Survival or outcome information is important for clinical routine as well as for clinical research and should be collected completely, timely and precisely. This information is relevant for multiple usages including quality control, clinical trials, observational studies and epidemiological registries. However, the local hospital information system (HIS) does not support this documentation and therefore this data has to generated by paper based or spreadsheet methods which can result in redundantly documented data. Therefore we investigated, whether integrating the follow-up documentation of different departments in the HIS and reusing it for survival analysis can enable the physician to obtain survival curves in a timely manner and to avoid redundant documentation.
We analysed the current follow-up process of oncological patients in two departments (urology, haematology) with respect to different documentation forms. We developed a concept for comprehensive survival documentation based on a generic data model and implemented a follow-up form within the HIS of the University Hospital Muenster which is suitable for a secondary use of these data. We designed a query to extract the relevant data from the HIS and implemented Kaplan-Meier plots based on these data. To re-use this data sufficient data quality is needed. We measured completeness of forms with respect to all tumour cases in the clinic and completeness of documented items per form as incomplete information can bias results of the survival analysis.
Based on the form analysis we discovered differences and concordances between both departments. We identified 52 attributes from which 13 were common (e.g. procedures and diagnosis dates) and were used for the generic data model. The electronic follow-up form was integrated in the clinical workflow. Survival data was also retrospectively entered in order to perform survival and quality analyses on a comprehensive data set. Physicians are now able to generate timely Kaplan-Meier plots on current data. We analysed 1029 follow-up forms of 965 patients with survival information between 1992 and 2010. Completeness of forms was 60.2%, completeness of items ranges between 94.3% and 98.5%. Median overall survival time was 16.4 years; median event-free survival time was 7.7 years.
It is feasible to integrate survival information into routine HIS documentation such that Kaplan-Meier plots can be generated directly and in a timely manner.
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"In this regard, the so-called single-source approach attempts to capture data once and reuses it again for different purposes. Theoretical frameworks   have been developed for the secondary use as well as concrete implementations within EHR systems      and data warehouses   . However, the secondary use is not unrestrictedly possible, because EHR data is not always available in a structured manner as needed to facilitate the research process . "
[Show abstract][Hide abstract] ABSTRACT: Objectives
The first objective of this study is to evaluate the impact of integrating a single-source system into the routine patient care documentation workflow with respect to process modifications, data quality and execution times in patient care as well as research documentation. The second one is to evaluate whether it is cost-efficient using a single-source system in in terms of achieved savings in documentation expenditures.
We analyzed the documentation workflow of routine patient care and research documentation in the medical field of pruritus to identify redundant and error-prone process steps. Based on this, we established a novel documentation workflow including the x4 T (exchange for Trials) system to connect hospital information systems with electronic data capture systems for the exchange of study data. To evaluate the workflow modifications, we performed a before/after analysis as well as a time-motion study. Data quality was assessed by measuring completeness, correctness and concordance of previously and newly collected data. A cost-benefit analysis was conducted to estimate the savings using x4 T per collected data element and the additional costs for introducing x4 T.
The documentation workflow of patient care as well as clinical research was modified due to the introduction of the x4 T system. After x4 T implementation and workflow modifications, half of the redundant and error-prone process steps were eliminated. The generic x4 T system allows direct transfer of routinely collected health care data into the x4 T research database and avoids manual transcription steps. Since x4 T has been introduced in March 2012, the number of included patients has increased by about 1,000 per year. The average entire documentation time per patient visit has been significantly decreased by 70.1% (from 1,116 ± 185 to 334 ± 83 sec.). After the introduction of the x4 T system and associated workflow changes, the completeness of mandatory data elements raised from 82.2% to 100%. In case of the pruritus research study, the additional costs for introducing the x4 T system are €434.01 and the savings are 0.48ct per collected data element. So, with the assumption of a 5-year runtime and 82 collected data elements per patient, the amount of documented patients has to be higher than 1,102 to create a benefit.
Introduction of the x4 T system into the clinical and research documentation workflow can optimize the data collection workflow in both areas. Redundant and cumbersome process steps can be eliminated in the research documentation, with the result of reduced documentation times as well as increased data quality. The usage of the x4 T system is especially worthwhile in a study with a large amount of collected data or a high number of included patients.
International Journal of Medical Informatics 08/2014; DOI:10.1016/j.ijmedinf.2014.08.007 · 2.00 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Semantic interoperability between routine healthcare and clinical research is an unsolved issue, as information systems in the healthcare domain still use proprietary and site-specific data models. However, information exchange and data harmonization are essential for physicians and scientists if they want to collect and analyze data from different hospitals in order to build up registries and perform multicenter clinical trials. Consequently, there is a need for a standardized metadata exchange based on common data models. Currently this is mainly done by informatics experts instead of medical experts.
We propose to enable physicians to exchange, rate, comment and discuss their own medical data models in a collaborative web-based repository of medical forms in a standardized format.
Based on a comprehensive requirement analysis, a web-based portal for medical data models was specified. In this context, a data model is the technical specification (attributes, data types, value lists) of a medical form without any layout information. The CDISC Operational Data Model (ODM) was chosen as the appropriate format for the standardized representation of data models. The system was implemented with Ruby on Rails and applies web 2.0 technologies to provide a community based solution. Forms from different source systems - both routine care and clinical research - were converted into ODM format and uploaded into the portal.
A portal for medical data models based on ODM-files was implemented (http://www.medical-data-models.org). Physicians are able to upload, comment, rate and download medical data models. More than 250 forms with approximately 8000 items are provided in different views (overview and detailed presentation) and in multiple languages. For instance, the portal contains forms from clinical and research information systems.
The portal provides a system-independent repository for multilingual data models in ODM format which can be used by physicians. It serves as a platform for discussion and enables the exchange of multilingual medical data models in a standardized way.
[Show abstract][Hide abstract] ABSTRACT: Decisions to adopt a particular innovation may vary between stakeholders because individual stakeholders may disagree on the costs and benefits involved. This may translate to disagreement between stakeholders on priorities in the implementation process, possibly explaining the slow diffusion of innovations in health care. In this study, we explore the differences in stakeholder preferences for innovations, and quantify the difference in stakeholder priorities regarding costs and benefits.
The decision support technique called the analytic hierarchy process was used to quantify the preferences of stakeholders for nine information technology (IT) innovations in hospital care. The selection of the innovations was based on a literature review and expert judgments. Decision criteria related to the costs and benefits of the innovations were defined. These criteria were improvement in efficiency, health gains, satisfaction with care process, and investments required. Stakeholders judged the importance of the decision criteria and subsequently prioritized the selected IT innovations according to their expectations of how well the innovations would perform for these decision criteria.
The stakeholder groups (patients, nurses, physicians, managers, health care insurers, and policy makers) had different preference structures for the innovations selected. For instance, self-tests were one of the innovations most preferred by health care insurers and managers, owing to their expected positive impacts on efficiency and health gains. However, physicians, nurses and patients strongly doubted the health gains of self-tests, and accordingly ranked self-tests as the least-preferred innovation.
The various stakeholder groups had different expectations of the value of the nine IT innovations. The differences are likely due to perceived stakeholder benefits of each innovation, and less to the costs to individual stakeholder groups. This study provides a first exploratory quantitative insight into stakeholder positions concerning innovation in health care, and presents a novel way to study differences in stakeholder preferences. The results may be taken into account by decision makers involved in the implementation of innovations.
BMC Medical Informatics and Decision Making 08/2013; 13(1):91. DOI:10.1186/1472-6947-13-91 · 1.83 Impact Factor