Agile co-development for clinical adoption and adaptation of innovative technologies

Objectives The objective of this study is the conceptual design, implementation and evaluation of a system for generic, standard-compliant data transfer into electronic health records (EHRs). This includes patient data from clinical research and medical care that has been semantically annotated and enhanced with metadata. The implementation is based on the single-source approach. Technical and clinical feasibilities, as well as cost-benefit efficiency, were investigated in everyday clinical practice. Methods Münster University Hospital is a tertiary care hospital with 1,457 beds and 10,823 staff who treated 548,110 patients in 2018. Single-source metadata architecture transformation (SMA:T) was implemented as an extension to the EHR system. This architecture uses Model Driven Software Development (MDSD) to generate documentation forms according to the Clinical Data Interchange Standards Consortium (CDISC) operational data model (ODM). Clinical data are stored in ODM format in the EHR system database. Documentation forms are based on Google's Material Design Standard. SMA:T was used at a total of five clinics and one administrative department in the period from March 1, 2018 until March 31, 2019 in everyday clinical practice. Results The technical and clinical feasibility of SMA:T was demonstrated in the course of the study. Seventeen documentation forms including 373 data items were created with SMA:T. Those were created for 2,484 patients by 283 users in everyday clinical practice. A total of 121 documentation forms were examined retrospectively. The Constructive cost model (COCOMO II) was used to calculate cost and time savings. The form development mean time was reduced by 83.4% from 3,357 to 557 hours. Average costs per form went down from EUR 953 to 158. Conclusion Automated generic transfer of standard-compliant data and metadata into EHRs is technically and clinically feasible, cost efficient, and a useful method to establish comprehensive and semantically annotated clinical documentation. Savings of time and personnel resources are possible.

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“…Agile methods were used on the Project Life Cycle and Development Cycle. 58 Additional content is available via Figshare. 59…”

Section: Resultsmentioning

confidence: 99%

Conceptual Design, Implementation, and Evaluation of Generic and Standard-Compliant Data Transfer into Electronic Health Records

Blitz

Dugas

2020

Appl Clin Inform

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“…The implementation of the architecture is divided into four areas: data collection, data transfer, data storage and data visualization. Agile methods were used for Project Life Cycle and Development Cycle [50].…”

Section: System Implementationmentioning

confidence: 99%

Design, Implementation and Feasibility of an Informatics Infrastructure for Standardized Data Acquisition, Transfer, Storage and Export in Psychiatric Clinical Routine

Blitz

Storck

Baune

et al. 2020

Preprint

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BackgroundEmpirically 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.ObjectiveHere 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.MethodsWe 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.ResultsThe 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.ConclusionsWith 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.

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“…In an ideal world, upfront efforts for project-specific technical setup, data cleansing, and data structuring can be avoided, and development starts rapidly, while repeated discussions with clinicians and medical researchers are carried out in parallel [ 14 ]. Technical solutions that facilitate this approach have been called dataspace management systems [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Enabling Agile Clinical and Translational Data Warehousing: Platform Development and Evaluation

Spengler¹,

Lang²,

Mahapatra³

et al. 2020

JMIR Med Inform

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Background Modern data-driven medical research provides new insights into the development and course of diseases and enables novel methods of clinical decision support. Clinical and translational data warehouses, such as Informatics for Integrating Biology and the Bedside (i2b2) and tranSMART, are important infrastructure components that provide users with unified access to the large heterogeneous data sets needed to realize this and support use cases such as cohort selection, hypothesis generation, and ad hoc data analysis. Objective Often, different warehousing platforms are needed to support different use cases and different types of data. Moreover, to achieve an optimal data representation within the target systems, specific domain knowledge is needed when designing data-loading processes. Consequently, informaticians need to work closely with clinicians and researchers in short iterations. This is a challenging task as installing and maintaining warehousing platforms can be complex and time consuming. Furthermore, data loading typically requires significant effort in terms of data preprocessing, cleansing, and restructuring. The platform described in this study aims to address these challenges. Methods We formulated system requirements to achieve agility in terms of platform management and data loading. The derived system architecture includes a cloud infrastructure with unified management interfaces for multiple warehouse platforms and a data-loading pipeline with a declarative configuration paradigm and meta-loading approach. The latter compiles data and configuration files into forms required by existing loading tools, thereby automating a wide range of data restructuring and cleansing tasks. We demonstrated the fulfillment of the requirements and the originality of our approach by an experimental evaluation and a comparison with previous work. Results The platform supports both i2b2 and tranSMART with built-in security. Our experiments showed that the loading pipeline accepts input data that cannot be loaded with existing tools without preprocessing. Moreover, it lowered efforts significantly, reducing the size of configuration files required by factors of up to 22 for tranSMART and 1135 for i2b2. The time required to perform the compilation process was roughly equivalent to the time required for actual data loading. Comparison with other tools showed that our solution was the only tool fulfilling all requirements. Conclusions Our platform significantly reduces the efforts required for managing clinical and translational warehouses and for loading data in various formats and structures, such as complex entity-attribute-value structures often found in laboratory data. Moreover, it facilitates the iterative refinement of data representations in the target platforms, as the required configuration files are very compact. The quantitative measurements presented are consistent with our experiences of significantly reduced efforts for building warehousing platforms in close cooperation with medical researchers. Both the cloud-based hosting infrastructure and the data-loading pipeline are available to the community as open source software with comprehensive documentation.

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Agile co-development for clinical adoption and adaptation of innovative technologies

Cited by 14 publications

References 7 publications

Conceptual Design, Implementation, and Evaluation of Generic and Standard-Compliant Data Transfer into Electronic Health Records

Conceptual Design, Implementation, and Evaluation of Generic and Standard-Compliant Data Transfer into Electronic Health Records

Design, Implementation and Feasibility of an Informatics Infrastructure for Standardized Data Acquisition, Transfer, Storage and Export in Psychiatric Clinical Routine

Enabling Agile Clinical and Translational Data Warehousing: Platform Development and Evaluation

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