Automated provision of clinical routine data for a complex clinical follow-up study: A data warehouse solution

Kaspar, Mathias; Fette, Georg; Hanke, Monika; Ertl, M. Anton; Puppe, Frank; Störk, Stefan

doi:10.1177/14604582211058081

Cited by 5 publications

(3 citation statements)

References 30 publications

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“…The proportion of variables for the clinical study that could be transferred automatically (24% of all variables) was somewhat lower in our model than in other approaches for automated data transfer [ 15 , 16 ]. Due to differing approaches toward automated data transfer, however, solely considering the proportion of automatically transferable variables is inadequate and resembles an apples-to-oranges comparison.…”

Section: Discussionmentioning

confidence: 87%

Automated Electronic Health Record to Electronic Data Capture Transfer in Clinical Studies in the German Health Care System: Feasibility Study and Gap Analysis

Mueller¹,

Herrmann²,

Cichos³

et al. 2023

J Med Internet Res

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Background Data transfer between electronic health records (EHRs) at the point of care and electronic data capture (EDC) systems for clinical research is still mainly carried out manually, which is error-prone as well as cost- and time-intensive. Automated digital transfer from EHRs to EDC systems (EHR2EDC) would enable more accurate and efficient data capture but has so far encountered technological barriers primarily related to data format and the technological environment: in Germany, health care data are collected at the point of care in a variety of often individualized practice management systems (PMSs), most of them not interoperable. Data quality for research purposes within EDC systems must meet the requirements of regulatory authorities for standardized submission of clinical trial data and safety reports. Objective We aimed to develop a model for automated data transfer as part of an observational study that allows data of sufficient quality to be captured at the point of care, extracted from various PMSs, and automatically transferred to electronic case report forms in EDC systems. This required addressing aspects of data security, as well as the lack of compatibility between EHR health care data and the data quality required in EDC systems for clinical research. Methods The SaniQ software platform (Qurasoft GmbH) is already used to extract and harmonize predefined variables from electronic medical records of different Compu Group Medical–hosted PMSs. From there, data are automatically transferred to the validated AlcedisTRIAL EDC system (Alcedis GmbH) for data collection and management. EHR2EDC synchronization occurs automatically overnight, and real-time updates can be initiated manually following each data entry in the EHR. The electronic case report form (eCRF) contains 13 forms with 274 variables. Of these, 5 forms with 185 variables contain 67 automatically transferable variables (67/274, 24% of all variables and 67/185, 36% of eligible variables). Results This model for automated data transfer bridges the current gap between clinical practice data capture at the point of care and the data sets required by regulatory agencies; it also enables automated EHR2EDC data transfer in compliance with the General Data Protection Regulation (GDPR). It addresses feasibility, connectivity, and system compatibility of currently used PMSs in health care and clinical research and is therefore directly applicable. Conclusions This use case demonstrates that secure, consistent, and automated end-to-end data transmission from the treating physician to the regulatory authority is feasible. Automated data transmission can be expected to reduce effort and save resources and costs while ensuring high data quality. This may facilitate the conduct of studies for both study sites and sponsors, thereby accelerating the development of new drugs. Nevertheless, the industry-wide implementation of EHR2EDC requires policy decisions that set the framework for the use of research data based on routine PMS data.

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Section: Discussionmentioning

confidence: 87%

Automated Electronic Health Record to Electronic Data Capture Transfer in Clinical Studies in the German Health Care System: Feasibility Study and Gap Analysis

Mueller¹,

Herrmann²,

Cichos³

et al. 2023

J Med Internet Res

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“…This was a retrospective analysis based on medical information retrieved from the dedicated electronic data warehouse of the University Hospital of Würzburg [ 10 ]. The system facilitates a customizable, in-depth search and can track patient information over time.…”

Section: Methodsmentioning

confidence: 99%

Improved Interpretation of Pulmonary Artery Wedge Pressures through Left Atrial Volumetry—A Cardiac Magnetic Resonance Imaging Study

Güder,

Reiter,

Drayss

et al. 2024

JCDD

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Background: The pulmonary artery wedge pressure (PAWP) is regarded as a reliable indicator of left ventricular end-diastolic pressure (LVEDP), but this association is weaker in patients with left-sided heart disease (LHD). We compared morphological differences in cardiac magnetic resonance imaging (CMR) in patients with heart failure (HF) and a reduced left ventricular ejection fraction (LVEF), with or without elevation of PAWP or LVEDP. Methods: We retrospectively identified 121 patients with LVEF < 50% who had undergone right heart catheterization (RHC) and CMR. LVEDP data were available for 75 patients. Results: The mean age of the study sample was 63 ± 14 years, the mean LVEF was 32 ± 10%, and 72% were men. About 53% of the patients had an elevated PAWP (>15 mmHg). In multivariable logistic regression analysis, NT-proBNP, left atrial ejection fraction (LAEF), and LV end-systolic volume index independently predicted an elevated PAWP. Of the 75 patients with available LVEDP data, 79% had an elevated LVEDP, and 70% had concomitant PAWP elevation. By contrast, all but one patient with elevated PAWP and half of the patients with normal PAWP had concomitant LVEDP elevation. The Bland–Altman plot revealed a systematic bias of +5.0 mmHg between LVEDP and PAWP. Notably, LAEF was the only CMR variable that differed significantly between patients with elevated LVEDP and a PAWP ≤ or >15 mmHg. Conclusions: In patients with LVEF < 50%, a normal PAWP did not reliably exclude LHD, and an elevated LVEDP was more frequent than an elevated PAWP. LAEF was the most relevant determinant of an increased PAWP, suggesting that a preserved LAEF in LHD may protect against backward failure into the lungs and the subsequent increase in pulmonary pressure.

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“…Finally, new digital services may enable automation in healthcare by integrating AI modules. Although the most popular benefit of automation is probably the implementation of medical decision support systems [ 19 ], automation can help in many other tasks, such as data entry and knowledge abstraction from literature [ 20 , 21 ], scheduling [ 22 ], billing [ 23 ], repetitive tasks in clinical workflows [ 24 ], data security or dashboard analytics [ 25 ], to name a few. However, among the many challenges to a wider uptake of AI-based algorithms in clinical practice [ 26–28 ], the lack of integration into HIS is one of the most prominent: modern health information system will be required to enable easy integration of digital applications based on AI and advanced analytics capabilities [ 29 , 30 ].…”

Section: Introductionmentioning

confidence: 99%

From electronic health records to clinical management systems: how the digital transformation can support healthcare services

Barbieri¹,

Neri²,

Stuard³

et al. 2023

Clinical Kidney Journal

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Healthcare systems worldwide are currently undergoing significant transformations in response to increasing costs, a shortage of healthcare professionals, and the growing complexity of medical needs among the population. Value-based healthcare reimbursement systems are emerging as an attempt to incentivize patient-centricity and cost containment. From a technological perspective, the transition to digitalized services is intended to support these transformations. A Health Information System is a technological solution designed to govern the data flow generated and consumed by healthcare professionals and administrative staff during the delivery of healthcare services. However, the exponential growth of digital capabilities and applied advanced analytics has expanded their traditional functionalities and brought the promise of automating administrative procedures and simple repetitive tasks, while enhancing the efficiency and outcomes of healthcare services by incorporating decision support tools for clinical management. The future of health information systems is headed towards modular architectures that can facilitate implementation and adaptation to different environments and systems, as well as the integration of various tools, such as Artificial Intelligence (AI) models, in a seamless way. As an example, we present the experience and future developments of the European Clinical Database (EuCliD®). EuCliD is a multilingual health information system used by 20 000 nurses and physicians on a daily basis to manage 105 000 patients belonging to 1 100 clinics in 43 different countries. EuCliD encompasses patients’ follow-up, automatic reporting, and mobile applications while enabling efficient management of clinical processes. It is also designed to incorporate multiagent systems to automate repetitive task, AI modules and advanced dynamic dashboards.

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Automated provision of clinical routine data for a complex clinical follow-up study: A data warehouse solution

Cited by 5 publications

References 30 publications

Automated Electronic Health Record to Electronic Data Capture Transfer in Clinical Studies in the German Health Care System: Feasibility Study and Gap Analysis

Automated Electronic Health Record to Electronic Data Capture Transfer in Clinical Studies in the German Health Care System: Feasibility Study and Gap Analysis

Improved Interpretation of Pulmonary Artery Wedge Pressures through Left Atrial Volumetry—A Cardiac Magnetic Resonance Imaging Study

From electronic health records to clinical management systems: how the digital transformation can support healthcare services

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