Integrating clinical decision support systems for pharmacogenomic testing into clinical routine - a scoping review of designs of user-system interactions in recent system development.

BACKGROUND: Pharmacogenomic clinical decision support systems (CDSS) have the potential to help overcome some of the barriers for translating pharmacogenomic knowledge into clinical routine. Before developing a prototype it is crucial for developers to know which pharmacogenomic CDSS features and user-system interactions have yet been developed, implemented and tested in previous pharmacogenomic CDSS efforts and if they have been successfully applied. We address this issue by providing an overview of the designs of user-system interactions of recently developed pharmacogenomic CDSS. METHODS: We searched PubMed for pharmacogenomic CDSS published between January 1, 2012 and November 15, 2016. Thirty-two out of 118 identified articles were summarized and included in the final analysis. We then compared the designs of user-system interactions of the 20 pharmacogenomic CDSS we had identified. RESULTS: Alerts are the most widespread tools for physician-system interactions, but need to be implemented carefully to prevent alert fatigue and avoid liabilities. Pharmacogenomic test results and override reasons stored in the local EHR might help communicate pharmacogenomic information to other internal care providers. Integrating patients into user-system interactions through patient letters and online portals might be crucial for transferring pharmacogenomic data to external health care providers. Inbox messages inform physicians about new pharmacogenomic test results and enable them to request pharmacogenomic consultations. Search engines enable physicians to compare medical treatment options based on a patient's genotype. CONCLUSIONS: Within the last 5 years, several pharmacogenomic CDSS have been developed. However, most of the included articles are solely describing prototypes of pharmacogenomic CDSS rather than evaluating them. To support the development of prototypes further evaluation efforts will be necessary. In the future, pharmacogenomic CDSS will likely include prediction models to identify patients who are suitable for preemptive genotyping.

Controlling my genome with my smartphone: first clinical experiences of the PROMISE system.

BACKGROUND: The development of Precision Medicine strategies requires high-dimensional phenotypic and genomic data, both of which are highly privacy-sensitive data types. Conventional data management systems lack the capabilities to sufficiently handle the expected large quantities of such sensitive data in a secure manner. PROMISE is a genetic data management concept that implements a highly secure platform for data exchange while preserving patient interests, privacy, and autonomy. METHODS: The concept of PROMISE to democratize genetic data was developed by an interdisciplinary team. It integrates a sophisticated cryptographic concept that allows only the patient to grant selective access to defined parts of his genetic information with single DNA base-pair resolution cryptography. The PROMISE system was developed for research purposes to evaluate the concept in a pilot study with nineteen cardiomyopathy patients undergoing genotyping, questionnaires, and longitudinal follow-up. RESULTS: The safety of genetic data was very important to 79%, and patients generally regarded the data as highly sensitive. More than half the patients reported that their attitude towards the handling of genetic data has changed after using the PROMISE app for 4 months (median). The patients reported higher confidence in data security and willingness to share their data with commercial third parties, including pharmaceutical companies (increase from 5 to 32%). CONCLUSION: PROMISE democratizes genomic data by a transparent, secure, and patient-centric approach. This clinical pilot study evaluating a genetic data infrastructure is unique and shows that patient's acceptance of data sharing can be increased by patient-centric decision-making.

The Patient as Genomic Data Manager - Evaluation of the PROMISE App.

PROMISE (Personal Medical Safe) was a German research project which aimed to provide the responsibility of genomic data to the patient via a mobile app. The patient should accept or decline study requests to use his/her genomic data via the app. In the evaluation of the app the experiences with mobile health as well as the opinion on being the genomic data manager were measured. Furthermore, the test patients were asked about their opinion and their concerns on the PROMISE app. Most of the 19 test patients were aware of the high sensibility of genomic data and thought that the PROMISE app was a suitable solution. The largest part found it good that they were the responsible data owner. However, several participants also found it important to have a permanent contact person when it comes to questions on inquiries or the app.

Requirements Analysis and Specification for a Molecular Tumor Board Platform Based on cBioPortal.

Clinicians in molecular tumor boards (MTB) are confronted with a growing amount of genetic high-throughput sequencing data. Today, at German university hospitals, these data are usually handled in complex spreadsheets from which clinicians have to obtain the necessary information. The aim of this work was to gather a comprehensive list of requirements to be met by cBioPortal to support processes in MTBs according to clinical needs. Therefore, oncology experts at nine German university hospitals were surveyed in two rounds of interviews. To generate an interview guideline a scoping review was conducted. For visual support in the second round, screenshot mockups illustrating the requirements from the first round were created. Requirements that cBioPortal already meets were skipped during the second round. In the end, 24 requirements with sometimes several conceivable options were identified and 54 screenshot mockups were created. Some of the identified requirements have already been suggested to the community by other users or are currently being implemented in cBioPortal. This shows, that the results are in line with the needs expressed by various disciplines. According to our findings, cBioPortal has the potential to significantly improve the processes and analyses of an MTB after the implementation of the identified requirements.

Extractive summarization of clinical trial descriptions.

PURPOSE: Text summarization of clinical trial descriptions has the potential to reduce the time required to familiarize oneself with the subject of studies by condensing long-form detailed descriptions to concise, meaning-preserving synopses. This work describes the process and quality of automatically generated summaries of clinical trial descriptions using extractive text summarization methods. METHODS: We generated a novel dataset from the detailed descriptions and brief summaries of trials registered on clinicaltrials.gov. We executed several text summarization algorithms on the detailed descriptions in this corpus and calculated the standard ROUGE metrics using the brief summaries included in the record as a reference. To investigate the correlation of these metrics with human sentiments, four reviewers assessed the content-completeness of the generated summaries and the helpfulness of both the generated and reference summaries via a Likert scale questionnaire. RESULTS: The filtering stages of the dataset generation process reduce the 277,228 trials registered on clinicaltrials.gov to 101,016 records usable for the summarization task. On average, the summaries in this corpus are 25% the length of the detailed descriptions. Of the evaluated text summarization methods, the TextRank algorithm exhibits the overall best performance with a ROUGE-1 F1 score of 0.3531, ROUGE-2 F1 score of 0.1723, and ROUGE-L F1 score of 0.3003. These scores correlate with the assessment of the helpfulness and content similarity by the human reviewers. Inter-rater agreement for the helpfulness and content similarity was slight and fair respectively (Fleiss' kappa of 0.12 and 0.22). CONCLUSIONS: Extractive summarization is a viable tool for generating meaning-preserving synopses of detailed clinical trial descriptions. Further, the human evaluation has shown that the ROUGE-L F1 score is useful for rating the general quality of generated summaries of clinical trial descriptions in an automated way.

Implementing Pharmacogenomic Clinical Decision Support into German Hospitals.

BACKGROUND: Pharmacogenomic Clinical Decision Support Systems (CDSS) are considered to be the most feasible tool for adopting pharmacogenomic testing into clinical routine. OBJECTIVE: To discuss important factors for implementing pharmacogenomic CDSS into German hospitals. METHODS: We analyzed two relevant studies. Furthermore, we interviewed data privacy officers of three German university hospitals and examined relevant legal regulations in literature. RESULTS: There are three major barriers for implementing pharmacogenomic CDSS into German hospitals: (i) the legal uncertainty; (ii) the lack of machine-readable data; (iii) the remaining knowledge gap of both genetics and pharmacogenomics among physicians. CONCLUSION: The implementation of passive clinical decision support (CDS) for somatic mutations in the form of structured pharmacogenomic reports might be the most feasible CDS feature for clinicians in German hospitals.

Requirements Analysis for a Clinical Decision Support System Aiming at Improving the Artificial Nutrition of Critically Ill Patients.

BACKGROUND: Nutrition support is an important aspect regarding the care of critically ill patients. Malnutrition affects the recovery process negatively. However, the impact on the clinical outcome is often underestimated in complex clinical settings due to several factors hindering optimization of nutrition. OBJECTIVE: To identify the requirements for a clinical decision support system that enables the medical staff to improve its patients' nutritional status. METHODS: A literature review and interviews with two senior physicians were conducted to refine the requirements for the support system as well as to determine the inclusion criteria for a subsequent intervention study. RESULTS: The analysis resulted in: (i) the identification of 4 measurement parameters for the assessment of the nutrition status; (ii) the graphical layout in adherence to the standards-based implementation approach for the creation of multi-patient dashboards; (iii) the definition of the study group. The nutrition dashboard will be implemented and integrated based on the set requirements, followed by an intervention study evaluating the dashboard's efficacy.

Supporting Molecular Tumor Boards in Molecular-Guided Decision-Making - The Current Status of Five German University Hospitals.

BACKGROUND: German university hospitals have started to establish molecular tumor boards in order to enable physicians to make molecular-guided decisions. OBJECTIVE: Our aim was to describe the organizational structure and procedures which are currently supporting the molecular tumor boards of five German university hospitals. METHODS: We conducted semi-structured interviews with experts of five university hospitals between December 2016 and February 2017. RESULTS: We observed heterogeneity in both the organization of genetic testing and the management of the molecular tumor boards among the five hospitals. They used free-text documents in most of their support procedures rather than machine-readable documents. CONCLUSION: There are three potentialities to support the process from genetic testing to reporting within the molecular tumor boards: (i) standardized pipeline to integrate automated variant calling and annotation; (ii) tools supporting the experts in creating their reports and presentations and (iii) implementing pharmacogenomic CDSS into clinical routine.

The experience of physicians in pharmacogenomic clinical decision support within eight German university hospitals.

AIM: The aim of this study was to assess the physicians' attitude, their knowledge and their experience in pharmacogenomic clinical decision support in German hospitals. MATERIALS & METHODS: We conducted an online survey to address physicians of 13 different medical specialties across eight German university hospitals. In total, 564 returned questionnaires were analyzed. RESULTS: The remaining knowledge gap, the uncertainty of test reimbursement and the physicians' lack of awareness of existing pharmacogenomic clinical decision support systems (CDSS) are the major barriers for implementing pharmacogenomic CDSS into German hospitals. Furthermore, pharmacogenomic CDSS are most effective in the form of real-time decision support for internists. CONCLUSION: Physicians in German hospitals require additional education of both genetics and pharmacogenomics. They need to be provided with access to relevant pharmacogenomic CDSS.