 2 km x 2 km grids). These above mentioned findings might be useful in decision making to identify small areas or settlement types where the disease burden is high and to see whether the care is being performed according to clinical guidelines. With this information, the management of type 2 diabetes care could be more effectively tailored and improved to small areas, sub-regions and settlement types in most need and to socioeconomic groups at risk.
The findings of this thesis reveal two main points. First, when various geospatial areal classifications are utilised, it is possible to explore broadly or in more detail the disease burden and where the care is according to treatment guidelines. Varying areal classifications could be used for different purposes to support decision making when planning and managing type 2 diabetes care. Second, when combining EHRs with register- and GIS- based data, it enables the possibility to target more effective disease management to certain areas with certain population structure, such as to areas with old or low educated people. This thesis provides valuable information about the primary health care quality in the treatment of type 2 diabetes for the entire
health care district of Siun sote, in North Karelia, Finland. Similar approaches that link EHRs with register and geospatial data could be used for other diseases and regions.
The use of EHRs of type 2 diabetes patients or patients suffering from other chronic diseases and combining it with contextual geospatial data and areal classifications should be used more in the evaluation of chronic disease management and disease monitoring.
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