Evidence map›Paper›PMID 37131499›Full record

ArticleSN computer science2023

Software-Tool Support for Collaborative, Virtual, Multi-Site Molecular Tumor Boards.

Matthieu-P Schapranow, Florian Borchert, Nina Bougatf, Hauke Hund, Roland Eils

Abstract read
In one paragraph

Article in SN computer science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

8 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Matthieu-P SchapranowHasso Plattner Institute for Digital Engineering, University of Potsdam, Prof.-Dr.-Helmert-Str. 2-3, 14482 Potsdam, Germany.ORCID 0000-0001-6601-2942
Florian BorchertHasso Plattner Institute for Digital Engineering, University of Potsdam, Prof.-Dr.-Helmert-Str. 2-3, 14482 Potsdam, Germany.
Nina BougatfDepartment of Radiation Oncology, Heidelberg Ion-Beam Therapy Center (HIT), Heidelberg University Hospital, Im Neuenheimer Feld 450, 69120 Heidelberg, Germany.
Hauke HundGECKO Institute, Heilbronn University of Applied Sciences, Max-Planck-Straße 39, 74081 Heilbronn, Germany.
Roland EilsCenter for Digital Health, Berlin Institute of Health and Charité Universitätsmedizin Berlin, Kapelle-Ufer 2, 10117 Berlin, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The availability of high-throughput molecular diagnostics builds the foundation for Molecular Tumor Boards (MTBs). Although more fine-grained data is expected to support decision making of oncologists, assessment of data is complex and time-consuming slowing down the implementation of MTBs, e.g., due to retrieval of the latest medical publications, assessment of clinical evidence, or linkage to the latest clinical guidelines. We share our findings from analysis of existing tumor board processes and defininion of clinical processes for the adoption of MTBs. Building on our findings, we have developed a real-world software prototype together with oncologists and medical professionals, which supports the preparation and conduct of MTBs and enables collaboration between medical experts by sharing medical knowledge even across the hospital locations. We worked in interdisciplinary teams of clinicians, oncologists, medical experts, medical informaticians, and software engineers using design thinking methodology. With their input, we identified challenges and limitations of the current MTB approaches, derived clinical process models using Business Process and Modeling Notation (BMPN), and defined personas, functional and non-functional requirements for software tool support. Based on it, we developed software prototypes and evaluated them with clinical experts from major university hospitals across Germany. We extended the Kanban methodology enabling holistic tracking of patient cases from "backlog" to "follow-up" in our app. The feedback from interviewed medical professionals showed that our clinical process models and software prototype provide suitable process support for the preparation and conduction of molecular tumor boards. The combination of oncology knowledge across hospitals and the documentation of treatment decision can be used to form a unique medical knowledge base by oncologists for oncologists. Due to the high heterogeneity of tumor diseases and the spread of the latest medical knowledge, a cooperative decision-making process including insights from similar patient cases was considered as a very valuable feature. The ability to transform prepared case data into a screen presentation was recognized as an essential feature speeding up the preparation process. Oncologists require special software tool support to incorporate and assess molecular data for the decision-making process. In particular, the need for linkage to the latest medical knowledge, clinical evidence, and collaborative tools to discuss individual cases were named to be of importance. With the experiences from the COVID-19 pandemic, the acceptance of online tools and collaborative working is expected to grow. Our virtual multi-site approach proved to allow a collaborative decision-making process for the first time, which we consider to have a positive impact on the overall treatment quality.

Indexed as

Clinical processCollaborativeMolecular tumor boardMulti-sitePersonalized medicineSoftware toolVirtual

Identifiers

PMID37131499
PMCPMC10136394

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.