ORCID Profile
0000-0001-9740-0788
Current Organisations
Heidelberg University Hospital
,
Westfälische Wilhelms-Universität Münster
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: Cold Spring Harbor Laboratory
Date: 22-12-2020
DOI: 10.1101/2020.12.21.20248639
Abstract: Empirically driven personalized diagnostic and treatment is widely perceived as a major hallmark in psychiatry. However, databased personalized decision making requires standardized data acquisition and data access, which is currently absent in psychiatric clinical routine. Here we describe the informatics infrastructure implemented at the psychiatric university hospital Münster allowing for standardized acquisition, transfer, storage and export of clinical data for future real-time predictive modelling in psychiatric routine. We designed and implemented a technical architecture that includes an extension of the EHR via scalable standardized data collection, data transfer between EHR and research databases thus allowing to pool EHR and research data in a unified database and technical solutions for the visual presentation of collected data and analyses results in the EHR. The Single-source Metadata ARchitecture Transformation (SMA:T) was used as the software architecture. SMA:T is an extension of the EHR system and uses Module Driven Software Development to generate standardized applications and interfaces. The Operational Data Model (ODM) was used as the standard. Standardized data was entered on iPads via the Mobile Patient Survey (MoPat) and the web application Mopat@home, the standardized transmission, processing, display and export of data was realized via SMA:T. The technical feasibility was demonstrated in the course of the study. 19 standardized documentation forms with 241 items were created. In 317 patients, 6,451 instances were automatically transferred to the EHR system without errors. 96,323 instances were automatically transferred from the EHR system to the research database for further analyses. With the present study, we present the successful implementation of the informatics infrastructure enabling standardized data acquisition, and data access for future real-time predictive modelling in clinical routine in psychiatry. The technical solution presented here might guide similar initiatives at other sites and thus help to pave the way towards future application of predictive models in psychiatric clinical routine.
Publisher: Elsevier BV
Date: 02-2023
Publisher: JMIR Publications Inc.
Date: 12-2020
DOI: 10.2196/24066
Abstract: Predictive models have revealed promising results for the in idual prognosis of treatment response and relapse risk as well as for differential diagnosis in affective disorders. Yet, in order to translate personalized predictive modeling from research contexts to psychiatric clinical routine, standardized collection of information of sufficient detail and temporal resolution in day-to-day clinical care is needed. Digital collection of self-report measures by patients is a time- and cost-efficient approach to gain such data throughout treatment. The objective of this study was to investigate whether patients with severe affective disorders were willing and able to participate in such efforts, whether the feasibility of such systems might vary depending on in idual patient characteristics, and if digitally acquired assessments were of sufficient diagnostic validity. We implemented a system for longitudinal digital collection of risk and symptom profiles based on repeated self-reports via tablet computers throughout inpatient treatment of affective disorders at the Department of Psychiatry at the University of Münster. Tablet-handling competency and the speed of data entry were assessed. Depression severity was additionally assessed by a clinical interviewer at baseline and before discharge. Of 364 affective disorder patients who were approached, 242 (66.5%) participated in the study 88.8% of participants (215/242) were diagnosed with major depressive disorder, and 27 (11.2%) had bipolar disorder. During the duration of inpatient treatment, 79% of expected assessments were completed, with an average of 4 completed assessments per participant 4 participants (4/242, 1.6%) dropped out of the study prematurely. During data entry, 89.3% of participants (216/242) did not require additional support. Needing support with tablet handling and slower data entry pace were predicted by older age, whereas depression severity at baseline did not influence these measures. Patient self-reporting of depression severity showed high agreement with standardized external assessments by a clinical interviewer. Our results indicate that digital collection of self-report measures is a feasible, accessible, and valid method for longitudinal data collection in psychiatric routine, which will eventually facilitate the identification of in idual risk and resilience factors for affective disorders and pave the way toward personalized psychiatric care.
Publisher: JMIR Publications Inc.
Date: 21-12-2020
Abstract: mpirically driven personalized diagnostic applications and treatment stratification is widely perceived as a major hallmark in psychiatry. However, databased personalized decision making requires standardized data acquisition and data access, which are currently absent in psychiatric clinical routine. ere, we describe the informatics infrastructure implemented at the psychiatric Münster University Hospital, which allows standardized acquisition, transfer, storage, and export of clinical data for future real-time predictive modelling in psychiatric routine. e designed and implemented a technical architecture that includes an extension of the electronic health record (EHR) via scalable standardized data collection and data transfer between EHRs and research databases, thus allowing the pooling of EHRs and research data in a unified database and technical solutions for the visual presentation of collected data and analyses results in the EHR. The Single-source Metadata ARchitecture Transformation (SMA:T) was used as the software architecture. SMA:T is an extension of the EHR system and uses module-driven engineering to generate standardized applications and interfaces. The operational data model was used as the standard. Standardized data were entered on iPads via the Mobile Patient Survey (MoPat) and the web application Mopat@home, and the standardized transmission, processing, display, and export of data were realized via SMA:T. he technical feasibility of the informatics infrastructure was demonstrated in the course of this study. We created 19 standardized documentation forms with 241 items. For 317 patients, 6451 instances were automatically transferred to the EHR system without errors. Moreover, 96,323 instances were automatically transferred from the EHR system to the research database for further analyses. n this study, we present the successful implementation of the informatics infrastructure enabling standardized data acquisition and data access for future real-time predictive modelling in clinical routine in psychiatry. The technical solution presented here might guide similar initiatives at other sites and thus help to pave the way toward future application of predictive models in psychiatric clinical routine.
Publisher: JMIR Publications Inc.
Date: 09-06-2021
DOI: 10.2196/26681
Abstract: Empirically driven personalized diagnostic applications and treatment stratification is widely perceived as a major hallmark in psychiatry. However, databased personalized decision making requires standardized data acquisition and data access, which are currently absent in psychiatric clinical routine. Here, we describe the informatics infrastructure implemented at the psychiatric Münster University Hospital, which allows standardized acquisition, transfer, storage, and export of clinical data for future real-time predictive modelling in psychiatric routine. We designed and implemented a technical architecture that includes an extension of the electronic health record (EHR) via scalable standardized data collection and data transfer between EHRs and research databases, thus allowing the pooling of EHRs and research data in a unified database and technical solutions for the visual presentation of collected data and analyses results in the EHR. The Single-source Metadata ARchitecture Transformation (SMA:T) was used as the software architecture. SMA:T is an extension of the EHR system and uses module-driven engineering to generate standardized applications and interfaces. The operational data model was used as the standard. Standardized data were entered on iPads via the Mobile Patient Survey (MoPat) and the web application Mopat@home, and the standardized transmission, processing, display, and export of data were realized via SMA:T. The technical feasibility of the informatics infrastructure was demonstrated in the course of this study. We created 19 standardized documentation forms with 241 items. For 317 patients, 6451 instances were automatically transferred to the EHR system without errors. Moreover, 96,323 instances were automatically transferred from the EHR system to the research database for further analyses. In this study, we present the successful implementation of the informatics infrastructure enabling standardized data acquisition and data access for future real-time predictive modelling in clinical routine in psychiatry. The technical solution presented here might guide similar initiatives at other sites and thus help to pave the way toward future application of predictive models in psychiatric clinical routine.
Publisher: Elsevier BV
Date: 08-2020
Publisher: Springer Science and Business Media LLC
Date: 26-04-2019
DOI: 10.1007/S00701-018-03792-2
Abstract: The current draft of the German Hospital Structure Law requires remuneration to incorporate quality indicators. For neurosurgery, several quality indicators have been discussed, such as 30-day readmission, reoperation, or mortality rates the rates of infections or the length of stay. When comparing neurosurgical departments regarding these indicators, very heterogeneous patient spectrums complicate benchmarking due to the lack of risk adjustment. In this study, we performed an analysis of quality indicators and possible risk adjustment, based only on administrative data. All adult patients that were treated as inpatients for a brain or spinal tumour at our neurosurgical department between 2013 and 2017 were assessed for the abovementioned quality indicators. DRG-related data such as relative weight, PCCL (patient clinical complexity level), ICD-10 major diagnosis category, secondary diagnoses, age and sex were obtained. The age-adjusted Charlson Comorbidity Index (CCI) was calculated. Logistic regression analyses were performed in order to correlate quality indicators with administrative data. Overall, 2623 cases were enrolled into the study. Most patients were treated for glioma (n = 1055, 40.2%). The CCI did not correlate with the quality indicators, whereas PCCL showed a positive correlation with 30-day readmission and reoperation, SSI and nosocomial infection rates. All previously discussed quality indicators are easily derived from administrative data. Administrative data alone might not be sufficient for adequate risk adjustment as they do not reflect the endogenous risk of the patient and are influenced by certain complications during inpatient stay. Appropriate concepts for risk adjustment should be compiled on the basis of prospectively designed registry studies.
Publisher: JMIR Publications Inc.
Date: 02-09-2020
Abstract: redictive models have revealed promising results for the in idual prognosis of treatment response and relapse risk as well as for differential diagnosis in affective disorders. Yet, in order to translate personalized predictive modeling from research contexts to psychiatric clinical routine, standardized collection of information of sufficient detail and temporal resolution in day-to-day clinical care is needed. Digital collection of self-report measures by patients is a time- and cost-efficient approach to gain such data throughout treatment. he objective of this study was to investigate whether patients with severe affective disorders were willing and able to participate in such efforts, whether the feasibility of such systems might vary depending on in idual patient characteristics, and if digitally acquired assessments were of sufficient diagnostic validity. e implemented a system for longitudinal digital collection of risk and symptom profiles based on repeated self-reports via tablet computers throughout inpatient treatment of affective disorders at the Department of Psychiatry at the University of Münster. Tablet-handling competency and the speed of data entry were assessed. Depression severity was additionally assessed by a clinical interviewer at baseline and before discharge. f 364 affective disorder patients who were approached, 242 (66.5%) participated in the study 88.8% of participants (215/242) were diagnosed with major depressive disorder, and 27 (11.2%) had bipolar disorder. During the duration of inpatient treatment, 79% of expected assessments were completed, with an average of 4 completed assessments per participant 4 participants (4/242, 1.6%) dropped out of the study prematurely. During data entry, 89.3% of participants (216/242) did not require additional support. Needing support with tablet handling and slower data entry pace were predicted by older age, whereas depression severity at baseline did not influence these measures. Patient self-reporting of depression severity showed high agreement with standardized external assessments by a clinical interviewer. ur results indicate that digital collection of self-report measures is a feasible, accessible, and valid method for longitudinal data collection in psychiatric routine, which will eventually facilitate the identification of in idual risk and resilience factors for affective disorders and pave the way toward personalized psychiatric care.
No related grants have been discovered for Martin Dugas.