In rural regions with a low population density, distances to health care providers as well as insufficient public transport may be barriers for the accessibility of health care. In this analysis it was examined whether the accessibility of gynecologists and GPs, measured as travel time both by car and public transport has an influence on the utilization of health care in the rural region of Western Pomerania in Northern Germany.

Background: The accessibility of medical care facilities in sparsely populated rural regions is relevant especially for elderly people which often represent a large segment of the population in such regions. Elderly people have higher morbidity risks and a higher demand for medical care. Although travelling with private cars is the dominating traffic mode in rural regions, accessibility by public transport is increasingly important especially because of limited mobility of elderly people. The aim of this study was to determine accessibility both by car and public transport to general practitioners ( GP) and selected specialist physicians for a whole region and to detect areas with poor to no access in the county Vorpommern-Greifswald, which is a rural and sparsely populated region in the very northeast of Germany.Methods: Accessibility of medical care facilities by car was calculated on the basis of a network analysis within a geographic information system ( GIS) with routable street data. Accessibility by public transport was calculated using GIS and a network analysis based on the implementation of Dijkstra's algorithm.Results: The travelling time to general practitioners ( GP) by car in the study region ranges from 0.1 to 22.9 min. This is a significant difference compared to other physician groups. Traveling times to specialist physicians are 0.4 to 42.9 min. A minority of 80 % of the inhabitants reach the specialist physicians within 20 min. The accessibility of specialist physicians by public transport is poor. The travel time ( round trip) to GPs averages 99.3 min, to internists 143.0, to ophthalmologists 129.3 and to urologists 159.9 min. These differences were significant. Assumed was a one hour appointment on a Tuesday at 11 am. 8,973 inhabitants ( 3.8 %) have no connection to a GP by public transport. 15,455 inhabitants ( 6.5 %) have no connection to specialist internists.Conclusions: Good accessibility by public transport is not a question of distance but of transport connections. GIS analyses can detect areas with imminent or manifest deficits in the accessibility of health care providers. Accessibility analyses should be established instruments in planning issues.

ABSTRACT Background: A computerized Intervention-Management-System (IMS) has been developed and implemented to facilitate dementia care management. IMS is a rule-based expert decision support system that matches individual patient characteristics to a computerized knowledge base. One of the most important functionalities of IMS is to support the compilation of the individual intervention plan by systematically identifying unmet needs and suggesting the corresponding specific interventions for recommendation to the general practitioner (GP). The present analysis aimed to determine if the implementation of IMS improves the identification of unmet needs and the recommendation of adequate specific interventions. In addition, the feasibility and acceptability of the IMS were evaluated. Methods: Delphi-MV is an on-going GP-based, cluster-randomized, controlled intervention trial to implement and evaluate a collaborative dementia care management program for community-dwelling PWDs and their caregivers. IMS was developed and implemented over the course of the DelpHi-trial. The identified unmet needs and the interventions that were recommended to the GP before and after the implementation of IMS were compared. To evaluate the feasibility and acceptability of the IMS, a survey was conducted among the current users of IMS. Results and Conclusions: After the implementation of IMS, the number of specific interventions recommended to the GP increased by 85%. Our findings provide evidence that IMS improves the systematic identification of unmet needs and the subsequent recommendation of interventions to address these needs. The users evaluated IMS as very helpful and would like to use it for their future work. However, the usability could be further improved.

A Central Data Management (CDM) system based on electronic data capture (EDC) software and study specific databases is an essential component for assessment and management of large data volumes in epidemiologic studies. Conventional CDM systems using web applications for data capture depend on permanent access to a network. However, in many study settings permanent network access cannot be guaranteed, e.g. when participants/patients are visited in their homes. In such cases a different concept for data capture is needed. The utilized EDC software must be able to ensure data capture as stand-alone instance and to synchronize captured data with the server at a later point in time. This article describes the design of the mobile information capture (MInCa) system an EDC software meeting these requirements. In particular, we focus on client and server design, data synchronization, and data privacy as well as data security measures. The MInCa software has already proven its efficiency in epidemiologic studies revealing strengths and weaknesses concerning both concept and practical application which will be addressed in this article.

This article describes the concept of a "Central Data Management" (CDM) and its implementation within the large-scale population-based medical research project "Personalized Medicine". The CDM can be summarized as a conjunction of data capturing, data integration, data storage, data refinement, and data transfer. A wide spectrum of reliable "Extract Transform Load" (ETL) software for automatic integration of data as well as "electronic Case Report Forms" (eCRFs) was developed, in order to integrate decentralized and heterogeneously captured data. Due to the high sensitivity of the captured data, high system resource availability, data privacy, data security and quality assurance are of utmost importance. A complex data model was developed and implemented using an Oracle database in high availability cluster mode in order to integrate different types of participant-related data. Intelligent data capturing and storage mechanisms are improving the quality of data. Data privacy is ensured by a multi-layered role/right system for access control and de-identification of identifying data. A well defined backup process prevents data loss. Over the period of one and a half year, the CDM has captured a wide variety of data in the magnitude of approximately 5terabytes without experiencing any critical incidents of system breakdown or loss of data. The aim of this article is to demonstrate one possible way of establishing a Central Data Management in large-scale medical and epidemiological studies.