Development of consensus quality indicators for cancer supportive care: a Delphi study and pilot testing.

BACKGROUND: High quality supportive care is fundamental to achieve optimal health outcomes for people affected by cancer. Use of quality indicators provides comparative information for monitoring, management, and improvement of care within and across healthcare systems. The aim of this Australian study was to develop and test a minimum viable set of cancer supportive care quality indicators that would be feasible to implement and generate usable data for policy and practice. METHODS: A two-round, modified reactive Delphi process was employed firstto develop the proposed indicators. Participants with expertise in cancer control in Australia, the United Kingdom, and Canada rated their level of agreement on a 7-point Likert scale against criteria assessing the importance, feasibility, and usability of proposed indicators. Relative response frequencies were assessed against pre-specified consensus criteria and a ranking exercise, which delivered the list of proposed indicators. Draft indicators were then presented to a purposive sample of clinicial and health management staff via qualitative interviews at two acute care settings in Melbourne, Australia for feedback regarding feasibility. Desktop audits of online published health service policy and practice descriptions were also conducted at participating acute care settings to confirm health service data availability and feasibility of collection to report against proposed indicators. RESULTS: Sixteen quality indicators associated with the delivery of quality cancer supportive care in Australian acute healthcare settings met pre-specified criteria for inclusion. Indicators deemed 'necessary' were mapped and ranked across five key categories: Screening, Referrals, Data Management, Communication and Training, and Culturally Safe and Accessible Care. Testing confirmed indicators were viewed as feasible by clinical and health management staff, and desktop audits could provide a fast and reasonably effective method to assess general adherence and performance. CONCLUSIONS: The development of quality indicators specific to cancer supportive care provides a strong framework for measurement and monitoring, service improvement, and practice change with the potential to improve health outcomes for people affected by cancer. Evaluation of implementation feasibility of these expert consensus generated quality indicators is recommended.

Social return on investment economic evaluation of supportive care for lung cancer patients in acute care settings in Australia.

BACKGROUND: Unmanaged consequences of cancer and its treatment are high among patients with lung cancer and their informal carers, resulting in avoidable healthcare use and financial burden. Provision of cancer supportive care addressing the impacts of cancer and its treatment has demonstrated efficacy in mitigating these consequences; however, globally, there is a lack of investment in these services. Paucity of robust economic evidence regarding benefit of cancer supportive care has limited policy impact and allocation of resources. This study therefore utilised a Social Return on Investment (SROI) methodology to conduct a forecast evaluation of lung cancer supportive care services, to ascertain potential social value and return on investment. METHODS: An SROI economic evaluation model was developed using qualitative stakeholder consultations synthesised with published evidence to develop the inputs, outcomes and financial value associated with the delivery of a hypothetical model of quality lung cancer supportive care services over a one and five year period. SROI ratios were generated to determine the social value and cost savings associated per every $1AUD invested in cancer supportive care for both the healthcare system and patients. Deadweight, drop off and attribution were calculated, and sensitivity analysis was performed to confirm the stability of the model. RESULTS: The value generated from modelled supportive care service investments in a one-year period resulted in an SROI ratio of 1:9; that is, for every dollar invested in supportive care, AUD$9.00 social return is obtained when savings to the healthcare system and benefits to the patients are combined. At five-years, these same investments resulted in greater cumulative value generated for both the patient and the healthcare system, with a SROI ratio of 1:11. CONCLUSION: Our study provides strong evidence for policy makers, clinicians and consumers to advocate for further investment in cancer supportive care, as demonstrated cost savings could be achieved through implementation of the proposed supportive care service model, with these accruing over a five-year period. The SROI model provides a comprehensive framework detailing supportive care services and the health workforce necessary to achieve value-based outcomes for patients and the healthcare system.

Evaluating for impact: what type of data can assist a health promoting school approach?

There is debate within the health promoting school (HPS) movement on whether schools should monitor health behaviour outcomes as part of an evaluation or rely more on process type measures, such as changes to school policies and the physical and social environment which yield information about (in)effective implementation. The debate is often framed around ideological considerations of the role of schools and there is little empirical work on how these indicators of effective implementation can influence change at a policy and practice level in real world settings. Information has potentially powerful effects in motivating a change process, but this will vary according to the type of information and the type of organizational culture into which it is presented. The current predominant model relies on process data, policy and environmental audit monitoring and benchmarking approaches, and there is little evidence of whether this engages school communities. Theoretical assertions on the importance of monitoring data to motivate change need to be empirically tested and, in doing so, we can learn which types of data influence adoption of HPS in which types of school and policy contexts.