Treatment decisions are usually based on the individual experience, medical knowledge and skill of a physician. But with the advent of personalised patient-centered treatment and its many different aspects this poses a major challenge for clinicians, especially in the field of oncology.
For instance, to choose the appropriate cancer therapy, an oncologist must consider patient specific biology such as genetics, epigenetics, and micro-environmental factors and balance it with various treatment options, median survival, treatment toxicity, comorbidities, patient preferences, and cost-effectiveness. In addition, the increasing focus on shared decision making (SDM) places additional demand on the physicians time and effort to engage and inform patients. And as the field of medicine is getting more and more complex, there are constantly new guidelines and rules in how to conduct specific treatments that are released on a continually basis. This enormous number of different tasks is far too intricate for a clinician to handle single-handed, whether it‘s too time consuming or due to the fact that the limited human cognitive capacity can only process five factors per decision.
For this reason, various CDS tools have been developed to help reduce diagnostic errors, improve quality of care and alleviate intense time demand placed on clinicians. Most of these tools focus on information management and situational awareness, and include features such as automated information retrieval, alerts, summaries, and reminders. However, the demand for these CDS tools is limited, mainly due to the fact that they do not show an improvement in outcome and disrupt existing clinical workflows.
To be truly useful for treatment selection, a CDS should provide enhanced insights and contain epidemiological and research data in combination with electronic health records, imaging data, and patient genetic information. This would give the clinicians a comprehensive picture of the patient‘s disease and its treatment options, right up to distinct treatment outcome prediction models. However, a CDS with this range would require the interoperability of patient data with predictive models and the uniformity of medical ontology. And there is the catch: The many different data standards have made this impossible so far.
But with DECIDE‘s cross-data technology these most significant data sources can finally be accessed and used for clinical decision support.
DECIDE is utilizing the SeDI 2.0 technology developed by SOHARD SOFTWARE to retrieve and search existing data from various sources for specific attributes. After querying the patient data from PACS and EHR via SEDI 2.0 and creating a personalised patient locker for storage of the queried patient information, the data is further processed via feature extraction and the prediction can be derived.
DECIDE provides a clinician interface (cUI) that enables healthcare professionals to view these results in clinically validated models for predicting treatment outcome. After reviewing the outcome, the clinician can then select one of the various treatment options to best suit the patient.
With DECIDE, clinicians are supported with the right information and knowledge at the right time to choose the best treatment for any given patient condition.