Service quality and accessibility of healthcare facilities: digital healthcare potential in Ho Chi Minh City.

BACKGROUND: Effective delivery of health services requires adequate quality in healthcare facilities and easy accessibility to health services physically or virtually. The purpose of this study was to reveal how the quality of healthcare facilities varies across the different parts of Ho Chi Minh City and how well residents (N = 9 million) can reach healthcare facilities. By demarcating the deficiently served areas of low accessibility, the study shows where urban planning and digital healthcare could improve accessibility to health services and the quality of services efficiently. METHODS: The analysis utilised geocoded information on hospitals, clinics, roads and population and the data of the quality scores of healthcare facilities. Quality scores were analysed by hot spot analysis and inverse distance weighting. Accessibility and formation of travel time-based service areas by travel time distances were calculated using road network, driving speed and population data. RESULTS: The results unveiled a centripetal spatial pattern of healthcare facilities and a similar pattern in their quality. Outside the travel time of 30 min for hospitals and 15 min for clinics, the deficiently served areas have a population of 1.1 to 1.2 million. Based on the results and the evidence of digital healthcare, this paper highlights how to develop and plan spatially effective service provision. Especially, it gives grounds to discuss how cost-effective digital healthcare could be applied to improve the accessibility and quality of health services in an urban structure of extensively varying accessibility to health services. CONCLUSIONS: The results bring up the need and the means for improving the quality of health services and their cost-efficient availability by location optimisation, road improvements and implementing digital healthcare provided by hospitals and clinics in the city. At the same, this study provides a multidisciplinary approach for planning more equal and efficient health service provision geographically.

Patient-specific and healthcare real-world costs of atrial fibrillation in individuals treated with direct oral anticoagulant agents or warfarin.

BACKGROUND: Anticoagulant therapies are used to prevent atrial fibrillation-related strokes, with warfarin and direct oral anticoagulant (DOAC) the most common. In this study, we incorporate direct health care costs, drug costs, travel costs, and lost working and leisure time costs to estimate the total costs of the two therapies. METHODS: This retrospective study used individual-level patient data from 4000 atrial fibrillation (AF) patients from North Karelia, Finland. Real-world data on healthcare use was obtained from the regional patient information system and data on reimbursed travel costs from the database of the Social Insurance Institution of Finland. The costs of the therapies were estimated between June 2017 and May 2018. Using a Geographical Information System (GIS), we estimated travel time and costs for each journey related to anticoagulant therapies. We ultimately applied therapy and travel costs to a cost model to reflect real-world expenditures. RESULTS: The costs of anticoagulant therapies were calculated from the standpoint of patient and the healthcare service when considering all costs from AF-related healthcare visits, including major complications arising from atrial fibrillation. On average, the annual cost per patient for healthcare in the form of public expenditure was higher when using DOAC therapy than warfarin therapy (average cost = € 927 vs. € 805). Additionally, the average annual cost for patients was also higher with DOAC therapy (average cost = € 406.5 vs. € 296.7). In warfarin therapy, patients had considerable more travel and time costs due the different implementation practices of therapies. CONCLUSION: The results indicated that DOAC therapy had higher costs over warfarin from the perspectives of the patient and healthcare service in the study area on average. Currently, the cost of the DOAC drug is the largest determinator of total therapy costs from both perspectives. Despite slightly higher costs, the patients on DOAC therapy experienced less AF-related complications during the study period.

A geospatial model to determine the spatial cost-efficiency of anticoagulation drug therapy: Patients' perspective.

Most atrial fibrillation (AF) patients need anticoagulation management to reduce the risk of thromboembolic events and stroke. Currently, two major drug therapies are available: warfarin and direct oral anticoagulant (DOAC). This study examined the spatial costs of these therapies and derived the least-cost market areas for both therapies in the study area. The concepts of spatial costs and the principles of forming market areas were used as theoretical starting points, and the patients' travel, time-loss, and medication cost parameters combined with geographical information systems methods were incorporated into the geospatial model. Results showed that for AF patients who live near the international normalized ratio (INR) monitoring sample collection point and have less than 15 annual INR monitoring visits, warfarin therapy resulted in the lowest cost regardless of patient's travel mode and their assumed working or retirement status. If the AF patient needs more frequent INR monitoring visits or lives farther from the nearest sample collection point, DOAC would be the least costly option. The modelled results reveal the variety and importance of patients' cost of time loss and travel costs when a physician selects the appropriate anticoagulation therapy.

Modeling patients' time, travel, and monitoring costs in anticoagulation management: societal savings achievable with the shift from warfarin to direct oral anticoagulants.

BACKGROUND: Anticoagulation therapy is used for atrial fibrillation (AF) patients for reducing the risk of cardioembolic complications such as stroke. The previously recommended anticoagulant, warfarin, has a narrow therapeutic window, and it requires regular laboratory monitoring, unlike direct oral anticoagulants (DOAC). From a societal perspective, it is important to measure time and travel costs associated with warfarin monitoring to better compare the total therapy costs of these two alternative forms of anticoagulation management. In this study we design a georeferenced cost model to investigate societal savings achievable with the shift from warfarin to DOACs in the study region of North Karelia in Eastern Finland. METHODS: Individual-level patient data of 6519 AF patients was obtained from the regional patient database. Patients' geocoded home addresses and other GIS data were used to perform a network analysis for the optimal routes for warfarin monitoring visits. These measures of revealed accessibility were then used in the cost model to measure monetary time and travel costs in addition to direct healthcare costs of anticoagulation management. RESULTS: The share of time and travel costs in warfarin monitoring is 26.6% of the total therapy costs in our study region. With current drug retail prices in Finland, the societal expense of anticoagulation management is only 2.6% higher with DOACs than in the baseline with warfarin. However, when 25% lower distributor's prices are used, the total societal cost decreases by 13.6% with DOACs. CONCLUSIONS: Our results indicate that patients' time and travel costs critically increase the societal cost of warfarin therapy; and despite the higher price of DOACs, they are already cost-efficient alternatives to warfarin in anticoagulation management. In the future, the cost of AF complications should be included in the cost comparison between warfarin and DOACs. Our modeling approach applies to different geographical regions and to different healthcare processes requiring patient monitoring.

The usefulness of small-area-based socioeconomic characteristics in assessing the treatment outcomes of type 2 diabetes patients: a register-based mixed-effect study.

BACKGROUND: Assessment of the differences in the outcomes of care by socioeconomic status (SES) is beneficial for both the efficient targeting of health care services and to decrease health inequalities. This study compares the effects of three patient-based SES predictors (earned income, educational attainment, employment status) with three small-area-based SES predictors (median income, educational attainment, proportion of the unemployed) on the treatment outcomes of type 2 diabetes patients. METHODS: Mixed-effect modeling was applied to analyse how SES factors affect the treatment outcomes of type 2 diabetes patients. The treatment outcomes were assessed by the patients' latest available glycated hemoglobin A1C (HbA1c) value. We used electronic health records of type 2 diabetes patients from the regional electronic patient database, the patients' individual register-based SES information from Statistics Finland, and the SES information about the population of the postal code area of the patients from Statistics Finland. RESULTS: The effects of attained education on the treatment outcomes, both at the patient-level and the small-area-level are quite similar. Age and male gender were associated with higher HbA1c values and lower education indicated higher HbA1c values. Unemployment was not associated with HbA1c values at either the patient-level or the area-level. Income gave divergent results: high values of HbA1c were associated with low patient incomes but the median income of the postal code area did not predict the treatment outcomes of patients. CONCLUSIONS: Our comparative study of three SES factors shows that the effects of attained education on the treatment outcomes are rather similar, regardless of whether patient-based or small-area-based predictors are used. Small-area-based SES variables can be a good way to overcome the absence of individual SES information, but further research is needed to find the valid small-area factors by disease. This possibility of using more small-area-based data would be valuable in health service research and first-hand planning of health care services.

Self-monitoring induced savings on type 2 diabetes patients' travel and healthcare costs.

BACKGROUND: Type 2 diabetes (T2DM) is a major health concern in most regions. In addition to direct healthcare costs, diabetes causes many indirect costs that are often ignored in economic analyses. Patients' travel and time costs associated with the follow-up of T2DM patients have not been previously calculated systematically over an entire healthcare district. The aim of the study was to develop a georeferenced cost model that could be used to measure healthcare accessibility and patient travel and time costs in a sparsely populated healthcare district in Finland. Additionally, the model was used to test whether savings in the total costs of follow-up of T2DM patients are achieved by increasing self-monitoring and implementing electronic feedback practices between healthcare staff and patients. METHODS: Patient data for this study was obtained from the regional electronic patient database Mediatri. A georeferenced cost model of linear equations was developed with ESRI ArcGIS 10.3 software and ModelBuilder tool. The Model utilizes OD Cost Matrix method of network analysis to calculate optimal routes for primary-care follow-up visits. RESULTS: In the study region of North Karelia, the average annual total costs of T2DM follow-up screening of HbA1c (9070 patients) conforming to the national clinical guidelines are 280 EUR/297 USD per patient. Combined travel and time costs are 21 percent of the total costs. Implementing self-monitoring for a half of the follow-up still within the guidelines, the average annual total costs of HbA1c screening could be reduced by 57 percent from 280 EUR/297 USD to 121 EUR/129 USD per patient. CONCLUSIONS: Travel costs related to HbA1c screening of T2DM patients constitute a substantial cost item, the consideration of which in healthcare planning would enable the societal cost-efficiency of T2DM care to be improved. Even more savings in both travel costs and healthcare costs of T2DM can be achieved by utilizing more self-monitoring and electronic feedback practices. Additionally, the cost model composed in the study can be developed and expanded further to address other healthcare processes and patient groups.

Outcomes of Secondary Prevention among Coronary Heart Disease Patients in a High-Risk Region in Finland.

Despite comprehensive national treatment guidelines, goals for secondary prevention of coronary heart disease (CHD) have not been sufficiently met everywhere in Finland. We investigated the recorded risk factor rates of CHD and their spatial differences in North Karelia Hospital District, which has a very high cardiovascular burden, in order to form a general view of the state of secondary prevention in a high-risk region. Appropriate disease codes of CHD-diagnoses and coding for percutaneous coronary intervention (PCI) and coronary artery bypass grafting (CABG) were used to identify from the electronic patient records the patient group eligible for secondary prevention. The cumulative incidence rate of new patients (n = 2556) during 2011-2014 varied from 1.9% to 3.5% between municipalities. The success in secondary prevention of CHD was assessed using achievement of treatment targets as defined in national guidelines. Health centres are administrated by municipalities whereupon the main reporting units were municipalities, together with composed classification of patients by age, gender and dwelling location. Health disparities between municipalities, settlement types and patient groups were found and are interpreted. Moreover, spatial high-risk and low-risk clusters of acute CHD were detected. The proportion of patients achieving the treatment targets of low-density lipoprotein cholesterol (LDL-C) varied from 21% to 38% between municipalities. Variation was also observed in the follow-up of patients; e.g., the rate of follow-up measurements of LDL-C in municipalities varied from 72% to 86%. Spatial variation in patients' sociodemographic and neighbourhood characteristics and morbidity burden partly explain the differences in outcomes, but there are also very likely differences in the care process between municipalities which requires a study in its own right.

Do the classification of areas and distance matter to the assessment results of achieving the treatment targets among type 2 diabetes patients?

BACKGROUND: Type 2 diabetes is a major health concern all over the world. The prevention of diabetes is important but so is well-balanced diabetes care. Diabetes care can be influenced by individual and neighborhood socio-economic factors and geographical accessibility to health care services. The aim of the study is to find out whether two different area classifications of urban and rural areas give different area-level results of achieving the targets of control and treatment among type 2 diabetes patients exemplified by a Finnish region. The study exploits geo-referenced patient data from a regional primary health care patient database combined with postal code area-level socio-economic variables, digital road data and two grid based classifications of areas: an urban-rural dichotomy and a classification with seven area types. METHODS: The achievement of control and treatment targets were assessed using the patient's individual laboratory data among 9606 type 2 diabetes patients. It was assessed whether hemoglobin A1c (HbA1c) was controlled and whether the recommended level of HbA1c was achieved in patients by different area classes and as a function of distance. Chi square test and logistic regression analysis were used for testing. RESULTS: The study reveals that area-level inequalities exist in the care of type 2 diabetes in a detailed 7-class area classification but if the simple dichotomy of urban and rural is applied differences vanish. The patient's gender and age, area-level education and the area class they belonged to were associated with achievements of control and treatment targets. Longer distance to health care services was not a barrier to good achievements of control or treatment targets. CONCLUSIONS: A more detailed grid-based area classification is better for showing spatial differences in the care of type 2 diabetes patients. Inequalities exist but it would be misleading to state that the differences are simply due to urban or rural location or due to distance. From a planning point of view findings suggest that detailed geo-coded patient information could be utilized more in resourcing and targeting the health care services to find the area-level needs of care and to improve the cost-efficient allocation of resources.

Type 2 diabetes care in North Karelia Finland: do area-level socio-economic factors affect processes and outcomes?

AIMS: This research assessed the impact of area-level socio-economic factors on the prevalence and outcomes of type 2 diabetes in North Karelia, Finland. METHODS: All type 2 diabetes patients (n=10,204) were analyzed from the regional electronic patient database during the years 2011 and 2012. The patient's individual laboratory data was used to assess whether hemoglobin A1c (HbA1c) was measured and whether the recommended level of HbA1c <7% (<53 mmol/l) was achieved. The variables describing socio-economic characteristics of postal code areas were retrieved from the database of Statistics Finland. Linear and logistic regression analyses were used to determine associations. RESULTS: HbA1c had been measured in 83% of patients. Over 70% of those with HbA1c measured reached the recommended level of HbA1c. The worse the area-level socio-economic status, the more probably HbA1c was not measured. Achieving the recommended HbA1c level was associated with being female and having a better area-level socio-economic status. The age-adjusted prevalence of type 2 diabetes was not linearly dependent on the socio-economic circumstances of the postal code areas. CONCLUSIONS: This study shows that socio-economic factors at the small area-level are associated with treatment outcomes. The information from the regional electronic patient database linked with area-level socio-economic information could be effectively utilized to improve diabetes care.