Looking through the eyes of the multidisciplinary team: the design and clinical evaluation of a decision support system for lung cancer care

Improved guideline adherence in oncology through clinical decision-support systems: still hindered by current health IT infrastructures?

PURPOSE OF REVIEW

Despite several efforts to enhance guideline adherence in cancer management, the rate of adherence remains often dissatisfactory in clinical routine. Clinical decision-support systems (CDSS) have been developed to support the management of cancer patients by providing evidence-based recommendations. In this review, we focus on both current evidence supporting the beneficial effects of CDSS on guideline adherence as well as technical and structural requirements for CDSS implementation in clinical routine.

RECENT FINDINGS

Some studies have demonstrated a significant improvement of guideline adherence by CDSSs in oncologic diseases such as breast cancer, colon cancer, cervical cancer, prostate cancer, and hepatocellular carcinoma as well as in the management of cancer pain. However, most of these studies were rather small and designs rather simple. One reason for this limited evidence might be that CDSSs are only occasionally implemented in clinical routine. The main limitations for a broader implementation might lie in the currently existing clinical data infrastructures that do not sufficiently allow CDSS interoperability as well as in some CDSS tools themselves, if handling is hampered by poor usability.

SUMMARY

In principle, CDSSs improve guideline adherence in clinical cancer management. However, there are some technical und structural obstacles to overcome to fully implement CDSSs in clinical routine.

Artificial intelligence in clinical decision support systems for oncology

Artificial intelligence (AI) has been widely used in various medical fields, such as image diagnosis, pathological classification, selection of treatment schemes, and prognosis analysis. Especially in the image-aided diagnosis of tumors, the cooperation of human-computer interactions has become mature. However, the ethics of the application of AI as an emerging technology in clinical decision-making have not been fully supported, so the clinical decision support system (CDSS) based on AI technology has not fully realized human-computer interactions in clinical practice as the image-aided diagnosis system. The CDSS was currently used and promoted worldwide including Watson for Oncology, Chinese society of clinical oncology-artificial intelligence (CSCO AI) and so on. This paper summarized the applications and clarified the principle of AI in CDSS, analyzed the difficulties of AI in oncology decisions, and provided a reference scheme for the application of AI in oncology decisions in the future.

Optimising the quality of multidisciplinary team meetings: A narrative review

Background

Understanding of factors that contribute to implementation of effective cancer multidisciplinary team meetings (MDMs) is still limited. Published literature on the effect of teamwork function, leadership roles, decision‐making processes and structural components on the quality of MDMs was reviewed and synthesised.

Methods

In this paper, a MEDLINE review (September 2020) was performed to assess clinical decision‐making in the context of MDM discussions.

Results

Twenty‐nine eligible studies were included. Six studies addressed the infrastructural aspects of MDMs. Nine studies used either qualitative or mixed method approach to develop and validate observational tools to assess the quality of MDMs. Seven studies used qualitative approaches to explore the opinions of MDM members on factors that impact on the effectiveness of MDMs. Five studies used validated observational tools to observe and assess the effectiveness of MDMs. One prospective study explored the relationship between quality of information presented at MDMs and ability of MDM members to make clinical decisions. The final study prospectively tested the ability of a multicomponent intervention to improve decision‐making processes within MDMs.

Conclusions

A broad range of factors including teamwork, leadership, case complexity, decision‐making processes and availability of patient information were identified to impact the quality of MDMs. Evidence currently available largely focuses on the development of tools to identify factors in need of improvement to optimise MDMs. Robust research is required to identify the factors that are demonstrated to enhance MDM quality which can then aid the standardisation of how MDMs are conducted.

Virtual Multidisciplinary Tumor Boards: A Narrative Review Focused on Lung Cancer

To date, the virtual multidisciplinary tumor boards (vMTBs) are increasingly used to achieve high-quality treatment recommendations across health-care regions, which expands and develops the local MTB team to a regional or national expert network. This review describes the process of lung cancer-specific MTBs and the transition process from face-to-face tumor boards to virtual ones. The review also focuses on the project organization's description, advantages, and disadvantages. Semi-structured interviews identified five major themes for MTBs: current practice, attitudes, enablers, barriers, and benefits for the MTB. MTB teams exhibited positive responses to modeled data feedback. Virtualization reduces time spent for travel, allowing easier and timely patient discussions. This process requires a secure web platform to assure the respect of patients' privacy and presents the same unanswered problems. The implementation of vMTB also permits the implementation of networks especially in areas with geographical barriers facilitating interaction between large referral cancer centers and tertiary or community hospitals as well as easier access to clinical trial opportunities. Studies aimed to improve preparations, structure, and conduct of MTBs, research methods to monitor their performance, teamwork, and outcomes are also outlined in this article. Analysis of literature shows that MTB participants discuss 5-8 cases per meeting and that the use of a vMTB for lung cancer and in particular stage III NSCLC and complex stage IV cases is widely accepted by most health professionals. Despite still-existing gaps, overall vMTB represents a unique opportunity to optimize patient management in a patient-centered approach.

Quality indicators and excellence requirements for a multidisciplinary lung cancer tumor board by the Spanish Lung Cancer Group

Multidisciplinary care is needed to decide the best therapeutic approach and to provide optimal care to patients with lung cancer (LC). Multidisciplinary teams (MDTs) are optimal strategies for the management of patients with LC and have been associated with better outcomes, such as an increase in quality of life and survival. The Spanish Lung Cancer Group has promoted this review about the current situation of the existing national LC-MDTs, which also offers a set of excellence requirements and quality indicators to achieve the best care in any patient with LC. Time and sufficient resources; leadership; administrative and institutional support; and recording of activity are key factors for the success of LC-MDTs. A set of excellence requirements in terms of staff, resources and organization of the LC-MDT have been proposed. At last, a list of quality indicators has been agreed to achieve and measure the performance of current LC-MDTs.

The cancer multidisciplinary team meeting: in need of change? History, challenges and future perspectives

Two decades since their inception, multidisciplinary teams (MDTs) are widely regarded as the 'gold standard' of cancer care delivery. Benefits of MDT working include improved patient outcomes, adherence to guidelines, and even economic benefits. Benefits to MDT members have also been demonstrated. An increasing body of evidence supports the use of MDTs and provides guidance on best practise. The system of MDTs in cancer care has come under increasing pressure of late, due to the increasing incidence of cancer, the popularity of MDT working, and financial pressures. This pressure has resulted in recommendations by national bodies to implement streamlining to reduce workload and improve efficiency. In the present review we examine the historical evidence for MDT working, and the scientific developments that dictate best practise. We also explore how streamlining can be safely and effectively undertaken. Finally, we discuss the future of MDT working including the integration of artificial intelligence and decision support systems and propose a new model for improving patient centredness.

The Impact of Multidisciplinary Team Meetings on Patient Management in Oncologic Thoracic Surgery: A Single-Center Experience

BACKGROUND

the aim of this paper is to quantify multidisciplinary team meeting (MDT) impact on the decisional clinical pathway of thoracic cancer patients, assessing the modification rate of the initial out-patient evaluation.

METHODS

the impact of MDT was classified as follows: confirmation: same conclusions as out-patient hypothesis; modification: change of out-patient hypothesis; implementation: definition of a clear clinical track/conclusion for patients that did not receive any clinical hypothesis; further exams required: the findings that emerged in the MDT meeting require further exams.

RESULTS

one thousand consecutive patients evaluated at MDT meetings were enrolled. Clinical settings of patients were: early stage lung cancer (17.4%); locally advanced lung cancer (27.4%); stage IV lung cancer (9.8%); mesothelioma (1%); metastases to the lung from other primary tumors (4%); histologically proven or suspected recurrence from previous lung cancer (15%); solitary pulmonary nodule (19.2%); mediastinal tumors (3.4%); other settings (2.8%).

CONCLUSIONS

MDT meetings impact patient management in oncologic thoracic surgery by modifying the out-patient clinical hypothesis in 10.6% of cases; the clinical settings with the highest decisional modification rates are "solitary pulmonary nodule" and "proven or suspected recurrence" with modification rates of 14.6% and 13.3%, respectively.

Barriers and Facilitators for Implementation of a Computerized Clinical Decision Support System in Lung Cancer Multidisciplinary Team Meetings-A Qualitative Assessment

Simple Summary

Oncological computerized clinical decision support systems (CCDSSs) are currently being developed to facilitate workflows of multidisciplinary team meetings (MDTMs). To successfully implement these systems in MDTMs, the aim of this qualitative assessment was to identify barriers and facilitators for implementation and to provide actionable findings for an implementation strategy. The main facilitators for implementation of the CCDSS were considered to be easy access to well-structured data, and reducing time spent by clinicians on MDTM preparation and duration of the MDTMs. Main barriers for adoption were seen in incomplete or non-trustworthy output generated by the system and insufficient adaptability of the system to local and contextual needs. Actionable findings for an implementation strategy were a usability test and validation study involving key users in the organization’s real-life setting. Given the growing interest in CCDSSs in oncology care, insight in barriers and facilitators for successful implementation seems highly relevant.

Abstract

Background: Oncological computerized clinical decision support systems (CCDSSs) to facilitate workflows of multidisciplinary team meetings (MDTMs) are currently being developed. To successfully implement these CCDSSs in MDTMs, this study aims to: (a) identify barriers and facilitators for implementation for the use case of lung cancer; and (b) provide actionable findings for an implementation strategy. Methods: The Consolidated Framework for Implementation Science was used to create an interview protocol and to analyze the results. Semi-structured interviews were conducted among various health care professionals involved in MDTMs. The transcripts were analyzed using a thematic analysis following a deductive approach. Results: Twenty-six professionals participated in the interviews. The main facilitators for implementation of the CCDSS were considered to be easy access to well-structured patient data, and the resulting reduction of MDTM preparation time and of duration of MDTMs. Main barriers for adoption were seen in incomplete or non-trustworthy output generated by the system and insufficient adaptability of the system to local and contextual needs. Conclusion: Using a CCDSS in lung cancer MDTMs was expected to increase efficiency of workflows. Successful implementation was seen as dependent on the reliability and adaptability of the CCDSS and involvement of key users in the implementation process.