Building a precision oncology workforce by multidisciplinary and case-based learning

Interprofessional education in cancer care - a scoping review

BACKGROUND

Comprehensive cancer care requires effective collaboration by interprofessional healthcare teams. The need to develop educational initiatives to improve interprofessional collaboration is increasingly recognised. However, there is no agreement regarding the interprofessional competencies required for effective cancer care leading to much variation on the focus of research, planning and managing change. A scoping review was conducted to identify the current status of IPE in cancer care and to summarise the results of previous research in order to guide the development of interprofessional education in cancer care.

METHODS

The JBI Scoping Review guidelines were used to guide the process of the review. A search of the available literature was conducted in CINAHL, MEDLINE (Ovid), PubMed, PsycInfo, Scopus databases from January 2012 to March 2023 to investigate IPE for health professional clinicians working in cancer care.

RESULTS

Of the 825 initial references and 153 studies imported for screening, a total of 28 studies were included in the final review. From those studies, seven focused on the need for IPE and interprofessional competence for oncology healthcare professionals, four reviewed existing IPE programs and 17 described the development and evaluation of interprofessional education. Findings show variation and lack of concept definitions underpinning research in IPE in cancer care settings. Variation also exists in the range of research activities in IPE, most notably related to communication, teamwork and the development of interprofessional practice. The evaluation of impact of IPE is mainly focused on health care professionals' self-evaluation and general feedback. Impact on patient care was only evaluated in one study.

CONCLUSIONS

Based on the results, interprofessional education research in the field of cancer care is limited in Europe. Thus, there is a significant increase in publications in the last five years. A more systematic focus on the theoretical framework and definition of concepts would be of value. Research and programme development should be based on a shared understanding on what constitutes the interprofessional competences and IPE. Programmes to develop interprofessional practice should be developed and implemented systematically with inclusion of validated assessment methods, and evaluated and improved regularly.

Building a Hospital Pharmacist Workforce by a Diversified and Position-Oriented Learning System

Background

The role of hospital pharmacists has shifted from primarily ensuring drug supply to providing comprehensive pharmaceutical care. To accommodate this shift, new positions are needed. The traditional training model for hospital pharmacists is no longer sufficient for the evolving demands of pharmaceutical care and these new roles. This study aimed to describe the development of a position-oriented learning system explicitly tailored for hospital pharmacists and to assess its impact on workforce development and pharmacy service.

Methods

The position-oriented learning system for hospital pharmacists, aimed at enhancing training and workforce development, was evaluated based on two critical criteria: the completion rate of learning modules and the subsequent improvement in pharmaceutical care at the hospital. The completion rate assessed the engagement and effectiveness of the training content. At the same time, the improvement in pharmaceutical care evaluated practical outcomes such as percentages of patients who received pharmaceutical care and percentages of inappropriate medication orders intercepted.

Results

In 2021, 218 employees participated in the learning system. The pharmacy department has identified 22 pharmacists for various positions through this system. The quantity and quality of pharmaceutical care have improved significantly.

Conclusion

The position-oriented diversified learning system achieves the perfect combination of department development direction and individual career planning of employees. The learning system can significantly improve the learning efficiency of pharmacists, enhance the quality of various pharmaceutical care, and promote the development of disciplines.

Building a Competency Framework to Integrate Inter-disciplinary Precision Medicine Capabilities into the Medical Technology and Pharmaceutical Industry

INTRODUCTION

Integration of precision medicine (PM) competencies across the Medical Technology and Pharmaceutical industry is critical to enable industry professionals to understand and develop the skills needed to navigate the opportunities arising from rapid scientific and technological innovation in PM. Our objective was to identify the key competency domains required by industry professionals to enable them to upskill themselves in PM-related aspects of their roles.

METHODS

A desktop research review of current literature, curriculum, and healthcare trends identified a core set of domains and subdomains related to PM competencies that were consistent across multiple disciplines and competency frameworks. A survey was used to confirm the applicability of these domains to the cross-functional and multi-disciplinary work practices of industry professionals. Companies were requested to trial the domains to determine their relevance in practice and feedback was obtained.

RESULTS

Four PM-relevant domains were identified from the literature review: medical science and technology; translational and clinical application; governance and regulation and professional practice. Survey results refined these domains, and case studies within companies confirmed the potential for this framework to be used as an adjunct to current role specific competency frameworks to provide a specific focus on needed PM capabilities.

CONCLUSION

The framework was well accepted by local industry as a supplement to role specific competency frameworks to provide a structure on how to integrate new and evolving technologies into their current workforce development planning and build a continuous learning and cross-disciplinary mindset.

Comparison of case-based learning using Watson for oncology and traditional method in teaching undergraduate medical students

BACKGROUND

Watson for Oncology (WFO) is a decision-making system generated by artificial intelligence (AI) and has been widely used in treatment recommendations of cancer patients. However, the application of WFO in clinical teaching among medical students has not been reported.

OBJECTIVE

To establish a novel teaching and learning method with WFO in undergraduate medical students and evaluate its efficiency and students' satisfaction compared with traditional case-based learning model.

METHODS

72 undergraduates majoring in clinical medicine in Wuhan University were enrolled and were randomly divided into the WFO-based group and the control group. 36 students in the WFO-based group learned clinical oncology cases via WFO platform while 36 students in the control group using traditional teaching methods. After the course, final examination and questionnaire survey of teaching assessment were conducted on the two groups of students.

RESULTS

According to the questionnaire survey of teaching assessment, WFO-based group showed significant higher score in the aspect of cultivating ability of independent learning (17.67 ± 1.39 vs. 15.17 ± 2.02, P = 0.018), increasing knowledge mastery (17.75 ± 1.10 vs. 16.25 ± 1.18, P = 0.001), enhancing learning interest (18.41 ± 1.42 vs. 17.00 ± 1.37, P = 0.002), increasing course participation (18.33 ± 1.67 vs. 15.75 ± 1.67, P = 0.001) and the overall course satisfaction (89.25 ± 5.92 vs. 80.75 ± 3.42, P = 0.001) than those of the control group students.

CONCLUSION

Our practice has established a novel clinical case-based teaching pattern with WFO, providing undergraduate students with convenient and scientific training and guidance. It empowers students with improved learning experiences and equips them with essential tools for clinical practices.

Automated HL7v2 LRI informatics framework for streamlining genomics-EHR data integration

While VCF formatted files are the lingua franca of next-generation sequencing, most EHRs do not provide native VCF support. As a result, labs often must send non-structured PDF reports to the EHR. On the other hand, while FHIR adoption is growing, most EHRs support HL7 interoperability standards, particularly those based on the HL7 Version 2 (HL7v2) standard. The HL7 Version 2 genomics component of the HL7 Laboratory Results Interface (HL7v2 LRI) standard specifies a formalism for the structured communication of genomic data from lab to EHR. We previously described an open-source tool (vcf2fhir) that converts VCF files into HL7 FHIR format. In this report, we describe how the utility has been extended to output HL7v2 LRI data that contains both variants and variant annotations (e.g., predicted phenotypes and therapeutic implications). Using this HL7v2 converter, we implemented an automated pipeline for moving structured genomic data from the clinical laboratory to EHR. We developed an open source hl7v2GenomicsExtractor that converts genomic interpretation report files into a series of HL7v2 observations conformant to HL7v2 LRI. We further enhanced the converter to produce output conformant to Epic's genomic import specification and to support alternative input formats. An automated pipeline for pushing standards-based structured genomic data directly into the EHR was successfully implemented, where genetic variant data and the clinical annotations are now both available to be viewed in the EHR through Epic's genomics module. Issues encountered in the development and deployment of the HL7v2 converter primarily revolved around data variability issues, primarily lack of a standardized representation of data elements within various genomic interpretation report files. The technical implementation of a HL7v2 message transformation to feed genomic variant and clinical annotation data into an EHR has been successful. In addition to genetic variant data, the implementation described here releases the valuable asset of clinically relevant genomic annotations provided by labs from static PDFs to calculable, structured data in EHR systems.