%0 Journal Article %1 Hoffmann2020-ze %A Hoffmann, Katja %A Cazemier, Katja %A Baldow, Christoph %A Schuster, Silvio %A Kheifetz, Yuri %A Schirm, Sibylle %A Horn, Matthias %A Ernst, Thomas %A Volgmann, Constanze %A Thiede, Christian %A Hochhaus, Andreas %A Bornhäuser, Martin %A Suttorp, Meinolf %A Scholz, Markus %A Glauche, Ingmar %A Loeffler, Markus %A Roeder, Ingo %D 2020 %J BMC Med. Inform. Decis. Mak. %K Clinical Computer Data Haematology; Individual Mathematical Model-based Routine Support decision-making; management; modelling; optimization; planning; simulation; system therapy treatment workflow; %N 1 %P 28 %T Integration of mathematical model predictions into routine workflows to support clinical decision making in haematology %V 20 %X BACKGROUND: Individualization and patient-specific optimization of treatment is a major goal of modern health care. One way to achieve this goal is the application of high-resolution diagnostics together with the application of targeted therapies. However, the rising number of different treatment modalities also induces new challenges: Whereas randomized clinical trials focus on proving average treatment effects in specific groups of patients, direct conclusions at the individual patient level are problematic. Thus, the identification of the best patient-specific treatment options remains an open question. Systems medicine, specifically mechanistic mathematical models, can substantially support individual treatment optimization. In addition to providing a better general understanding of disease mechanisms and treatment effects, these models allow for an identification of patient-specific parameterizations and, therefore, provide individualized predictions for the effect of different treatment modalities. RESULTS: In the following we describe a software framework that facilitates the integration of mathematical models and computer simulations into routine clinical processes to support decision-making. This is achieved by combining standard data management and data exploration tools, with the generation and visualization of mathematical model predictions for treatment options at an individual patient level. CONCLUSIONS: By integrating model results in an audit trail compatible manner into established clinical workflows, our framework has the potential to foster the use of systems-medical approaches in clinical practice. We illustrate the framework application by two use cases from the field of haematological oncology.

@article{Hoffmann2020-ze, abstract = {BACKGROUND: Individualization and patient-specific optimization of treatment is a major goal of modern health care. One way to achieve this goal is the application of high-resolution diagnostics together with the application of targeted therapies. However, the rising number of different treatment modalities also induces new challenges: Whereas randomized clinical trials focus on proving average treatment effects in specific groups of patients, direct conclusions at the individual patient level are problematic. Thus, the identification of the best patient-specific treatment options remains an open question. Systems medicine, specifically mechanistic mathematical models, can substantially support individual treatment optimization. In addition to providing a better general understanding of disease mechanisms and treatment effects, these models allow for an identification of patient-specific parameterizations and, therefore, provide individualized predictions for the effect of different treatment modalities. RESULTS: In the following we describe a software framework that facilitates the integration of mathematical models and computer simulations into routine clinical processes to support decision-making. This is achieved by combining standard data management and data exploration tools, with the generation and visualization of mathematical model predictions for treatment options at an individual patient level. CONCLUSIONS: By integrating model results in an audit trail compatible manner into established clinical workflows, our framework has the potential to foster the use of systems-medical approaches in clinical practice. We illustrate the framework application by two use cases from the field of haematological oncology.}, added-at = {2024-09-10T11:54:51.000+0200}, author = {Hoffmann, Katja and Cazemier, Katja and Baldow, Christoph and Schuster, Silvio and Kheifetz, Yuri and Schirm, Sibylle and Horn, Matthias and Ernst, Thomas and Volgmann, Constanze and Thiede, Christian and Hochhaus, Andreas and Bornh{\"a}user, Martin and Suttorp, Meinolf and Scholz, Markus and Glauche, Ingmar and Loeffler, Markus and Roeder, Ingo}, biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/2e63be435835693e239e4176a7960fde8/scadsfct}, interhash = {264c16bc2db1e188d942af8362ea1ff7}, intrahash = {e63be435835693e239e4176a7960fde8}, journal = {BMC Med. Inform. Decis. Mak.}, keywords = {Clinical Computer Data Haematology; Individual Mathematical Model-based Routine Support decision-making; management; modelling; optimization; planning; simulation; system therapy treatment workflow;}, language = {en}, month = feb, number = 1, pages = 28, timestamp = {2024-09-10T11:54:51.000+0200}, title = {Integration of mathematical model predictions into routine workflows to support clinical decision making in haematology}, volume = 20, year = 2020 }