Supporting cancer care clinicians to 'hold' their patients during and beyond the COVID-19 pandemic: a role for reflective ethics discussions.

The COVID-19 pandemic has placed an overwhelming burden on healthcare delivery globally. This paper examines how COVID-19 has affected cancer care clinicians' capacity to deliver cancer care in the Australian context. We use the lens of 'holding patients' (drawing from attachment theory, psychology and from Australian Indigenous knowledge) to conceptualise cancer clinicians' processes of care and therapeutic relationships with patients. These notions of 'holding' resonate with the deep responsibility cancer care clinicians feel towards their patients. They enrich ethical language beyond duties to benefit, avoid harm, respect patients' autonomy and provide just treatment. We consider the disruptive effects of COVID-19 on care delivery and on clinicians themselves. We then show how models of clinical ethics and other similar reflective discussion approaches are a relevant support mechanism to assist clinicians to process and make sense of COVID-19's disruptions to their professional ethical role of holding patients during and beyond the pandemic.

Victorian Lung Cancer Service Redesign Project: impacts of a quality improvement collaborative on timeliness and management in lung cancer.

BACKGROUND: Lung cancer management is characterised by a high disease burden, poor survival and substantial variation in management and outcomes. Service redesign provides opportunities for quality improvement (QI) and this improvement may be leveraged across multiple sites using QI collaboration. AIM: This initiative targeted Quality Improvement (QI) in lung cancer management, engaging a QI collaborative using service redesign methodologies in five Victorian hospitals. QI targets included timeliness from referral and diagnosis to treatment, multi-disciplinary meeting (MDM) presentation and supportive care screening. Redesign strategies targeted process sustainability through enhanced team capability. METHODS: This study engaged a prospective quality improvement cohort design targeting newly diagnosed tissue confirmed lung cancer with 6-month pre-intervention period and 6-month redesign implementation period, between September 2016 and August 2017, evaluated using Interrupted Time Series Analysis. Hospital sites included three regional and two metropolitan hospitals in Victoria. QI redesign targeted time intervals from referral to first specialist appointment (FSA), referral to diagnosis, diagnosis to first treatment (any intent), MDM documented in medical records and Supportive Care Screening Tool documented in medical records. RESULTS: There was a marked reduction in referral to FSA interval across all sites, with median (interquartile range) falling from 6 (0-15) to 4 (1-10) days, and proportion seen by a specialist within 14 days increased from 74.3% to 84.2%. The interval between diagnosis and treatment was not substantively changed in the 6-month implementation period. The proportion of subjects with documented presentation to the MDM increased from 61% to 67%. The proportion for which Supportive Care Screening documentation remained low at 26.3% post-intervention. CONCLUSIONS: Data-driven redesign initiatives enable identification and analysis of clinical practice variation and may be utilised to enhance timeliness of cancer care and improve local data service capabilities.

Consensus minimum data set for lung cancer multidisciplinary teams: Results of a Delphi process.

BACKGROUND AND OBJECTIVE: While multidisciplinary team (MDT) care in lung cancer is widely practiced, there are few guidelines for MDT on best data collection strategies. MDT meetings need ready access to information for the provision of optimal treatment recommendations (the primary purpose of the meeting), audit of team performance and benchmarking. This study aimed to develop a practical data set designed for these goals through a recognized consensus process with health professionals who participate in formal MDT settings. METHODS: A modified Delphi process with three iterations (two surveys and one consensus conference) was carried out involving over 100 Australian lung cancer MDT health professionals. RESULTS: In total, 122 lung cancer MDT health professionals responded to the Round 1 survey from over 350 invitees. Of the 122, 98 were available for invitation to Round 2. Of 98, 52 (53%) invitees responded to the Round 2 survey. After two rounds, 51 data elements across 8 domains (patient demographics, risk factors, biopsy data, staging, timeliness, treatment, follow-up and patient selection) achieved consensus, defined as 80% agreement. For Round 3, 33 MDT lead clinicians were invited to participate in a consensus conference. Of 33, 14 (42%) invitees distilled the 47 data elements into 23 elements across 8 domains to address the study objectives. CONCLUSION: A practical data set for lung cancer MDT to use for optimal treatment recommendations and to evaluate team performance was developed through recognized consensus methodology. Access to streamlined, relevant and feasible data collection strategies may improve MDT decision-making, audit of team performance and facilitate benchmarking.

Lung cancer interval times from point of referral to the acute health sector to the start of first treatment.

Objective The aim of the present study was to compare lung cancer diagnostic and treatment intervals with agreed target measures across three large public health services in Victoria and assess any differences in interval times by treatment type and health service. Methods A retrospective medical record audit of 78 patients admitted with a new diagnosis of lung cancer was conducted. Interval times from referral to diagnosis, diagnosis to first treatment and referral to first treatment were recorded in three treatment types: surgery, chemotherapy and radiotherapy. Results There was a significant difference in the mean number of days from referral to diagnosis by treatment type. Patients who underwent surgery waited significantly longer (mean (± s.d.) 41.6±38.4 days) to obtain a diagnosis than those who received radiotherapy (15.1±18.6 days). Only 47% of surgical patients obtained a diagnosis within the recommended 28 days. Moreover, only 45% and 44% of patients, respectively, met the diagnosis-to-treatment target of 14 days and referral-to-treatment target of 42 days. Conclusion The present study highlights the effect of treatment type on lung cancer referral interval times. It demonstrates the benefits of using evidenced-based interval target times to benchmark and compare performance outcomes in lung cancer. What is known about the topic? Lung cancer is the leading cause of cancer mortality in Australia and has the lowest 5-year survival rate of all cancer types. Delays in the diagnosis of lung cancer can change the prognosis from potentially curable to incurable, particularly in faster-growing tumours. What does this paper add? This study reveals treatment type was a greater factor in explaining variations in diagnosis and treatment than health service. Surgical patients were consistently lower in meeting the recommended interval targets across referral to diagnosis, diagnosis to treatment and referral to treatment. What are the implications for practitioners? This study demonstrates the value of using evidenced-based interval target times to benchmark and compare performance outcomes in lung cancer. Such measures may further improve prognostic outcomes in lung cancer by reducing unwanted delays.

Audit of referral pathways in the diagnosis of lung cancer: a pilot study.

This pilot study sought to describe the diagnostic pathways for patients with lung cancer and explore the feasibility of a medical record audit for this purpose. An audit of 25 medical records of patients with a confirmed diagnosis of lung cancer was conducted, at a single outer metropolitan hospital in Victoria. Patients were presented to secondary care from general practice (n=17, 68%), the emergency department (n=3, 12%) or specialist rooms (n=1, 4%). Those who journeyed through general practice experienced the longest median intervals to diagnosis (20 days, interquartile range 7-47). The majority of patients (n=15, 60%) were referred by a specialist to a multidisciplinary team after a diagnosis had been confirmed but before treatment commenced. These patients waited a median of 20 days from their first specialist appointment to a multidisciplinary team appointment. This research illustrated that a variety of pathways to diagnosis exist. Critically, it requires patient data and additional auditing of primary, public and private health sector records to determine generalisability of findings and the effectiveness of a medical record audit as a data collection tool.